Functional genomics provide new insights into regulation of ...

Functional genomics provide new insights into regulation

of morphogenesis and secondary metabolism in the

industrial penicillin producer

Penicillium chrysogenum

Dissertation to obtain the degree Doctor Rerum Naturalium (Dr. rer. nat.) Submitted to the International Graduate School of Biosciences,

Faculty of Biology and Biotechnology Ruhr-University Bochum, Germany

this thesis was performed at the

Department of General and Molecular Botany

submitted by

Kordula Becker

from

Essen, Germany

Bochum

April, 2015

1st supervisor: Prof. Dr. Ulrich Kück

2nd supervisor: Prof. Dr. Franz Narberhaus

Funktionelle Genomanalysen zur Regulation von

Morphogenese und Sekundärmetabolismus in dem

industriellen Penicillin-Produzenten


Dissertation zur Erlangung des Grades eines Doktors der Naturwissenschaften der Fakultät für Biologie und Biotechnologie

an der Internationalen Graduiertenschule Biowissenschaften der Ruhr-Universität Bochum

angefertigt am

Lehrstuhl für Allgemeine und Molekulare Botanik

vorgelegt von

Kordula Becker

aus

Essen

Bochum

April, 2015

Referent: Prof. Dr. Ulrich Kück

Korreferent: Prof. Dr. Franz Narberhaus

DANKSAGUNG

VIELEN DANK:

Meinem Doktorvater Prof. Dr. Ulrich Kück möchte ich für das mir entgegengebrachte Vertrauen, die hervorragende Betreuung, sowie für unzählige Gespräche danken, die mich nicht nur fachlich sondern auch persönlich voran gebracht haben. Ich habe in den vergangenen Jahren nicht nur die Privilegien sondern auch die Verpflichtungen, die mit der Arbeit an Ihrem Lehrstuhl einhergehen, zu schätzen gelernt und bin froh, mich für die Promotion in der Allgemeinen und Molekularen Botanik entschieden zu haben.

Herrn Prof. Dr. Franz Narberhaus gilt mein besonderer Dank für die Übernahme des Korreferates.

Allen aktuellen und ehemaligen Mitarbeitern des Lehrstuhls für Allgemeine und Molekulare Botanik danke ich für die freundschaftliche Zusammenarbeit während der vergangenen Jahre. Es waren viele kleine und große Dinge, die dazu beigetragen haben, dass ich meine Zeit als Doktorandin in guter Erinnerung behalten werde.

Mein Dank gilt insbesondere Ingeborg Godehardt, die mich tatkräftig bei der Durchführung sämtlicher DNA-Bindungsstudien unterstützt hat. Besser als mit den Worten eines Gutachters kann auch ich deine Arbeit nicht beschreiben: “The binding site analysis is compelling”. Mein Dank für hervorragende technische Unterstützung gilt außerdem Kerstin Kalkreuter und Stefanie Mertens. Ihr danke ich außerdem für viele ermutigende Worte, gute Ratschläge und ihr immer offenes Ohr. PD Dr. Minou Nowrousian danke ich für die professionelle Unterstützung bei zahlreichen bioinformatischen Analysen. Darüber hinaus möchte ich ihr und Dr. Julia Böhm ganz herzlich für die gewissenhafte und kritische Korrektur dieser Arbeit danken.

Meinen Doktorschwestern und Doktorbrüdern danke ich für die schöne gemeinsame Zeit. Ohne euch wäre alles nur halb so schön gewesen! Insbesondere möchte ich mich bei Tim Dahlmann bedanken – dank dir bin ich nicht nur zu einer realistischen Selbsteinschätzung meiner mathematischen Fähigkeiten gelangt, sondern hatte einen Büro- und Laborpartner, auf den ich mich stets verlassen konnte. – „Keks? Heißes Wasser?!“

Prof. Dr. Michael Freitag und seinen Mitarbeitern danke ich für den schönen Forschungsaufenthalt an der Oregon State University im Herbst 2012, der die Etablierung der ChIP-seq Technologie für die Anwendung in P. chrysogenum überhaupt erst ermöglicht hat.

Der Studienstiftung des Deutschen Volkes, der Sandoz GmbH, der Christian Doppler Forschungsgesellschaft und der RUB Research School danke ich für die großzügige finanzielle Unterstützung. Darüber hinaus gilt mein Dank unseren Kooperationspartnern bei der Sandoz GmbH, insbesondere Dr. Ivo Zadra und Dr. Hubert Kürnsteiner, für ihr fortwährendes Interesse am Fortgang dieser Arbeit.

TABLE OF CONTENTS 1

TABLE OF CONTENTS

ABBREVIATIONS ........................................................................................................................... 2

I. INTRODUCTION ......................................................................................................................... 3

1. From Sanger sequencing to next-generation sequencing .............................................................................. 3

2. New directions in functional genomics........................................................................................................... 5

3. Location-based NGS approaches .................................................................................................................... 7

4. Bioinformatics analysis of ChIP-seq data ...................................................................................................... 11

5. Functional downstream analysis: from physical context to biological function ........................................... 13

6. ChIP-seq analyses in fungi ............................................................................................................................ 15

7. Summary ....................................................................................................................................................... 15

II. SCOPE OF THE THESIS ............................................................................................................. 18

1. Regulation of fungal secondary metabolism ................................................................................................ 18

2. Aim of this thesis .......................................................................................................................................... 20

III. BECKER et al. 2015a ............................................................................................................... 23

IV. BECKER et al. 2015b ............................................................................................................... 24

V. DISCUSSION ........................................................................................................................... 25

1. ChIP-seq analyses with MAT1-1-1 .......................................................................................................... 25

1.1 MAT1-1-1 regulates target genes beyond sexual development ............................................................ 26

1.2 Rewiring of MAT-regulated transcriptional networks ............................................................................ 30

1.3 A new MAT1-1-1 working model ............................................................................................................ 33

2. ChIP-seq analyses with PcVelA ............................................................................................................... 36

2.1 PcVelA acts as a transcriptional regulator on DNA level ........................................................................ 37

2.2 The putative SAM-dependent methyltransferase PcLlmA is a direct interaction partner of PcVelA ..... 40

2.3 An expanded model of PcVelA regulatory functions .............................................................................. 42

3. Overall analysis of ChIP-seq data ........................................................................................................... 43

3.1 Genome-wide TF binding beyond direct target-gene control ................................................................ 43

3.2 MAT1-1-1 and PcVelA bind DNA via specific DNA-consensus sequences .............................................. 45

3.3 Concluding remarks ................................................................................................................................ 47

VI. SUMMARY ............................................................................................................................ 49

VII. ZUSAMMENFASSUNG ........................................................................................................... 50

VIII. REFERENCES ........................................................................................................................ 51

IX. EIGENANTEIL AN PUBLIKATIONEN .......................................................................................... 76

X. CURRICULUM VITAE ............................................................................................................... 77

XI. ERKLÄRUNG .......................................................................................................................... 79

ABBREVIATIONS 2

ABBREVIATIONS

bp base pairs BiFC bimolecular fluorescence complementation ChIP chromatin immunoprecipitation ChIP-chip ChIP combined with microarray hybridization ChIP-DNA DNA obtained from ChIP ChIP-PCR ChIP combined with PCR ChIP-seq ChIP combined with NGS DNA deoxyribonucleic acid ENCODE Encyclopedia of DNA Elements GRN gene regulatory network HMG high-mobility group input-DNA DNA sample removed prior to ChIP MAT mating type NGS next-generation sequencing NHGRI National Human Genome Research Institute nt nucleotide PCR polymerase chain reaction qRT-PCR quantitative real time PCR RNA ribonucleic acid RNA-seq RNA sequencing SAM S-adenosyl-L-methionine SM secondary metabolite TF transcription factor TFBS transcription factor binding site TSS transcription start site WGS whole-genome sequencing Y2H yeast two-hybrid analysis αsg mating-type α-specific gene Δ deletion

I. INTRODUCTION 3

I. INTRODUCTION

1. From Sanger sequencing to next-generation sequencing

When Frederick Sanger first introduced his method to sequence DNA by “dideoxy

chain-termination” and fragmentation techniques in 1977 (Sanger et al. 1977), few might

have envisioned the revolutionary character of his discovery, for which he was awarded the

Nobel Prize in Chemistry less than ten years later. The technique, commonly referred to as

Sanger sequencing, dominated the DNA-analysis field for the next 30 years and, ultimately,

enabled the completion of the first human genome sequence in 2004 (The International

Human Genome Sequencing Consortium(2004). However, the Human Genome Project

required vast amounts of time and resources, and an increasing demand for faster, cheaper,

and higher-throughput technologies emerged. As a consequence, the National Human

Genome Research Institute (NHGRI) initiated a funding program to reduce the sequencing

cost of a human genome to US$1,000 within the next 10 years (Schloss 2008). Shortly

afterwards, a new generation of sequencing technologies, summarily termed next-generation

sequencing (NGS) technologies, arrived on the scene (Table 1). Compared to the Sanger

method, which is considered a first-generation technology, NGS technologies share three

characteristic features: [1] they rely on the preparation of NGS libraries in a cell-free system

in place of bacterial cloning of DNA fragments, [2] instead of hundreds, thousands to many

millions of sequencing reactions are produced in parallel, and [3] the sequencing output is

directly detected without need for electrophoresis (van Dijk et al. 2014). Nevertheless, error

rates are high and far apart from those of the Sanger sequencings’ > 0.001%. Furthermore,

with some exceptions, read lengths are restricted to a maximum of some hundred base pairs.

While brief descriptions of the most important NGS platforms, namely 454, Illumina, and

SOLiD, will be given in the next paragraph, please refer to Mardis (2008), Metzker (2010),

and van Dijk (2014) for descriptions of less commonly used techniques and detailed

information.

The 454 Genome Sequencer, the first commercial NGS system for individual laboratory use

was introduced by 454 Life Sciences (today Roche) in 2005. The system is based on the

principle of “pyrosequencing”, a sequencing-by-synthesis technique that measures the release

of inorganic pyrophosphate by chemiluminescence (Margulies et al. 2005). The DNA library

is amplified by emulsion PCR (Tawfik and Griffiths 1998, Nakano et al. 2003) on the surface

I. INTRODUCTION 4

Table 1: Sequencing technologies at a glance

Platform Mechanism Max. read length [bp]

Output data/run

Run time Error rate

Sanger 3730xl1)

1st

generation; Dideoxy chain termination

400 – 900 1.9 – 84 Kb 20 min – 3 hours

> 0.001%

454 FLX+2)

2nd

generation (NGS); Pyrosequencing

1000 0.7 Gb 23 hours > 0.8%

Illumina HiSeq 2500

3)

2nd

generation (NGS); Sequencing-by-synthesis

2*150 95 Gb 10 days > 0.8%

SOLiD 5500xl1)

2nd

generation (NGS); Ligation-based sequencing

75 15 Gb 8 days > 0.5%

MiSeq3)

2nd

generation (NGS); Sequencing-by-synthesis

2*300 15 Gb 3 days > 0.8%

PacBio RS II4)

3rd

generation; Real-time sequencing

up to > 40,000 0.3 – 1 Gb 4 hours > 10%

1) http://www.lifetechnologies.com;

2) http://www.454.com;

3) http://www.illumina.com;

4) http://www.pacificbiosciences.com

of 28-µm agarose beads, covered with millions of oligomers, which are complementary to the

adaptor sequences used for NGS-library construction. After amplification, several hundred

thousand such agarose beads, which harbor up to 1,000,000 copies of the originally annealed

DNA fragment, are added to 454 picotiter plates for sequencing. Nucleotides are added in a

defined manner and imaging of light signals, produced by a chemiluminescent enzyme

present in the reaction mix, is used to record their incorporation. In 2006, one year after the

introduction of the 454 platform, Solexa (today Illumina) commercialized the Genome

Analyzer, which is based on the concept of “sequencing-by-synthesis” (Bentley et al. 2008).

Here, during the so-called cluster generation, single-stranded DNA fragments are hybridized

and amplified on the surface of a glass flow cell prior to sequencing. After the amplification

step, flow cells contain more than 40 million clusters, each composed of approximately 1,000

copies of a single template molecule. The templates are sequenced in a massively parallel

fashion using differentially 3’-labeled fluorescent nucleotides. Each base incorporation cycle

is followed by an imaging step, identifying the incorporated nucleotide, and the chemical

removal of the fluorescent group, introducing the next incorporation cycle. In 2007, shortly

after commercialization of the Illumina platform, Applied Biosystems (today Life

Technologies) released the SOLiD (Sequencing by Oligo Ligation Detection) system

(Shendure and Ji 2008). SOLiD is based on massive parallel sequencing by ligation, using a

ligation technique referred to as polony sequencing (Shendure et al. 2005). Here,

adaptor-linked DNA fragments are coupled to magnetic beads, covered with complementary

oligonucleotides. Bead-DNA complexes are amplified using emulsion PCR and subsequently

beads are covalently attached to glass slides for sequencing. The “ligation-based sequencing”

process starts with the annealing of universal sequencing primers, complementary to the

I. INTRODUCTION 5

adaptor sequences used for library preparation. Next, 8mer oligonucleotides and a DNA ligase

are added. Matching 8mer oligonucleotides next to the universal sequencing primer 3’-end are

linked by the DNA ligase and, depending on the cycle number, either the fifth or second

position of the 8mer is identified using a fluorescent readout. The 8mer is chemically cleaved

between positions five and six, removing the fluorescent group and enabling the next round of

ligation. This way, the sequence of each DNA fragment is identified at five-nucleotide

intervals upon completion of the sequencing cycle. Synthesized fragments are denaturized and

removed, and a second round of sequencing starts, using either an universal primer that is set

back one or more bases from the adaptor-insert junction or differentially labelled 8mers.

Meanwhile, benchtop-sequencing machines, like Ion Torrent Systems’ (today Life

Technologies) Personal Genome Machine (Rothberg et al. 2011) and Illumina’s MiSeq, are

available on the market, making large-scale sequencing affordable even for small laboratories.

Driven by the competition between the main players on the NGS market, sequencing costs for

a human genome have fallen well below US$5,000 until today and, finally, Illumina’s

HiSeq X Ten platform was announced to burst the US$1,000 boundary set by the NHGRI in

2004 (McPherson 2014).

The next step in development of new DNA-sequencing platforms now meets the challenge of

single-molecule sequencing without any amplification of the DNA template, ushering in the

era of third-generation sequencing. Currently, this field of DNA analysis is dominated by

Pacific Biosciences (PacBio), which released the PacBio RS, a system based on the detection

of natural DNA synthesis by a single DNA polymerase (Eid et al. 2009), and Oxford

Nanopore Technologies (ONT), which started a testing phase for the MinION sequencing

device, reading the sequences of individual DNA strands while driving them through

biological nanopores, in 2014 (Schneider and Dekker 2012, Jain et al. 2015).

2. New directions in functional genomics

The advent of NGS technologies marked a radical change in genomics research and provided

a virtually inexhaustible basis for a variety of functional-genomics approaches, aiming at

turning the huge amount of data obtained by genomic projects into a description of the

interactions between the genome, gene products, and metabolites. Doing so,

functional-genomics approaches characteristically focus on dynamic aspects, such as

protein-DNA interactions, transcription, and translation (Werner 2010).

I. INTRODUCTION 6

One of the first NGS-based applications in the field of functional genomics was RNA

sequencing (RNA-seq), enabling the identification and quantification of transcripts, even

without prior knowledge of particular genes, and providing insights into alternative splicing

events and sequence variation (Wang et al. 2009). Early applications of RNA-seq comprised

the generation of high-resolution transcriptome maps of Saccharomyces cerevisiae and

Schizosaccharomyces pombe (Nagalakshmi et al. 2008, Wilhelm et al. 2008), transcriptome

analyses in Arabidopsis thaliana (Lister et al. 2008) and human HeLa cells (Morin et al.

2008), as well as mapping and quantification of mouse transcriptomes from brain, liver, and

skeletal muscle tissues (Mortazavi et al. 2008). Another early application of NGS was

chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq),

allowing the genome-wide identification of protein-DNA interactions and epigenetic marks,

e.g. DNA methylation and/or histone modifications (Park 2009). The first studies using

ChIP-seq were published in 2007. They described the identification of DNA-binding sites of

the human neuron-restrictive silencer factor (NRSF) (Johnson et al. 2007), mapping of target

regions of the transcriptional regulator STAT1 in interferon-γ-stimulated and unstimulated

human HeLa cells (Robertson et al. 2007), and high-resolution profiling of 20 histone

methylations along with histone variant H2A.Z, RNA Pol II, and the DNA-binding protein

CTCF in the human genome (Barski et al. 2007).

Besides RNA-seq and ChIP-seq, one other major field of NGS applications is whole-genome

sequencing (WGS). Most importantly, NGS-based WGS approaches not only reveal the

genome sequence of interest, but also provide valuable information about genomic deletions,

rearrangements, copy number variations (CNVs), and short nucleotide polymorphisms

(SNPs). The first eukaryotic organisms to be sequenced exclusively by NGS were the giant

panda (Li et al. 2010) and the filamentous ascomycete Sordaria macrospora (Nowrousian et

al. 2010). Since then, NGS-based WGS has been successfully used in a variety of

experimental approaches, e.g. for analyzing the epidemiology of Staphylococcus aureus

clinical isolates (Francois et al. 2007), generation of a draft genome sequence of the

Neandertal (Green et al. 2010), and determination of the genome sequence of an 18.5 weeks

unborn human fetus (Kitzman et al. 2012). In fungi, NGS-based WGS pipelines have been

applied for sequencing of Pyronema confluens (Traeger et al. 2013), as well as strains of the

biotechnologically relevant species Penicillium chrysogenum (Specht et al. 2014) and

Acremonium chrysogenum (Terfehr et al. 2014). Moreover, NGS-based workflows have been

developed for the discovery of fungal secondary metabolite (SM) gene clusters (Cacho et al.

2015). One of the earliest examples describes the identification of two Penicillium

I. INTRODUCTION 7

aethiopicum SM gene clusters, encoding the tetracycline-like viridicatum toxin 1 and the

antifungal agent griseofulvin 2, by 454 shotgun sequencing (Chooi et al. 2010).

Until today, the enormous power of NGS promoted its establishment in various new fields of

applications, such as non-invasive prenatal diagnostics (Nepomnyashchaya et al. 2013),

clinical diagnostics (Desai and Jere 2012), forensics (Yang et al. 2014), molecular barcoding

(Smith et al. 2010), metagenomics (Kim et al. 2013, Burton et al. 2014), and drug

development (Rodriguez and Miller 2014).

3. Location-based NGS approaches

Transcription factors (TFs) are the most important players in gene regulatory networks

(GRNs). They orchestrate the gene expression control of the cell and thereby determine

organismal complexity and diversity (Shelest 2008, Charoensawan et al. 2010). Hence, a

precise map of binding sites of DNA-binding proteins and epigenetic marks is vital for

understanding the regulatory mechanisms that underlie various biological processes (Farnham

2009, Park 2009). Since the ascent of NGS technologies, large-scale projects aiming at

creating comprehensive maps of GRNs have been initiated. Prominent examples are The

Encyclopedia of DNA Elements (ENCODE), an international collaboration of research groups

funded by the NHGRI (The_ENCODE_Project_Consortium 2004, 2012), and the Human

Epigenome Project (Esteller 2006), both aiming at the provision of a platform for the

improvement of human biology and health.

For a long time, binding sites of DNA-binding proteins and histone modifications have been

analyzed using chromatin immunoprecipitation (ChIP) (Hecht et al. 1996, Strahl-Bolsinger et

al. 1997). ChIP provides a snapshot of all factors bound to specific chromatin regions in

different functional states, and therefore provides an exquisite tool to investigate the interplay

between structural or regulatory proteins and DNA (Won and Kim 2006). A typical

DNA-binding protein ChIP experiment is based on the enrichment of DNA fragments

associated with a protein of interest, such as TFs, components of the core transcriptional

machinery, or histones. Therefore, interactions between proteins and DNA are crosslinked in

vivo by treating the starting material with formaldehyde, a highly reactive compound, which

reacts with the amino groups of proteins and amino acids (McGhee and Hippel 1975a, 1975b,

Orlando 2000). In the following step, the chromatin is sheared, e.g. by sonication or

endonuclease treatment, to small fragments of ~ 200-500 nt in length, and protein-specific

antibodies are used to immunoprecipitate the protein-DNA complexes of interest. Finally, the

I. INTRODUCTION 8

crosslinks are reversed and the released DNA fragments are applied to downstream analyses.

Downstream strategies reach from quantitative PCR (ChIP-PCR) to microarray hybridization

(ChIP-chip) and NGS (ChIP-seq) approaches (Figure 1).

In case of ChIP-PCR analysis, the immunoprecipitated DNA (ChIP-DNA) is analyzed by

PCR, in order to verify the association of the protein of interest to selected DNA regions

(Tanaka et al. 1997, Chen et al. 1999). However, each protein-DNA interaction must be

validated on its own, strongly restricting the information content of ChIP-PCR analyses in

comparison to genome-wide approaches. This drawback was - up to a certain point -

overcome by ChIP-chip, combining ChIP with microarray hybridization. Here, single-

stranded ChIP-DNA fragments, labeled with fluorescent tags, are hybridized to a DNA

microarray, and probe-target hybridization is quantified using a fluorescent readout. The first

ChIP-chip analysis was performed in 1999 and described the distribution of two cohesins

along the yeast chromosome III (Blat and Kleckner 1999). Soon after, the first combinatorial

approach, using location and expression profiles from ChIP-chip and microarray experiments,

was used to identify direct target genes of S. cerevisiae TFs Gal4 and Ste12 in response to

changes in carbon source and the mating pheromone, respectively (Ren et al. 2000). In 2002,

the first high-throughput ChIP-chip analysis, focusing on determination of genome-wide

positions of over 100 S. cerevisiae TFs followed (Lee et al. 2002). For long, ChIP-chip has

been the method of choice for studying gene regulation and epigenetic mechanisms on an

almost genome-wide scale. However, application of this method was limited by the fact that

whole-genome microarrays are very expensive and not available for many organisms. As a

consequence and fostered by a tremendous progress in NGS technology, ChIP-seq took

rapidly over from its array-based predecessor. Today, ChIP-seq is the gold standard for

determining binding sites of DNA-binding proteins in vivo (Hahn and Young 2011). As

shown in Table 2, compared to ChIP-chip, ChIP-seq offers higher, up to nucleotide-level

resolution, fewer artefacts, a larger dynamic range, less noise and greater coverage (Park

2009).

Figure 1: Chromatin immunoprecipitation - from quantitative PCR to ChIP-seq. DNA-fragments, isolated using chromatin

immunoprecipitation (ChIP), can be applied to single-gene studies based on quantitative PCR (ChIP-PCR) or genome-wide

approaches using ChIP coupled to microarray hybridization (ChIP-chip) and next-generation sequencing (ChIP-seq).

(adapted from Thürmer (2014))

I. INTRODUCTION 9

Originally developed for the identification of in vivo protein-DNA interactions on a

genome-wide scale (Johnson et al. 2007), ChIP-seq soon has been adapted for the

investigation of a wide variety of biological processes. The experimental procedures of some

of the most important adaptations, which have been described in detail in Furey (2012),

Dekker et al. (2013), and de Wit and de Laat (2012), are depicted in Figure 2. They can be

used for studying RNA-protein interactions (cross-linked immunoprecipitation followed by

next-generation sequencing = CLIP-seq) (Sanford et al. 2009), RNA-DNA interactions

(chromatin isolation through RNA purification = ChIRP-seq) (Chu et al. 2011, Simon et al.

2011), as well as DNA-DNA interactions (chromosome conformation capture = Hi-C, 5C;

chromatin-interaction analysis by paired-end sequencing = ChIA-PET) (Dostie et al. 2006,

Dostie and Dekker 2007, Fullwood et al. 2009, Lieberman-Aiden et al. 2009). Furthermore,

nucleosome-depleted open chromatin can be mapped using DNase-seq (DNase I

hypersensitive sites sequencing) (Crawford et al. 2006, Boyle et al. 2008) and FAIRE-seq

(formaldehyde-assisted identification of regulatory elements) (Gaulton et al. 2010, Song et al.

2011).

Development and standardization of the above mentioned and new pipelines, as well as

ongoing cost reduction will surely lead to the establishment of NGS and, in particular,

ChIP-seq as key technologies in basic science. However, a number of technical considerations

must be taken into account when setting up ChIP-seq pipelines, aiming at exploiting the full

strength of this technology (Landt et al. 2012, Flensburg et al. 2014, Jung et al. 2014).

Important issues in experimental design are selection and validation of high-quality

antibodies, optimization of sample quantity (dependent on the abundance of the chromatin-

Table 2: Comparison of ChIP-chip and ChIP-seq

ChIP-chip ChIP-seq

Max. resolution Array-specific, generally 30-100 bp Up to single nucleotide, depends on size of chromatin fragments and sequencing depth

Coverage Limited by sequences on the array; repetitive regions are usually masked out

Limited only by alignability of reads to the genome; increases with read length; many repetitive regions can be covered

Source of platform noise Cross-hybridization between probes and non-specific targets

Some GC bias can be present

Cost-effective cases Profiling of selected regions; when a large fraction of the genome is enriched for the modification or protein of interest

Large genomes; when a small fraction of the genome is enriched for the modification or protein of interest

Required amount of ChIP-DNA High (a few micrograms) Low (10 – 50 ng)

Dynamic range Lower detection limit; saturation at high signal

Not limited

Multiplexing Not possible Possible

(adapted from Park (2009))

I. INTRODUCTION 10

I. INTRODUCTION 11

Figure 2: Comparison of selected experimental pipelines for location-based NGS approaches. Simplified schematics of the

main steps are shown. (A) Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-seq) for the

genome-wide analysis of DNA-binding patterns of DNA-binding proteins, such as TFs. (B) ChIP-seq of histone modifications,

such as H3K4me3 or H3K9me3, can be performed for the identification of heterochromatic and euchromatic genomic

regions. (C) DNase-seq and (D) formaldehyde-assisted identification of regulatory elements (FAIRE-seq) can be used for the

identification of nucleosome-depleted open chromatin. (E) Analogous to ChIP-seq, CLIP-seq can be used to identify binding

sites of RNA-binding proteins and (F) ChIRP-seq can be applied for the identification of genomic regions which are bound by

a specific RNA or a ribonucleoprotein containing the RNA of interest. (G) Interactions between distal genomic regions on

the same or different chromosomes can be analyzed using chromatin conformation capture, providing information about

possible targets for DNA-bound proteins. (H) Similarly, chromatin interaction analysis with paired-end tag sequencing (ChIA-

PET) provides information about chromatin interactions mediated by a specific DNA-binding protein, such as RNA

polymerase II. (adapted from Park (2009) and Furey (2012))

associated protein targets and/or histone modifications), sequencing depth, and

implementation of control experiments. Currently, there is no consensus on which control

experiment is the most appropriate, but the most commonly used strategies are:

[1] sequencing of input-DNA (DNA sample removed prior to ChIP), [2] mock IP DNA (DNA

obtained from ChIP without antibody), or [3] DNA from non-specific ChIP (using an

antibody against a protein not involved in DNA binding or chromatin modification).

4. Bioinformatics analysis of ChIP-seq data

NGS technologies are well suited to provide all the primary data required for a variety of

functional-genomics approaches, reaching from genomics to epigenetics, transcriptomics, and

TF-binding studies. Nevertheless, a thorough down-stream analysis is necessary to bridge

mere data, represented by sequence tags, and functional connections of biological relevance

(Werner 2010).

Bioinformatics analysis of location-based NGS projects starts with a vast amount of raw data,

which are converted to sequence reads during the so-called base-calling process (Figure 3).

Sequence reads are then mapped to the corresponding reference genome using short read

aligners, optimized for extremely fast mapping of short NGS reads. Next, genomic regions

that show statistically significant enrichment in the ChIP sample relative to the control are

identified during the so-called peak-calling process. As peaks can be generally divided into

three categories, selection of the right peak-calling software is essential for effective data

analysis. Peak categories are based on the architecture of peak regions, which in turn is caused

by the DNA-binding properties of the associated proteins. Peak categories are: [1] point

source (highly localized signals, e.g. for TFs), [2] broad source (signals spanning lager

domains, e.g. for some histone modifications such as H3K36me3), and [3] mixed source

(signals that have elements of both, e.g. RNA polymerase II binding) (Pepke et al. 2009).

I. INTRODUCTION 12

Several “peak-callers”, such as the very popular open source peak-caller MACS (Model-

based Analysis of ChIP-Seq) (Zhang et al. 2008), are currently available. They have been

summarized in great detail by Bailey (2013), Furey (2012), and Wilbanks and Facciotti

(2010).

Since bioinformatics analysis of NGS data can be challenging for users who are not skilled in

advanced bioinformatics, several platform based analysis tools have been released, some of

them even enabling data analysis via desktop applications. Prominent examples are HOMER,

offering solid tools and methods for analyzing and interpreting ChIP-seq experiments (Heinz

et al. 2010), Nebula, a web service provided by the Institute Curie (Boeva et al. 2012), and the

Cistrome Project, an integrative and reproducible bioinformatics data-analysis platform

featuring 29 ChIP-seq-specific tools from preliminary peak calling to downstream analysis

(Liu et al. 2011). However, even if the growing number of bioinformatics tools simplifies data

analysis in a great measure, it has to be regarded that varieties in sample preparation as well

as data acquisition, processing, and interpretation can introduce bias, which results in

divergent conclusions that do not necessarily reflect the biology of the factors of interest

(Bailey et al. 2013, Park et al. 2013, Meyer and Liu 2014).

Figure 3: Overview of ChIP-seq data analysis. Raw sequencing data obtained from the sequencing platform is converted

into sequence reads during the base-calling process. Subsequently, sequencing reads are mapped to a reference genome.

Several quality-control analyses, such as examination of base-calling reliability, clonal-tag distribution, and check for

nucleotide frequency relative to read positions and GC bias, can be performed in order to provide information about the

quality of the experiment. Next, peak calling, using data from the ChIP-DNA profile and, if available, a control profile

(usually input-DNA), is performed. Regions showing statistically significant enrichment in ChIP-DNA compared to the input

profile are submitted to downstream analyses. (adapted from Park (2009))

I. INTRODUCTION 13

5. Functional downstream analysis: from physical context to biological

function

Starting from a set of peak regions, follow-on bioinformatics analyses can be used to address

the various biological implications of ChIP-seq data (Figure 3). For DNA-binding protein

ChIP-seq approaches, the most common follow-up analysis is the identification of

DNA-binding sequence motifs, either by de novo motif prediction or by comparison with

pre-defined weight matrices of known DNA-binding factors. However, as de novo pattern

detection algorithms are capable of defining novel patterns without prior experience, they are

more in line with the genome-wide unbiased approach of ChIP-seq (Werner 2010). To date,

hundreds of algorithms for the prediction of DNA motifs have been developed (Tompa et al.

2005, Weirauch et al. 2013), most of them using position weight matrices (PWMs) for

representation of protein-DNA binding specificity (Stormo and Zhao 2010). One of the most

widely used tool for de novo motif prediction, MEME (Multiple Em for Motif Elicitation),

also allows the analysis of very large ChIP-seq datasets (Machanick and Bailey 2011). Based

on sequences obtained from ChIP-seq peak regions, MEME performs all steps necessary for

efficient DNA-binding motif analysis, ranging from ab initio motif discovery to motif

enrichment analysis, motif visualization, binding-affinity analysis, and motif identification.

Nevertheless, these and other models are restricted to a description of the DNA-base readout

by a DNA-binding protein, and they rely on the assumption that positions within a TF binding

site (TFBS) independently contribute to the binding affinity of the corresponding protein

(Slattery et al. 2014). In order to overcome these restrictions, efforts have been made to

develop new, more complex models of protein-DNA interactions. For example, algorithms

based on Bayesian networks (Friedman 2004), Markov networks (Sharon et al. 2008), or

thermodynamic/energy-based models (Zhao et al. 2012) have been introduced.

When aiming at the identification of a DNA-binding consensus sequence based on the

comparison to known DNA-binding motifs, the easiest way is the use publicly available

databases. One of the most popular open-access databases is JASPAR, representing a

collection of annotated, high-quality, matrix-based TFBS profiles for multicellular eukaryotes

(Sandelin et al. 2004). The latest release of the database contains a curated non-redundant set

of 593 DNA-binding motifs derived from published collections of experimentally defined

TFBSs. 205 of them were validated in vertebrates and 177 in fungi (Mathelier et al. 2014).

Even in case of successful de novo prediction of a new DNA-binding motif, comparison to a

I. INTRODUCTION 14

DNA-binding motif database can be rewarding, as similarities to known motifs might point to

related regulatory functions or binding properties of the corresponding proteins.

In case of TF-binding studies, another important step in functional downstream analysis is the

elucidation of the biological relevance of the identified TFBSs in terms of transcriptional

regulation of neighboring genes. Based on the observation that transcriptional control is

highly combinatorial, which means that cooperative binding of multiple TFs and/or

cooperative recruitment of RNA polymerase II is often required for transcriptional activation,

it has been postulated that TF binding only indicates the potential for a neighboring gene to be

regulated (Gao et al. 2004). Furthermore, based on TF binding alone, it is not possible to

determine whether the respective protein acts as a transcriptional activator or as a repressor.

Hence, incorporation of other data types into the analysis is inevitable to establish functional

TF–target gene relations. For example, TF ChIP-seq data can be complemented by expression

data from RNA-seq or microarray analyses. Doing so, direct TF target genes can be identified

based on the correlation between binding strength of the protein, as deduced from ChIP-seq

data, and the expression levels of neighboring genes. Corresponding approaches were

successfully used for the identification of genes under direct control of TF SrbA in

Aspergillus fumigatus (Chung et al. 2014) and analysis of the binding properties of the core

circadian TF WCC in Neurospora crassa (Hurley et al. 2014). Alternatively, data from

DNA-binding protein ChIP-seq experiments can be complemented by the corresponding data

from histone-modification ChIP-seq analyses, enabling the identification of transcriptional

active genomic regions next to peak regions. While trimethylation of H3K4, H3K36, and

H3K79 is generally accepted to be associated with actively transcribed euchromatic regions,

trimethylation of H3K9, H3K27, and H4K20 is characteristically for heterochromatin

formation and transcriptional silencing (Noma et al. 2001, Berger 2007). A corresponding

strategy was used by Thurtle and Rine (2014), who used ChIP-seq of Sir proteins, histones,

and histone modification H4K16-ac for mapping of silenced chromatin in S. cerevisiae.

Once a set of differentially expressed target genes has been identified, Gene Ontology (GO)

analyses can be performed to identify over-representation of genes assigned to particular

molecular functions or biological processes (Ashburner et al. 2000) and, finally, comparative

analysis of multiple ChIP-seq data sets can be used for the generation of differential binding

profiles, e.g. as a function of developmental processes (Li et al. 2015), in response to external

stimuli (Chung et al. 2014), or for functional distinction of multiple factors involved in the

same regulatory network (Fitzgerald et al. 2014).

I. INTRODUCTION 15

6. ChIP-seq analyses in fungi

While ChIP-seq has been widely used for analyzing DNA-binding proteins and epigenetic

mechanisms, especially in mice and human tissues, the number of published experiments

performed in fungi is rather limited (Table 3). The very first publication dates back to 2007

and describes the generation of a comprehensive map of H2A.Z nucleosomes in S. cerevisiae

by sequencing of DNA from 322,000 individual nucleosomes (Albert et al. 2007). In 2011,

the first ChIP-seq analysis performed in a filamentous ascomycete, namely N. crassa,

followed. Here, extensive co-localization of centromeric proteins CenH3, CEN-C, CEN-T,

and histone H3K9me3 was demonstrated, and a model, in which centromere proteins nucleate

at the core kinetochore but require additional factors for spreading, was proposed (Smith et al.

2011). Meanwhile, ChIP-seq experiments in various other fungi followed. For example, in the

biotechnologically relevant species Fusarium fujikuroi and Trichoderma reesei, ChIP-seq was

used for the analysis of epigenetic marks linked to SM gene-cluster expression (Seiboth et al.

2012, Karimi-Aghcheh et al. 2013, Niehaus et al. 2013, Studt et al. 2013, Wiemann et al.

2013). Furthermore, DNA-binding protein ChIP-seq analysis was used for the generation of a

genome-wide binding profile of TF Tri6, which is involved in the regulation of trichothecene

gene-cluster expression in Fusarium graminearum (Nasmith et al. 2011).

7. Summary

Driven by an increasing demand for faster, cheaper, and higher-throughput sequencing

technologies, development of NGS technologies led to dramatic changes in genomics research

during the past decade. Today, virtually all functional-genomics approaches can be addressed

by NGS-based experimental pipelines. Specialized protocols have been published for use of

NGS in WGS and transcriptomics (RNA-seq), as well as for the investigation of protein-DNA

interactions (ChIP-seq, DNase-seq, FAIRE-seq), protein-RNA interactions (CLIP-seq),

RNA-RNA interactions (ChIRP-seq), and chromatin conformation studies (Hi-C, 5C,

ChIA-PET) (Furey 2012).

When focusing on location-based NGS approaches aiming at the generation of precise maps

of epigenetic marks and genome-wide TF DNA-binding profiles, the advantages of ChIP-seq

compared to its predecessor ChIP-chip are obvious. Compared to ChIP-chip, ChIP-seq offers

higher resolution, fewer artefacts, a larger dynamic range, less noise, and greater coverage

(Park, 2009). However, conscientious experimental design and thorough downstream analysis

are necessary to exploit the full strength of this versatile technology (Landt et al. 2012).

I. INTRODUCTION 16

Table 3: ChIP-seq analyses in fungi

Species Experimental approach Outcome Reference

Aspergillus fumigatus ChIP-seq of transcription factor SrbA; RNA-seq

Identification of genes under direct SrbA transcriptional regulation in hypoxia

(Chung et al. 2014)

Candida albicans ChIP-seq of histone deacetylase Set3C; RNA-seq

Set3C acts as a transcriptional co-factor of metabolic and morphogenesis-related genes

(Hnisz et al. 2012)

Candida parapsilosis ChIP-seq of TF Efg1 Genome-wide binding profile of Efg1, a transcriptional regulator of morphogenesis, biofilm formation, and virulence

(Connolly et al. 2013a)

Cryptococcus neoformans

ChIP-seq of H3K9ac; RNA-seq

A homolog of the yeast protein Ada2, a member of the SAGA complex, is involved in the direct regulation of capsule and mating responses; it may also play a direct role in regulating capsule-independent anti-phagocytic virulence factors

(Haynes et al. 2011)

Fusarium fujikuroi ChIP-seq of H3K4me2, H3K9me3, and H3K9ac

Identification of epigenetic marks linked to SM gene-cluster expression

(Wiemann et al. 2013)

Fusarium fujikuroi ChIP-seq of H3K9ac Identification of epigenetic marks linked to SM gene-cluster expression

(Niehaus et al. 2013)

Fusarium fujikuroi ChIP-seq of H3K9ac in ffdah1 deletion strains

Identification of genome-wide changes in histone acetylation-patterns

(Studt et al. 2013)

Fusarium graminearum

ChIP-seq of TF Tri6 Genome-wide binding profile of Tri6, involved in trichothecene gene-cluster expression

(Nasmith et al. 2011)

Neurospora crassa ChIP-seq of circadian TF WCC; RNA-seq

Mapping of binding sites of the core circadian TF WCC

(Hurley et al. 2014)

Neurospora crassa ChIP-seq of histone variant yH2A, H3K9me3, H3K4me2

Mapping of binding sites of yH2A, important for stabilization of stalled replication forks and promotion of DNA double-strand-break repair

(Sasaki et al. 2014)

Neurospora crassa ChIP-seq of centromere proteins CenH3/CEN-C, kinetochore protein CEN-T, and H3K4me2, H3K4me3, H3K9me3

Identification of centromeric DNA (Smith et al. 2011)

Saccharomyces cerevisiae

ChIP-seq of histone H3 Genome-wide mapping of nucleosome positions

(Wal and Pugh 2012)


ChIP-seq of Sir proteins, histone H3, H4K16ac

Analysis of the molecular topography of silenced chromatin

(Thurtle and Rine 2014)


ChIP-seq of linker histone Hho1

Hho1 is required for efficient sporulation and full compaction of the spore genome

(Bryant et al. 2012)


ChIP-seq of H2A.Z Generation of a H2A.Z nucleosome map (Albert et al. 2007)


ChIP-seq of Cse4, Ste12 and Pol II

Concurrent analysis of TFBSs (Lefrançois et al. 2009)

Schizosaccharomyces pombe

ChIP-seq of TF Pho7 Characterization of the phosphate starvation response and hydrogen peroxide-mediated oxidative stress response

(Carter-O'Connell et al. 2012)

Trichoderma reesei ChIP-seq of H3K4me3, H3K9me3, and H3K4me2

Identification of epigenetic marks linked to SM gene-cluster expression

(Seiboth et al. 2012, Karimi-Aghcheh et al. 2013)

Zymoseptoria tritici ChIP-seq of H3K9me3 and H3K4me2

Mapping of euchromatic and heterochromatic genomic regions

(Soyer et al. 2015)

I. INTRODUCTION 17

While ChIP-seq has been widely used in mice and human tissues, the number of experiments

performed in fungi has remained rather low until today. Nevertheless, further application of

ChIP-seq in a broader variety of fungi harbors a great potential, especially in terms of

functional analyses in biotechnologically relevant species.

II. SCOPE OF THE THESIS 18

II. SCOPE OF THE THESIS

1. Regulation of fungal secondary metabolism

Filamentous fungi are renowned for their ability to produce SMs, low-molecular-weight

molecules, which are not essential for normal growth or survival of the producing organism

(Keller et al. 2005). Generally, at least four major classes of fungal SMs, namely

non-ribosomal peptides, polyketides, alkaloids, and terpenes, can be distinguished. As shown

in Table 4, some of the most prominent fungal SMs include important pharmaceuticals, such

as penicillins, cyclosporines and statins, as well as high-potency fungal toxins, e.g. aflatoxins

and trichothecenes.

Fungal secondary metabolism is influenced by a number of genetic and environmental factors,

which are ranging from light to the availability and type of carbon/nitrogen sources, as well as

the pH of the surrounding medium and temperature (Calvo et al. 2002). Furthermore, a tight

association between production of SMs and developmental processes has been described

(Reiss 1982, Hicks et al. 1997, Guzmán-de-Peña et al. 1998).

Genes for fungal SMs are organized in clusters (Keller et al. 2005), which are mostly located

in sub-telomeric regions (Palmer and Keller 2010). Transcriptional regulation of these SM

Table 4: Classes and examples of fungal SMs

Compound Application Species Reference

Non-ribosomal peptides

Cephalosporin Antibiotic Acremonium chrysogenum (Elander 2003)

Cyclosporin Immunosuppressive drug Tolypocladium inflatum (Survase et al. 2011)

Gliotoxin Mycotoxin, Immunosuppressive drug

Aspergillus fumigatus (Sutton et al. 1994, Scharf et al. 2012)

Penicillin Antibiotic Penicillium chrysogenum (Brakhage et al. 2004)

Polyketides

Aflatoxin B1 Mycotoxin Aspergillus flavus (Yu et al. 2004)

Compactin Cholesterol-lowering drug Penicillium citrinum (Chakravarti and Sahai 2004)

Fumonisin B Mycotoxin Fusarium verticillioides (Nelson et al. 1993)

Lovastatin Cholesterol-lowering drug Aspergillus terreus (Manzoni and Rollini 2002)

Mycophenolic acid Immunosuppressive drug Penicillium brevicompactum (Regueira et al. 2011)

Alkaloids

Ergotamin Acute treatment of migraine Claviceps purpurea (Tudzynski et al. 1999b, Tfelt-Hansen and Koehler 2008)

Fumigaclavine C Anti-atherosclerotic agent Aspergillus fumigatus (Du et al. 2011)

Terpenes

Aflatrem Tremorgenic mycotoxin Aspergillus flavus (TePaske et al. 1992)

Deoxynivalenol (DON) Mycotoxin Fusarium graminearum (Audenaert et al. 2014)

Gibberellin GA3 Plant hormone Gibberella fujikuroi (Tudzynski 1999)

Trichothecene T2 toxin Mycotoxin Fusarium sporotrichoides (Desjardins et al. 1993)


gene clusters is mediated by a variety of transcriptional regulatory elements, ranging from

pathway specific TFs to broad domain TFs and multiple-subunit protein complexes (Yin and

Keller 2011). Pathway specific TFs include AflR, a sequence-specific Zn(II)2Cys6 protein

necessary for regulation of sterigmatocystin/aflatoxin biosynthesis in Aspergillus species

(Woloshuk et al. 1994, Yu et al. 1996, Fernandes et al. 1998), as well as AcFKH1, a member

of the forkhead family of TFs, and CPCR1, a eukaryotic regulatory X (RFX) family TF, both

involved in regulation of cephalosporin C production in A. chrysogenum (Schmitt et al.

2004a, Schmitt et al. 2004b). Furthermore, PcRFX1 was shown to be a direct regulator of the

expression of the penicillin-biosynthesis genes pcbAB, pcbC and penDE in P. chrysogenum

(Domínguez-Santos et al. 2012). Besides pathway-specific transcriptional regulators, a

number of broad domain TFs establishing a link between fungal SM production and

environmental signals have been described. Important representatives of this category are

CreA (Cre1), involved in carbon signaling (Dowzer and Kelly 1989), AreA, involved in

nitrogen signaling (Hynes 1975), and PacC, involved in pH sensing (Tilburn et al. 1995). In

A. chrysogenum, evidence was provided for Cre1 acting as a carbon catabolite-dependent

repressor of the cephalosporin-gene cluster (Jekosch and Kück 2000), and a homolog of AreA

was shown to directly influence the expression of the gibberellin-gene cluster in Gibberella

fujikuroi (Tudzynski et al. 1999a, Mihlan et al. 2003). Furthermore, PacC was demonstrated

to positively regulate penicillin production and negatively regulate production of

sterigmatocystin in P. chrysogenum and Aspergillus species, respectively (Espeso et al. 1993,

Suarez and Peñalva 1996, Keller et al. 1997). A totally new feature of regulation of SM

biosynthesis in filamentous fungi was uncovered by recent work in P. chrysogenum,

providing evidence for the mating-type (MAT) TF MAT1-1-1 to be involved in regulation of

penicillin biosynthesis. It was shown that expression of the penicillin-biosynthesis genes is

significantly down-regulated in a ΔMAT1-1-1 strain compared to wild type (Böhm et al.

2013). This finding was of exceptional importance, because for long MAT1-1-1 has been

regarded as a regulatory protein restricted to the orchestration of sexual reproduction alone

(Martin et al. 2010). The last group of transcriptional regulatory elements involved in

regulation of fungal secondary metabolism involves multi-subunit protein complexes, such as

the CCAAT-binding complex AnCF/PNR1 (Then Bergh et al. 1996, Brakhage et al. 1999)

and the velvet complex (Bayram et al. 2008, Hoff et al. 2010, Wiemann et al. 2010, Kopke et

al. 2013). Both are involved in regulation of SM biosynthesis in a number of fungal species.

The founding member of the velvet complex, VeA (Velvet A), was shown to interact with the

putative methyltransferase LaeA to positively regulate SM production and, together with


another velvet protein, VelB, to induce sexual development in Aspergillus nidulans (Bayram

and Braus 2012). Similar effects were also observed in P. chrysogenum, where PcVelA, a

homolog of VeA, plays an important regulatory role during penicillin biosynthesis and

conidiation (Hoff et al. 2010).

Although a broad variety of pathway-specific TFs, global transcriptional regulators, and

multi-subunit complexes, which are involved in the regulation of fungal secondary

metabolism, have been identified until today, the complexity of these regulatory networks,

including multiple target sites and interconnections to other regulatory circuits, is far away

from being understood (Calvo et al. 2002). This is mainly due to the fact that almost all works

on the isolation and characterization of fungal TFs focused on the detailed analysis of

particular genes or families until today. Although these studies played a large part in

explaining the fundamental principles of gene regulation, they only presaged the whole

dynamics of large GRNs. Today, NGS-based approaches provide excellent tools to analyze

genome-wide TF-binding patterns and gene regulation at unprecedented depth. Using

ChIP-seq combined with microarray/RNA-seq analysis, TFBSs and genome-wide expression

profiles can be determined for various cell types, different developmental stages, or in

different environmental conditions. This provides the information needed to uncover the

regulatory dynamics associated with changes in cell physiology and development, and

establishes a framework for a comprehensive understanding of multi-layer GRNs (Stormo and

Zhao 2010).

2. Aim of this thesis

The aim of this thesis was the genome-wide analysis of GRNs controlling morphogenesis

and secondary metabolism in the filamentous fungus P. chrysogenum by using ChIP-seq.

P. chrysogenum is the main industrial producer of the pharmaceutically relevant β-lactam

antibiotic penicillin (Fleming 1929), the most commonly used drug in the treatment of

bacterial infections. With yearly sales of about US$ 8 billion, penicillin is one of the most

valued products in the global anti-infective market (Barber et al. 2004). Progressive

optimization of P. chysogenum strains used for industrial penicillin production was started in

1943, after isolation of P. chrysogenum strain NRRL 1951 from a moldy cantaloupe in Peoria,

IL, USA (Raper et al. 1944, Raper 1946). Using random-mutagenesis approaches, based on

X-ray, ultraviolet irradiation, and nitrogen-mustard mutagenesis, penicillin-biosynthesis

performance was increased from 60 µg/ml penicillin to more than 50 mg/ml in modern


overproducer strains (Backus and Stauffer 1955, Peñalva et al. 1998, Barreiro et al. 2012).

However, release of the P. chrysogenum genome sequence in 2008 (van den Berg et al. 2008)

paved the way for the replacement of random mutagenesis by targeted genetic engineering.

Today, detailed knowledge of cellular and developmental processes affecting penicillin

biosynthesis and other traits of biotechnological relevance, such as growth rates, hyphal

morphology and pellet formation, sporulation, and stress tolerance, is crucial for further

optimization of this industrially highly relevant organism. As a consequence, the functional

investigation of regulators of secondary metabolism and morphogenesis in P. chrysogenum

has been in the limelight of numerous research projects during the past years (Kosalková et al.

2009, Hoff et al. 2010, Kamerewerd et al. 2011, Veiga et al. 2012, Böhm et al. 2013, Kopke et

al. 2013, Böhm et al. 2015, Wolfers et al. 2015). However, as these studies were mainly built

on phenotypic characterization of deletion and overexpression strains, protein-protein

interaction studies, and microarray analyses, little is known about genome-wide GRNs and

direct target genes of regulators of secondary metabolism and morphogenesis in

P. chrysogenum until today.

This work describes the application of DNA-binding protein ChIP-seq analysis for

genome-wide DNA-binding studies of two regulators of penicillin biosynthesis, development,

and morphogenesis in P. chrysogenum, namely MAT1-1-1 and PcVelA. The α-box MAT TF

MAT1-1-1 is one of the main regulators of the sexual life cycle in P. chrysogenum (Hoff et al.

2008, Böhm et al. 2013) and PcVelA acts as one of the core components of the multi-subunit

velvet complex (Hoff et al. 2010, Kopke et al. 2013). Previous studies pointed to MAT1-1-1

and PcVelA regulatory functions that are not restricted to one cellular or developmental

process alone. For example, MAT1-1-1 was shown to affect various traits of biotechnological

relevance, including penicillin biosynthesis, hyphal morphology, and formation of asexual

conidiospores (Böhm et al. 2013), extending its regulatory function far beyond its recognized

role in orchestration of the sexual life cycle. Correspondingly, besides its well-known

regulatory functions in terms of secondary metabolism and formation of asexual

conidiospores, PcVelA was shown to influence pellet formation and hyphal morphology in

P. chrysogenum (Hoff et al. 2010, Kopke et al. 2013). Additionally, recent work in related

species described the ability of homologs of PcVelA to bind DNA in a sequence-dependent

manner and to specifically interact with putative methyltransferases outside the velvet

complex (Jiang et al. 2011, Palmer et al. 2013, Sarikaya-Bayram et al. 2014, Sarikaya-Bayram

et al. 2015). These observations suggest that MAT1-1-1 and PcVelA might perform as global


transcriptional regulators, marking them as extremely interesting candidates for further

characterization on a genome-wide scale.

Within the scope of this thesis, ChIP-seq will be adapted for the application in

P. chrysogenum and an experimental pipeline will be established for sample preparation,

bioinformatics analysis of sequencing data, and further downstream analysis. A

comprehensive ChIP-seq approach will be used in order to identify as many MAT1-1-1 and

PcVelA DNA-binding sites as possible, independent of physiological culture conditions,

developmental stages, or external stimuli. Downstream analyses will include the validation of

data obtained from ChIP-seq analyses by ChIP-PCR as well as the identification of direct

target genes of both, MAT1-1-1 and PcVelA, by integration of previous microarray data and

qRT-PCR analyses. Based on peak regions identified in ChIP-seq analyses, de novo

prediction of DNA-binding motifs specific for MAT1-1-1 as well as PcVelA will be

performed. Subsequently, predicted DNA-binding consensus sequences will be tested for

functionality and specificity in vitro, ex vitro, and in vivo by applying DNA-binding studies

(electrophoretic mobility shift essays; EMSAs), yeast one-hybrid (Y1H), and DsRed reporter

gene assays. Furthermore, functional characterization of new MAT1-1-1 and PcVelA

downstream factors will complete this work.

III. BECKER et al. 2015a 23

III. BECKER et al. 2015a

Genome-wide identification of target genes of a mating-type

α-domain transcription factor reveals functions beyond sexual

development

Kordula Becker, Christina Beer, Michael Freitag, and Ulrich Kück (2015)

Molecular Microbiology doi:10.1111/mmi.12987

Genome-wide identification of target genes of a mating-typeα-domain transcription factor reveals functions beyondsexual development

Kordula Becker,1 Christina Beer,1 Michael Freitag2

and Ulrich Kück1*1Christian Doppler Laboratory for Fungal Biotechnology,Lehrstuhl für Allgemeine und Molekulare Botanik,Ruhr-Universität Bochum, Universitätsstr. 150, D-44780Bochum, Germany.2Department of Biochemistry and Biophysics, OregonState University, Corvallis, Oregon 97331-7305, USA.

Summary

Penicillium chrysogenum is the main industrial pro-ducer of the β-lactam antibiotic penicillin, the mostcommonly used drug in the treatment of bacterialinfections. Recently, a functional MAT1-1 locusencoding the α-box transcription factor MAT1-1-1 wasdiscovered to control sexual development in P. chry-sogenum. As only little was known from any organ-ism about the regulatory functions mediated byMAT1-1-1, we applied chromatin immunoprecipitationcombined with next-generation sequencing (ChIP-seq) to gain new insights into the factors that influ-ence MAT1-1-1 functions on a molecular level and itsrole in genome-wide transcriptional regulatory net-works. Most importantly, our data provide evidencefor mating-type transcription factor functions thatreach far beyond their previously understood role insexual development. These new roles include regula-tion of hyphal morphology, asexual development, aswell as amino acid, iron, and secondary metabolism.Furthermore, in vitro DNA–protein binding studiesand downstream analysis in yeast and P. chrysoge-num enabled the identification of a MAT1-1-1 DNA-binding motif, which is highly conserved amongeuascomycetes. Our studies pave the way to a moregeneral understanding of these master switches fordevelopment and metabolism in all fungi, and openup new options for optimization of fungal high pro-duction strains.

Introduction

Sexual propagation in euascomycetes is controlled by twoalternative mating-type loci, namely MAT1-1 and MAT1-2,which consist of dissimilar sequences occupying thesame locus on the chromosome. These sequences aretermed idiomorphs to indicate that they do not representthe alleles of a single gene (Metzenberg and Glass,1990). A common feature specific to mating types fromeuascomycetes is the presence of a MAT1-1-1 gene,defining the MAT1-1 idiomorph and encoding anα-domain transcription factor (TF). The alternative idi-omorph, MAT1-2, is characterized by the presence of aMAT1-2-1 gene, encoding a TF carrying a high mobilitygroup (HMG) domain (Turgeon and Yoder, 2000; Leeet al., 2010).

While DNA-binding HMG-domain proteins are ubiqui-tous and well characterized, α-domain proteins havelimited distribution and their evolutionary origin is stillobscure (Martin et al., 2010). In Saccharomyces cerevi-siae, MATα1, one of two proteins encoded by the α-typemating locus, acts as a transcriptional co-activator and isinvolved in the regulation of mating-type-specific geneexpression (Herskowitz, 1989). MATα1 binds coopera-tively with the MADS-box TF Mcm1 to 26-bp P′Q promoterelements to activate the expression of α-specific genes(αsgs) (Bender and Sprague, 1987). Surprisingly, only afew direct target genes of mating-type TFs are knownuntil today. For example, chromatin immunoprecipitation(ChIP)-chip analysis in S. cerevisiae identified five αsgsand six a-specific genes (asgs), which, with the exceptionof one αsg, were all involved directly in some aspect ofmating, e.g. those encoding the mating pheromoneα-factor and the a-pheromone receptor Ste3 (Galgoczyet al., 2004). Similarly, microarray analysis in Candidaalbicans identified two αsgs and at least two asgs(Tsong et al., 2003), and genome-wide ChIP analysis inLachancea kluyveri identified a total of nine asgs, of whichsix were orthologs of asgs in either C. albicans or S. cer-evisiae (Baker et al., 2012). Against this background,it appears somehow contradictory that several micro-array analyses demonstrated that MAT genes have arather wide-ranging effect on fungal gene expression

Accepted 26 February, 2015. *For correspondence. [email protected]; Tel.: (+49) 23 4322 6212; Fax (+49) 23 43214184.

Molecular Microbiology (2015) ■ doi:10.1111/mmi.12987

© 2015 The Authors. Molecular Microbiology published by John Wiley & Sons LtdThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use,distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

mailto:[email protected]

http://creativecommons.org/licenses/by-nc/4.0/

(Pöggeler et al., 2006; Bidard et al., 2011; Wada et al.,2012; Böhm et al., 2013). Hence, further research isneeded in order to distinguish between primary and sec-ondary target genes of mating-type encoded TFs, and toprovide a comprehensive understanding of mating-typecontrolled regulatory circuits on a genome-wide level.

As most research performed on characterizing mating-type locus-encoded TFs used yeasts as a model organ-ism, little is known about mating-type protein function ineuascomycetes. Lack of research on many euascomy-cetes, especially those of major medical and industrialimportance, was fostered by the fact that these fungi havebeen considered to be asexual since no sexual propaga-tion had been observed under laboratory conditions for avery long time (Dyer and O’Gorman, 2012; Kück andBöhm, 2013). Recent description of a hetherothallicsexual cycle in P. chrysogenum now makes the fungus avaluable object for the investigation of mating-type con-trolled transcriptional regulatory networks and fungalsexual reproduction in general. These mechanisms are ofmajor importance, as the possibility to generate offspringwith novel combinations of traits relevant to penicillin pro-duction provides promising starting points for industrialstrain development purposes (Böhm et al., 2013).

Chromatin immunoprecipitation combined with next-generation sequencing analysis (ChIP-seq) is one of themost powerful tools for genome-wide profiling of DNA-binding proteins, which has greatly benefited from tre-mendous progress in next-generation sequencingtechnology (Smith et al., 2010; Magnúsdóttir et al., 2013;Myers et al., 2013). Today, ChIP-seq is an indispensabletool for studying gene regulation and epigenetic mecha-nisms at the genomic level (Park, 2009). Here, we presentthe first application of ChIP-seq for the functional charac-terization of a TF from P. chrysogenum, and, more impor-tantly, the first genome-wide analysis focusing onunraveling the transcriptional regulatory network con-trolled by a mating-type locus-encoded TF.

While MAT1-1-1 has been described as a regulatoryprotein restricted to the orchestration of sexual reproduc-tion (Debuchy et al., 2010), our data clearly expand thiscurrent view of MAT1-1-1 function beyond transcriptionalregulation of sexual development alone. We providestrong evidence of new and additional roles for MAT1-1-1in regulating asexual development and morphogenesis,as well as amino acid, iron, and secondary metabolism.Furthermore, our analyses, using bioinformatics, electro-phoretic mobility shift assays (EMSAs), yeast one-hybrid(Y1H), and DsRed reporter gene assays in P. chrysoge-num, led to the identification of a MAT1-1-1 DNA-bindingmotif that shows a high degree of conservation withineuascomycetes.

Taken together, our data extend the general under-standing of the biological functions of mating-type-

encoded TFs and should thus open new avenues for thestudy of fungal sexual development. Finally, as we per-formed ChIP-seq experiments with a laboratory strain thathas already undergone several rounds of mutagenesis toincrease penicillin production (Nielsen, 1997), our resultsare applicable to fungal strains used for today’s industrialproduction of pharmaceutically relevant secondarymetabolites.

Results

Construction of MAT1-1-1 strains for ChIP-seq analysis

A Pgpd::egfp::MAT1-1-1 fusion construct (pGFP-MAT1,Fig. S1A) was transformed into recipient P2niaD18 togenerate strain MAT1-ChIP. Pgpd was used to obtain anelevated expression level of the MAT1-1-1 gene, sinceexpression of mating-type genes under control of theirnative promoter is known to be low. For example, RMA-express (http://rmaexpress.bmbolstad.com) analysis ofnormalized raw data obtained from microarray analysisusing P. chrysogenum strain P2niaD18 revealed relativeMAT1-1-1 expression levels of about 13.6% and 3.1%referred to actin (Pc20g11630) and myosin (Pc21g00710)expression levels, respectively (Fig. S1B) (Böhm et al.,2013). Furthermore, transcripts of mating-type geneswere reported to be barely detectable by Northern hybridi-zation in Podospora anserina as well as RNA-seq analy-sis in Neurospora crassa (Coppin and Debuchy, 2000;Wang et al., 2014).

Successful transformation was verified by polymerasechain reaction (PCR) and sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS–PAGE)/Western blot analysis, confirming the presence of theepitope-tagged protein EGFP-MAT1-1-1 in crude proteinextract from recombinant strains (Fig. S1C and D). Usingfluorescence microscopy, the presence and nuclear locali-zation of the fusion protein were verified prior to eachChIP experiment (Fig. S1E). Functionality of the fusionprotein was further confirmed when pellet formation wasinvestigated in shaking cultures (Fig. S1F). Overexpres-sion of MAT1-1-1 in the MAT1-ChIP strain resulted in theformation of significantly larger pellets (Ø 4–5 mm) whencompared with P2niaD18 (Ø 1–2 mm), matching the phe-notypic characteristics of a previously described MAT1-1-1 overexpression strain (OE MAT1-1-1) (Böhm et al.,2013).

ChIP-seq analysis reveals a genome-wide bindingprofile of MAT1-1-1

We performed ChIP-seq experiments on three independ-ent biological samples, namely ‘shaking 1’, ‘shaking 2’,and ‘surface’ (Table 1). In an effort to identify as many

2 K. Becker, C. Beer, M. Freitag and U. Kück ■

© 2015 The Authors. Molecular Microbiology published by John Wiley & Sons Ltd, Molecular Microbiology

http://rmaexpress.bmbolstad.com

MAT1-1-1 binding sites as possible, independent of physi-ological culture conditions, two samples were derivedfrom shaking cultures and one was obtained from asurface-grown culture. Input-DNA from shaking (‘shaking-_input’) and surface cultures (‘surface_input’) wassequenced as a control. Only regions meeting the follow-ing criteria were considered as specific peak regions: (1)at least fourfold enrichment in ChIP-DNA versus input-DNA, (2) a false discovery rate (FDR) threshold ≤ 0.001,and (3) a Poisson p-value ≤ 1.00e–04. Intersection of ourdatasets identified 243 sites that were specifically boundby MAT1-1-1 in at least two independent biological repli-cates, thus meeting the standards set by the ENCODEand modENCODE consortia (Landt et al., 2012) (DatasetS1, Fig. 1A).

Starting from ChIP-seq datasets, we classified peaksaccording to their genomic location with regard to neigh-boring coding sequences. Seventy-nine percent (193/243) of peaks were exclusively located within intergenicregions and 21% (50/243) showed intragenic localization(Fig. 1B). Of 193 peaks showing intergenic localization,21 were positioned within the 3′ region of both neighbor-ing open reading frames, and 90 showed 5′ localization toonly one adjacent gene. Eighty-two peak regions were,however, positioned within divergent promoters, resultingin a total of 254 genes that may be directly controlled byMAT1-1-1. Comparison to expression data obtained fromprevious microarray analyses (Böhm et al., 2013) con-firmed changes in expression profiles by at least twofoldin a ΔMAT1-1-1 strain compared with P2niaD18 for 29.9%

Table 1. ChIP-seq design and results.

Sample # Readsa # Mappedb % Mappedc# PeaksFDR ≤ 0.001d

# Differentialpeakse

# Totalpeaksf

Estimatedfragment lengthg

shaking 1 44,608,426 27,190,663 60.9 % 7453 430 327 237shaking 2 39,771,172 23,994,317 60.3 % 6523 379 276 226surface 14,364,485 12,890,352 89.7 % 6324 218 102 212shaking_input 16,952,199 15,380,186 90.7 % – – – –surface_input 12,879,889 11,422,613 88.7 % – – – –

a. Total number of sequenced reads.b. Total number of reads mapped to P. chrysogenum P2niaD18 genome (Specht et al., 2014).c. Fraction of tags found in peaks versus genomic background determined by HOMER (Heinz et al., 2010).d. Number of peaks passing FDR ≤ 0.001 threshold.e. Number of peak regions showing at least fourfold enrichment in ChIP-sample compared to input.f. Total number of peak regions after local background filtering and clonal filtering.g. Estimated fragment length used for sequencing, determined from tag auto correlation analysis.

Fig. 1. Genome-wide distribution ofMAT1-1-1 binding regions.A. Venn-diagram showing intersectionbetween MAT1-1-1 ‘shaking 1’, ‘shaking 2’,and ‘surface’ datasets. Only peaks within amaximum distance of 100 nt were regardedas overlapping.B. Distribution of ChIP-enriched regionsoverlapping or positioned within intragenicregions vs. ChIP-enriched regions that wereexclusively located within intergenic regions(based on peak regions present in at leasttwo independent datasets).C. Distance between MAT1-1-1 ChIP-seqpeak summits and ATG of neighboring genespositioned in 5′–3′ orientation with regard tothe corresponding peak region (based onpeak regions present in at least twoindependent datasets).

Target genes of a mating-type transcription factor 3


(76/254) of these genes. Analysis of the distance betweenpeak summits and the predicted translation start sites(nearest ATG in good initiation context) revealed anaverage distance of 200–500 nt (Fig. 1C). Approximately50% of all analyzed genes fit this pattern.

Categorization of putative MAT1-1-1 target genes

Gene ontology (GO) analysis of proteins encoded by the254 putative MAT1-1-1 target genes revealed a significant(p ≤ 0.05) overrepresentation of the following categories:(1) metabolism, including proteins related to amino acidand secondary metabolism, (2) energy, (20) cellular trans-port, transport facilities, and transport routes, (32) cellrescue, defense, and virulence, (34) interaction withthe environment, including proteins involved in cellularsensing and response to external stimuli (e.g. pheromoneresponse) (Fig. S2). Besides expected putative MAT1-1-1target genes that could be directly assigned to sexualdevelopment, e.g. ppg1 (Pc14g01160), the homolog ofS. cerevisiae MFα1/2, encoding the α-factor pheromone,and pre1 (Pc22g15650), the homolog of the S. cerevisiaea-factor receptor encoding gene STE3 (Galgoczy et al.,2004), ChIP-seq analysis identified many new putativeMAT1-1-1 target genes that had never been linked tomating-type-encoded TFs before. Table 2 provides adetailed summary of selected MAT1-1-1 target genesarranged according to the description and proposed func-tion of encoded proteins, as obtained from blastp analysisand literature. All genes listed are positioned in 5′–3′orientation with regard to neighboring MAT1-1-1 peakregions. Corresponding peak values, expression profilesof each gene in a MAT1-1-1 deletion strain compared withwild type P2niaD18 and occurrence of the MAT1.1 motif(to be described later) are given. For reasons of clarityand comprehensibility, the categories mentioned here donot necessarily correspond directly to categories used inGO analysis.

Validation of MAT1-1-1 targets

To validate MAT1-1-1 DNA-binding regions identified byour ChIP-seq approach, we performed ChIP-PCR analy-sis (Fig. 2A). Five representative MAT1-1-1 target regionswere analyzed for MAT1-1-1-specific enrichment in ChIP-DNA compared to input-DNA, obtained from shakingcultures. Target regions were selected according to thefollowing two key criteria: (1) they either possessed astatistically highly significant peak value (Pc20g00090) or(2) proteins encoded by adjacent genes were known to beinvolved in regulation of sexual reproduction in yeast(pre1, kex1, kex2, ppg1). Enrichment was calculated asthe ratio of the region of interest to a control regionshowing no MAT1-1-1-specific enrichment in ChIP-DNA

relative to this ratio in the input-DNA sample. An additionalcontrol region (NC) is shown as a negative control. ChIP-PCR results showed significant overlap with the corre-sponding peak values obtained from bioinformaticsanalysis, confirming specific enrichment of all testedtarget regions in ChIP-DNA vs. input-DNA, and validatingpeak values as a convincing parameter for estimation ofMAT1-1-1 binding affinity to target regions identified inChIP-seq analyses.

Next, quantitative real time (qRT)-PCR analyses wereperformed to validate MAT1-1-1 target genes next to thepeak regions mentioned earlier as well as a selection ofadditional, non-mating-related target genes, covering allfunctional protein categories mentioned in Table 2. Com-pared with wild type P2niaD18, expression levels of puta-tive target genes were examined in shaking cultures of aMAT1-1-1 deletion strain (ΔMAT1) or MAT1-1-1 overex-pression strain (MAT1-ChIP), grown under the same con-ditions as for ChIP-seq sample preparation. A total of fourmating- and 13 non-mating-related genes were selectedfor our investigation. Compared with P2niaD18, overex-pression of MAT1-1-1 in the MAT1-ChIP strain led tosignificant changes in expression levels of 3 genes relatedto some aspect of sexual reproduction, namely pre1, kex1,and ppg1, as well as seven non-mating-related genes,namely Pc20g00090, dewA, atf21, Pc19g00140, sidD,Pc22g27040, and Pc22g22160 (Fig. 2B and Fig. S3A).Similar results were obtained when ΔMAT1 expressionlevels were measured. It is remarkable that all these genesare located in 5′–3′ orientation relative to the adjacentpeaks, thus, validating our criteria applied for identificationof putative MAT1-1-1 target genes based on data obtainedfrom ChIP-seq analysis.

De novo prediction of a MAT1-1-1 DNA-binding motif

To gain further insight into MAT1-1-1 DNA-binding prop-erties, de novo motif prediction based on MAT1-1-1-binding regions, identified in our ChIP-seq analysis, wasperformed. We used MEME to identify conserved motifs,and therefore the most likely binding site of MAT1-1-1 inP. chrysogenum. MEME analysis, based on 62 MAT1-1-1binding regions, present in three independent ChIP-seqexperiments, identified one highly significant motif, desig-nated MAT1.1, which showed a high degree of centralenrichment across MAT1-1-1 peak regions in CentriMoanalysis (Fig. 3). Furthermore, FIMO analysis confirmedthe presence of at least one copy of MAT1.1 within 202 of243 (83.1%; p-value ≤ 0.01) MAT1-1-1 peak regions, indi-cating that the vast majority but not all MAT1-1-1 targetsites are bound at this motif.

Comparison of MAT1.1 to known binding motifs presentin the JASPAR CORE (2014) databases for fungi andvertebrates revealed strong similarity to the binding sites



Tab

le2.

Sel

ecte

dM

AT

1-1-

1ta

rget

regi

ons

obta

ined

from

ChI

P-s

eqan

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Iden

tifier

Des

crip

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Pro

pose

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eb

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roar

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MA

T1.

1d

36h

96h

p≤

0.00

1p

≤0.

01

Sex

uald

evel

opm

ent

Pc2

2g18

600e

serin

eca

rbox

ypep

tidas

eK

ex1

Hom

olog

ofS

.ce

revi

siae

α-ph

erom

one

proc

essi

ngen

dopr

otea

ses

KE

X1

(Dm

ocho

wsk

aet

al.,

1987

)45

40−0

.14

−1.0

73

6P

c22g

1565

0ea-

pher

omon

ere

cept

orP

re1

Hom

olog

ofth

eS

.cer

evis

iae

a-fa

ctor

rece

ptor

ST

E3

(Gal

gocz

yet

al.,

2004

)38

69−0

.11

−0.5

22

6P

c22g

0291

0eph

erom

one-

proc

essi

ngen

dopr

otea

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revi

siae

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one

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al.,

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960.

420.

022

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30.

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of the S. cerevisiae mating-type protein MATa1 (Haber,2012) and Mcm1, a TF involved in cell-type-specific tran-scription and pheromone response in yeast (Mead et al.,2002) (Fig. S4). Furthermore, MAT1.1 showed similarity toDNA-binding motifs for Yhp1, a homeobox transcriptionalrepressor known to bind Mcm1 (Pramila et al., 2002),and Hcm1, a forkhead TF regulating expression of genesinvolved in chromosome segregation, spindle poledynamics and budding (Pramila et al., 2006). Comparison

to motifs known from vertebrates revealed strong similar-ity to the binding sites of Sox9, a SRY-related HMG-boxprotein, regulating the development of the skeleton andthe reproductive system (Mertin et al., 1999), Nkx2-5, ahomeobox TF involved in the regulation of heart formationand development (Chen and Schwartz, 1995), as well asSox17 and Sox2, SRY-related HMG-box proteins involvedin the regulation of embryonic development and cell fate(Kanai et al., 1996; Maruyama et al., 2005).

Fig. 2. Verification of MAT1-1-1 ChIP-seqdata.A. ChIP-PCR analysis was performed to verifyenrichment of selected MAT1-1-1 bindingregions in ChIP-DNA compared withinput-DNA. Enrichment was calculated as theratio of the region of interest to a controlregion showing no MAT1-1-1-specificenrichment in ChIP-DNA, relative to this ratioin the input-DNA sample. A region showing noMAT1-1-1-specific enrichment in ChIP-seqanalysis is shown as a control (NC). EachqPCR ratio (gray bars) is shown incomparison to the corresponding peak valuegenerated during bioinformatics analysis ofChIP-seq data (black bars). Values for qPCRsare the mean score of three biologicalreplicates; average ± standard deviations areindicated. Tested peak regions are namedaccording to neighboring genes (see DatasetS1).B. Analysis of relative log2 fold geneexpression ratios in a MAT1-1-1overexpression strain (MAT1-ChIP; gray bars)or MAT1-1-1 deletion strain (ΔMAT1; blackbars) compared with wild type strainP2niaD18 led to the identification of MAT1-1-1specific target genes. Values are the meanscore of three biological replicates. Testedgenes represent pairs of genes positionedupstream and downstream of MAT1-1-1 targetregions identified in ChIP-seq analysis (seeDataset S1). Directions of open readingframes are indicated by arrows.

Fig. 3. De novo prediction of a MAT1-1-1DNA-binding motif. The central 100 nt regionof 62 MAT1-1-1 specific peak regionsidentified in three independent ChIP-seqexperiments was submitted to MEME foridentification of enriched motifs. Only the mostsignificant putative DNA-binding motif(‘MAT1.1’) is shown. The size of each letter isproportional to the frequency of eachnucleotide at this position within theconsensus sequence. CentriMo analysis,using MAT1.1 as an input, revealed centralenrichment of the motif within a 500 nt rangearound MAT1-1-1 binding regions used formotif prediction.



The MAT1.1 binding motif shows conservationwithin euascomycetes

To address the question whether the predicted MAT1-1-1DNA-binding consensus sequence MAT1.1 is conservedamong ascomycetes, we performed FIMO analysis. Forthis purpose, the 1000 nt upstream region of ppg1, pre1,kex2, and kex1 from P. chrysogenum and the correspond-ing homologs from A. fumigatus, A. nidulans, C. albicans,Fusarium graminearum, N. crassa, S. cerevisiae, and Tri-choderma reesei were screened for occurrences ofMAT1.1. The corresponding 1000 nt upstream sequencesof the actin gene (act) were used as a negative control(Fig. 4). A high degree of conservation of MAT1.1 withinthe tested promoter regions of euascomycetes becameobvious, whereas significant deviations were recognizedwhen compared with hemiascomycetes. For example,applying a statistical threshold of p ≤ 0.0001, occurrencesof MAT1.1 were detected in seven out of eight ppg1(no occurrence in C. albicans) and six out of eight pre1(no occurrence in S. cerevisiae and C. albicans)upstream sequences. These observations were furtherconfirmed when sequence alignments of the proteinsequences of MAT1-1-1 DNA-binding domains revealeda significantly higher degree of conservation within

the MAT_alpha1 domain (pfam04769), especially theregion spanning the MATA_HMG-box (cd01389), of euas-comycetes compared to hemiascomycetes, in particular,C. albicans (Fig. S5).

MAT1-1-1 binds in vitro to MAT1.1

Since our motif analysis suggested that MAT1-1-1 asso-ciates with DNA via the predicted DNA-binding consensussequence CTATTGAG (MAT1.1), EMSAs were performedto test direct binding between DNA and protein. For thispurpose, a GST-MAT1-1-1 fusion protein was purifiedfrom Escherichia coli BL21 (DE3) and the quality of theisolated protein was verified by SDS–PAGE/Western blotanalysis using an antibody to GST (Fig. S6). The promoterregions of mating-related genes pre1, kex1, kex2, andppg1, as well as 13 non-mating-related genes (marked inTable 2), which were bound by MAT1-1-1 in ChIP-seqanalysis, were used to design oligonucleotide probes cov-ering a region with at least one copy of MAT1.1 (Fig. 5A,Table S3).

All oligonucleotides harboring a complete, central copyof MAT1.1 were bound by GST-MAT1-1-1 (e.g. mating-related Pre1-2, Kex1-2, Kex2-2, Ppg1-2, and non-mating-related Pc20g00090-2, Pc22g27040-1, ArtA-1, DewA-1/-2, FetC/FtrA-1, and SidD-1), whereas probes lackingMAT1.1 (e.g. Pc20g00090-3, Pre1-3, Kex1-1) showed nobinding (Fig. 5B and S3B). Only weak binding or nobinding between DNA and protein was observed whenMAT1.1 was positioned at the very end of the oligonucleo-tide or contained obvious deviations from the predictedconsensus sequence (e.g. Kex1-3, Kex2-1, Ppg1-1,TrxA-1, and Pc16g06630-1). GST alone showed nobinding to oligonucleotide Ppg1-2, confirming that theobserved formation of protein–DNA complexes is medi-ated by MAT1-1-1, and not by the tag.

Specificity of MAT1-1-1 binding to MAT1.1 was furtherverified using mutated Ppg1-2 oligonucleotides. A singleA → G or T → C substitution at position three of one oftwo copies of MAT1.1 present in oligonucleotide Ppg1-2_m1 led to a drastic reduction of protein–DNA complexformation, whereas mutation of both motifs (Ppg1-2_m2)totally abolished complex formation (Fig. 6A). Further-more, competition assays using Ppg1-2 as a probe andunlabeled Ppg1-2 oligonucleotide as a competitor showedthat the level of MAT1-1-1 binding to the labeled probe isdiminished by addition of increasing amounts of the unla-beled competitor. In the corresponding autoradiogram, anattenuation of the shift band and accumulation of freelabeled probe became visible (Fig. 6B; left panel). In con-trast, Western blotting of the shift gel and immunodetec-tion using an antibody to GST clearly showed an increasein complex signal strength when competing with unla-beled Ppg1-2 probe (Fig. 6B; right panel). Both, EMSA

Fig. 4. Conservation of the MAT1.1 binding motif withinascomycetes. The 1000 nt upstream region of ppg1, pre1, kex2,and kex1 from selected ascomycetes was screened foroccurrences of MAT1.1 using FIMO. The 1000 nt upstream regionof the actin gene act was used as a negative control. Totalnumbers of detected MAT1.1 copies within input sequencesmeeting a statistical threshold of p ≤ 0.001 and p ≤ 0.0001,respectively, are given. Locus tags according to NCBI database(http://www.ncbi.nlm.nih.gov/) are: ppg1: Pc14g01160,AFUA_6G06360, AN5791.2, CaO19.11961*, FG05061.1,NCU02500.1, YPL187W, TRIREDRAFT_104292; pre1:Pc22g15650, AFUA_5G07880, AN7743.2, CaO19.2492*,FG07270.1, NCU00138, YKL178C*, TRIREDRAFT_57526; kex2:Pc22g02910, AFUA_4G12970, AN3583.2, CaO19.12219,FG09156.1, NCU03219, YNL238W*, TRIREDRAFT_123561*; kex1:Pc22g18600, AFUA_1G08940, AN1384.2, CaO19.7020*,FG10145.1, NCU04316, YGL203C, TRIREDRAFT_74517; act:Pc20g11600, AFUA_6G04740, AN6542.2, CaO19.5007,FG07335.1, NCU04173, YFL039C*, TRIREDRAFT_77541.Asterisks are sequences shorter than 1000 nt.



http://www.ncbi.nlm.nih.gov/

and Shift–Western analyses, confirmed the specificity ofMAT1-1-1 binding to the Ppg1-2 oligonucleotide sinceaddition of unlabeled DNA minimized binding of MAT1-1-1to the radiolabeled probe, while overall complex formationwas maximized. As expected, unlabeled Ppg1-2_m2 didnot compete for binding to MAT1-1-1 with labeled Ppg1-2,leading to a steady protein–DNA complex signal in shiftanalysis and a decrease in signal intensity in Shift–Western analysis due to interference in overall complexformation as a result of a great excess of unbound com-petitor DNA (Fig. 6C).

MAT1-1-1 binding to MAT1.1 activates reporter geneexpression in an ex vivo yeast one-hybrid (Y1H) assay

As biochemical assays confirmed MAT1-1-1 binding to thenewly identified MAT1-1-1 DNA-binding motif MAT1.1 invitro, yeast one-hybrid (Y1H) reporter gene assays were

performed to validate binding ex vitro. Triple repeats ofoligonucleotides Kex1-2 and Ppg1-2, as well as Ppg1-2_m1 and Ppg1-2_m2 in the promoter of the lacZ or HIS3reporter gene, were used as preys for MAT1-1-1. As bait,we used vector pMAT1-AD, containing the MAT1-1-1cDNA sequence and the activation domain of yeast Gal4TF. Both prey and bait vectors were integrated into yeasta- and α-strains. Diploid strains, generated by mating andcarrying one of the prey and the bait vector, were identi-fied by growth on selective media lacking uracil andleucine. Furthermore, HIS3 reporter gene activity, indicat-ing MAT1-1-1 binding to the respective prey sequence,was analyzed on selective media lacking uracil, leucine,and histidine, but containing increasing amounts of 3-AT.In addition, qualitative and quantitative β-galactosidaseassays were performed to measure lacZ reporter geneactivity, thereby enabling evaluation of protein–DNA inter-actions based on two independent reporter gene systems.

Fig. 5. Electrophoretic mobility shift assays (EMSAs) confirm MAT1-1-1-binding to ChIP-enriched genomic regions.A. Zoomed ChIP-seq profiles of selected MAT1-1-1 ChIP-enriched regions. Positions and sequences of oligonucleotides used for shift analysisare indicated. Occurrences of the predicted MAT1-1-1 DNA-binding motif MAT1.1 are marked in red. Single nucleotides that do not fit thepredicted consensus sequence are indicated in small letters. Maximum read counts at the summit of ChIP-seq peaks are indicated at the left.ORFs next to MAT1-1-1 ChIP-seq peak regions are marked by black boxes; arrowheads indicate 5′–3′ orientation.B. EMSAs were performed using radiolabeled double-stranded oligonucleotide probes covering the central region of selected MAT1-1-1 targetregions, identified in ChIP-seq analysis. Addition of GST-MAT1-1-1 protein is marked by (+), samples without protein are marked by (−).Positions of free probe (*) and protein–DNA complexes (→) are indicated.

Fig. 6. Single-bp substitutions and Shift–Western analyses confirm specificity of MAT1-1-1 DNA-binding.A. GST-MAT1-1-1 shows strong binding to a 30 nt double-stranded oligonucleotide derived from the ppg1 promoter sequence (Ppg1-2),carrying two copies of the predicted MAT1-1-1 DNA-binding motif MAT1.1. A single A→G/T→C substitution at position 3 within one of twomotif sequences (Ppg1-2_m1) results in a diminished formation of protein–DNA complexes. Complex formation is completely suppressedwhen both consensus sequences are mutated (Ppg1-2_m2).B. Competition with increasing amounts of unlabeled Ppg1-2 oligonucleotide decreased the level of MAT1-1-1 binding to the labeled probe,leading to an attenuation of the shift band and accumulation of free labeled probe in shift experiments (autoradiogram, left panel). Westernblotting and immunodetection (Shift–Western analysis), using an antibody to GST, showed an increase in complex signal strength whencompeting with unlabeled Ppg1-2 probe (GST-immunoblot, right panel).C. Unlabeled Ppg1-2_m2 oligonucleotide did not compete for binding to MAT1-1-1 with labeled Ppg1-2, leading to a steady signal forprotein–DNA complexes in shift experiments (autoradiogram, left panel) and a decrease in signal intensity in Shift–Western analysis(GST-immunoblot, right panel). The amount of protein used for shift analyses is indicated on top of each lane (1 μg of GST-MAT1-1-1 equals amolar concentration of 0.76 μM). Positions of free probe (*) and protein–DNA complexes (→) are indicated.



Previously successfully employed Y1H plasmids wereused as a positive and negative control, and served asstandard for quantitative β-galactosidase assays (Schmittand Kück, 2000) (Fig. 7).

Y1H analysis confirmed binding of MAT1-1-1 to oligo-nucleotides Kex1-2, Ppg1-2, and Ppg1-2_m1 based onboth, HIS3 and lacZ, reporter gene activity. Moreover,quantitative β-galactosidase assays confirmed our resultsobtained from Shift–Western assays using oligonucleo-tide Ppg1-2_m1. In both analyses, binding betweenMAT1-1-1 and the oligonucleotide was reduced due to asingle point mutation within one copy of MAT1.1. As inte-gration of Ppg1-2_m2 into the prey vector pHISi led totransactivation with the empty bait vector pGADT7, Y1Hanalysis did not yield reliable results in this particularcase. Most probably, this activation was mediated by ayeast protein that binds with high affinity to the mutatedbinding sequence. Additional control experiments wereperformed to exclude transactivation between pGADT7and the remaining prey vectors (Fig. S7).

DsRed reporter gene assays confirm MAT1-1-1 bindingto the kex1 and ppg1 promoter sequence in vivo

To further verify binding between MAT1-1-1 and the pro-moter regions of kex1 and ppg1 in vivo, we performedDsRed reporter gene assays in P. chrysogenum. For thispurpose, reporter gene constructs carrying the DsRedgene under control of the upstream sequence of kex1 andppg1 were transformed into P. chrysogenum recipients

MAT1-ChIP and P2niaD18, and plasmid integration wasconfirmed using PCR analysis. A plasmid containing theDsRed gene without a promoter sequence (pDsRed) wasintegrated into MAT1-ChIP as a control. As MAT1-ChIPcontained the Pgpd::egfp::MAT1-1-1 overexpression con-struct used for ChIP analysis, all derivatives of this strainshowed clear nuclear EGFP signals, while no signalswere detectable in the P2niaD18 background. DsRedexpression in MAT1-ChIP+Pppg1::DsRed and MAT1-ChIP+Pkex1::DsRed confirmed binding of the MAT1-1-1protein to the promoter regions of kex1 and ppg1, while nofluorescence was recorded for the MAT1-ChIP+pDsRedcontrol strain (Fig. 8). Because only weak DsRedfluorescence was detectable for Pkex1::DsRed in P2niaD18and no DsRed fluorescence was detectable forP2niaD18+Pppg1::DsRed, overall activation of reportergene expression could be clearly attributed to high MAT1-1-1 gene expression in the MAT1-ChIP background.Thus, fluorescence microscopy confirmed the in vivospecificity of MAT1-1-1 binding to promoter regions ofkex1 and ppg1.

Characterization of a MAT1-1-1 target gene thatfunctions beyond sexual development

To further validate functionality of a new MAT1-1-1 targetgene, identified in our ChIP-seq approach and unlikelyto be involved in regulation of sexual development, wegenerated artA (Pc18g03940) deletion strains (ΔartA) byhomolog integration of a PtrpC-nat1 resistance cassette

Fig. 7. Yeast one-hybrid analysis confirmsMAT1-1-1 binding to MAT1.1. Yeast strainswere grown on SD-ura-leu in order to confirmthe presence of both, a bait and a preyvector, after mating. HIS3 reporter geneactivity was analyzed on SD-ura-leu-hissupplemented with 3-AT as indicated. lacZreporter gene activity was analyzed usingqualitative and quantitative β-galactosidaseassays. A diploid strain harboring the mutatedCPCR1 binding site BSIIm1 as a prey and thetranscription factor CPCR1 as a bait constructwas used as a negative control. A diploidstrain carrying the native BSII binding site asa prey and CPCR1 as a bait is shown as apositive control (Schmitt et al., 2004).



in Δku70FRT2 background. Correct integration of theknockout construct was verified using PCR analysis.ArtA codes for a 14-3-3 family protein, which was pre-viously shown to be involved in a pathway controllingconidiospore germination in A. nidulans (Kraus et al.,2002). As shown in Fig. 9A, deletion of the correspond-ing homolog in P. chrysogenum results in a severe

reduction in conidiospore germination (∼ 30% germina-tion after 24 h), when compared with the recipientΔku70FRT2 and wild type P2niaD18 (∼ 90% germinationafter 24 h). This effect was further verified using micro-scopic analysis, confirming an impaired growth in ΔartAcompared with the reference strains after 24 h of culti-vation (Fig. 9B).

Fig. 8. In vivo DsRed reporter gene analysis confirms MAT1-1-1 binding to selected target gene promoter regions. Reporter gene constructscarrying the DsRed gene under control of the upstream sequence of kex1 and ppg1 (Pkex1: 1445 nt; Pppg1: 843 nt) were transformed intoP. chrysogenum strains MAT1-ChIP and P2niaD18. Fluorescence microscopy confirmed EGFP-MAT1-1-1 expression and nuclear localizationin the MAT1-ChIP background. DsRed protein expression confirmed binding of MAT1-1-1 to the respective promoter regions. Scalebar = 20 μm.

Fig. 9. Characterization of artA deletion strains.A. Three independent artA deletion (ΔartA) mutants, recipient Δku70FRT2, and wild type P2niaD18 were grown on solid CCM. For each timepoint 400 conidiospores from each strain were investigated for determination of germination rates after 12, 15, 18, 21, and 24 h (given in %).B. Microscopic analysis of strains used in (A) after 24 h of cultivation. Scale bar = 50 μm.



Discussion

ChIP-seq analysis identifies MAT1-1-1 target genes thathave functions other than for sexual development

Although concerted research efforts have been made toanalyze regulatory circuits controlled by mating-type-encoded TFs, little is still known about their specific targetgenes. Our work expands the current understanding ofmating-type protein functions far beyond the regulation ofsexual development alone, and provides unambiguousevidence for a participation of the mating-type α-domainTF MAT1-1-1 in regulation of asexual developmentand morphogenesis, as well as amino acid, iron and sec-ondary metabolism in P. chrysogenum. Furthermore, wepresent the first genome-wide analysis focusing onunraveling the transcriptional regulatory network con-trolled by a mating-type locus-encoded TF and the com-prehensive characterization of a MAT1-1-1 DNA-bindingmotif in euascomycetes.

Identification of Pc14g01160 and Pc22g15650 asMAT1-1-1 specific target genes confirms the biologicalsignificance of our ChIP-seq analyses, as they arehomologs of S. cerevisiae MFα1 and STE3, respectively,both αsgs (Galgoczy et al., 2004). Since their correspond-ing peak regions revealed significantly high statisticalpeak values, these observations are consistent with thegeneral acceptance that the most highly bound regionsin ChIP experiments occur near generally known func-tional targets, while many of the regions bound at muchlower levels may represent ‘non-functional’ binding sites(Todeschini et al., 2014). Nevertheless, low-affinity TFbinding may have a functional role in chromatin remod-eling (Cao et al., 2010) or nucleosome positioning (Zaretand Carroll, 2011), which can influence gene expressionat later developmental stages or have an additional non-transcriptional function (Spitz and Furlong, 2012).

We used previous microarray data (Böhm et al., 2013),which identified a total of 2421 genes as MAT1-1-1-dependent in a MAT1-1-1 deletion strain (ΔMAT1) com-pared with wild type P2niaD18, to align ChIP-seq resultsand expression profiles of putative MAT1-1-1 targetgenes, identified in our ChIP-seq analysis. This compari-son revealed an overlap of 29.9% (76/254), which is con-sistent to comparisons between TF binding events andexpression profiling data in yeast and higher eukaryotes,showing a relatively small overlap of ∼ 50% and 10–25%between TF occupancy and expression of neighboringgenes (Spitz and Furlong, 2012). Nevertheless, ourassumption that most of the 243 MAT1-1-1 binding sitesidentified in ChIP-seq experiments affect the expressionof neighboring genes at some point during developmentwas strengthened when DsRed reporter gene assaysshowed that MAT1-1-1 binding to promoter regions, iden-tified as specific target regions in ChIP-seq analyses

(Pppg1, Pkex1), can be used for controlled expression ofdownstream reporter genes in a MAT1-1-1-dependentmanner. Furthermore, DNA-binding assays and qRT-PCRanalyses confirmed functionality of at least 13 non-mating-related MAT1-1-1 target genes, identified in ourChIP-seq approach and covering the functional catego-ries morphogenesis and development, amino acid andsecondary metabolism, iron metabolism as well as TFs.Important examples are the spore wall fungal hydro-phobin encoding dewA, the non-ribosomal peptide syn-thetase encoding sidD, the bZIP TF encoding atf21and the F-box domain protein encoding Pc22g22160.Moreover, functional characterization of artA, a furtherMAT1-1-1 target gene, confirmed its role in regulation ofconidiospore germination in P. chrysogenum. A compara-ble function was previously shown for A. nidulans (Krauset al., 2002). This observation supports our hypothesisthat MAT1-1-1 functions on a genome-wide level are morefar-ranging than expected, and that the number of primaryMAT1-1-1 target genes might be significantly higher thanpreviously assumed. To improve clarity, all MAT1-1-1target genes, verified by EMSAs and/or qRT-PCR analy-sis, are labeled with an asterisk (*) throughout this discus-sion (see Table 2 for further information).

Interestingly, none of the identified MAT1-1-1 targetgenes, assigned to sexual development in P. chrysoge-num, showed MAT1-1-1-dependent changes in expres-sion profiles in microarray analysis comparing ΔMAT1 towild type P2niaD18, except for kex1* and the homolog ofpac2 (Pc12g15890). Pac2 encodes a cAMP-independentregulatory protein, modulating onset of sexual develop-ment in S. pombe and M. oryzae, and regulation of sporu-lation in Ashbya gossypii (Kunitomo et al., 1995;Wasserstrom et al., 2013; Chen et al., 2014). On the con-trary, qRT-PCR analysis revealed a significant upregula-tion of pre1*, kex1*, and ppg1* expression in a MAT1-1-1overexpression strain (MAT1-ChIP) and significant down-regulation of pre1* in ΔMAT1 compared with P2niaD18after 48 h of cultivation in shaking cultures. Accordingly,pre1* and ppg1* expression was shown to be significantlydownregulated in ΔMAT1 compared with the parentalstrain after 72 h of cultivation in liquid shaking cultures(Böhm et al., 2013). These findings are consistent withreports, demonstrating that expression of pheromone pre-cursor genes, and most probably receptor genes, is con-trolled by mating-type gene expression in heterothallicspecies, e.g. N. crassa (Kim and Borkovich, 2006). Theoverexpression of MAT1-1-1 thus has an impact on theexpression of genes involved in regulation and onset ofsexual reproduction. Similar observations were made inA. nidulans and N. crassa, in which sexual reproductioncorrelates significantly with an increased expression ofmating-type genes and key genes of a pheromone-response MAP-kinase signaling pathway (Paoletti et al.,



2007; Wang et al., 2014). However, deletion of MAT1-1-1in P. chrysogenum does not lead to significant changes inexpression levels of key genes from the pheromone-response signaling pathway at early developmentalstages, suggesting that these are, to a certain degree,independent of MAT1-1-1.

A significant number of MAT1-1-1 target genes, identi-fied in our ChIP-seq analyses, might be involved in themanifestation of the phenotypic characteristics of MAT1-1-1 overexpression and deletion strains, showing alteredpolarity of germinating hyphae, unusual branching behav-ior, and impaired hyphal growth and pellet formation(Böhm et al., 2013). Examples are dewA* (Pc16g06690),encoding a spore wall fungal hydrophobin responsiblefor hydrophobicity of conidiospores in A. nidulans(Stringer and Timberlake, 1995; Grünbacher et al., 2014),artA* (Pc18g03940), coding for a 14-3-3 family proteininvolved in regulation of polarization of germinating con-idiospores in A. nidulans (Kraus et al., 2002), and PAG1(Pc21g20900), encoding a cell morphogenesis proteinrelated to polarized morphogenesis and proliferationin S. cerevisiae (Du and Novick, 2002; Nelson et al.,2003). Furthermore, genes assigned to the formation ofconidiospores were identified and showed significantupregulation in ΔMAT1, e.g. atf21* (Pc22g26820) andPc19g00140*. Atf21* codes for a basic leucine zipper(bZIP) TF and repressor of sexual development in A. nidu-lans (Lara-Rojas et al., 2011), while Pc19g00140* showshigh similarity to trehalose-6-phosphate synthase subunitencoding genes from Aspergilli, involved in the biosynthe-sis of trehalose, a compound necessary for long-termviability of fungal spores (Elbein et al., 2003). As formationof conidiospores is generally accepted to be restricted toasexual development, this observation fits the notion ofMAT1-1-1 being a positive regulator of sexual reproduc-tion and a negative regulator of asexual development.Consistent with this hypothesis is our recent finding thatsporulation was increased by about 25% in a ΔMAT1-1-1strain compared with wild type (Böhm et al., 2013).

It is known that the developmental decision betweensexual and asexual reproduction in A. nidulans isdependent on environmental factors, such as nutritionalstatus and culture conditions (Han et al., 2003). Conse-quently, in most out-crossing ascomycetes, such asN. crassa and S. cerevisiae, nitrogen limitation is a keyinducing condition for mating or sexual sporulation(Glass and Lorimer, 1991). As we identified meaB*(Pc18g00880), a bZIP TF involved in regulation ofexpression of nitrogen-dependent genes in A. nidulans(Wong et al., 2007), as a target gene of MAT1-1-1 inChIP-seq analyses and microarray analysis indicatedupregulation in ΔMAT1 compared with wild type, MAT1-1-1 seems to act as a negative regulator of meaB*expression in P. chrysogenum, thus, supporting the idea

of nitrogen limitation as a key feature of induction ofsexual reproduction in P. chrysogenum.

We found a variety of MAT1-1-1 target genes linked toamino acid and secondary metabolism, most of themshowing downregulation in ΔMAT1 compared with wildtype. Pc18g02620, encoding a cyanide hydratase/nitrilase, and Pc22g18630*, encoding an enzyme catalyz-ing the chemical reaction of L-homocysteine toL-methionine, are important candidates, as they mighthave a direct impact on penicillin biosynthesis, whichstarts with the formation of a tripeptide based onL-cysteine, L-valine and L-α-aminoadipic acid. Severalmultidrug (Pc12g00820, Pc16g06630, Pc16g11470,Pc20g03900) and amino acid transporter encoding genes(Pc06g01080, Pc22g06500) complete this selection. Asdeletion of MAT1-1-1 was shown to lead to a significantreduction in penicillin production (Böhm et al., 2013),these observations strengthen our idea of MAT1-1-1being a positive regulator of secondary metabolism inP. chrysogenum.

Furthermore, integration of ChIP-seq and microarraydata led to the identification of MAT1-1-1 target genesinvolved in iron transport and iron acquisition, e.g. sidD*(Pc22g20400), encoding a non-ribosomal siderophorepeptide synthetase important for biosynthesis of the intra-cellular siderophore triacetylfusarinine C (TAFC) (Schrettlet al., 2007), fetC* (Pc21g08020), encoding for a ferroxi-dase, and ftrA* (Pc21g08030), encoding for a high affinityiron permease that mediates uptake of Fe2+ during reduc-tive iron acquisition (Schrettl and Haas, 2011). It is knownfrom Aspergillus species that imbalance in iron homeo-stasis affects a variety of cellular functions, e.g. growthrates, germination, sensitivity of conidia to oxidativestress and formation of cleistothecia (Eisendle et al.,2006a). Furthermore, deletion of sidD* in A. fumigatuswas shown to lead to decreased conidiation during iron-depleted conditions (Schrettl et al., 2007), whereas dele-tion of ftrA* displayed an eightfold increase in TAFCsiderophore production under iron-depleted conditions,demonstrating that lack of FtrA brings forward the onset ofsiderophore production (Schrettl et al., 2004).

Identification of a new MAT1-1-1 DNA-binding motif

Using EMSAs, Shift–Western and Y1H analysis, weshowed that MAT1-1-1 binds with high specificity tothe newly identified MAT1.1 DNA-binding consensussequence ‘CTATTGAG’. The motif was further shown tobe conserved among euascomycetes and showed simi-larities to known DNA-binding motifs of proteins known tobe involved in regulation of sexual reproduction in yeast,e.g. MATa1, Mcm1, and Hcm1, and embryonic develop-ment in vertebrates, e.g. Sox9, Nkx2-5, and Sox17. Eventhough DNA-sequence recognition by TFs can be con-



served across large evolutionary distances, binding speci-ficity of MATα1 has been shown to have changedsubstantially over small evolutionary distances (Tuchet al., 2008). As our analysis pointed to obvious differ-ences between MAT1-1-1 binding sites in euascomycetesand hemiascomycetes, especially C. albicans, these find-ings are consistent with the hypothesis that hemiascomy-cetes and euascomycetes share a common ancestor, butthat binding specificity of modern MATα1 proteins fromC. albicans and euascomycetes might have changed sub-stantially during evolution (Baker et al., 2011).

The strongest protein–DNA interaction was observedbetween MAT1-1-1 and an oligonucleotide probe harbor-ing two copies of the MAT1.1 binding motif, forming theimperfect palindrome 5′-TCAATA-N7-TATTGA-3′. Corre-spondingly, the strongest interaction between DNAand MAT1-1-1, as deduced from ChIP-seq data, wasobserved for those peak regions characterized by anoticeable high frequency of MAT1.1 with close matchesto the consensus sequence, whereas weak interactionswere characterized by a relatively low abundance of themotif (compare with Table 2 and Dataset S1). Sinceeukaryotic TFs tend to recognize shorter DNA sequencemotifs compared with bacterial TFs, clustering of sites isoften required to achieve specific recognition (Wunderlichand Mirny, 2009).

Although a large number of MAT1-1-1 peak regionscontained at least one copy of MAT1.1, some peaks com-pletely lacked it. However, this might be due to statisticalthresholds applied during motif prediction and motifdetection procedures. On the other hand, this is acommon observation: even if ChIP-seq peaks are typi-cally enriched in the consensus motif for the TF in ques-tion, a significant proportion of peaks lacks clearlyidentifiable motifs (Robertson et al., 2007; Valouev et al.,2008). For example, the consensus sequence for E2Ffamily proteins that control various cellular and organismalfunctions in higher eukaryotes is present in less than 20%of the regions recognized in ChIP-chip experiments inhuman and mouse cells (Rabinovich et al., 2008). Thisobservation might be ascribed to the fact that most TFsnot only interact with DNA through a consensus site butalso recognize divergent sequences. For example, astudy of approximately 100 mouse TF revealed thatalmost half of these proteins can recognize several differ-ent sequences in addition to the known DNA-binding con-sensus sequences (Badis et al., 2009). Furthermore,specific recognition of regulatory elements by a TF isstrongly influenced by its ability to interact with other pro-teins that bind to neighboring DNA sites. The simplestexample of this mechanism is the formation of TF dimersor higher order structures (Amoutzias et al., 2008). Sincecooperative binding was described for the mating-type α1HMG domain TF and Mcm1 from S. cerevisiae (Carr

et al., 2004; Baker et al., 2011), dimerization might also bea regulatory feature of MAT1-1-1 in P. chrysogenum.

Interestingly, the most prominent MAT1-1-1 targetgenes, characterized by high statistical peak values com-bined with an accumulation of MAT1.1 (p ≤ 0.001), wereassigned to sexual reproduction. This finding might indi-cate a regulatory feature ensuring high-affinity binding ofMAT1-1-1 to the corresponding promoter regions, evenunder conditions where only a low amount of MAT1-1-1protein is available. Moreover, the occurrence of MAT1.1within the upstream regions of new direct MAT1-1-1 targetgenes presented within this work points to an evolutionarylink between mating and other cellular functions whichwere believed to be independent of MAT1-1-1 proteinfunctions until now. This hypothesis was further strength-ened by EMSAs and qRT-PCR analyses, verifying func-tionality of selected MAT1-1-1 target genes identified inour ChIP-seq approach. Further research is needed toidentify interaction partners of MAT1-1-1 on protein leveland to understand interactions between the TF, enhancerelements and other cis-regulatory elements. Furthermore,as our analysis was designed to identify as many MAT1-1-1 target genes as possible, further studies, however, willbe needed in order to decipher MAT1-1-1 mediated tran-scriptional regulation under control of its native promotersequence, e.g. as a function of developmental stages orphysiological culture conditions.

Taken together, our discoveries concerning the sexualbiology of P. chrysogenum presented within this workgreatly advance the current understanding of sexualreproduction within ascomycetes, and open up newavenues for the study of fungal development as a whole.Based on our finding that the mating-type encoded TFMAT1-1-1 not only regulates expression of αsgs related tosexual reproduction but also other key biological pro-cesses, it appears that mating-type regulated transcrip-tional networks have undergone drastic reorganization,resulting in the presence of DNA binding sites in thepromoters of – at first glance – unrelated target genes thatare bound and controlled by highly conserved transcrip-tional regulators in different fungi. This hypothesis is sup-ported by a recent discovery showing that targets of themating-type TF heterodimer Sxi2a-Sxiα1 from Cryptococ-cus neoformans not only include genes known to beinvolved in sexual reproduction but also several wellstudied virulence genes (Mead et al., 2015). Microarrayanalyses in other euascomycetes also pointed to an unex-pectedly large number of genes that are expressed ina mating-type dependent manner (Lee et al., 2006;Pöggeler et al., 2006; Keszthelyi et al., 2007; Bidardet al., 2011). In combination, these data suggest thatmating-type protein regulatory functions might reach farbeyond sexual development in these species as well.Future research will be necessary in order to determine



exactly which changes in MAT1-1-1 and its correspondingDNA binding site were necessary to allow for the expan-sion in MAT1-1-1 regulatory functions during evolution.

The observation that MAT1-1-1 is involved in regulationof development, morphogenesis and metabolism inP. chrysogenum supports the idea that MAT genes arefunctionally retained even during the asexual part of thelife cycle and the apparent absence of a sexual phase,presumably because of the impact of positive selection onimportant processes unrelated to sexual development inasexual fungal populations (Ádám et al., 2011). Since wedemonstrated that MAT1-1-1 regulates expression of anumber of genes related to various traits of morphologyand development, it is conceivable that the mating-typeprotein mediated regulation is necessary for efficientbalance between morphologic features characteristic tothe sexual and asexual parts of the life cycle. This mightalso be true for an involvement of MAT1-1-1 in regulationof secondary, amino acid and iron metabolism. It is knownfrom various euascomycetes that there is a concertedbalance between sexual development and secondarymetabolism (Bayram et al., 2008; Hoff et al., 2010;Wiemann et al., 2010; Kopke et al., 2012). Another impor-tant example is fungal iron metabolism, which was shownto affect both asexual and sexual development (Eisendleet al., 2003; 2006b; Schrettl et al., 2007; Johnson, 2008).Since these traits are also crucial in terms of appliedmicrobiology, our work will further not only contribute tothe advanced improvement of P. chrysogenum strainsused for industrial production of β-lactam antibioticsbut also to other filamentous fungi with biotechnologicalrelevance.

Experimental procedures

Strains and culture conditions

Penicillium chrysogenum strains (Table S1) were grown inshaking or surface cultures in complete culture medium (CCM;0.3% (w/v) sucrose, 0.05% (w/v) NaCl, 0.05% (w/v) K2HPO4,0.05% (w/v) MgSO4, 0.001% (w/v) FeSO4, 0.5% (w/v) trypticsoy broth, 0.1% (w/v) yeast extract, 0.1% (w/v) meat extract,0.15% (w/v) dextrin, pH 7.0) at 27°C. For inoculation, 0.5 × 107

spores derived from cultures grown on M322 solid medium(0.35% (w/v) (NH4)2SO4, 0.2% (w/v) KSO4, 0.02% (w/v)KHSO4, 1 g N/l soy flour, 0.5% (w/v) lime stone powder, 5%(w/v) lactose, pH 6.3) for 4–5 days were used. Escherichia colistrain XL1 blue was used for cloning and plasmid propagationpurposes, while BL21 (DE3) served as a host for heterologousoverexpression of MAT1-1-1 (Bullock et al., 1987; Miroux andWalker, 1996). Saccharomyces cerevisiae strains PJ69-4aand PJ69-4α were used for yeast one-hybrid analysis (Jameset al., 1996). Strains were grown at 30°C on synthetic defined(SD) medium lacking selected amino acids used for auxotro-phy marker selection. Mating of PJ69-4a and -4α strains wasperformed in liquid yeast peptone dextrose adenine (YPDA)medium at 30°C and 50 rpm.

Construction of recombinant P. chrysogenum strains

For generation of strains used for ChIP-seq analysis, DsRedreporter gene assays and deletion mutants (Table S1), thecorresponding plasmids (Table S2) were transformed intoP. chrysogenum strain P2niaD18 and Δku70FRT2, respec-tively. Transformation was performed as described previously(Hoff et al., 2010; Kamerewerd et al., 2011) with some modi-fications. Cultures were grown for 72 h and protoplasts weretransformed with circular plasmid DNA for ectopic, and linearplasmid DNA for homologous integration. Transformantswere selected on CCM media containing 150 μg mL−1 nour-seothricin (Werner BioAgents, Jena, Germany) and40 μg ml−1 phleomycin (Invivogen, CA, USA) as necessary.Resistant colonies were isolated and tested for correct inte-gration of plasmid DNA as previously described (Hoff et al.,2010).

Sample preparation for ChIP-seq

Chromatin immunoprecipitation (ChIP) was carried outessentially as described previously (Tamaru et al., 2003;Smith et al., 2011) with the following modifications. P. chrys-ogenum strains were grown in 100 mL CCM cultures inocu-lated with 0.5 × 107 spores for 48 h at 120 rpm and 27°C. Forchromatin fixation, freshly prepared formaldehyde (in NaOH)was added to a final concentration of 1%, and cultures wereincubated at 27°C and 100 rpm for 30 min. Five milliliters of2.5 M glycine was added to quench formaldehyde, and cul-tures were incubated at room temperature with gentleshaking for 5 min. Approximately 250 mg mycelium wereresuspended in 750 μL lysis buffer (50 mM HEPES–KOH pH7.5, 90 mM NaCl, 1 mM ethylenediaminetetraacetic acid(EDTA), 1% Triton X-100, 0.1% sodium deoxycholate (DOC)supplemented with fresh protease inhibitors) and chromatinwas sheared using a Branson 250 sonifier (output 2, dutycycle 0.8, 6 × 20 impulses). After pre-clearing with protein Aagarose beads (Invitrogen, Darmstadt, Germany) the solublechromatin fraction was immunoprecipitated using anti-GFPantibody (ab290; Abcam, Cambridge, UK). Fresh protein Aagarose beads were added to bind antibody–protein–DNAcomplexes. The supernatant was discarded and beads werewashed several times (1 × TE buffer: 10 mM Tris–HCl pH 8.8,1 mM EDTA; 2 × lysis buffer without protease inhibitors;1 × lysis buffer without protease inhibitors + 0.5 M NaCl;1 × LiCl wash buffer: 0.25 M LiCl, 1 mM EDTA, 10 mM Tris–HCl pH 8.0, 0.5% NP-40, 0.5% DOC). Beads were incubatedtwo times in TE(S) (50 mM Tris–HCl pH 8.0, 10 mM EDTA,1% SDS) at 65°C for 10 min with gentle agitation to eluteprotein–DNA complexes. To reverse the crosslinking,samples were incubated at 65°C for 6–16 h. After RNaseAand ProteinaseK digestion, DNA from immunoprecipitatedchromatin (ChIP-DNA) and input samples (input-DNA) wasisolated. Construction of ChIP-libraries and sequencing of 50nt single-end reads on a Illumina HiSeq 2000 were performedby GATC Biotech AG (Konstanz, Germany) or at the OSUCGRB core facility.

Data analysis and visualization

Sequences corresponding to adaptors were removed fromreads, and remaining sequences were subsequently mapped



to the latest version of the P. chrysogenum P2niaD18genome (Specht et al., 2014) using Bowtie version 1.0.1(Langmead et al., 2009) with the following settings: ‘–S –q –m 1’, which only retains unique alignments. Binary Alignment/Map (BAM) files were sorted and indexed using SAMtools(Li et al., 2009), and visualized using the Integrative Genom-ics Viewer (IGV) (Thorvaldsdóttir et al., 2012). A genome-wide distribution figure of MAT1-1-1 binding sites is providedin Fig. S8. Further data analysis was performed using theHOMER software for motif discovery and next-generationsequencing analysis (Heinz et al., 2010). Quality controlanalysis included examination of clonal tag counts in orderto determine the non-redundant fraction of mapped reads,autocorrelation analysis to enable sequencing fragmentlength estimation, nucleotide frequency analysis and frag-ment GC % distribution to rule out sequence biases andanalysis of ChIP-fragment density near MAT1-1-1-specificpeak regions. Peaks were called using findPeaks.pl usingthe -style factor option, a FDR ≤ 0.001, and a p-value overlocal background cutoff of 1.00e–04. Peak regions for eachindividual experiment were intersected using mergePeak-s.pl –d 100, reporting peaks within a maximum distance of100 nt as overlapping. Peaks were assigned to neighboringand overlapping genes using a custom-made Perl scriptbased on BioPerl modules and blast2go analysis (Conesaet al., 2005). Functional category enrichment analysisof genes associated with peaks was performed using theMIPS functional catalogue database (FunCat) (Ruepp et al.,2004). Raw sequencing data from ChIP experimentsare available from the NCBI SRA database (http://www.ncbi.nlm.nih.gov/sra), study ID PRJNA257456, Acces-sion # SRP045261.

Sequence motif analysis

The central 100 nt region of selected MAT1-1-1 peak regionswas submitted to MEME (Multiple Em for Motif Elicitation;http://meme.nbcr.net/meme/) (Bailey and Elkan, 1994) forde novo motif prediction. Further analysis was performedusing CentriMo (Bailey and Machanick, 2012) and FIMO(Grant et al., 2011). For comparison of the newly identifiedMAT1-1-1 DNA-binding consensus sequence against theJASPAR CORE (2014) fungi and vertebrates databases,results were submitted to TOMTOM (Gupta et al., 2007)using default parameters.

Expression and purification of recombinantGST-MAT1-1-1 protein

The MAT1-1-1 cDNA sequence was integrated into theexpression vector pGEX-4T3 (Amersham Bioscience, Frei-burg, Germany) to generate plasmid pGEX-MAT1 (seeTable S2). GST and GST-MAT1-1-1 were purified fromE. coli BL21 (DE3) cells. Purification of recombinantprotein and GST alone was performed as describedearlier using an elution buffer containing 50 mM Tris/HCl,30 mM reduced glutathione, 100 mM NaCl, pH 8.0 (Januset al., 2007). Purified protein was supplemented with87% glycerol and stored at −70°C until used for furtherapplications.

Quantification of protein levels and immunodetection

The concentration of purified GST-MAT1-1-1 and GST alonewas determined by using Bradford reagent (BioRad,München, Germany). Western blotting and immunodetectionof GST-tagged proteins were performed using RPN1236 anti-GST HRP conjugate (GE Healthcare, Freiburg, Germany).Detection of GFP-MAT1-1-1 from P. chrysogenum totalprotein isolates was performed using JL-8 antibody to GFP(Clontech, Saint-Germain-en-Laye, France) and HRP-coupled secondary antibody #7076 (Cell Signaling Technol-ogy, Leiden, The Netherlands).

Electrophoretic mobility shift assays (EMSAs) andShift–Western analysis

Gel shift assays were performed using oligonucleotidesderived from ChIP-enriched regions and purified GST-MAT1-1-1. Double-stranded oligonucleotides were 5′-end-labeledusing polynucleotide kinase (Roche, Basel, Switzerland) and[γ-32P]-ATP (Hartmann Analytic, Braunschweig, Germany).For shift experiments, 3.5–7.0 fmol (∼ 50–100 cps) of radi-olabeled oligonucleotides was incubated with varying proteinconcentrations in the presence of 2 μL binding buffer(250 mM Tris/HCl pH 8.0, 1 M KCl, 50 % glycerol) and 1 μgpoly(dI-dC)-poly(dI-dC) (Affymetrix USB, CA, USA) in a totalvolume of 20 μL for 20 min at room temperature. Sampleswere run on 5% polyacrylamide gels at 4°C in 190 mMglycine, 27 mM Tris/HCl pH 8.5. Competition experimentswere performed by adding unlabeled oligonucleotide. Prepa-ration of gels used for Shift–Western analysis (Demczuket al., 1993) was performed as described earlier. Denatura-tion of proteins and blotting to a PVDF membrane (Perki-nElmer, MA, USA) was performed as described previously(Granger-Schnarr et al., 1988) with a transfer time of180 min at 1.3 A and a transfer buffer containing 25 mM Tris,192 mM glycine and 10 % methanol. The sequences of alloligonucleotides used for shift analyses are provided in TableS3.

Nucleic acids isolation, cDNA synthesis, quantitativeRT-PCR and ChIP-PCR

Isolation of nucleic acids, cDNA synthesis and qRT-PCRanalysis were carried out as described earlier (Hoff et al.,2009; Böhm et al., 2013). ChIP-PCR analysis was performedas described for qRT-PCR analysis, using ChIP- and input-DNA from independent ChIP experiments as a template. Thesequences of all oligonucleotides used for PCR analyses aregiven in Table S3.

Microarray data analysis

Analysis of microarray data was performed as describedpreviously using the affylmGUI R package (Wettenhallet al., 2006; Wolfers et al., 2014). p-Values for single timepoints were generated by treating datasets from light-grownΔMAT1 (48 h, 60 h, 96 h) as independent biologicalreplicates.



http://www.ncbi.nlm.nih.gov/sra

http://www.ncbi.nlm.nih.gov/sra

http://meme.nbcr.net/meme/

Yeast one-hybrid analysis

Complementary oligonucleotides harboring three copies ofthe corresponding oligonucleotide sequence used for EMSAswere cloned into plasmids pHISi and pLacZi to generate preyvectors for yeast one-hybrid analysis (see Table S3), asdescribed previously (Schmitt and Kück, 2000). As a bait, theMAT1-1-1 cDNA sequence was integrated into plasmidpGADT7 to generate plasmid pMAT1-AD (see Table S2). Baitand prey vectors were transferred into S. cerevisiae strainsPJ69-4α and PJ69-4a, respectively. Diploid reporter strainsharboring both, the bait and a prey vector, were generated bymating. For analyzing DNA–protein interactions betweenMAT1-1-1 and putative DNA-binding sites, reporter strainswere tested for growth on -his/-leu/-ura selective media sup-plemented with 3-amino-1,2,4-triazole (3-AT) (Merck, Darm-stadt, Germany) as indicated. Further, β-galactosidase activityof reporter strains was analyzed by qualitative and quantitativedetermination of 5-bromo-4-chloro-3-indolyl-beta-D-galacto-pyranoside and O-nitrophenyl β-d-galactopyranoside turno-ver, respectively.

Microscopy

P. chrysogenum strains were grown on glass slides with athin layer of CCM at 27°C. Fluorescence and light microscopywas carried out with an AxioImager M1 fluorescence micro-scope (Zeiss, Jena, Germany) using a SPECTRA LightEngine® LED lamp (Lumencor, OR, USA) as described pre-viously (Engh et al., 2007). Images were captured with aPhotometrix Cool SnapHQ camera (Roper Scientific, AZ,USA) and MetaMorph software version 6.3.1. Recordedimages were edited with MetaMorph and Adobe PhotoshopCS4. Counter staining of nuclei was performed usingNucBlue® Live Cell Stain (Life Technologies GmbH, Darm-stadt, Germany) as specified by the manufacturer. Pelletquantification assays were conducted as described earlier(Böhm et al., 2013).

Multiple sequence alignments

Multiple sequence alignments were performed using theGuidance server (http://guidance.tau.ac.il/) and MAFFTdefault settings (Penn et al., 2010). Alignments were visual-ized using Jalview according to the Clustalx color scheme(http://www.jalview.org/) (Waterhouse et al., 2009).

Acknowledgements

We thank L. Connolly, Dr. J. Galazka, Dr. E. Bredeweg, S.Friedman and M. Dasenko for help with ChIP-seq analyses,PD Dr. M. Nowrousian, M. Sc. T. A. Dahlmann and M. Sc. D.Terfehr for help with bioinformatics, and I. Godehardt fortechnical assistance. We thank Dr. I. Zadra, Dr. H. Kürnsteiner,Dr. E. Friedlin, and Dr. T. Specht for their ongoing interest andsupport, and Dr. I. Teichert for critical reading of the manu-script. This work was funded by Sandoz GmbH, the ChristianDoppler Society, the German National Academic Foundation,and the Ruhr-University Bochum Research School.

The authors declare no conflict of interest.

Author contributions

K.B., U.K., M.F. designed experiments; K.B., C.B. performedexperiments; K.B. analyzed data; K.B., U.K., M.F. wrote themanuscript. All authors discussed results and commented onthe manuscript.

References

Ádám, A.L., García-Martínez, J., Szücs, E.P., Avalos, J., andHornok, L. (2011) The MAT1-2-1 mating-type gene upregu-lates photo-inducible carotenoid biosynthesis in Fusariumverticillioides. FEMS Microbiol Lett 318: 76–83.

Amoutzias, G.D., Robertson, D.L., Van de Peer, Y., andOliver, S.G. (2008) Choose your partners: dimerization ineukaryotic transcription factors. Trends Biochem Sci 33:220–229.

Badis, G., Berger, M.F., Philippakis, A.A., Talukder, S.,Gehrke, A.R., Jaeger, S.A., et al. (2009) Diversity andcomplexity in DNA recognition by transcription factors.Science 324: 1720–1723.

Bailey, T.L., and Elkan, C. (1994) Fitting a mixture model byexpectation maximization to discover motifs in biopoly-mers. Proc Int Conf Intell Syst Mol Biol 2: 28–36.

Bailey, T.L., and Machanick, P. (2012) Inferring directDNA binding from ChIP-seq. Nucleic Acids Res 40:e128.

Baker, C.R., Tuch, B.B., and Johnson, A.D. (2011) ExtensiveDNA-binding specificity divergence of a conserved tran-scription regulator. Proc Natl Acad Sci USA 108: 7493–7498.

Baker, C.R., Booth, L.N., Sorrells, T.R., and Johnson, A.D.(2012) Protein modularity, cooperative binding, and hybridregulatory states underlie transcriptional network diversifi-cation. Cell 151: 80–95.

Bayram, Ö., Krappmann, S., Ni, M., Bok, J.W., Helmstaedt,K., Valerius, O., et al. (2008) VelB/VeA/LaeA complex coor-dinates light signal with fungal development and secondarymetabolism. Science 320: 1504–1506.

Bender, A., and Sprague, G.F., Jr (1987) MAT alpha 1protein, a yeast transcription activator, binds synergisticallywith a second protein to a set of cell-type-specific genes.Cell 50: 681–691.

Bidard, F., Ait Benkhali, J., Coppin, E., Imbeaud, S., Grognet,P., Delacroix, H., and Debuchy, R. (2011) Genome-widegene expression profiling of fertilization competent myce-lium in opposite mating types in the heterothallic fungusPodospora anserina. PLoS ONE 6: e21476.

Böhm, J., Hoff, B., O’Gorman, C.M., Wolfers, S., Klix, V.,Binger, D., et al. (2013) Sexual reproduction and mating-type-mediated strain development in the penicillin-producing fungus Penicillium chrysogenum. Proc NatlAcad Sci USA 110: 1476–1481.

Bullock, W.O., Fernandez, J.M., and Short, J.M. (1987) Xl1-Blue – a high-efficiency plasmid transforming reca Escheri-chia coli strain with beta-galactosidase selection.Biotechniques 5: 376–378.

Cao, Y., Yao, Z., Sarkar, D., Lawrence, M., Sanchez, G.J.,Parker, M.H., et al. (2010) Genome-wide MyoD binding inskeletal muscle cells: a potential for broad cellular repro-gramming. Dev Cell 18: 662–674.



http://guidance.tau.ac.il/

http://www.jalview.org/

Carr, E.A., Mead, J., and Vershon, A.K. (2004) Alpha1-induced DNA bending is required for transcriptional activa-tion by the Mcm1-alpha1 complex. Nucleic Acids Res 32:2298–2305.

Chen, C.Y., and Schwartz, R.J. (1995) Identification of novelDNA binding targets and regulatory domains of a murinetinman homeodomain factor, Nkx-2.5. J Biol Chem 270:15628–15633.

Chen, Y., Zhai, S., Zhang, H., Zuo, R., Wang, J., Guo, M.,et al. (2014) Shared and distinct functions of two Gti1/Pac2 family proteins in growth, morphogenesis and patho-genicity of Magnaporthe oryzae. Environ Microbiol 16:788–801.

Conesa, A., Götz, S., García-Gómez, J.M., Terol, J., Talón,M., and Robles, M. (2005) Blast2GO: a universal tool forannotation, visualization and analysis in functional genom-ics research. Bioinformatics 21: 3674–3676.

Coppin, E., and Debuchy, R. (2000) Co-expression of themating-type genes involved in internuclear recognition islethal in Podospora anserina. Genetics 155: 657–669.

Debuchy, R., Berteaux-Lecellier, V., and Silar, P. (2010)Mating systems and sexual morphogenesis in ascomy-cetes. In Cellular and Molecular Biology of FilamentousFungi. Washington, DC: ASM Press, pp. 501–535.

Demczuk, S., Harbers, M., and Vennstrom, B. (1993) Identi-fication and analysis of all components of a gel retardationassay by combination with immunoblotting. Proc Natl AcadSci USA 90: 2574–2578.

DeZwaan, T.M., Carroll, A.M., Valent, B., and Sweigard, J.A.(1999) Magnaporthe grisea Pth11p is a novel plasma mem-brane protein that mediates appressorium differentiation inresponse to inductive substrate cues. Plant Cell 11: 2013–2030.

Dmochowska, A., Dignard, D., Henning, D., Thomas, D.Y.,and Bussey, H. (1987) Yeast KEX1 gene encodes a puta-tive protease with a carboxypeptidase B-like functioninvolved in killer toxin and alpha-factor precursor process-ing. Cell 50: 573–584.

Du, L.L., and Novick, P. (2002) Pag1p, a novel protein asso-ciated with protein kinase Cbk1p, is required for cell mor-phogenesis and proliferation in Saccharomyces cerevisiae.Mol Biol Cell 13: 503–514.

Dyer, P.S., and O’Gorman, C.M. (2012) Sexual developmentand cryptic sexuality in fungi: insights from Aspergillusspecies. FEMS Microbiol Rev 36: 165–192.

Eisendle, M., Oberegger, H., Zadra, I., and Haas, H. (2003)The siderophore system is essential for viability of Asper-gillus nidulans: functional analysis of two genes encodingl-ornithine N 5-monooxygenase (sidA) and a non-ribosomal peptide synthetase (sidC). Mol Microbiol 49:359–375.

Eisendle, M., Schrettl, M., Kragl, C., Müller, D., Illmer, P., andHaas, H. (2006a) The intracellular siderophore ferricrocinis involved in iron storage, oxidative-stress resistance, ger-mination, and sexual development in Aspergillus nidulans.Eukaryot Cell 5: 1596–1603.

Eisendle, M., Schrettl, M., Kragl, C., Müller, D., Illmer, P., andHaas, H. (2006b) The intracellular siderophore ferricrocinis involved in iron storage, oxidative-stress resistance, ger-mination, and sexual development in Aspergillus nidulans.Eukaryot Cell 5: 1596–1603.

Elbein, A.D., Pan, Y.T., Pastuszak, I., and Carroll, D. (2003)New insights on trehalose: a multifunctional molecule. Gly-cobiology 13: 17R–27R.

Engh, I., Würtz, C., Witzel-Schlomp, K., Zhang, H.Y., Hoff, B.,Nowrousian, M., et al. (2007) The WW domain proteinPRO40 is required for fungal fertility and associates withworonin bodies. Eukaryot Cell 6: 831–843.

Fukuda, K., Yamada, K., Deoka, K., Yamashita, S., Ohta, A.,and Horiuchi, H. (2009) Class III chitin synthase ChsB ofAspergillus nidulans localizes at the sites of polarized cellwall synthesis and is required for conidial development.Eukaryot Cell 8: 945–956.

Galgoczy, D.J., Cassidy-Stone, A., Llinas, M., O’Rourke,S.M., Herskowitz, I., DeRisi, J.L., and Johnson, A.D. (2004)Genomic dissection of the cell-type-specification circuit inSaccharomyces cerevisiae. Proc Natl Acad Sci USA 101:18069–18074.

Glass, N.L., and Lorimer, I. (1991) More gene manipulationsin fungi. In Ascomycete Mating Types. Bennett, J.W., andLasure, L.S. (eds). San Diego: CA: Academic Press, pp.193–216.

Granger-Schnarr, M., Lloubes, R., de Murcia, G., andSchnarr, M. (1988) Specific protein-DNA complexes:immunodetection of the protein component after gel elec-trophoresis and Western-blotting. Anal Biochem 174: 235–238.

Grant, C.E., Bailey, T.L., and Noble, W.S. (2011) FIMO: scan-ning for occurrences of a given motif. Bioinformatics 27:1017–1018.

Grünbacher, A., Throm, T., Seidel, C., Gutt, B., Rohrig, J.,Strunk, T., et al. (2014) Six hydrophobins are involved inhydrophobin rodlet formation in Aspergillus nidulans andcontribute to hydrophobicity of the spore surface. PLoSONE 9: e94546.

Grzenda, A., Lomberk, G., Zhang, J.S., and Urrutia, R. (2009)Sin3: master scaffold and transcriptional corepressor.Biochim Biophys Acta 1789: 443–450.

Gupta, S., Stamatoyannopoulos, J.A., Bailey, T.L., andNoble, W.S. (2007) Quantifying similarity between motifs.Genome Biol 8: R24.

Haber, J.E. (2012) Mating-type genes and MAT switching inSaccharomyces cerevisiae. Genetics 191: 33–64.

Han, K.H., Lee, D.B., Kim, J.H., Kim, M.S., Han, K.Y.,Kim, W.S., et al. (2003) Environmental factors affectingdevelopment of Aspergillus nidulans. J Microbiol 41:34–40.

Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y.C.,Laslo, P., et al. (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory ele-ments required for macrophage and B cell identities. MolCell 38: 576–589.

Herskowitz, I. (1989) A regulatory hierarchy for cell speciali-zation in yeast. Nature 342: 749–757.

Hoff, B., Kamerewerd, J., Sigl, C., Zadra, I., and Kück, U.(2009) Homologous recombination in the antibiotic pro-ducer Penicillium chrysogenum: strain ΔPcku70 showsup-regulation of genes from the HOG pathway. Appl Micro-biol Biotechnol 85: 1081–1094.

Hoff, B., Kamerewerd, J., Sigl, C., Mitterbauer, R., Zadra, I.,Kürnsteiner, H., and Kück, U. (2010) Two componentsof a velvet-like complex control hyphal morphogenesis,



conidiophore development, and penicillin biosynthesis inPenicillium chrysogenum. Eukaryot Cell 9: 1236–1250.

Irie, K., Nomoto, S., Miyajima, I., and Matsumoto, K. (1991)SGV1 encodes a CDC28/cdc2-related kinase required fora G alpha subunit-mediated adaptive response to phero-mone in S. cerevisiae. Cell 65: 785–795.

James, P., Halladay, J., and Craig, E.A. (1996) Genomiclibraries and a host strain designed for highly efficient two-hybrid selection in yeast. Genetics 144: 1425–1436.

Janus, D., Hortschansky, P., and Kück, U. (2007) Identifica-tion of a minimal cre1 promoter sequence promotingglucose-dependent gene expression in the β-lactam pro-ducer Acremonium chrysogenum. Curr Genet 53: 35–48.

Johnson, L. (2008) Iron and siderophores in fungal-host inter-actions. Mycol Res 112: 170–183.

Julius, D., Brake, A., Blair, L., Kunisawa, R., and Thorner, J.(1984) Isolation of the putative structural gene for thelysine-arginine-cleaving endopeptidase required for pro-cessing of yeast prepro-alpha-factor. Cell 37: 1075–1089.

Kamerewerd, J., Zadra, I., Kürnsteiner, H., and Kück, U.(2011) PcchiB1, encoding a class V chitinase, is affected byPcVelA and PcLaeA, and is responsible for cell wall integrityin Penicillium chrysogenum. Microbiology 157: 3036–3048.

Kanai, Y., Kanai-Azuma, M., Noce, T., Saido, T.C., Shiroishi,T., Hayashi, Y., and Yazaki, K. (1996) Identification of twoSox17 messenger RNA isoforms, with and without the highmobility group box region, and their differential expressionin mouse spermatogenesis. J Cell Biol 133: 667–681.

Keszthelyi, A., Jeney, A., Kerényi, Z., Mendes, O., Waalwijk,C., and Hornok, L. (2007) Tagging target genes of theMAT1-2-1 transcription factor in Fusarium verticillioides(Gibberella fujikuroi MP-A). Antonie Van Leeuwenhoek 91:373–391.

Kim, H., and Borkovich, K.A. (2006) Pheromones are essen-tial for male fertility and sufficient to direct chemotropicpolarized growth of trichogynes during mating in Neuros-pora crassa. Eukaryot Cell 5: 544–554.

Kopke, K., Hoff, B., Bloemendal, S., Katschorowski, A.,Kamerewerd, J., and Kück, U. (2012) Members of thePenicillium chrysogenum velvet complex play functionallyopposing roles in the regulation of penicillin biosynthesisand conidiation. Eukaryot Cell 12: 299–310.

Kraus, P.R., Hofmann, A.F., and Harris, S.D. (2002) Charac-terization of the Aspergillus nidulans 14-3-3 homologue,ArtA. FEMS Microbiol Lett 210: 61–66.

Krüger, A., Vowinckel, J., Mulleder, M., Grote, P., Capuano,F., Bluemlein, K., and Ralser, M. (2013) Tpo1-mediatedspermine and spermidine export controls cell cycle delayand times antioxidant protein expression during the oxida-tive stress response. EMBO Rep 14: 1113–1119.

Kunitomo, H., Sugimoto, A., Wilkinson, C.R., and Yamamoto,M. (1995) Schizosaccharomyces pombe pac2+ controlsthe onset of sexual development via a pathway independ-ent of the cAMP cascade. Curr Genet 28: 32–38.

Kück, U., and Böhm, J. (2013) Mating type genes and crypticsexuality as tools for genetically manipulating industrialmolds. Appl Microbiol Biotechnol 97: 9609–9620.

Landt, S.G., Marinov, G.K., Kundaje, A., Kheradpour, P.,Pauli, F., Batzoglou, S., et al. (2012) ChIP-seq guidelinesand practices of the ENCODE and modENCODE consor-tia. Genome Res 22: 1813–1831.

Langmead, B., Trapnell, C., Pop, M., and Salzberg, S.L.(2009) Ultrafast and memory-efficient alignment of shortDNA sequences to the human genome. Genome Biol 10:R25.

Lara-Rojas, F., Sanchez, O., Kawasaki, L., and Aguirre, J.(2011) Aspergillus nidulans transcription factor AtfA inter-acts with the MAPK SakA to regulate general stressresponses, development and spore functions. Mol Micro-biol 80: 436–454.

Lee, S.C., Ni, M., Li, W., Shertz, C., and Heitman, J. (2010)The evolution of sex: a perspective from the fungalkingdom. Microbiol Mol Biol Rev 74: 298–340.

Lee, S.H., Lee, S., Choi, D., Lee, Y.W., and Yun, S.H. (2006)Identification of the down-regulated genes in a mat1-2-deleted strain of Gibberella zeae, using cDNA subtractionand microarray analysis. Fungal Genet Biol 43: 295–310.

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J.,Homer, N., et al. (2009) The sequence alignment/mapformat and SAMtools. Bioinformatics 25: 2078–2079.

Magnúsdóttir, E., Dietmann, S., Murakami, K., Günesdogan,U., Tang, F.C., Bao, S.Q., et al. (2013) A tripartite transcrip-tion factor network regulates primordial germ cell specifi-cation in mice. Nat Cell Biol 15: 905–U322.

Martin, T., Lu, S.W., van Tilbeurgh, H., Ripoll, D.R., Dixelius,C., Turgeon, B.G., and Debuchy, R. (2010) Tracing theorigin of the fungal alpha 1 domain places its ancestor inthe HMG-box superfamily: implication for fungal mating-type evolution. PLoS ONE 5: e15199.

Maruyama, M., Ichisaka, T., Nakagawa, M., and Yamanaka,S. (2005) Differential roles for Sox15 and Sox2 in transcrip-tional control in mouse embryonic stem cells. J Biol Chem280: 24371–24379.

Mead, J., Bruning, A.R., Gill, M.K., Steiner, A.M., Acton, T.B.,and Vershon, A.K. (2002) Interactions of the Mcm1 MADSbox protein with cofactors that regulate mating in yeast.Mol Cell Biol 22: 4607–4621.

Mead, M.E., Stanton, B.C., Kruzel, E.K., and Hull, C.M.(2015) Targets of the Sex Inducer homeodomain proteinsare required for fungal development and virulence in Cryp-tococcus neoformans. Mol Microbiol 95: 804–818.

Mertin, S., McDowall, S.G., and Harley, V.R. (1999) TheDNA-binding specificity of SOX9 and other SOX proteins.Nucleic Acids Res 27: 1359–1364.

Metzenberg, R.L., and Glass, N.L. (1990) Mating type andmating strategies in Neurospora. Bioessays 12: 53–59.

Miroux, B., and Walker, J.E. (1996) Over-production of pro-teins in Escherichia coli: mutant hosts that allow synthesisof some membrane proteins and globular proteins at highlevels. J Mol Biol 260: 289–298.

Morita, T., Yamada, T., Yamada, S., Matsumoto, K., and Ohta,K. (2011) Fission yeast ATF/CREB family protein Atf21plays important roles in production of normal spores.Genes Cells 16: 217–230.

Myers, K.S., Yan, H., Ong, I.M., Chung, D., Liang, K., Tran, F.,et al. (2013) Genome-scale analysis of Escherichia coliFNR reveals complex features of transcription factorbinding. PLoS Genet 9: e1003565.

Nelson, B., Kurischko, C., Horecka, J., Mody, M., Nair, P.,Pratt, L., et al. (2003) RAM: a conserved signaling networkthat regulates Ace2p transcription factor and polarizedmorphogenesis. Mol Biol Cell 14: 3782–3803.



Nielsen, J. (1997) Physiological Engineering Aspects of Peni-cillium chrysogenum. Singapore: World Scientific Publish-ing Co. Pte. Ltd.

Paoletti, M., Seymour, F.A., Alcocer, M.J., Kaur, N., Calvo,A.M., Archer, D.B., and Dyer, P.S. (2007) Mating type andthe genetic basis of self-fertility in the model fungus Asper-gillus nidulans. Curr Biol 17: 1384–1389.

Park, P.J. (2009) ChIP-seq: advantages and challenges of amaturing technology. Nat Rev Genet 10: 669–680.

Penn, O., Privman, E., Ashkenazy, H., Landan, G., Graur, D.,and Pupko, T. (2010) GUIDANCE: a web server for assess-ing alignment confidence scores. Nucleic Acids Res 38:W23–W28.

Pöggeler, S., Nowrousian, M., Ringelberg, C., Loros, J.J.,Dunlap, J.C., and Kück, U. (2006) Microarray and real-timePCR analyses reveal mating type-dependent gene expres-sion in a homothallic fungus. Mol Genet Genomics 275:492–503.

Pramila, T., Miles, S., GuhaThakurta, D., Jemiolo, D., andBreeden, L.L. (2002) Conserved homeodomain proteinsinteract with MADS box protein Mcm1 to restrict ECB-dependent transcription to the M/G1 phase of the cellcycle. Genes Dev 16: 3034–3045.

Pramila, T., Wu, W., Miles, S., Noble, W.S., and Breeden, L.L.(2006) The forkhead transcription factor Hcm1 regulateschromosome segregation genes and fills the S-phase gapin the transcriptional circuitry of the cell cycle. Genes Dev20: 2266–2278.

Rabinovich, A., Jin, V.X., Rabinovich, R., Xu, X., andFarnham, P.J. (2008) E2F in vivo binding specificity: com-parison of consensus versus nonconsensus binding sites.Genome Res 18: 1763–1777.

Robertson, G., Hirst, M., Bainbridge, M., Bilenky, M., Zhao,Y., Zeng, T., et al. (2007) Genome-wide profiles of STAT1DNA association using chromatin immunoprecipitation andmassively parallel sequencing. Nat Methods 4: 651–657.

Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J.,Mokrejs, M., et al. (2004) The FunCat, a functional anno-tation scheme for systematic classification of proteins fromwhole genomes. Nucleic Acids Res 32: 5539–5545.

Schmitt, E.K., and Kück, U. (2000) The fungal CPCR1protein, which binds specifically to beta-lactam biosynthe-sis genes, is related to human regulatory factor X transcrip-tion factors. J Biol Chem 275: 9348–9357.

Schmitt, E.K., Bunse, A., Janus, D., Hoff, B., Friedlin, E.,Kürnsteiner, H., and Kück, U. (2004) Winged helixtranscription factor CPCR1 is involved in regulation of beta-lactam biosynthesis in the fungus Acremonium chrysoge-num. Eukaryot Cell 3: 121–134.

Schönig, B., Brown, D.W., Oeser, B., and Tudzynski, B.(2008) Cross-species hybridization with Fusarium verticil-lioides microarrays reveals new insights into Fusarium fuji-kuroi nitrogen regulation and the role of AreA and NMR.Eukaryot Cell 7: 1831–1846.

Schrettl, M., and Haas, H. (2011) Iron homeostasis – Achilles’heel of Aspergillus fumigatus? Curr Opin Microbiol 14:400–405.

Schrettl, M., Bignell, E., Kragl, C., Joechl, C., Rogers, T., Arst,H., et al. (2004) Siderophore biosynthesis but not reductiveiron assimilation is essential for Aspergillus fumigatus viru-lence. J Exp Med 200: 1213–1219.

Schrettl, M., Bignell, E., Kragl, C., Sabiha, Y., Loss, O.,Eisendle, M., et al. (2007) Distinct roles for intra- and extra-cellular siderophores during Aspergillus fumigatus infec-tion. PLoS Pathog 3: 1195–1207.

Slaven, J.W., Anderson, M.J., Sanglard, D., Dixon, G.K.,Bille, J., Roberts, I.S., and Denning, D.W. (2002) Increasedexpression of a novel Aspergillus fumigatus ABC trans-porter gene, atrF, in the presence of itraconazole in anitraconazole resistant clinical isolate. Fungal Genet Biol 36:199–206.

Smith, K.M., Sancar, G., Dekhang, R., Sullivan, C.M., Li, S.J.,Tag, A.G., et al. (2010) Transcription factors in light andcircadian clock signaling networks revealed by genom-ewide mapping of direct targets for Neurospora white collarcomplex. Eukaryot Cell 9: 1549–1556.

Smith, K.M., Phatale, P.A., Sullivan, C.M., Pomraning, K.R.,and Freitag, M. (2011) Heterochromatin is required fornormal distribution of Neurospora crassa CenH3. Mol CellBiol 31: 2528–2542.

Specht, T., Dahlmann, T.A., Zadra, I., Kürnsteiner, H., andKück, U. (2014) Complete sequencing and chromosome-scale genome assembly of the industrial progenitor strainP2niaD18 from the penicillin producer Penicillium chrys-ogenum. Genome Announc 2: e00577-14.

Spitz, F., and Furlong, E.E. (2012) Transcription factors: fromenhancer binding to developmental control. Nat Rev Genet13: 613–626.

Stringer, M.A., and Timberlake, W.E. (1995) DewA encodes afungal hydrophobin component of the Aspergillus sporewall. Mol Microbiol 16: 33–44.

Tamaru, H., Zhang, X., McMillen, D., Singh, P.B., Nakayama,J., Grewal, S.I., et al. (2003) Trimethylated lysine 9 ofhistone H3 is a mark for DNA methylation in Neurosporacrassa. Nat Genet 34: 75–79.

Thorvaldsdóttir, H., Robinson, J.T., and Mesirov, J.P. (2012)Integrative Genomics Viewer (IGV): high-performancegenomics data visualization and exploration. Brief Bioin-form 14: 178–192.

Thön, M., Al-Abdallah, Q., Hortschansky, P., and Brakhage,A.A. (2007) The thioredoxin system of the filamentousfungus Aspergillus nidulans: impact on developmentand oxidative stress response. J Biol Chem 282: 27259–27269.

Todeschini, A.L., Georges, A., and Veitia, R.A. (2014) Tran-scription factors: specific DNA binding and specific generegulation. Trends Genet 30: 211–219.

Tsong, A.E., Miller, M.G., Raisner, R.M., and Johnson, A.D.(2003) Evolution of a combinatorial transcriptional circuit: acase study in yeasts. Cell 115: 389–399.

Tuch, B.B., Galgoczy, D.J., Hernday, A.D., Li, H., andJohnson, A.D. (2008) The evolution of combinatorial generegulation in fungi. PLoS Biol 6: e38.

Turgeon, B.G., and Yoder, O.C. (2000) Proposed nomencla-ture for mating type genes of filamentous ascomycetes.Fungal Genet Biol 31: 1–5.

Valouev, A., Johnson, D.S., Sundquist, A., Medina, C., Anton,E., Batzoglou, S., et al. (2008) Genome-wide analysis oftranscription factor binding sites based on ChIP-Seq data.Nat Methods 5: 829–834.

Wada, R., Maruyama, J., Yamaguchi, H., Yamamoto, N.,Wagu, Y., Paoletti, M., et al. (2012) Presence and



functionality of mating type genes in the supposedlyasexual filamentous fungus Aspergillus oryzae. ApplEnviron Microbiol 78: 2819–2829.

Wang, Z., Lopez-Giraldez, F., Lehr, N., Farré, M., Common,R., Trail, F., and Townsend, J.P. (2014) Global geneexpression and focused knockout analysis reveals genesassociated with fungal fruiting body development in Neu-rospora crassa. Eukaryot Cell 13: 154–169.

Wasserstrom, L., Lengeler, K.B., Walther, A., and Wendland,J. (2013) Molecular determinants of sporulation in Ashbyagossypii. Genetics 195: 87–99.

Waterhouse, A.M., Procter, J.B., Martin, D.M., Clamp, M.,and Barton, G.J. (2009) Jalview Version 2-a multiplesequence alignment editor and analysis workbench. Bioin-formatics 25: 1189–1191.

Wettenhall, J.M., Simpson, K.M., Satterley, K., and Smyth,G.K. (2006) affylmGUI: a graphical user interface for linearmodeling of single channel microarray data. Bioinformatics22: 897–899.

Wiemann, P., Brown, D.W., Kleigrewe, K., Bok, J.W., Keller,N.P., Humpf, H.U., and Tudzynski, B. (2010) FfVel1 andFfLae1, components of a velvet-like complex in Fusarium

fujikuroi, affect differentiation, secondary metabolism andvirulence. Mol Microbiol 77: 972–994.

Wolfers, S., Kamerewerd, J., Nowrousian, M., Sigl, C., Zadra,I., Kürnsteiner, H., et al. (2014) Microarray hybridizationanalysis of light-dependent gene expression in Penicilliumchrysogenum identifies bZIP transcription factor PcAtfA. JBasic Microbiol 54: 1–10.

Wong, K.H., Hynes, M.J., Todd, R.B., and Davis, M.A. (2007)Transcriptional control of nmrA by the bZIP transcriptionfactor MeaB reveals a new level of nitrogen regulation inAspergillus nidulans. Mol Microbiol 66: 534–551.

Wunderlich, Z., and Mirny, L.A. (2009) Different gene regu-lation strategies revealed by analysis of binding motifs.Trends Genet 25: 434–440.

Zaret, K.S., and Carroll, J.S. (2011) Pioneer transcriptionfactors: establishing competence for gene expression.Genes Dev 25: 2227–2241.

Supporting information

Additional supporting information may be found in the onlineversion of this article at the publisher’s web-site.



Genome-wide identification of target genes of a mating-type α-domain transcription factor reveals functions beyond sexual

development

- SUPPLEMENTARY MATERIAL -

Authors:

Kordula Beckera, Christina Beera, Michael Freitagb, and Ulrich Kücka,1

Affiliations:

aChristian Doppler Laboratory for „Fungal Biotechnology“, Lehrstuhl für Allgemeine und Molekulare Botanik, Ruhr-Universität Bochum, Universitätsstr. 150, 44780 Bochum, Germany bDepartment of Biochemistry and Biophysics, Oregon State University, Corvallis, Oregon 97331-7305, USA

1To whom correspondence should be addressed:

Ulrich Kück, Christian Doppler Laboratory for „Fungal Biotechnology“, Lehrstuhl für Allgemeine und Molekulare Botanik, Ruhr-Universität Bochum, Universitätsstr. 150, 44780 Bochum, Germany

Tel.: +49 234 - 32 - 26212, [email protected]

Table S1: P. chrysogenum strains used in this work. Table S2: Plasmids used in this work. Table S3: Oligonucleotides used in this work. Figure S1: Construction of P. chrysogenum strains used for ChIP analysis. Figure S2: Functional categorization of putative MAT1-1-1 target genes. Figure S3: Validation of non-mating related MAT1-1-1 target genes. Figure S4: Comparison of the predicted MAT1-1-1 DNA-binding motif MAT1.1 to known DNA-

binding motifs. Figure S5: Alignment of MAT1-1-1 α-domain region amino acid sequences. Figure S6: Purification of recombinant GST-MAT1-1-1 from E. coli BL21 (DE3). Figure S7: Control experiments for yeast one-hybrid analyses. Figure S8: Genome-wide distribution of MAT1-1-1 binding sites.

Table S1: P. chrysogenum strains used in this work.

strain characteristics and genotype source P2niaD18 niaD- (Hoff et al., 2008) Δku70FRT2 ΔPcku70::FRT2; niaD− (Kopke et al., 2010) OE MAT1-1-1 (T2) Pgpd::MAT1-1–1; ptrA; niaD− (Böhm et al., 2013) ΔMAT1 (EK6) MAT1-1-1Δ::ble; Pcku70Δ::nat1; niaD− (Böhm et al., 2013) MAT1-ChIP (T28.8) Pgpd::EGFP::MAT1-1-1::TtrpC; nat1; niaD− this study T4.11 Pgpd::EGFP::MAT1-1-1::TtrpC; DsRed::TtrpC; nat1; ble; niaD− this study T2.7.1 Pppg1::DsRed::TtrpC; Pgpd::EGFP::MAT1-1-1::TtrpC; nat1; ble; niaD− this study T11.7 Pppg1::DsRed::TtrpC; ble; niaD- this study T3.15 Pkex1::DsRed::TtrpC; Pgpd::EGFP::MAT1-1-1::TtrpC; nat1; ble; niaD− this study T10.6 Pkex1::DsRed::TtrpC; ble; niaD- this study T34.2 artAΔ::nat1; ΔPcku70::FRT2; niaD− this study T34.3 artAΔ::nat1; ΔPcku70::FRT2; niaD− this study T34.4 artAΔ::nat1; ΔPcku70::FRT2; niaD− this study

Table S2: Plasmids used in this work.

name characteristics source pGEX-MAT1 MAT1-1-1 cDNA sequence of P. chrysogenum; used for heterologous

expression of a GST-MAT1-1-1 fusion construct in E. coli BL21 (DE3) this study

pGFP-MAT1 Pgpd of A. nidulans, egfp, MAT1-1-1 gene of P. chrysogenum, TtrpC of A. nidulans, nat resistance gene of Streptomyces noursei; used for construction of P. chrysogenum ChIP-strains

this study

pLacZi prey vector for yeast one-hybrid analyses, lacZ reporter gene Clontech, (Luo et al., 1996)

pLacZ-kex1-2 triple repeat of Pkex1 fragment (chr1, 2536477-2536502) of P. chrysogenum integrated into MCS of pLacZi

this study

pLacZ-ppg1-2 triple repeat of Pppg1 fragment (chr1: 278174-278202) of P. chrysogenum integrated into MCS of pLacZi

this study

pLacZ-ppg1-2_m1 triple repeat of Pppg1 fragment (chr1: 278174-278202, A G at pos. 278184) of P. chrysogenum integrated into MCS of pLacZi

this study

pLacZ-ppg1-2_m2 triple repeat of Pppg1 fragment (chr1: 278174-278202, A G at pos. 278184 and 278194) of P. chrysogenum integrated into MCS of pLacZi

this study

pHISi prey vector for yeast one-hybrid analyses, his3 reporter gene Clontech, (Alexandre et al., 1993)

pHIS-kex1-2 triple repeat of Pkex1 fragment (chr1, 2536477-2536502) of P. chrysogenum integrated into MCS of pHISi

this study

pHIS-ppg1-2 triple repeat of Pppg1 fragment (chr1: 278174-278202) of P. chrysogenum integrated into MCS of pHISi

this study

pHIS-ppg1-2_m1 triple repeat of Pppg1 fragment (chr1: 278174-278202, A G at pos. 278184) of P. chrysogenum integrated into MCS of pHISi

this study

pHIS-ppg1-2_m2 triple repeat of Pppg1 fragment (chr1: 278174-278202, A G at pos. 278184 and 278194) of P. chrysogenum integrated into MCS of pHISi

this study

pGADT7 bait vector for yeast one-hybrid analyses, GAL4 transcription factor activation domain

Clontech, (Chien et al., 1991)

pMAT1-AD MAT1-1-1 cDNA sequence of P. chrysogenum integrated into MCS of pGADT7

this study

pDsRed dsRed gene of Discosoma sp., TtrpC of A. nidulans this study pPkex1-dsRed_ble

Pkex1 (chr1:2536272-2537717) of P. chrysogenum, dsRed gene of Discosoma sp., TtrpC of A. nidulans, ble resistance gene of Streptoalluteichus hindustanus

this study

pPppg1-dsRed_ble

Pppg1 (chr1:277853-278696) of P. chrysogenum, dsRed gene of Discosoma sp., TtrpC of A. nidulans, ble resistance gene of Streptoalluteichus hindustanus

this study

pKO_artA 5’-flank Pc18g03940, nat resistance gene of Streptomyces noursei, 3’-flank Pc18g03940; used for construction of artA deletion strains

this study

Table S3: Oligonucleotides used in this work. name sequence (5’ 3’) specificity plasmid and strain construction, PCR analysis

MAT1-1-1_f TCTCGGCATGGACGAGCTGTACAAGATGTCTACCTCTCTTGATGC

Pc20g07800 gene; chr2:1892222-1892241

MAT1-1-1_r CGTTAAGTGGATCCACTAGTTCTAGCTAGTTGTGCCCAAAGATCCGGTC

Pc20g07800 gene; chr2:1891165-1891188

MAT1_SmaI_f CCCGGGATGTCTACCTCTCTTGATGC Pc20g07800 gene; chr2:1892222-1892241 MAT1_SmaI_r CCCGGGCTAGTTGTGCCCAAAGATCC Pc20g07800 gene; chr2:1891165-1891184 egfp_f GGTGAACTTCAAGATCCG egfp gene MAT1_r CTAGTTGTGCCCAAAGATCCGGTC Pc20g07800 gene; chr2:1891165-1891188 Pkex1_f ATTCTCGAGCGCTAATCACGGTATATCTG chr1:2537698-2537717 Pkex1_r GACTGAGCTCGATTGGAACTCGCGCTTTG chr1:2536272-2536290 Pppg1_f ATTGGGGCCCTGTGCCGTTCTGGAGAAC chr1:278679-278697 Pppg1_r GATTAAGCTTCTTGATGGTTGGAGGAGAAAC chr1:277852-277873

5’artA_f* GTAACGCCAGGGTTTTCCCAGTCACGACGGAATTCAGGCGCAAGTAAGGAACC

chr1:6422911-6422928

5’artA_r* ATGCTCCTTCAATATCAGTTGAATTGAGGTGAGGGTTGAAAATC

chr1:6423803-6423821

3’artA_f* TGAGCATGCCCTGCCCCTGAGGGCCGACCTTGTTGATGGACACG

chr1:6424895-6424913

3’artA_r* GCGGATAACAATTTCACACAGGAAACAGCGAATTCTACGCAGTATACCTTTCGC

chr1:6425858-6425876

ChIP-PCR qPCR_Pex20_f CCCCATGAACAATAGCAATCACGT chr2:3627752-3627775 qPCR_Pex20_r AGACAGCCAATCAGAGACCGT chr2:3627840-3627860 qPCR_Pre1_f TCCATCGAAAATATGAGGCGGATG chr1:3250668-3250691 qPCR_Pre1_r TGAGCGAATCGTGTGAGGCA chr1:3250591-3250610 qPCR_Kex1_f CCAACTCCACTGGCCATGTCT chr1:2536505-2536525 qPCR_Kex1_r GACATGGTGGCGTTATTGAGCT chr1:2536418-2536439 qPCR_Kex2_f GAGGATCGTCAGAGGCCACC chr3:4731876-4731895 qPCR_Kex2_r TGGAGTAGAAACCGTGGCCTT chr3:4731747-4731767 qPCR_Ppg1_f GTGTTCTTTGTCGACATCAGCCT chr1:278126-278148 qPCR_Ppg1_r CGACAGGATGGCTTGCCCTA chr1:278253-278272 qPCR_NC1_f TTCTTCCGCAATCAAGCTCA chr1:5375234-5375253 qPCR_NC1_r GAAAAATTGCCGCTGGACTC chr1:5375364-5375383 qPCR_NC2_f GGTCGTTGATTCCCTTGAGC chr2:7621179-7621198 qPCR_NC2_r GGATCGGATTATTCGGGTGA chr2:7621294-7621313

qRT-PCR Pc20g00090_f CTGTCATCATCGCTGCGCTG Pc20g00090 gene; chr2:3628372-3628391 Pc20g00090_r GCTTGCGACCGTTGCTTTCT Pc20g00090 gene; chr2:3628547-3628566 Pex20_f TGGCTCAACAACAAGGGCCT Pc20g00100 gene; chr2:3626658-3626677 Pex20_r GGATTCGTCGAACTGCTGCG Pc20g00100 gene; chr2:3626811-3626830 Pc22g15640_f TTGTCGTTCATCAAGCCCGC Pc22g15640 gene; chr1:3251329-3251348 Pc22g15640_r AGACGGACCTGGCGTTCAAT Pc22g15640 gene; chr1:3251116-3251135 Pre1_f TGGGACACTGCTGGATGATCT Pc22g15650 gene; chr1:3248820-3248840 Pre1_r GCTAATAACCTGCCGCACATG Pc22g15650 gene; chr1:3248690-3248710 Sok1_f ACAAAAGAAGCCCGGACCCA Pc22g18590 gene; chr1:2537733-2537752 Sok1_r AGGCGAAGGGTTGTTGACGA Pc22g18590 gene; chr1:2537882-2537901 Kex1_f GGCTTCAACGACGTGCTAGC Pc22g18600 gene; chr1:2535442-2535461 Kex1_r CTTCTTCCGGGGTTGTGGGT Pc22g18600 gene; chr1:2535308-2535327 Kex2_f CAATACGTTGCAGCCCGGAC Pc22g02910 gene; chr3:4732652-4732671 Kex2_r TCCATGTCCAGCCCATCGTC Pc22g02910 gene; chr3:4732740-4732759 Pc22g02930_f GCACGTGAAGCACCAACACA Pc22g02930 gene; chr3:4729905-4729924 Pc22g02930_r CCGTGCCTTCTGATTGTCGC Pc22g02930 gene; chr3:4729771-4729790 Ppg1_f GCTTGCCCCTTGTCCTTCAGA Pc14g01160 gene; chr1:277648-277668 Ppg1_r CGCTGGTACGCTTGACCTCA Pc14g01160 gene; chr1:277567-277586 Pc14g01170_f CGCCAGAACCTTTGCCAGTG Pc14g01170 gene; chr1:278866-278885 Pc14g01170_r AGCACCGATACCGTCACCAG Pc14g01170 gene; chr1:279084-279104 DewA_f GGAGGCCTTCTGAACGGTGT chr4:861331-861350 DewA_r CGGTGCAAGTTGTGGTTCCC chr4:861596-861615 SidD_f GCTGGAAGTTCTGGATGCGC chr1:2128333-2128352 SidD_r TCCTTGCGGCCAACAAACAC chr1:2128417-2128436 Sin3_f GAACAGGCCGAGAAGTACGG chr3:712273-712292 Sin3_r GCAGTTCAGCCACGTCAAAG chr3:712353-712372

Pc22g27040_f GAATTCACACTGGCCAGCGG chr1:586115-586134 Pc22g27040_r GGAGGGTGAGAGCGGTGTTT chr1:586259-586278 Pc22g22160_f CGTGGTGAGAGTCTGGTGCA chr1:1697134-1697153 Pc22g22160_r TTCCCGAAAGCCCAAACCCT chr1:1696969-1696988 ArtA_f TACCACCGCTACCTTGCTGA chr1:6424345-6424364 ArtA_r CAGTGGAGGCGATCTCAGTG chr1:6424428-6424447 Atf21_f CCAGCGAATCAACCAGCAGC chr1:631651-631670 Atf21_r CGCACCATCTGAGACCGACT chr1:631523-631542 Pc19g00140_f GCCTGCGGTTCTCACATTGG chr2:4622762-4622781 Pc19g00140_r TCTCTGGCAGTCAATCCCCG chr2:4622615-4622634 Pc22g18630_f TGTGATCCAGGTCGACGAGC chr1:2530647-2530666 Pc22g18630_r GCGGTGGAGAGCTTGAAGGA chr1:2530738-2530757 FetC_f CCGAAGCAGATCCAGGAGCA chr2:6607858-6607877 FetC_r AAGCTGCTTGTTTTGGCCGG chr2:6607740-6607759 FtrA_f ACAACACCTGGAACCACGCT chr2:6610998-6611017 FtrA_r GAGCGCGTTGAAGATTCCCC chr2:6611127-6611146 MeaB_f CCCAACCCCGGACTTTCAGT chr1:5714759-5714778 MeaB_r CGAGGACCCATAGCTCCACC chr1:5714673-5714692

EMSAs and Y1H**

Pc20g00090-1 CATGAACAATAGCAATCACGTGATCTCTA chr2:3627755-3627783 Pc20g00090-2 TCACGTGATCTCTATTGAGAACAATAGAA chr2:3627770-3627798 Pc20g00090-3 TGAGAACAATAGAAGTCCATTCAAGGATC chr2:3627785-3627813 Pre1-1 TCAATAGACTAGAAAGTCTAGATCAATAA chr1, 3250705-3250733 Pre1-2 AGATCAATAATAAACTCATTCATTCCATC chr1, 3250686-3250714 Pre1-3 ACTCATTCATTCCATCGAAAATATGAGGC chr1, 3250673-3250701 Kex1-1 ACTCCACTGGCCATGTCTTTGGCCACAAT chr1, 2536494-2536522 Kex1-2 GGCCACAATAACCCCACCGGCCTTATTGA chr1, 2536474-2536502 Kex1-3 CCTTATTGACACCCAAATCTGGCTCAACA chr1, 2536454-2536482 Kex2-1 TCCTATTGAGTCTCCTAAGAGGTCTATTG chr3, 4731883-4731866 Kex2-2 GGTCTATTGAGCTCAACTTAGCTTATTCA chr3, 4731818-4731846 Kex2-3 GCTTATTCAAACAGAAGCTAATTCCTTTG chr3, 4731798-4731826 Ppg1-1 CTCGAGATCGGCAGTTCTCAATAGGAATC chr1,278163-278191 Ppg1-2 CAGTTCTCAATAGGAATCTTATTGACCGA chr1, 278174-278202 Ppg1-3 ATAGGAATCTTATTGACCGACGTCAGTGT chr1,278183-278211 Ppg1-2_m1 CAGTTCTCAACAGGAATCTTATTGACCGA chr1, 278174-278202, A G at 278184 Ppg1-2_m2 CAGTTCTCAACAGGAATCTTGTTGACCGA chr1, 278174-278202, A G at 278184 and

278194 DewA-1 CAGTAGGCATTCTCAATAATCAAAGCGTC chr4:860854-860882

DewA-2 ATTGTCTGATTGATTCTCATTGACTTGAG chr4:859279-859307 SidD-1 GGAAATACCGAGTATTGATACCACGGTAT chr1:2122707-2122735 Sin3-1 TCGCCTGTCCACTCAATGCCAGTCATGTT chr3:708154-708182 Pc22g27040-1 CCAAGATGAGGTTATTGAGGCATTTCTTT chr1:590547-590575 Pc22g22160-1 TGCAAAGCTTGCAATTGAGTGACGCAAAG chr1:1699087-1699115 ArtA-1 GCAGGACGGGCAAATTGACGAAGCATGAT chr1:6422421-6422449 Atf21-1 CATACCGGAGGATTCATTGATCCTATGCA chr1:632283-632311 Pc19g00140-1 TATCCTGACTGTTCTCAATTGCAACCTTG chr2:4623848-4623876 Pc22g18630-1 GCCTTATCGGTGTCTCAATGGCGTGATCA chr1:2528324-2528352 FetC/FtrA-1 CAAAAGGCTAAGCTCAATACGAGTGGGTC chr2:6609927-6609955 MeaB-1 TAGGTATTCAAGTATTGACTCGGTATTGG chr1:5716368-5716396

*5‘-overhang for homologous recombination in yeast is marked by underlining **in case of double-stranded oligonucleotides only the sense sequences are given.

Figure S1: Construction of P. chrysogenum strains used for ChIP analysis. (A) A Pgpd::egfp::MAT1-1-1 fusion construct was used for the construction of MAT1-1-1 ChIP-strains. Plasmid pGFP-MAT1 was integrated ectopically into P. chrysogenum P2niaD18. (B) RMAexpress (http://rmaexpress.bmbolstad.com/) analysis of normalized raw data from microarray analysis using P. chrysogenum P2niaD18 after 48 h of cultivation was performed to obtain relative MAT1-1-1 expression levels. Actin (Pc20g11600) and myosin (Pc21g00710) relative expression levels are shown as a reference. (C) Integration of Pgpd::egfp::MAT1-1-1 was confirmed using PCR. Binding positions of primers egfp_f and MAT1_r are indicated as arrows in (A). NC = water control. (D) Presence of the epitope-tagged protein EGFP-MAT1-1-1 in crude protein extract from recombinant P. chrysogenum strains was confirmed using SDS-Page/Western blot analysis prior to ChIP-experiments. (E) Fluorescence microscopy confirmed nuclear localization of EGFP-MAT1-1-1 in the MAT1-ChIP strain. Strains were grown on solid medium for 48 h. Scale bar = 10 μm. (F) Pellet formation in MAT1-ChIP was analyzed in comparison to a previously described MAT1-1-1 overexpression strain (OE MAT1-1-1) (Böhm et al., 2013) and the parental strain P2niaD18 in shaking cultures after 72 h of cultivation. Scale bar = 5 mm.

Figure S2: Functional categorization of putative MAT1-1-1 target genes. (A) Illustration of functional categories assigned to proteins encoded by putative MAT1-1-1 target genes. For each functional category, the fold representation among all predicted proteins is given. Statistically overrepresented functional groups (p-value ≤ 0.05) are indicated by an asterisk. (B) For each functional category, the absolute and relative number of proteins from our dataset (abs SET, rel SET) and the reference genome (abs GENOME, rel GENOME), the fold representation among all predicted proteins (rel SET/rel GENOME), and the corresponding p-value are indicated.

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Figure S4: Comparison of the predicted MAT1-1-1 DNA-binding motif MAT1.1 to known DNA-binding motifs. MAT1.1 was submitted to TOMTOM for comparison with the JASPAR CORE (2014) databases for fungi and vertebrates. For each database, the top four hits, showing similarities to the predicted MAT1-1-1 DNA-binding sequence, are presented. The associated proteins, IDs from the JASPAR CORE database, p-values and E-values are indicated. The size of each letter is proportional to the frequency of each nucleotide at this position within the consensus sequence. Motifs are centered on common central nucleotide.

Figure S5: Alignment of MAT1-1-1 α-domain region amino acid sequences. Accession numbers for MAT1-1-1 proteins according to UniProt database (http://www.uniprot.org/): A. fumigatus (Q4G285), A. nidulans (G5EAT5), C. albicans (Q9UW19), F. graminearum (I1RX43), N. crassa (P19392), P. chrysogenum (B6HEL2), S. cerevisiae (P0CY06), T. reesei (C7SQ97. Localization of the MAT_alpha1 (pfam04769), MATA_HMG-box (cd01389), and Y-[LM]-x(3)-G-[WL] (Martin et al., 2010) domains are indicated by red, green, and blue labels. Alignments were visualized using Jalview according to the Clustalx colour scheme (http://www.jalview.org/).

Figure S6: Purification of recombinant GST-MAT1-1-1 from E. coli BL21 (DE3). Purified protein was analyzed using SDS-PAGE/Western blot analysis using an antibody to GST prior to DNA-binding experiments.

Figure S7: Control experiments for yeast one-hybrid analyses. Control experiments were performed to analyze trans-activation between prey constructs and the empty bait vector and vice versa. All strains used for reporter gene assays were grown on SD-ura-leu to confirm presence of both, a bait and a prey vector, in diploid yeast strains. HIS3 reporter gene activity was analyzed using growth tests on SD-ura-leu-his supplemented with 3-AT as indicated. lacZ reporter gene activity was analyzed using qualitative and quantitative ß-galactosidase assays.

Figure S8: Genome-wide distribution of MAT1-1-1 binding sites. The tracks are (from top): “shaking 1”, “shaking 2”, “surface”, “shaking IP (input)”, and “surface IP (input)” for each of the four P. chrysogenum P2niaD18 chromosomes. Selected peaks upstream of genes mentioned within this study are labeled with the name of the corresponding gene.

REFERENCES

Alexandre, C., Grueneberg, D. A., Gilman, M. Z. (1993) Studying heterologous transcription factors in yeast. METHODS: A Companion to Methods in Enzymology 5: 147–155.

Böhm, J., Hoff, B., O'Gorman, C. M., Wolfers, S., Klix, V., Binger, D., et al. (2013) Sexual reproduction and mating-type-mediated strain development in the penicillin-producing fungus Penicillium chrysogenum. Proc Natl Acad Sci U S A 110: 1476-1481.

Chien, C. T., Bartel, P. L., Sternglanz, R., Fields, S. (1991) The two-hybrid system: a method to identify and clone genes for proteins that interact with a protein of interest. Proc Natl Acad Sci U S A 88: 9578-9582.

Hoff, B., Pöggeler, S., Kück, U. (2008) Eighty years after its discovery, Fleming's Penicillium strain discloses the secret of its sex. Eukaryot Cell 7: 465-470.

Kopke, K., Hoff, B., Kück, U. (2010) Application of the Saccharomyces cerevisiae FLP/FRT recombination system in filamentous fungi for marker recycling and construction of knockout strains devoid of heterologous genes. Appl Environ Microbiol 76: 4664-4674.

Luo, Y., Vijaychander, S., Stile, J., Zhu, L. (1996) Cloning and analysis of DNA-binding proteins by yeast one-hybrid and one-two-hybrid systems. Biotechniques 20: 564-568.

Martin, T., Lu, S. W., van Tilbeurgh, H., Ripoll, D. R., Dixelius, C., Turgeon, B. G., Debuchy, R. (2010) Tracing the origin of the fungal alpha 1 domain places its ancestor in the HMG-box superfamily: implication for fungal mating-type evolution. PLoS One 5: e15199.

IV. BECKER et al. 2015b 24

IV. BECKER et al. 2015b

New insights into PcVelA regulatory functions on a genome-wide

scale reveal evidence for methyltransferase PcLlmA acting as a

downstream factor and direct interaction partner of PcVelA in


Kordula Becker, Sandra Bloemendal, and Ulrich Kück (2015)

– prepared for submission –

1

New insights into PcVelA regulatory functions on a genome-wide scale

reveal evidence for methyltransferase PcLlmA acting as a downstream

factor and direct interaction partner of PcVelA in Penicillium chrysogenum

Authors:

Kordula Beckera, Sandra Bloemendala, and Ulrich Kücka,1

Affiliations:

aLehrstuhl für Allgemeine und Molekulare Botanik, Ruhr-Universität Bochum,

Universitätsstr. 150, D–44780 Bochum, Germany

1To whom correspondence should be addressed:

Ulrich Kück, Lehrstuhl für Allgemeine und Molekulare Botanik, Ruhr-Universität Bochum, Universitätsstr. 150, 44780 Bochum, Germany

Tel.: +49 234 - 32 - 26212, [email protected]

Short title:

Target genes of PcVelA

Key words: Penicillium chrysogenum, velvet complex, PcVelA, PcLaeA, PcLlmA, ChIP-sequencing, protein-DNA interaction, methyltransferase

2

ABSTRACT

Penicillium chrysogenum is the only industrial producer of the β-lactam antibiotic penicillin,

the most commonly used drug in the treatment of bacterial infections. In P. chrysogenum and

other fungi, secondary metabolism and morphogenesis were shown to be controlled via

velvet, a highly conserved multi-subunit protein complex. However, only little is known about

how velvet complex-mediated regulation is exerted on a molecular level. To address this

question, we performed chromatin immunoprecipitation combined with next-generation

sequencing (ChIP-seq) analysis of PcVelA, the founding member and one of the core

components of the velvet complex. We present a genome-wide DNA binding profile and

DNA-binding motif of PcVelA, providing the first experimental evidence for PcVelA acting

as a direct transcriptional regulator on DNA level, possibly even as a transcription factor.

Based on ChIP-seq data, we identified a total of 592 PcVelA-specific DNA-binding regions

and 631 putative direct target genes. Besides a remarkable number of genes related to known

PcVelA regulatory functions, e.g. in terms of conidiation, we also identified at least seven

PcVelA-specific target genes coding for putative methyltransferases. Furthermore, by using

yeast two-hybrid analysis and bimolecular fluorescence complementation (BiFC), one of the

encoded putative methyltransferases, PcLlmA, was shown to interact with PcVelA.

3

INTRODUCTION

The discovery of β-lactam antibiotics has been described as one of the most significant

milestones of the human history, and entailed a revolution in modern chemotherapy (Barreiro

et al. 2012). Penicillin, the most commonly used drug within this group of antibiotics, is

produced by the filamentous ascomycete P. chrysogenum, which was firstly described in 1928

(Fleming 1929). Since then, huge efforts have been made in order to maximize penicillin

yields in large-scale industrial production. For long, strain improvement programs were based

on random mutagenesis approaches, such as treatment with X-ray, ultraviolet irradiation, and

nitrogen mustard mutagenesis (Backus and Stauffer 1955, Peñalva et al. 1998, Barreiro et al.

2012). One of the main drawbacks of random mutagenesis is that it introduces both intended

and unintended mutations, and large-scale screening processes are necessary to identify those

mutant strains displaying the desired characteristics. As a consequence, one main goal of

modern strain-improvement programs is to replace random mutagenesis by targeted genetic

engineering approaches in order to fasten and simplify the generation of new strains with

optimized production properties. Hence, detailed knowledge of determinants regulating

morphology and secondary metabolism in P. chrysogenum is crucial for further optimization

of this industrially highly relevant organism.

Secondary metabolism and differentiation processes in various filamentous fungi are

orchestrated by the multi-subunit velvet complex (Bayram et al. 2008, Hoff et al. 2010,

Wiemann et al. 2010). The founding member of the complex, VeA (Velvet A), was firstly

described as a light-dependent regulator in Aspergillus nidulans (Käfer 1965). Since then,

characterization of veA deletion and overexpression mutants confirmed its regulatory

functions in terms of sexual and asexual development, morphogenesis, virulence, and

secondary metabolism in numerous species (Kim et al. 2002, Kato et al. 2003, Dreyer et al.

2007, Hoff et al. 2010, Wiemann et al. 2010, Merhej et al. 2012). In P. chrysogenum, deletion

of PcvelA was shown to result in reduced production of penicillin, together with light-

independent formation of conidiospores, dichotomous branching of hyphae, and increased

pellet formation in shaking cultures (Hoff et al. 2010). Besides PcVelA, members of the

velvet family of proteins in P. chrysogenum include PcVelB, PcVelC, and PcVosA (Kopke et

al. 2013). Furthermore, the putative S-adenosyl-L-methionine (SAM)-dependent

methyltransferase LaeA (loss of aflR expression A), which functions as a global regulator of

secondary metabolism and development in a huge number of euascomycetes (Bok and Keller

2004, Hoff et al. 2010, Sarikaya Bayram et al. 2010, Wiemann et al. 2010), has been shown to

4

be part of the fungal velvet complex. According to our current working model, all velvet

subunits, including PcLaeA, are able to interact with one or more other subunits (Hoff et al.

2010, Kopke et al. 2013). Using a comprehensive set of single and double deletion mutants, it

has been shown that PcVelA, together with PcLaeA and PcVelC, is an activator of penicillin

biosynthesis, whereas PcVelB represses this process. Moreover, PcVelB and PcVosA were

identified as promoters of conidiation, while PcVelC has an inhibitory effect (Hoff et al. 2010,

Kopke et al. 2013). In contrast to A. nidulans, it has not been solved if velvet sub-complexes

are formed at distinct time points or as a function of developmental stages in P. chrysogenum.

Velvet proteins are characterized by the so-called velvet domain, a conserved protein domain

that can be found in most fungi (Kopke et al. 2013, Gerke and Braus 2014). While velvet

regulatory functions were for long thought to be solely mediated by the putative

methyltransferase LaeA, Ni and Yu hypothesized that the velvet proteins might be acting as

global transcriptional regulators, representing a new fungus-specific class of transcription

factors (TFs) (Ni and Yu 2007). Further evidence for this assumption was provided by

microarray analyses, revealing that the expression of 13.6 % of all nuclear genes in

P. chrysogenum is influenced by PcVelA (Hoff et al. 2010). Accordingly, RNA-seq analyses

in A. fumigatus and A. nidulans revealed that a total of 32 % and 26 % of all protein coding

genes are differentially regulated in a ∆veA strain compared to wild type (Lind et al. 2015).

The first experimental evidence for velvet proteins acting as regulators on DNA level was

provided in 2013. Ahmed et al. (2013) not only showed that the velvet domain is involved in

the dimerization of different velvet proteins, leading to the formation of different homo- and

heterodimers, but also demonstrated that the velvet domains of A. nidulans VosA and the

VosA-VelB heterodimer are acting as DNA-binding domains. Besides the finding that velvet

proteins are able to bind DNA in a sequence-specific manner, another interesting new feature

of velvet-protein properties deals with the observation that several putative

methyltransferases, others than LaeA, are able to directly interact with VeA on the protein

level. For example, a reverse genetics screen in A. nidulans identified LlmF (LaeA-like

methyltransferase F) as a direct interaction partner of VeA and a negative regulator of

sterigmatocystin production and sexual development. While over-expression of llmF was

shown to decrease the nuclear to cytoplasmic ratio of VeA, deletion of llmF resulted in an

increased nuclear accumulation of the protein (Palmer et al. 2013). Furthermore, A. nidulans

VipC (velvet interacting protein C, also referred to as LlmB) and VapB (VipC associated

protein B), both putative methyltransferases and interaction partners of VeA, were shown to

act in the nucleus to promote asexual development or, together with the membrane protein

5

VapA, at the plasma membrane to support sexual development (Sarikaya-Bayram et al. 2014).

Finally, using a yeast two-hybrid approach (Y2H), Fusarium graminearum FgVeA was

shown to directly interact with a total of six putative methyltransferases that show sequence

homologies to FgLaeA1 (Jiang et al. 2011).

A general understanding of PcVelA regulatory functions on a genome-wide scale as well as

the identification and characterization of additional interaction partners and downstream

factors is crucial for further optimization of P. chrysogenum high production strains.

Although huge efforts have been made in order to decipher the molecular mechanisms that

control velvet protein-mediated regulation in various fungi, they are still poorly understood.

Here, we present the first application of chromatin immunoprecipitation combined with next-

generation sequencing (ChIP-seq) for the functional characterization of PcVelA, one of the

core components of the velvet complex from P. chrysogenum. Most importantly, we provide

evidence for PcVelA acting as a direct regulator of transcription on DNA-level and introduce

the putative methyltransferase PcLlmA as a new downstream factor and interaction partner of

PcVelA.

6

RESULTS

Generation of a genome-wide PcVelA DNA-binding profile. For use in ChIP-seq

experiments, a Pgpd::PcvelA::egfp fusion construct was ectopically integrated into

P. chrysogenum ΔPcvelA, a marker-free PcvelA deletion strain (Fig. 1A). Successful

transformation and expression of PcvelA::egfp in strain PcVelA-ChIP was verified using PCR

and SDS-PAGE/Western blot analysis (Fig. 1B-C). Furthermore, fluorescence microscopy

confirmed presence and nuclear localization of PcVelA-EGFP prior to ChIP-experiments

(Fig. 1D). ChIP-seq experiments were performed on two independent biological samples

obtained from shaking cultures, designated “PcVelA_shaking_1” and “PcVelA_shaking_2”.

As a control, input-DNA (“PcVelA_shaking_input”; DNA sample removed prior to ChIP)

was sequenced in parallel. During bioinformatics analysis, only those regions accomplishing

the following criteria were regarded as specific PcVelA binding regions: [1] at least fourfold

enrichment in ChIP-DNA versus input-DNA, [2] a false discovery rate (FDR) threshold ≤

0.001, and [3] a Poisson p-value ≤ 1.00e-04. In total, we identified 764 and 1,001 regions to

be specifically bound to PcVelA in the “PcVelA_shaking1” and “PcVelA_shaking2” dataset,

respectively (see Table 1). Intersection of both datasets, regarding peaks within a maximum

distance of 100 nt as overlapping, identified 592 sites that were specifically bound by PcVelA

in both biological replicates (Fig. 2A and Dataset S1), thus meeting the standards set by the

ENCODE and modENCODE consortia (Landt et al. 2012).

As part of our initial analysis, peak regions were classified according to their genomic

location with regard to neighboring open reading frames (ORFs). 78.9 % (467/592) of peaks

showed intergenic localization and 21.1 % (125/592) were positioned within intragenic

regions (Fig. 2B). Of 467 peaks showing intergenic localization, 39 were positioned within

the 3’-region of both adjacent ORFs, 225 showed 5’-localization to one neighboring gene, and

203 peak regions were positioned within divergent promoter regions, resulting in a total of

631 genes that may be directly controlled by PcVelA. Previous microarray analyses (Hoff et

al. 2010) confirmed PcVelA-dependent changes in expression levels by at least twofold for

18.9 % (119/631) of these genes. Furthermore, analysis of the distance between peak summits

and the predicted translation start site (ATG) of those genes showing 5’-3’ orientation with

regard to neighboring peaks revealed an average distance within a range of 100-600 nt

(Fig. 2C). This observation is in accordance with the general understanding that most

regulatory DNA sequences for a given gene fall within a few hundred bp from its

transcription start site (TSS) in S. cerevisiae (Nguyen and D'Haeseleer 2006, Lin et al. 2010).

7

Validation of PcVelA ChIP-seq data. In order to validate the biological significance of our

dataset and to rule out bias from bioinformatics analysis, PcVelA-specific enrichment of four

selected target regions identified in ChIP-seq analyses was confirmed using quantitative

ChIP-PCR. Target regions were selected to cover a range from high-affinity to mid-affinity

PcVelA binding sites, as deduced from ChIP data. Based on data obtained from ChIP-PCR

analysis, the ratio of the region of interest to a control region showing no PcVelA-specific

enrichment in ChIP-DNA relative to this ratio in input-DNA was calculated. Another region,

showing no enrichment in ChIP-seq data, was analyzed as a negative control (NC). Data from

ChIP-PCR analysis was compared to peak values obtained from bioinformatics analysis,

representing the average number of sequence tags found within a peak region after

normalization to a total of 10 million mapped tags. As shown in Fig. 3, ChIP-PCR results

were consistent with peak values, thus confirming specific enrichment of all tested PcVelA

target regions, and validating peak values as a convincing parameter for estimation of PcVelA

binding affinity to target regions identified in ChIP-seq analyses.

Categorization of putative PcVelA target genes. Screening of our ChIP-seq dataset identified

a remarkable number of putative PcVelA target genes, directly related to cellular and

developmental processes known to be under velvet-mediated control (Table 2). Most of these

genes exhibited PcVelA-dependent expression profiles in previous microarray analyses, when

expression levels in a PcvelA deletion strain were compared to the corresponding wild type

strain ΔPcku70 (Hoff et al. 2010). Prominent examples for direct PcVelA target genes include

con-6 (Pc16g03240), flbC (Pc12g12190), flbD (Pc13g03170), artA (Pc18g03940), and brlA

(Pc23g00400), all related to different aspects of conidiation (Adams et al. 1988, Kraus et al.

2002, Kwon et al. 2010, Olmedo et al. 2010, Arratia-Quijada et al. 2012). Interestingly, a

PcVelA DNA-binding region was also identified within the upstream region of PcvelB,

encoding another component of the velvet complex that acts as an activator of conidiospore

formation in various filamentous fungi (Bayram et al. 2008, Wiemann et al. 2010, Kopke et

al. 2013). Putative targets assigned to functions related to spore viability and protection

included treA/ath1 (Pc16g11870), coding for an α,α-trehalose glucohydrolase, Pc16g06690,

encoding a precursor of the spore-wall fungal hydrophobin DewA, and Pc13g09910, coding

for a late embryogenesis abundant (LEA) domain protein, known to protect other proteins

from aggregation due to osmotic stresses associated with low temperatures in plants and

animals (Stringer and Timberlake 1995, d'Enfert and Fontaine 1997, Goyal et al. 2005).

Furthermore, several genes encoding proteins related to various aspects of secondary

metabolism have been identified, such as Pc21g08920, coding for a norsolorinic acid

8

reductase, Pc21g12630, coding for a non-ribosomal peptide synthetase, and stuA

(Pc13g04920), encoding a basic helix-loop-helix (bHLH) domain TF. Remarkably, StuA was

not only shown to be involved in regulation of penicillin biosynthesis in P. chrysogenum (Sigl

et al. 2011), but also in regulation of asexual reproduction, especially conidiophore

development in A. nidulans (Miller et al. 1992).

Besides this selection of somehow obvious PcVelA target genes, ChIP-seq analysis also

identified a remarkable number of target genes never related to PcVelA or any other

component of the velvet complex before. Among these genes, we identified five genes

encoding proteins with Acetyl-CoA/Acyl-CoA-related functions, numerous genes coding for

uncharacterized TFs (only those showing PcVelA-dependent expression in microarray

analysis are shown in Table 2), as well as seven genes encoding putative methyltransferases.

As recent reports indicated a close link between VeA and various putative methyltransferases

in A. nidulans and F. graminearum (Jiang et al. 2011, Palmer et al. 2013, Sarikaya-Bayram et

al. 2014), we decided to dedicate follow-on analyses to this group of new PcVelA target

genes.

Further characterization of putative methyltransferases. Interestingly, five out of seven

genes coding for putative methyltransferases showed highly significant PcVelA-dependent

expression profiles in previous microarray analyses. To further validate PcVelA-dependent

expression of these genes, qRT-PCR analysis was performed with RNA from strains that were

grown under exactly the same conditions as for ChIP-seq sample preparation. We compared

expression levels from a PcvelA overexpression strain, PcVelA-ChIP, and PcvelA deletion

strain, ΔPcvelA, with those from wild type P2niaD18. As shown in Fig. 4, PcVelA-dependent

expression profiles were confirmed for four out of seven tested genes, namely PcllmA

(Pc21g02240), PcvipC (Pc18g01840), Pc21g12700, and Pc22g01170. Moreover,

Pc18g06010 showed PcVelA-dependent expression in qRT-PCR but not in microarray

analysis (Fig. 4, Table 2).

Next, amino acid sequences of putative methyltransferases were compared to that of the

velvet-complex component PcLaeA, in order to draw inferences to possible similarities in

terms of their functional properties. In general, a set of three conserved sequence motifs

(motif I-III), essential for catalytic activity, can be identified in most methyltransferases

(Kagan and Clarke 1994, Hacker et al. 2000). The most prominent one, “motif I”, is

characterized by three consecutive glycine (G) residues, which are conserved in fungi, plants,

and humans (Kozbial and Mushegian 2005, Sarikaya-Bayram et al. 2014). As shown in

9

Fig. 5, comparison of amino acid sequences revealed strong accordance for PcLlmA, PcVipC,

the one encoded by Pc21g12700, and PcLaeA. Interestingly, all of these putative

methyltransferases are regarded as S-adenosyl-methionine (SAM)-dependent

methyltransferases, and conservation seemed to be restricted to the regions spanning the

methyltransferase sequence motifs. This finding fits the observation that although

SAM-dependent methyltransferases share a highly conserved structural fold and - in most

cases - carry a set of three conserved methyltransferase sequence motifs, they share little

sequence similarity (Kagan and Clarke 1994, Hacker et al. 2000, Martin and McMillan 2002).

De novo prediction and validation of a PcVelA DNA-binding motif. We used MEME to

perform a de novo prediction of a PcVelA DNA-binding motif, based on peak regions present

in two independent ChIP-experiments. Our analysis revealed one highly significant motif

sequence, designated PcVelA.M1, which was found to be present in 275 (46.5 %) out of 592

peak regions when applying a statistical threshold of p ≤ 0.001. While comparison of

PcVelA.M1 to known binding motifs within the JASPAR CORE (2014) fungi database did

not reveal any significant matches, comparison to the JASPER CORE (2014) vertebrates’

database revealed some interesting similarities. PcVelA.M1 most closely resembled

DNA-binding motifs of NR2E3, a photoreceptor nuclear receptor TF involved in human

photoreceptor development (Kobayashi et al. 1999, Milam et al. 2002), and NR2F1, a nuclear

hormone receptor and transcriptional regulator playing an important role in the

neurodevelopment of the visual system in humans (Bosch et al. 2014). Independently to

bioinformatics analysis, we observed weak similarity between PcVelA.M1 and a

DNA-binding consensus sequence that has recently been described for A. nidulans VosA

(Ahmed et al. 2013).

To further verify biological significance of PcVelA.M1, we performed electrophoretic

mobility shift assays (EMSAs). As shown in Fig. 7, a GST-tagged version of the PcVelA

N-terminal region (PcVelA1-256), purified from E. coli, showed specific binding to 50 nt

oligonucleotides (PcLlmA_2 and PcLlmA_4) derived from a region within the PcllmA

upstream sequence and harboring exactly one copy of PcVelA.M1. Specific binding was also

documented for full-length PcVelA (data not shown), however, the PcVelA N-terminus seems

to be sufficient to mediate effective DNA binding. As PcVelA1-256 harbors the complete velvet

domain, this might indicate that the velvet domain mediates PcVelA DNA-binding, like it has

been previously demonstrated for A. nidulans VosA and VelB (Ahmed et al. 2013). Binding

specificity between PcVelA and the DNA-binding consensus sequence PcVelA.M1 was

10

further verified, when mutated versions of the aforementioned oligonucleotides

(PcLlmA_2_m and PcLlmA_4_m) were tested for binding to PcVelA1-256 (Fig. 7). Complex

formation was diminished drastically due to four single nucleotide mutations within the motif

sequence, thus, confirming PcVelA.M1 as a specific PcVelA DNA-binding motif.

PcVelA directly interacts with PcLlmA, a putative SAM-dependent methyltransferase.

Starting from the observation that we identified PcllmA as a direct target gene of PcVelA, and

comparison of amino acid sequences revealed high conservation within the methyltransferase

motifs I-III of PcLaeA and PcLlmA (Fig. 5), we decided to submit PcLlmA to further

functional characterization. As shown in Fig. 8A, we were able to confirm direct interaction

between PcVelA and PcLlmA by using an ex vivo yeast two–hybrid (Y2H) approach. Here,

diploid strains synthesizing both the bait and the prey protein were spotted on selective media,

lacking adenine and histidine and supplemented with X-α-Gal, to demonstrate ADE2 and

HIS3, as well as lacZ reporter gene activity. Interestingly, PcLlmA did interact with PcVelA

but not with other velvet components (data not shown). This is also true for the interaction

between PcLaeA and the velvet complex, which is solely mediated by PcVelA (Hoff et al.

2010, Kopke et al. 2013). To confirm the observed interaction between PcVelA and PcLlmA

in vivo in the homologous system, we used bimolecular fluorescence complementation

analysis (BiFC) (Hoff and Kück 2005). Genes encoding PcVelA and PcLlmA were fused to

eyfp fragments encoding either the N- or the C-terminus of the yellow fluorescent protein, and

strains harboring both constructs were analyzed using fluorescence microscopy. As a control,

we investigated strains producing only split EYFPs and strains producing one split EYFP

together with either EYFP-PcVelA or PcLlmA-EYFP. As shown in Fig. 8B, strains carrying

both PcVelA and PcLlmA eyfp-fusion constructs showed clear EYFP signals while no

fluorescence was detectable in control strains. Additional DAPI staining demonstrated that

interaction between PcVelA and PcLlmA takes place in the nucleus, as it has previously been

shown for the interaction between PcVelA and PcLaeA, PcVelB, PcVelC, and PcVosA as

well as the interaction of PcVelA with itself (Hoff et al. 2010, Kopke et al. 2013).

11

DISCUSSION

Although functional characterization of the velvet-complex components in P. chrysogenum

and related species has been in the limelight of research during the past years (Bayram et al.

2008, Hoff et al. 2010, Wiemann et al. 2010), the output mechanisms of genome-wide velvet

protein-mediated regulatory functions on a molecular level remained enigmatic. Within this

work, we present the first ChIP-seq analysis of one of the core components of the velvet

complex and provide unambiguous evidence for an involvement of PcVelA in genome-wide

transcriptional regulation on DNA-level, possibly even as a TF.

PcVelA acts as a global regulator of transcriptional regulation. Based on data obtained from

ChIP-seq analysis, we identified a total of 592 highly specific PcVelA DNA-binding sites and

631 putative direct PcVelA target genes, of which 18.9 % showed PcVelA-dependent changes

in expression levels by at least twofold in previous microarray analyses (Hoff et al. 2010).

This apparently small overlap is in accordance with previous ChIP-seq analyses in

P. chrysogenum and comparable analyses in yeast and higher eukaryotes, which revealed an

overlap of ~50 % and 10-25 % between TF occupancy and expression of neighboring genes

(Gao et al. 2004, Sandmann et al. 2006, Jakobsen et al. 2007, Vokes et al. 2008, Becker et al.

2015). A remarkable number of PcVelA target genes, identified in our analysis, could be

clearly related to known velvet-regulated cellular and developmental processes. For example,

we identified at least 14 genes involved in the regulation of conidiation and development, as

well as 16 genes, which could be assigned to secondary metabolism (see Table 2). Genes

related to these categories were localized next to some of the most significant PcVelA DNA-

binding sites, which is in great accordance with the general acceptance that the most likely

bound regions identified in DNA-binding protein ChIP-seq analysis are positioned next to

generally known functional target genes (Todeschini et al. 2014). However, it was shown that

even low-affinity TF binding may have a functional role in chromatin remodeling (Cao et al.

2010) or nucleosome positioning (Zaret and Carroll 2011), which can influence gene

expression at later developmental stages or have an additional non-transcriptional function

(Spitz and Furlong 2012). Based on our ChIP-seq data, this might also be true for a PcVelA

peak region within the upstream sequence of PcvelB, encoding another component of the

velvet complex that acts as an activator of conidiospores formation in various fungi (Bayram

et al. 2008, Wiemann et al. 2010, Kopke et al. 2013). Although PcvelB did not show PcVelA-

dependent expression in previous microarray analyses, and the peak value of the

corresponding ChIP-seq peak region pointed to a rather low-affinity target region, further

12

research will be needed in order to analyze this remarkable observation and to draw

inferences about its biological relevance.

Besides somehow expected PcVelA target genes involved in regulation of spore formation

and secondary metabolism, our dataset also included a large number of new putative PcVelA

target genes, e.g. five genes encoding proteins with functions related to

Acyl-CoA/Acetyl-CoA synthesis and utilization and several genes encoding TFs with so far

unknown functions. Acetyl-CoA is one of the key biochemical precursors used in

fundamental cellular metabolism, such as fatty acid metabolism, and secondary metabolite

synthesis (Hutchinson and Fujii 1995, Brown et al. 1996, Kistler and Broz 2015). For

example, Acetyl-CoA is needed during the final enzymatic steps of penicillin biosynthesis in

P. chrysogenum, where the enzyme acyl-coenzyme A:isopenicillin N acyltransferase converts

isopenicillin N (IPN) to penicillin G by exchange of the α-amino adipyl side chain of IPN

with CoA-activated phenylacetic acid (Brakhage et al. 2004). Among genes encoding

proteins with functions related to Acyl-CoA/Acetyl-CoA, we identified a homolog of AnfacA,

coding for a cytoplasmic Acetyl-CoA synthetase in A. nidulans. It has been shown that

loss-of-function mutations in the AnfacA gene result in resistance to fluoroacetate in the

absence of a repressing carbon source, which otherwise inhibits development, conidiation,

and conidial pigmentation (Hynes and Murray 2010). Based on the diversity of

Acetyl-CoA/Acyl-CoA functions, it will be highly interesting to characterize the functions of

Acetyl-CoA- and Acyl-CoA-utilizing enzymes, encoded by specific target genes of PcVelA,

in more detail. Another promising starting point for future experiments includes the

characterization of TFs encoded by PcVelA target genes. As these proteins might be acting as

important downstream factors of the velvet complex, knowledge of their specific regulatory

functions will play a crucial part in improving our general understanding of the regulatory

circuits governed by velvet.

PcVelA binds DNA in a sequence-dependent manner. Besides the identification of direct

target genes, ChIP-seq data were also used for the de novo prediction of a PcVelA DNA-

binding motif, PcVelA.M1, which was further verified by DNA-binding studies (EMSAs).

Interestingly, with the exception of the DNA-binding motif described for A. nidulans VosA,

we were not able to identify any fungal DNA-binding consensus sequences similar to

PcVelA.M1. This finding is in accordance to a theory advanced by Ni and Yu in 2007, which

implies that A. nidulans VeA, VelB, and VosA might be acting as global transcriptional

regulators, representing a new fungus-specific class of TFs. Furthermore, when comparing

13

PcVelA.M1 to known DNA-binding motifs from vertebrates, close similarities to motif

sequences of transcriptional regulators involved in development of the visual system became

obvious. As PcVelA is known as a regulator of light-dependent formation of conidiospores in

P. chrysogenum and related species, this finding points to a possible evolutionary

interconnection between light sensing systems in filamentous fungi and vertebrates. Most

importantly, PcVelA.M1 resembled the DNA-binding motif of NR2E3, a photoreceptor

nuclear receptor TF involved in the regulation of human photoreceptor development

(Kobayashi et al. 1999, Milam et al. 2002). It was shown that mutations in NR2E3 are

associated with the enhanced S-cone syndrome (ESCS), characterized by night blindness,

varying degrees of cone vision, and retinal degeneration in humans (Haider et al. 2000).

Remarkably, this phenotype somehow resembles those of PcvelA/veA deletion mutants in

P. chrysogenum and A. nidulans, which are characterized by an impaired light-sensing ability,

leading to the formation of conidiospores in the absence of light, whereas conidiogenesis in

the wild type is light-dependent (Käfer 1965, Mooney and Yager 1990, Hoff et al. 2010).

The response to light and the corresponding molecular mechanisms that regulate many

different aspects of the biology of organisms have been extensively studied in plants and

many fungi, in particular Neurospora crassa (Chen et al. 2004, Chen et al. 2010). However,

only little is known about the mechanisms underlying light perception in P. chrysogenum and

closely related species. In A. nidulans, direct interaction between VeA and FphA, a

phytochrome acting as a red-light sensor, as well as FphA-mediated interaction between VeA

and LreA/LreB, orthologs of the respective N. crassa blue-light responsive WC-1 and WC-2,

have been demonstrated (Blumenstein et al. 2005, Purschwitz et al. 2009). Accordingly, it has

been hypothesized that development in A. nidulans is regulated through an interplay of two

light-sensing systems, FphA and LreA/LreB, with VeA, interacting at the protein level (Calvo

2008). In A. nidulans, one of the major responses to light is the regulation of asexual

development, a pathway that is controlled by the master regulator BrlA (Adams et al. 1988,

Ruger-Herreros et al. 2011). It was demonstrated that the fluffy genes fluG and flbA-E encode

regulators of light-dependent brlA expression. Their deletion reduces expression of brlA,

resulting in aconidial, fluffy phenotypes (Ruger-Herreros et al. 2011). As deletion of lreB

results in a complete loss of brlA expression but not of the fluffy genes, it was hypothesized

that the photoreceptor complex interacts directly with brlA (Ruger-Herreros et al. 2011).

Identification and validation of a PcVelA DNA-binding motif with high similarity to those of

regulators of development of the visual system in vertebrates as well as the identification of

specific PcVelA DNA-binding sites within the upstream region of the fluffy genes flbC and

14

flbD, as well as brlA, clearly indicates a need for reconsideration of the regulatory functions

of PcVelA in terms of light perception in P. chrysogenum and related species. Most

importantly, our findings might help to finally answer the question how photoreceptors induce

the expression of the regulators of conidiation and whether it is a direct or indirect event

through other components. Based on our data, a regulatory function of PcVelA on DNA-level

instead or simultaneously to a regulatory function on protein level has to be taken into

consideration. Further research will be needed in order to clarify the exact involvement of

PcVelA in transcriptional regulation of light-dependent gene expression and to elucidate the

possible evolutionary interconnection between velvet proteins and regulators of the visual

system in vertebrates.

PcVelA directly interacts with the putative SAM-dependent methyltransferase PcLlmA.

Recently, a number of putative methyltransferases other than LaeA have been described to

directly interact with VeA in A. nidulans and F. graminearum. In A. nidulans,

methyltransferase LlmF was shown to be involved in determination of VeA localization and

methyltransferases VipC and VapB were demonstrated to be involved in regulation of sexual

and asexual development (Palmer et al. 2013, Sarikaya-Bayram et al. 2014). In

F. graminearum, a total of six putative methyltransferases were shown to directly interact

with FgVeA using a Y2H screen (Jiang et al. 2011). Moreover, a number of putative

methyltransferases have been demonstrated to exert LaeA-similar functions in various fungi.

For example, in the maize pathogen Cochliobolus heterostrophus, Lae1-like

methyltransferase Llm1, a homolog of A. nidulans LlmF, was shown to act as a negative

regulator of T-toxin production and asexual sporulation (Bi et al. 2013), and in

F. graminearum, methyltransferase KMT6 was shown to be involved in regulation of

development as well as expression of genes for mycotoxins, pigments, and other secondary

metabolites (Connolly et al. 2013). Against this background, it appeared highly interesting

that we identified a total of seven putative methyltransferase-encoding genes as PcVelA

targets. To the best of our knowledge, no putative methyltransferase-encoding gene has been

identified as a specific downstream factor of VeA in P. chrysogenum or related species until

today. As the methyltransferase-specific sequence motifs (motif I-III) of one of the encoded

putative methyltransferases, PcLlmA, exhibited noticeably high consistency to those of

PcLaeA, and the corresponding gene was located next to a highly significant PcVelA

DNA-binding region obtained from ChIP-seq analysis, we decided to submit PcLlmA to

further functional characterization. Using Y2H and BiFC analysis we were able to verify

direct interaction between PcVelA and PcLlmA on protein level. Interestingly, this interaction

15

seemed to be restricted to PcVelA, as no other interaction between PcLlmA and components

of the velvet complex could be demonstrated. This observation is in great accordance with

previous Y2H analyses, revealing that interaction of the putative methyltransferase PcLaeA is

restricted to PcVelA (Kopke et al. 2013). Furthermore, DAPI staining confirmed nuclear

localization of PcVelA-PcLlmA in vivo, as it was previously described for PcVelA-PcLaeA

and PcVelA-PcVelB (Hoff et al. 2010), PcVelA-PcVelC and PcVelA-PcVosA (Kopke et al.

2013) as well as VeA-LlmF (Palmer et al. 2013), VeA-VipC, and VipC-VapB in A. nidulans

(Sarikaya-Bayram et al. 2014).

PcLaeA as well as PcLlmA belong to the family of SAM-dependent methyltransferases,

which catalyze the transfer of methyl groups from SAM to a large variety of acceptor

substrates. These substrates are ranging from small metabolites to bio-macromolecules,

including DNA, proteins and small-molecule secondary metabolites (Martin and McMillan

2002, Jiang et al. 2011, Struck et al. 2012). As the biological functions of methylation are

versatile, reaching from biosynthesis, metabolism, detoxification and signal transduction, to

protein sorting and repair as well as nucleic acid processing (Martin and McMillan 2002),

they have a significant potential for application in biotechnology (Struck et al. 2012).

Accordingly, the putative SAM-dependent methyltransferase LaeA has been shown to

influence regulation of secondary metabolite gene clusters in various fungi (Bok and Keller

2004, Kale et al. 2008, Kosalková et al. 2009, Sarikaya-Bayram et al. 2010, Wiemann et al.

2010, Karimi-Aghcheh et al. 2013). For example, deletion of P. chrysogenum PclaeA resulted

in a significant reduction of penicillin biosynthesis (Hoff et al. 2010). It remains a task of the

future to elucidate the regulatory functions of VeA-interacting methyltransferases on a

molecular level and to analyze how the interaction between VeA and the growing number of

methyltransferases described as direct interaction partners in P. chrysogenum and other fungi

is mediated on a structural level. Based on data from A. nidulans, it was suggested that VeA

should have an affinity domain for methyltransferases or a tertiary domain providing

interaction between VeA and methyltransferases (Bayram et al. 2008, Sarikaya-Bayram et al.

2015). However, experimental evidence is needed to verify sustainability of these hypotheses

and to elucidate the functional output of this interaction in more detail.

Taken together, our data provide unambiguous evidence for PcVelA acting as a global

transcriptional regulator, involved in a variety of cellular and developmental processes,

ranging from conidiation and development to secondary metabolism. Furthermore, at least

seven genes encoding putative methyltransferases were identified as specific PcVelA target

16

genes, and PcLlmA, a putative SAM-domain methyltransferase was introduced as a new

direct interaction partner of PcVelA on protein level. However, even if this work provided

unprecedented deep insight into PcVelA regulatory functions on a genome-wide scale, much

work remains to be done in order to fully understand PcVelA’s ambiguous nature as a

transcriptional regulator on the one hand and as one of the core components of the

multi-subunit velvet complex on the other hand.

17

ACKNOWLEDGEMENTS

We thank PD Dr. M. Nowrousian and M. Sc. T. A. Dahlmann for help with bioinformatics,

and S. Mertens, I. Godehardt, K. Kalkreuter and I. Schelberg for excellent technical

assistance. We further thank Drs. I. Zadra, H. Kürnsteiner, E. Friedlin, and T. Specht for their

ongoing interest and support. This work was funded by Sandoz GmbH, the Christian Doppler

Society, the German National Academic Foundation, and the Ruhr-University Bochum

Research School.

18

REFERENCES

Adams TH, Boylan MT, Timberlake WE (1988) BrlA is necessary and sufficient to direct conidiophore development in Aspergillus nidulans. Cell 54: 353-62

Ahmed YL, Gerke J, Park HS, Bayram Ö, Neumann P, Ni M, Dickmanns A, Kim SC, Yu JH,

Braus GH, Ficner R (2013) The velvet family of fungal regulators contains a DNA-binding domain structurally similar to NF-κB. PLoS Biol 11: e1001750

Arratia-Quijada J, Sanchez O, Scazzocchio C, Aguirre J (2012) FlbD, a Myb transcription factor

of Aspergillus nidulans, is uniquely involved in both asexual and sexual differentiation. Eukaryot Cell 11: 1132-42

Backus MP, Stauffer JF (1955) The production and selection of a family of strains in Penicillium

chrysogenum. Mycologia 47: 429-63 Bailey TL, Elkan C (1994) Fitting a mixture model by expectation maximization to discover motifs

in biopolymers. Proc Int Conf Intell Syst Mol Biol 2: 28-36 Balazs A, Pocsi I, Hamari Z, Leiter E, Emri T, Miskei M, Olah J, Toth V, Hegedus N, Prade RA,

Molnar M (2010) AtfA bZIP-type transcription factor regulates oxidative and osmotic stress responses in Aspergillus nidulans. Mol Genet Genomics 283: 289-303

Barreiro C, Martín JF, García-Estrada C (2012) Proteomics shows new faces for the old penicillin

producer Penicillium chrysogenum. J Biomed Biotechnol 2012: 1-15 Bayram Ö, Krappmann S, Ni M, Bok JW, Helmstaedt K, Valerius O, Braus-Stromeyer S, Kwon

NJ, Keller NP, Yu JH, Braus GH (2008) VelB/VeA/LaeA complex coordinates light signal with fungal development and secondary metabolism. Science 320: 1504-6

Becker K, Beer C, Freitag M, Kück U (2015) Genome-wide identification of target genes of a

mating-type α-domain transcription factor reveals functions beyond sexual development. Mol Microbiol (Epub ahead of print)

Bi Q, Wu D, Zhu X, Gillian Turgeon B (2013) Cochliobolus heterostrophus Llm1 - a Lae1-like

methyltransferase regulates T-toxin production, virulence, and development. Fungal Genet Biol 51: 21-33

Blumenstein A, Vienken K, Tasler R, Purschwitz J, Veith D, Frankenberg-Dinkel N, Fischer R

(2005) The Aspergillus nidulans phytochrome FphA represses sexual development in red light. Curr Biol 15: 1833-8

Böhm J, Hoff B, O'Gorman CM, Wolfers S, Klix V, Binger D, Zadra I, Kürnsteiner H, Pöggeler

S, Dyer PS, Kück U (2013) Sexual reproduction and mating-type-mediated strain development in the penicillin-producing fungus Penicillium chrysogenum. Proc Natl Acad Sci U S A 110: 1476-81

Bok JW, Keller NP (2004) LaeA, a regulator of secondary metabolism in Aspergillus spp. Eukaryot

Cell 3: 527-35 Bosch DG, Boonstra FN, Gonzaga-Jauregui C, Xu M, de Ligt J, Jhangiani S, Wiszniewski W,

Muzny DM, Yntema HG, Pfundt R, Vissers LE, Spruijt L, Blokland EA, Chen CA, Lewis RA, Tsai SY, Gibbs RA, Tsai MJ, Lupski JR, Zoghbi HY, Cremers FP, de Vries BB, Schaaf CP (2014) NR2F1 mutations cause optic atrophy with intellectual disability. Am J Hum Genet 94: 303-9

19

Brakhage AA, Spröte P, Al-Abdallah Q, Gehrke A, Plattner H, Tüncher A (2004) Regulation of penicillin biosynthesis in filamentous fungi. Adv Biochem Eng Biotechnol 88: 45-90

Brown DW, Adams TH, Keller NP (1996) Aspergillus has distinct fatty acid synthases for primary

and secondary metabolism. Proc Natl Acad Sci U S A 93: 14873-7 Bullock WO, Fernandez JM, Short JM (1987) Xl1-Blue - a high-efficiency plasmid transforming

recA Escherichia coli strain with β-galactosidase selection. Biotechniques 5: 376-8 Calvo AM (2008) The VeA regulatory system and its role in morphological and chemical

development in fungi. Fungal Genet Biol 45: 1053-61 Cao Y, Yao Z, Sarkar D, Lawrence M, Sanchez GJ, Parker MH, MacQuarrie KL, Davison J,

Morgan MT, Ruzzo WL, Gentleman RC, Tapscott SJ (2010) Genome-wide MyoD binding in skeletal muscle cells: a potential for broad cellular reprogramming. Dev Cell 18: 662-74

Chakrabortee S, Tripathi R, Watson M, Schierle GSK, Kurniawan DP, Kaminski CF, Wise MJ,

Tunnacliffe A (2012) Intrinsically disordered proteins as molecular shields. Mol Biosyst 8: 210-9

Chen CH, Dunlap JC, Loros JJ (2010) Neurospora illuminates fungal photoreception. Fungal Genet

Biol 47: 922-9 Chen M, Chory J, Fankhauser C (2004) Light signal transduction in higher plants. Annu Rev Genet

38: 87-117 Connolly LR, Smith KM, Freitag M (2013) The Fusarium graminearum histone H3 K27

methyltransferase KMT6 regulates development and expression of secondary metabolite gene clusters. PLoS Genet 9: e1003916

d'Enfert C, Fontaine T (1997) Molecular characterization of the Aspergillus nidulans treA gene

encoding an acid trehalase required for growth on trehalose. Mol Microbiol 24: 203-16 DeZwaan TM, Carroll AM, Valent B, Sweigard JA (1999) Magnaporthe grisea Pth11p is a novel

plasma membrane protein that mediates appressorium differentiation in response to inductive substrate cues. Plant Cell 11: 2013-30

Dreyer J, Eichhorn H, Friedlin E, Kürnsteiner H, Kück U (2007) A homologue of the Aspergillus

velvet gene regulates both cephalosporin C biosynthesis and hyphal fragmentation in Acremonium chrysogenum. Appl Environ Microbiol 73: 3412-22

Engh I, Würtz C, Witzel-Schlomp K, Zhang HY, Hoff B, Nowrousian M, Rottensteiner H, Kück

U (2007) The WW domain protein PRO40 is required for fungal fertility and associates with woronin bodies. Eukaryot Cell 6: 831-43

Fleming A (1929) On the antibacterial action of cultures of a Penicillium, with special reference to

their use in the isolation of B. influenzae. British Journal of Experimental Pathology 10: 226-36

Gao F, Foat BC, Bussemaker HJ (2004) Defining transcriptional networks through integrative

modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics 5: 31

Gerke J, Braus GH (2014) Manipulation of fungal development as source of novel secondary

metabolites for biotechnology. Appl Microbiol Biotechnol 98: 8443-55

20

Goyal K, Walton LJ, Tunnacliffe A (2005) LEA proteins prevent protein aggregation due to water stress. Biochem J 388: 151-7

Gupta S, Stamatoyannopoulos JA, Bailey TL, Noble WS (2007) Quantifying similarity between

motifs. Genome Biol 8: R24 Hacker C, Glinski M, Hornbogen T, Doller A, Zocher R (2000) Mutational analysis of the N-

methyltransferase domain of the multifunctional enzyme enniatin synthetase. J Biol Chem 275: 30826-32

Hagiwara D, Suzuki S, Kamei K, Gonoi T, Kawamoto S (2014) The role of AtfA and HOG MAPK

pathway in stress tolerance in conidia of Aspergillus fumigatus. Fungal Genet Biol 73: 138-49 Haider NB, Jacobson SG, Cideciyan AV, Swiderski R, Streb LM, Searby C, Beck G, Hockey R,

Hanna DB, Gorman S, Duhl D, Carmi R, Bennett J, Weleber RG, Fishman GA, Wright AF, Stone EM, Sheffield VC (2000) Mutation of a nuclear receptor gene, NR2E3, causes enhanced S cone syndrome, a disorder of retinal cell fate. Nat Genet 24: 127-31

Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass

CK (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38: 576-89

Hoff B, Kück U (2005) Use of bimolecular fluorescence complementation to demonstrate

transcription factor interaction in nuclei of living cells from the filamentous fungus Acremonium chrysogenum. Curr Genet 47: 132-8

Hoff B, Pöggeler S, Kück U (2008) Eighty years after its discovery, Fleming's Penicillium strain

discloses the secret of its sex. Eukaryot Cell 7: 465-70 Hoff B, Kamerewerd J, Sigl C, Zadra I, Kück U (2009) Homologous recombination in the

antibiotic producer Penicillium chrysogenum: strain ΔPcku70 shows up-regulation of genes from the HOG pathway. Appl Microbiol Biotechnol 85: 1081-94

Hoff B, Kamerewerd J, Sigl C, Mitterbauer R, Zadra I, Kürnsteiner H, Kück U (2010) Two

components of a velvet-like complex control hyphal morphogenesis, conidiophore development, and penicillin biosynthesis in Penicillium chrysogenum. Eukaryot Cell 9: 1236-50

Hutchinson CR, Fujii I (1995) Polyketide synthase gene manipulation: a structure-function approach

in engineering novel antibiotics. Annu Rev Microbiol 49: 201-38 Hynes MJ, Murray SL (2010) ATP-citrate lyase is required for production of cytosolic acetyl

coenzyme A and development in Aspergillus nidulans. Eukaryot Cell 9: 1039-48 Jakobsen JS, Braun M, Astorga J, Gustafson EH, Sandmann T, Karzynski M, Carlsson P,

Furlong EE (2007) Temporal ChIP-on-chip reveals Biniou as a universal regulator of the visceral muscle transcriptional network. Genes Dev 21: 2448-60

James P, Halladay J, Craig EA (1996) Genomic libraries and a host strain designed for highly

efficient two-hybrid selection in yeast. Genetics 144: 1425-36 Janus D, Hortschansky P, Kück U (2007) Identification of a minimal cre1 promoter sequence

promoting glucose-dependent gene expression in the β-lactam producer Acremonium chrysogenum. Curr Genet 53: 35-48

21

Jiang J, Liu X, Yin Y, Ma Z (2011) Involvement of a velvet protein FgVeA in the regulation of asexual development, lipid and secondary metabolisms and virulence in Fusarium graminearum. PLoS One 6: e28291

Käfer E (1965) Origins of translocations in Aspergillus nidulans. Genetics 52: 217-32 Kagan RM, Clarke S (1994) Widespread occurrence of three sequence motifs in diverse S-

adenosylmethionine-dependent methyltransferases suggests a common structure for these enzymes. Arch Biochem Biophys 310: 417-27

Kale SP, Milde L, Trapp MK, Frisvad JC, Keller NP, Bok JW (2008) Requirement of LaeA for

secondary metabolism and sclerotial production in Aspergillus flavus. Fungal Genet Biol 45: 1422-9

Kamerewerd J, Zadra I, Kürnsteiner H, Kück U (2011) PcchiB1, encoding a class V chitinase, is

affected by PcVelA and PcLaeA, and is responsible for cell wall integrity in Penicillium chrysogenum. Microbiology 157: 3036-48

Karimi-Aghcheh R, Bok JW, Phatale PA, Smith KM, Baker SE, Lichius A, Omann M, Zeilinger

S, Seiboth B, Rhee C, Keller NP, Freitag M, Kubicek CP (2013) Functional analyses of Trichoderma reesei LAE1 reveal conserved and contrasting roles of this regulator. G3 (Bethesda) 3: 369-78

Kato N, Brooks W, Calvo AM (2003) The expression of sterigmatocystin and penicillin genes in

Aspergillus nidulans is controlled by veA, a gene required for sexual development. Eukaryot Cell 2: 1178-86

Kim H, Han K, Kim K, Han D, Jahng K, Chae K (2002) The veA gene activates sexual

development in Aspergillus nidulans. Fungal Genet Biol 37: 72-80 Kistler HC, Broz K (2015) Cellular compartmentalization of secondary metabolism. Front Microbiol

6: 68 Kobayashi M, Takezawa S, Hara K, Yu RT, Umesono Y, Agata K, Taniwaki M, Yasuda K,

Umesono K (1999) Identification of a photoreceptor cell-specific nuclear receptor. Proc Natl Acad Sci U S A 96: 4814-9

Kopke K, Hoff B, Kück U (2010) Application of the Saccharomyces cerevisiae FLP/FRT

recombination system in filamentous fungi for marker recycling and construction of knockout strains devoid of heterologous genes. Appl Environ Microbiol 76: 4664-74

Kopke K, Hoff B, Bloemendal S, Katschorowski A, Kamerewerd J, Kück U (2013) Members of

the Penicillium chrysogenum velvet complex play functionally opposing roles in the regulation of penicillin biosynthesis and conidiation. Eukaryot Cell 12: 299-310

Kosalková K, García-Estrada C, Ullán RV, Godio RP, Feltrer R, Teijeira F, Mauriz E, Martín

JF (2009) The global regulator LaeA controls penicillin biosynthesis, pigmentation and sporulation, but not roquefortine C synthesis in Penicillium chrysogenum. Biochimie 91: 214-25

Kozbial PZ, Mushegian AR (2005) Natural history of S-adenosylmethionine-binding proteins. BMC

Struct Biol 5: 19 Kraus PR, Hofmann AF, Harris SD (2002) Characterization of the Aspergillus nidulans 14-3-3

homologue, ArtA. FEMS Microbiol Lett 210: 61-6

22

Kwon NJ, Garzia A, Espeso EA, Ugalde U, Yu JH (2010) FlbC is a putative nuclear C2H2 transcription factor regulating development in Aspergillus nidulans. Mol Microbiol 77: 1203-19

Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel

P, Brown JB, Cayting P, Chen Y, DeSalvo G, Epstein C, Fisher-Aylor KI, Euskirchen G, Gerstein M, Gertz J, Hartemink AJ, Hoffman MM, Iyer VR, Jung YL, Karmakar S, Kellis M, Kharchenko PV, Li Q, Liu T, Liu XS, Ma L, Milosavljevic A, Myers RM, Park PJ, Pazin MJ, Perry MD, Raha D, Reddy TE, Rozowsky J, Shoresh N, Sidow A, Slattery M, Stamatoyannopoulos JA, Tolstorukov MY, White KP, Xi S, Farnham PJ, Lieb JD, Wold BJ, Snyder M (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 22: 1813-31

Langmead B, Trapnell C, Pop M, Salzberg SL (2009) Ultrafast and memory-efficient alignment of

short DNA sequences to the human genome. Genome Biol 10: R25 Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, Marth G, Abecasis G, Durbin R

(2009) The sequence alignment/map format and SAMtools. Bioinformatics 25: 2078-9 Lin Z, Wu WS, Liang H, Woo Y, Li WH (2010) The spatial distribution of cis regulatory elements

in yeast promoters and its implications for transcriptional regulation. BMC Genomics 11: 581 Lind AL, Wisecaver JH, Smith TD, Feng X, Calvo AM, Rokas A (2015) Examining the evolution

of the regulatory circuit controlling secondary metabolism and development in the fungal genus Aspergillus. PLoS Genet 11: e1005096

Martin JL, McMillan FM (2002) SAM (dependent) I AM: the S-adenosylmethionine-dependent

methyltransferase fold. Curr Opin Struct Biol 12: 783-93 Merhej J, Urban M, Dufresne M, Hammond-Kosack KE, Richard-Forget F, Barreau C (2012)

The velvet gene, Fgve1, affects fungal development and positively regulates trichothecene biosynthesis and pathogenicity in Fusarium graminearum. Mol Plant Pathol 13: 363-74

Milam AH, Rose L, Cideciyan AV, Barakat MR, Tang WX, Gupta N, Aleman TS, Wright AF,

Stone EM, Sheffield VC, Jacobson SG (2002) The nuclear receptor NR2E3 plays a role in human retinal photoreceptor differentiation and degeneration. Proc Natl Acad Sci U S A 99: 473-8

Miller KY, Wu JG, Miller BL (1992) StuA is required for cell pattern-formation in Aspergillus.

Gene Dev 6: 1770-82 Miroux B, Walker JE (1996) Over-production of proteins in Escherichia coli: mutant hosts that

allow synthesis of some membrane proteins and globular proteins at high levels. J Mol Biol 260: 289-98

Mooney JL, Yager LN (1990) Light is required for conidiation in Aspergillus nidulans. Genes Dev 4:

1473-82 Nguyen DH, D'Haeseleer P (2006) Deciphering principles of transcription regulation in eukaryotic

genomes. Mol Syst Biol 2: 2006.0012 Ni M, Yu JH (2007) A novel regulator couples sporogenesis and trehalose biogenesis in Aspergillus

nidulans. PLoS One 2: e970

23

Olmedo M, Ruger-Herreros C, Luque EM, Corrochano LM (2010) A complex photoreceptor system mediates the regulation by light of the conidiation genes con-10 and con-6 in Neurospora crassa. Fungal Genet Biol 47: 352-63

Palmer JM, Theisen JM, Duran RM, Grayburn WS, Calvo AM, Keller NP (2013) Secondary

metabolism and development is mediated by LlmF control of VeA subcellular localization in Aspergillus nidulans. PLoS Genet 9: e1003193

Peñalva MA, Rowlands RT, Turner G (1998) The optimization of penicillin biosynthesis in fungi.

Trends Biotechnol 16: 483-9 Purschwitz J, Müller S, Fischer R (2009) Mapping the interaction sites of Aspergillus nidulans

phytochrome FphA with the global regulator VeA and the White Collar protein LreB. Mol Genet Genomics 281: 35-42

Ruger-Herreros C, Rodríguez-Romero J, Fernández-Barranco R, Olmedo M, Fischer R,

Corrochano LM, Canovas D (2011) Regulation of conidiation by light in Aspergillus nidulans. Genetics 188: 809-22

Sandmann T, Jensen LJ, Jakobsen JS, Karzynski MM, Eichenlaub MP, Bork P, Furlong EEM

(2006) A temporal map of transcription factor activity: Mef2 directly regulates at all stages of muscle target genes development. Dev Cell 10: 797-807

Sarikaya-Bayram Ö, Bayram Ö, Valerius O, Park HS, Irniger S, Gerke J, Ni M, Han KH, Yu

JH, Braus GH (2010) LaeA control of velvet family regulatory proteins for light-dependent development and fungal cell-type specificity. PLoS Genet 6: e1001226

Sarikaya-Bayram Ö, Bayram Ö, Feussner K, Kim JH, Kim HS, Kaever A, Feussner I, Chae KS,

Han DM, Han KH, Braus GH (2014) Membrane-bound methyltransferase complex VapA-VipC-VapB guides epigenetic control of fungal development. Dev Cell 29: 406-20

Sarikaya-Bayram Ö, Palmer JM, Keller N, Braus GH, Bayram Ö (2015) One Juliet and four

Romeos: VeA and its methyltransferases. Front Microbiol 6: 1 Sarikaya Bayram Ö, Bayram Ö, Valerius O, Park HS, Irniger S, Gerke J, Ni M, Han KH, Yu

JH, Braus GH (2010) LaeA control of velvet family regulatory proteins for light-dependent development and fungal cell-type specificity. Plos Genet 6: e1001226

Sigl C, Haas H, Specht T, Pfaller K, Kürnsteiner H, Zadra I (2011) Among developmental

regulators, StuA but not BrlA is essential for penicillin V production in Penicillium chrysogenum. Appl Environ Microbiol 77: 972-82

Spitz F, Furlong EE (2012) Transcription factors: from enhancer binding to developmental control.

Nat Rev Genet 13: 613-26 Stringer MA, Timberlake WE (1995) dewA encodes a fungal hydrophobin component of the

Aspergillus spore wall. Mol Microbiol 16: 33-44 Struck AW, Thompson ML, Wong LS, Micklefield J (2012) S-adenosyl-methionine-dependent

methyltransferases: highly versatile enzymes in biocatalysis, biosynthesis and other biotechnological applications. Chembiochem 13: 2642-55

Thorvaldsdóttir H, Robinson JT, Mesirov JP (2012) Integrative Genomics Viewer (IGV): high-

performance genomics data visualization and exploration. Brief Bioinform 14: 178-92

24

Todeschini AL, Georges A, Veitia RA (2014) Transcription factors: specific DNA binding and specific gene regulation. Trends Genet 30: 211-9

Vokes SA, Ji H, Wong WH, McMahon AP (2008) A genome-scale analysis of the cis-regulatory

circuitry underlying sonic hedgehog-mediated patterning of the mammalian limb. Genes Dev 22: 2651-63

Wiemann P, Brown DW, Kleigrewe K, Bok JW, Keller NP, Humpf HU, Tudzynski B (2010)

FfVel1 and FfLae1, components of a velvet-like complex in Fusarium fujikuroi, affect differentiation, secondary metabolism and virulence. Mol Microbiol 77: 972-94

Wolfers S, Kamerewerd J, Nowrousian M, Sigl C, Zadra I, Kürnsteiner H, Kück U, Bloemendal

S (2014) Microarray hybridization analysis of light-dependent gene expression in Penicillium chrysogenum identifies bZIP transcription factor PcAtfA. J Basic Microbiol 54: 1-10

Zaret KS, Carroll JS (2011) Pioneer transcription factors: establishing competence for gene

expression. Genes Dev 25: 2227-41

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Figure 1: Construction of PcVelA-ChIP strains. (A) Plasmid pVelA-EGFP, harboring a Pgpd::PcvelA::egfp fusion construct, was used for ectopic integration into a marker-free PcvelA deletion strain, ΔPcvelA. (B) PCR analysis confirmed integration of Pgpd::PcvelA::egfp. Binding positions of primers PcvelA_f and egfp_r are indicated as arrows in (A). (C) Presence of the epitope-tagged protein PcVelA-EGFP in crude protein extract from recombinant P. chrysogenum strains was confirmed using SDS-PAGE/Western blot analysis. (D) Fluorescence microscopy confirmed nuclear localization of PcVelA-EGFP in the PcVelA-ChIP strain. Strains were grown on solid medium for 48 h.

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Figure 2: Initial analysis of PcVelA ChIP-seq data. (A) Venn diagram showing intersection between peak regions identified within PcVelA_shaking_1 and PcVelA_shaking_2 datasets. Only peaks within a maximum distance of 100 nt were regarded as overlapping. (B) Distribution of ChIP-enriched regions overlapping with or positioned within intragenic regions vs. ChIP-enriched regions that were exclusively located within intergenic regions (based on peak regions identified in both biological replicates). (C) Distance between peak summits and ATG of neighboring genes positioned in 5’-3’ orientation with regard to the corresponding peak region (based on peak regions present in both biological replicates).

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Figure 3: Validation of PcVelA ChIP-seq data. (A) ChIP-PCR analysis was performed to analyze enrichment of selected PcVelA target regions in ChIP-DNA compared to input-DNA. Enrichment was calculated as the ratio of the region of interest to a control region, showing no PcVelA-specific enrichment in ChIP-seq experiments. Another region showing no PcVelA-specific enrichment in ChIP-seq analysis is shown as a control (NC). ChIP-PCR ratios (grey bars) are shown in comparison to the corresponding peak values, as obtained from bioinformatics analysis (black bars). Values for ChIP-PCR are the mean score of three biological replicates; average ± standard deviations are indicated. Peak regions are named according to neighboring genes (see Dataset S1).

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Figure 4: qRT-PCR analysis confirms PcVelA-dependent expression of putative methyltransferase-encoding genes. Analysis of relative log2fold gene expression ratios of putative methyltransferase-encoding genes, identified as specific PcVelA target genes in ChIP-seq analysis, confirmed PcVelA dependency. Expression ratios in PcVelA-ChIP (grey bars) and ΔPcvelA (black bars) compared to wild type P2niaD18 are shown. Values are the mean score of three biological replicates.

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Figure 5: Multiple sequence alignments of methyltransferase sequence motifs I-III. Alignment of amino acid sequences revealed a high degree of conservation between methyltransferase sequence motifs I-III from putative SAM-dependent methyltransferases PcLaeA and PcLlmA, PcVipC, and the one encoded by Pc21g12700. Alignments were visualized using Jalview according to the ClustalX color scheme (http://www.jalview.org/). Numbers indicate amino acids that separate conserved motifs.

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Figure 6: ChIP-seq reveals a PcVelA consensus-binding site. PcVelA-specific peak regions were submitted to MEME (Bailey and Elkan 1994) for de novo motif prediction. Only the most significant putative DNA-binding motif, PcVelA.M1, is shown. For comparison against the JASPAR CORE (2014) database, results were submitted to TOMTOM (Gupta et al. 2007), using default parameters. The top two matches to known DNA-binding motifs from vertebrates are given. The associated proteins, IDs from the JASPAR CORE database, p-values, and E-values are indicated. The size of each letter is proportional to the frequency of each nucleotide at this position within the consensus sequence. Motifs are centered on common central nucleotides.

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Figure 7: Electrophoretic mobility shift assays (EMSAs) confirm PcVelA-binding to the predicted DNA-binding consensus sequence PcVelA.M1. (A) Zoomed ChIP-seq profiles from PcVelA_shaking1, PcVelA_shaking2, and input control next to Pc21g02240, encoding the putative SAM-domain methyltransferase PcLlmA are shown. Positions of oligonucleotides used for shift analysis (black bars) and occurrences of PcVelA.M1 (red arrows) are indicated. Orientation of ORFs (black boxes) next to PcVelA ChIP-seq peak regions are indicated by arrowheads. (B) EMSAs were performed using 50 nt radiolabeled double stranded oligonucleotide probes (PcLlmA_2, PcLlmA_4) derived from the PcllmA promoter region and rising amounts of purified GST-PcVelA1-256 protein. Positions of free probe (*) and protein-DNA complexes ( ) are indicated. Single-bp substitutions within PcVelA.M1 in oligonucleotides PcLlmA_2_m and PcLlmA_4_m resulted in a diminished formation of protein-DNA complexes.

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Figure 8: PcVelA directly interacts with the putative SAM-dependent methyltransferase PcLlmA. (A) For yeast two-hybrid analysis, diploid strains were spotted on selective media lacking adenine and histidine and supplemented with X-α-Gal to demonstrate ADE2 and HIS3 as well as lacZ reporter gene activity. (B) For BiFC analysis, genes encoding PcVelA and PcLlmA were fused to eyfp fragments encoding either the N- or the C-terminus of the yellow fluorescent protein, and strains harboring both constructs were analyzed using fluorescence microscopy. DAPI straining confirmed nuclear localization of the PcVelA-PcLlmA interaction. As a control, strains producing either both split EYFPs or one split EYFP together with EYFP-PcVelA/PcLlmA-EYFP are shown.

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Table 1: ChIP-seq design and results

Sample # Readsa # Mappedb % Mappedc

# Peaks FDR ≤ 0.001d

# Differential peakse

# Total peaksf

Estimated fragment lengthg

PcVelA_shaking1 34.074.601 20.835.894 61.15% 6088 1937 764 235 PcVelA_shaking2 29.736.045 17.177.895 57.77% 6090 1362 1001 231 PcVelA_shaking_input 20.383.512 18.540.910 90.96% - - - -

a total number of sequenced reads b total number of reads mapped to P. chrysogenum P2niaD18 genome c fraction of tags found in peaks versus genomic background determined by HOMER d number of peaks passing FDR ≤ 0.001 threshold e number of peak regions showing at least fourfold enrichment in ChIP-sample compared to input-DNA f total number of peak regions after local background filtering and clonal filtering g estimated fragment length used for sequencing determined from tag auto correlation analysis

34

Table 2: Selected PcVelA target genes identified using ChIP-seq analysis

Identifier Descriptiona Proposed function Peak

valueb

Microarray ΔPcVelAc

48 h 60 h 96 h Development and conidiation

Pc18g03940 14-3-3 family protein ArtA over-expression causes a severe delay in the polarization of conidiospores in A. nidulans (Kraus et al. 2002)

1090 -0.1 0 -0.1

Pc13g04920 cell pattern formation-associated protein StuA

inactivation reduces expression of the penicillin gene cluster in P. chrysogenum (Sigl et al. 2011); required for differentiation and spatial organization of cell types of the A. nidulans conidiophore (Miller et al. 1992)

1034 -0.1 0.4 -0.1

Pc13g09580 bZIP transcription factor AtfA

deletion mutant conidia show significant sensitivity to high temperature and oxidative stress in A. fumigatus (Hagiwara et al. 2014); regulates different types of stress responses in A. nidulans (Balazs et al. 2010)

1027 -0.9 -0.7 -1.1

Pc16g03240 conidiation protein Con-6 expressed during the formation of asexual spores or after illumination of vegetative mycelia in N. crassa (Olmedo et al. 2010)

801 0.9 0.8 0.3

Pc16g11870 α,α-trehalose glucohydrolase TreA/Ath1

gene product is localized in the conidiospore wall; required for growth on trehalose as a carbon source in A. nidulans (d'Enfert and Fontaine 1997)

761 -0.1 -0.2 -0.3

Pc21g09870 related to integral membrane protein Pth11

functions at the cell cortex as an upstream effector of appressorium differentiation in M. grisea (DeZwaan et al. 1999)

746 0 1.5 1.6

Pc16g06690 spore-wall fungal hydrophobin DewA precursor

encodes a fungal hydrophobin component of the conidial wall (Stringer and Timberlake 1995) 730 -0.1 0.2 -0.2

Pc12g12190 C2H2 conidiation transcription factor FlbC

putative nuclear TF necessary for proper activation of conidiation, growth and development in A. nidulans (Kwon et al. 2010)

613 -0.5 0.3 -0.2

Pc13g09910 LEA (late embryogenesis abundant) domain protein

associated with tolerance to water stress resulting from desiccation and cold shock in plants and animals (Goyal et al. 2005, Chakrabortee et al. 2012)

560 -0.6 0 -0.8

Pc13g03170 MYB family conidiophore development protein FlbD

regulates both asexual and sexual differentiation in A. nidulans (Arratia-Quijada et al. 2012) 553 0.9 0.5 0.2

Pc22g22320 developmental regulator VelB

acts as an activator of conidiospore formation in various filamentous fungi (Bayram et al. 2008, Kopke et al. 2013)

506 0.2 0.6 0.4

Pc23g00400 C2H2 type conidiation transcription factor BrlA

mediates developmental switch from apical growth of vegetative cells to budding growth pattern of conidiophores (Adams et al. 1988)

396 -0.1 1.2 0.2

Secondary metabolism Pc22g06500 amino acid transporter 2544 0.9 1.0 1.2 Pc22g17530 ABC multidrug transporter aa5 2154 0 -0.3 1.5 Pc22g06610 neutral amino acid permease 1585 0.2 0.6 1.4 Pc20g05090 ABC multidrug transporter 1262 1.0 0.4 0.2 Pc16g11480 PKS, putative 870 -2.7 -3.7 -4.3 Pc16g11470 ABC multidrug transporter 870 0.3 -1.2 0.5 Pc20g03900 MFS multidrug transporter 716 0.4 1.2 1.3 Pc18g00380 hybrid NRPS PKS 644 0.1 0.5 0.1 Pc21g12630 NRPS 621 -2.6 -1.5 -0.1 Pc20g12260 ABC drug exporter AtrF 538 0.3 0.7 1.1 Pc12g14890 MFS multidrug transporter 538 0.3 0.8 3.4 Pc22g22420 MFS transporter 528 1.3 1.5 4 Pc18g03610 ABC multidrug transporter 481 -0.1 0 2.8 Pc18g03610 ABC multidrug transporter 481 -0.1 0 2.8 Pc21g08920 norsolorinic acid reductase 343 -0.2 0.5 1.9 Acyl-CoA-related processes

Pc22g24780 acetyl-CoA synthetase-like protein 1291 -0.1 0.3 1.3

Pc22g06680 acetyl-coenzyme A synthetase FacA

loss-of-function mutations result in resistance to fluoroacetate in the absence of a repressing carbon source, which otherwise inhibits development, conidiation, and conidial pigmentation in A. nidulans (Hynes and Murray 2010)

1150 0.2 0 0.3

Pc22g00420 acetyl-CoA-acetyltransferase 923 0.5 0 0.6

35

a as obtained from blastp analysis (http://blast.ncbi.nlm.nih.gov/Blast.cgi) b statistical peak value = average tag count found at peak normalized to 10 million total mapped tags

c microarray data showing expressional changes in ΔPcvelA compared to wild type ΔPcku70 after 48, 60, and 96 h of cultivation (Hoff et al. 2010)

Pc16g03600 acyl-CoA N-acyltransferase 606 0 1.4 1.9

Pc21g08470 acyl-CoA N-acyltransferase 467 -1.5 -1.2 -2.1

Transcription factors Pc20g05960 C2H2 transcription factor 1705 -0.7 -1.0 -1.7 Pc21g15330 bZIP TF 1040 -1.2 -1.1 -1.9 Pc06g02030 C2H2 finger domain protein 986 -3.1 -3.2 -2.3 Pc15g00130 F-box domain protein 885 -1.0 -0.2 -0.9 Pc21g15330 bZIP TF 725 -1.2 -1.1 -1.9 Pc12g10080 C6 finger domain protein 576 -1.4 -0.6 0 Methyltransferases

Pc21g02240 LaeA-like SAM-dependent methyltransferase PcLlmA 2756 2.6 1.6 0.8

Pc18g01840 LaeA-like SAM-dependent methyltransferase PcVipC

part of a membrane-associated trimeric complex that controls a signal transduction pathway for fungal differentiation in A. nidulans (Sarikaya-Bayram et al. 2014)

2139 1430 -2.6 -2.1 -1.7

Pc21g12700 SAM-dependent methyltransferase 1101 -1.4 -0.4 5

Pc18g04780 SAM-dependent methyltransferase 660 0.1 0.6 0.6

Pc18g06010 O-methyltransferase 470 0.1 0 0.1

Pc13g15570 nicotinamide N-methyltransferase 334 0.8 0.7 1.0

Pc22g01170 O-methyltransferase 328 4.5 4.6 4.5

36

MATERIALS AND METHODS

Strains and culture conditions. Penicillium chrysogenum strains (Table S1) were grown in CCM (Wolfers et al. 2014) shaking or surface cultures at 27°C. For inoculation, 0.5x107 spores derived from cultures grown on M322 solid medium (Wolfers et al. 2014) for 4-5 days were used. Escherichia coli strain XL1blue was used for cloning and plasmid propagation purposes, while BL21 (DE3) served as a host for heterologous overexpression of PcVelA-GST (Bullock et al. 1987, Miroux and Walker 1996). Saccharomyces cerevisiae strains PJ69-4a and PJ69-4α were used for yeast two-hybrid analysis (James et al. 1996). Strains were grown at 30°C on SD medium lacking selected amino acids used for auxotrophy marker selection. Mating of PJ69-4a and -α strains was performed in liquid YPDA medium at 30°C and 50 rpm. Construction of P. chrysogenum strains. Strains were constructed by ectopic or homologous integration of plasmid DNA (Table S2) as described previously (Hoff et al. 2010, Kamerewerd et al. 2011) with some modifications. Recipient strains were grown for 72 h in shaking cultures and protoplasts were transformed with either circular (for ectopic integration) or linear (for homologous recombination) plasmid DNA. Transformants were selected on CCM media containing 150 μg/ml nourseothricin (Werner BioAgents, Germany). Resistant colonies were isolated and tested for integration of plasmid DNA. PCR analysis and SDS-PAGE/Western blot analysis was performed as described previously (Hoff et al. 2010). Nucleic acids isolation, cDNA synthesis, qRT-PCR, and ChIP-PCR. Isolation of nucleic acids, cDNA synthesis, qRT-PCR, and ChIP-PCR analysis was carried out as described earlier (Hoff et al. 2009, Böhm et al. 2013, Becker et al. 2015). Oligonucleotides are listed in Table S3. Sample preparation for ChIP-seq, data analysis and visualization. ChIP and analysis of sequencing data was carried out as previously described (Becker et al. 2015), using Bowtie version 1.0.1 (Langmead et al. 2009), SAMtools (Li et al. 2009), the Integrative Genomics Viewer (IGV) (Thorvaldsdóttir et al. 2012), MEME (Multiple Em for Motif Elicitation; http://meme.nbcr.net/meme/) (Bailey and Elkan 1994), TOMTOM (Gupta et al. 2007), and the HOMER software for motif discovery and next-generation sequencing analysis (Heinz et al. 2010). Raw sequencing data is available from the NCBI SRA database; study ID n.a., Accession # n.a.. Electrophoretic mobility shift assays (EMSAs). Gel shift assays were performed using oligonucleotides derived from ChIP-enriched regions and purified GST-PcVelA1-256. 50 nt double-stranded oligonucleotides (Table S3) were 5’-end-labeled using polynucleotide kinase (Roche, Basel, Switzerland) and [ɣ-32P]-ATP (Hartmann Analytic, Braunschweig, Germany). For shift experiments, 3.5-7.0 fmol (~50 – 100 cps) of radiolabeled oligonucleotides were incubated with varying protein concentrations in the presence of 2 μl binding buffer (250 mM Tris/HCl pH 8.0, 1 M KCl, 50 % glycerol) and 1 μg poly(dI-dC)-poly(dI-dC) (Affymetrix USB, CA, USA) in a total volume of 20 μl for 20 min at room temperature. Samples were run on 5 % polyacrylamide gels at 4°C in 190 mM glycine, 27 mM Tris/HCl pH 8.5.

37

Expression, purification and immunodetection of recombinant PcVelA-GST protein. Purification of recombinant PcVelA-GST protein from E. coli was performed as described earlier (Janus et al. 2007) using an elution buffer containing 50 mM Tris/HCl, 30 mM reduced glutathione, 100 mM NaCl, pH 8.0. Western blotting and immunodetection was performed using RPN1236 anti-GST HRP conjugate (GE Healthcare, Germany). Yeast two-hybrid analysis. Yeast two-hybrid analysis was carried out as described by Kopke et al. (2013) using yeast strain PJ694a for Gal4 activation domain (AD) fusion derivatives and strain PJ69-4α for Gal4 DNA-binding domain (BD) fusion constructs. Microscopy. Fluorescence and light microscopy was carried out as described previously (Engh et al. 2007, Hoff et al. 2010) with minor modifications. Images were captured with a Photometrix Cool SnapHQ camera (Roper Scientific, USA) and Metamorph (version 7.7.5.0; Universal Imaging). Recorded images were processed with MetaMorph and Adobe Photoshop CS4. Staining of nuclei was performed using DAPI (Sigma Aldrich, Germany).

38

SUPPLEMENTS

Table S1: P. chrysogenum strains used in this work Strain Characteristics and genotype Source P2niaD18 niaD- (Hoff et al. 2008) PcVelA-ChIP (T16.2) Pgpd::PcVelA::EGFP::TtrpC; nat1; niaD− This study ΔPcvelAab Pcku70::FRT; PcVelA::FRT; niaD− (Kopke et al. 2013) ∆Pcku70b Pcku70::FRT; niaD- (Kopke et al. 2010)

BiFC-N/N Pgpd::eyfpC::TtrpC; Pgpd::eyfpN::TtrpC; PtrpC::nat1; niaD− This study BiFC-N/PcLlmA Pgpd::eyfpC::TtrpC; Pgpd::PcllmA::eyfpC::TtrpC; PtrpC::nat1; niaD− This study BiFC-PcVelA/N Pgpd::PcvelA::eyfpN::TtrpC; Pgpd::eyfpN::TtrpC; PtrpC::nat1; niaD− This study BiFC-PcVelA/PcLlmA Pgpd::PcvelA::eyfpN::TtrpC; Pgpd::PcllmA::eyfpC::TtrpC; PtrpC::nat1; niaD− This study

a strains that still carry a resistance marker from the flipper knockout construct

b strains without any resistance markers due to FLP/FRT marker recycling.

Table S2: Plasmids used in this work Name Characteristics Source pPcVelA-EGFP Pgpd of A. nidulans, egfp, PcvelA gene of P. chrysogenum, TtrpC of A. nidulans, nat

resistance gene of Streptomyces noursei; used for construction of P. chrysogenum ChIP-strains via ectopic integration into strain ΔPcVelA

This study

pEYFPC-nat gpd promoter of A. nidulans, eyfpC-fragment (aa 155-238), trpC terminator of A. nidulans, nat1 gene

(Hoff et al. 2010)

pEYFPN-nat gpd promoter of A. nidulans, eyfpN-fragment (aa 1-154), trpC terminator of A. nidulans, nat1 gene

(Hoff et al. 2010)

pYNVELA PcvelA ORF in NotI site of pEYFPN-nat (Hoff et al. 2010) pYCLLMA PcllmA ORF in NcoI and NotI site of pEYFPC-nat (Hoff et al. 2010) pGADT7 ADH1(p)::gal4 AD::LEU2 Clontech pGBKT7 ADH1(p)::gal4 BD::TRP1 Clontech pAD-PcvelA PcvelA cDNA in SmaI and SacI site in pGADT7 (Kopke et al. 2013) pAD-PcllmA PcllmA cDNA in EcoRI and BamHI site in pGADT7 This study pAD-PclaeA PclaeA cDNA in EcoRI and XhoI site in pGADT7 (Kopke et al. 2013) pBD-PcvelA PcvelA cDNA in SmaI and SacI site in pGBKT7 (Kopke et al. 2013) pBD-PcllmA PcllmA cDNA in EcoRI and BamHI site in pGADT7 This study pBD-PclaeA PclaeA cDNA in EcoRI and PstI site in pGBKT7 (Kopke et al. 2013)

Table S3: Oligonucleotides used in this work Name Sequence (5’ to 3’) Specificity Plasmid and strain construction

egfp_r ACTTCAGGGTCAGCTTGC egfp gene PcvelA_f TCGGTCGACATGGCCAACAGACCATCTC PcvelA gene

ChIP-PCR qPCR_NC1_f TTCTTCCGCAATCAAGCTCA chr1:5375234-5375253 qPCR_NC1_r GAAAAATTGCCGCTGGACTC chr1:5375364-5375383 qPCR_NC2_f GGTCGTTGATTCCCTTGAGC chr2:7621179-7621198 qPCR_NC2_r GGATCGGATTATTCGGGTGA chr2:7621294-7621313 qPCR_Pc21g02240_f CGAGAGAGAGGAACCCGGGA chr2:5277047-5277066 qPCR_Pc21g02240_r TTTCCCGTACCAGGCTGTCG chr2:5277176-5277195 qPCR_Pc22g17530_f AGGCACCGAAACCGTGAAGA chr1:2788884-2788903 qPCR_Pc22g17530_r ACGCCAGGCCAGAGTTCAAT chr1:2788799-2788818 qPCR_Pc20g02880_f CGTGAAATTCGAAGGTTCCCGA chr2:2995008-2995029 qPCR_Pc20g02880_r AGAAATTAAGCCGCAAAACCCAGA chr2:2994880-2994903 qPCR_Pc20g14090_f GTGGAAATTTCGGATGGGGTAGC chr2:378180-378202 qPCR_Pc20g14090_r GATGCCCTGGTATCGGCAAAA chr2:378053-378073

qRT-PCR qRT_Pc21g02240_f ACAAGGAAATCGGTCGCATC chr2:5275851-5275870 qRT_Pc21g02240_r GCCCTCTCCATATGCTCCTG chr2:5275747-5275766 qRT_Pc18g01840_f TTCGGCAAGGACATGACATC chr1:5930185-5930204 qRT_Pc18g01840_r TGGTACCGACCAAGCTCCTT chr1:5930361-5930380 qRT_Pc21g12700_f GGGTTTGTCGATACCCAGGA chr2:7743588-7743607

39

qRT_Pc21g12700_r CCGCAGTCCACTGATGGTAA chr2:7743677-7743696 qRT_Pc18g04780_f TGATGATGCGAAGACCATCC chr1:6636202-6636221 qRT_Pc18g04780_r TGAACCAATGCACCAGCTCT chr1:6636094-6636113 qRT_Pc18g06010_f CCCCGATACCAACGCATACT chr1:6920825-6920844 qRT_Pc18g06010_r CGTGATCTTCAACCCAGCAG chr1:6920718-6920737 qRT_Pc13g15570_f GGACCCGAACTCTGTTGCTC chr4:2949445-2949464 qRT_Pc13g15570_r GCATCCACCACCTTCTCAAA chr4:2949330-2949349 qRT_Pc22g01170_f TCGCTCGCTTCCTTGTATGA chr3:5122377-5122396 qRT_Pc22g01170_r CAGGACTCGCAGACCAACAG chr3:5122469-5122488

EMSAsa PcLlmA_2 TAGCGTCATTTATTTTTTTCTTCCAAGGTTTTTCCCTCTTCTTCGGAGT

chr2:5277074-5277122

PcLlmA_2_m TAGCGTCATTTATTTTTTTCcTtCAgaGTTTTTCCCTCTTCTTCGGAGT

chr2:5277074-5277122 (with mutations)

PcLlmA_4 TCCGACAGCCTGGTACGGGAAACCTTGGAACCCATTCCAAATCGGTCTG

chr2:5277174-5277222

PcLlmA_4_m TCCGACAGCCTGGTACGGGAAACtcTGaAgCCCATTCCAAATCGGTCTG

chr2:5277174-5277222 (with mutations)

a in case of double-stranded oligonucleotides used for EMSAs, only the sense sequences are given.

V. DISCUSSION 25

V. DISCUSSION

For long, functional analysis of transcriptional regulators in P. chrysogenum and many other

species has been restricted to the characterization of single genes or gene families, e.g. by

using deletion and overexpression mutants. Even though these studies contributed in many

ways to the investigation of fundamental principles of gene regulation, they only presaged the

whole dynamics of genome-wide GRNs, which control all kinds of cellular and

developmental processes. Accordingly, in order to improve our current knowledge of

transcriptional regulation of secondary metabolism and morphogenesis in the industrially

highly relevant filamentous fungus P. chrysogenum, a ChIP-seq approach on two regulatory

proteins, namely MAT1-1-1 and PcVelA, has been performed within the scope of this work.

1. ChIP-seq analyses with MAT1-1-1

For the most part, our general understanding of mating-related processes in ascomycetes is

based on knowledge obtained from studies using the baker’s yeast S. cerevisiae and the

filamentous fungus N. crassa (Ni et al. 2011). In S. cerevisiae, sex is determined by two

alternative MAT loci, namely MATα and MATa, which consist of dissimilar sequences

occupying the same locus on the chromosome (Astell et al. 1981). These sequences are

termed idiomorphs to indicate that they do not represent the alleles of a single gene

(Metzenberg and Glass 1990). In N. crassa, MAT loci are designated mata and matA, whereas

in most other euascomycetes the terms MAT1-1 and MAT1-2 are used (Coppin et al. 1997,

Pöggeler 2001). MAT loci from ascomycetes harbor one or more open reading frames (ORFs),

of which at least one codes for a MAT TF (Kück and Böhm 2013). As a rule, the MAT1-1

locus encodes an α-domain TF and the alternative idiomorph, MAT1-2, is characterized by a

gene coding for a TF carrying a high-mobility group (HMG)-domain. The corresponding

genes are generally referred to as MAT1-1-1 and MAT1-2-1 (Turgeon and Yoder 2000, Lee et

al. 2010).

While MAT genes were shown to control mating in all sexually reproducing ascomycetes

(Coppin et al. 1997, Merlini et al. 2013), a total of 64 % and 73 % of all described Aspergillus

and Penicillium species, respectively, are still lacking known sexual states (Dyer and

O'Gorman 2011). Nevertheless, the presence of fully functional, constitutively transcribed

MAT genes suggests that many of these supposed 'asexual' species do indeed have the

potential to undergo sexual reproduction under appropriate environmental conditions. Until

V. DISCUSSION 26

now, cryptic sexuality has been described for a growing number of fungi, which had been

considered to be asexual since no sexual propagation was observed under laboratory

conditions for a very long time. Prominent examples include the fungal pathogens

Candida albicans, Aspergillus flavus, Aspergillus parasiticus, and A. fumigatus (Magee and

Magee 2000, Horn et al. 2009a, Horn et al. 2009b, O'Gorman et al. 2009), as well as the

biotechnologically relevant species P. chrysogenum, Penicillium roqueforti, and T. reesei

(Seidl et al. 2009, Böhm et al. 2013, Ropars et al. 2014).

Functional MAT genes and homologs of pheromone and pheromone-receptor genes have been

first described in P. chrysogenum in 2008 (Hoff et al. 2008). Since then, huge efforts have

been made in order to elucidate the ability of P. chrysogenum to undergo sexual mating and,

in 2013, the sexual life cycle of P. chrysogenum, leading to the production of recombinant

ascospores, has been described (Böhm et al. 2013, Böhm et al. 2015). Furthermore,

phenotypic characterization of MAT1-1-1 and MAT1-2-1 deletion and overexpression strains

provided evidence for an involvement of MAT proteins in regulation of cellular and

developmental processes other than mating. For example, deletion of MAT1-1-1 was shown to

lead to a drastic reduction in penicillin biosynthesis and an increased formation of asexual

conidiospores, whereas both deletion and overexpression of the gene resulted in the formation

of significantly larger pellets compared to wild type (Böhm et al. 2013). On the contrary,

MAT1-2-1 was shown to be involved in regulation of conidiospore germination,

light-dependent asexual sporulation, and determination of surface properties of conidiospores

(Böhm et al. 2015).

1.1 MAT1-1-1 regulates target genes beyond sexual development

A comprehensive ChIP-seq approach, enabling the generation of a genome-wide MAT1-1-1

DNA-binding profile and identification of 254 putative direct MAT1-1-1 target genes, was

described within this work (see section III). Interestingly, some of the most highly bound

regions in ChIP experiments occurred near homologs of known functional targets of the

S. cerevisiae MATα1 protein, such as Pcppg1, a homolog of MFα1, encoding the

α-pheromone, and Pcpre1, a homolog of STE3, coding for the a-factor receptor (Ammerer et

al. 1985, Galgoczy et al. 2004). In combination with data obtained from qRT-PCR analyses,

this observation not only demonstrated MAT1-1-1-mediated regulation of these highly

conserved target genes in P. chrysogenum, but also confirmed biological significance of the

presented ChIP-seq dataset. It is consistent with reports demonstrating that expression of

pheromone-precursor genes and most probably receptor genes is controlled by expression of

V. DISCUSSION 27

MAT genes in heterothallic species (Kim and Borkovich 2006), and that sexual reproduction

correlates significantly with an increased expression of MAT genes and key genes of a

pheromone-response MAP-kinase signaling pathway in A. nidulans and N. crassa (Paoletti et

al. 2007, Wang et al. 2014). All in all, findings presented within this work fit the notion of

MAT1-1-1 being a positive regulator of sexual reproduction and a negative regulator of

asexual development in P. chrysogenum. This is consistent with the recent discovery that

sporulation was increased by about 25 % in a ΔMAT1-1-1 strain compared to wild type, and

that ΔMAT1-1-1 strains were sterile when crossed to a fertile MAT1-2 isolate (Böhm et al.

2013).

Besides known target genes of MAT TFs, ChIP-seq and downstream analyses identified a

large number of new MAT1-1-1 target genes, which have never been related to any MAT TF

before. A large part of these genes was assigned to the functional categories asexual

development, morphogenesis, amino acid and secondary metabolism, as well as iron

metabolism. It is noteworthy that for most of these processes indications for a relation to

fungal sexual development can be found in the literature, but no experimental evidence for a

direct regulatory impact of MAT encoded TFs has been reported until today. Two of the most

diverse groups, morphogenesis and asexual development, contained genes related to the

formation of conidiospores, hyphal growth, polarization of germinating conidiospores and

hyphae, as well as surface hydrophobicity. These genes can be used to explain the phenotypic

characteristics of MAT1-1-1 mutant strains that have previously been described by Böhm et

al. (2013). Within this context, one of the most promising new MAT1-1-1 target genes is

dewA, which codes for a fungal hydrophobin, likely to be involved in the formation of

extraordinarily large pellets in MAT1-1-1 overexpression and deletion mutants.

It is known that the developmental decision between sexual and asexual reproduction as well

as coordination of secondary metabolism is dependent on environmental factors, such as

nitrogen sources, iron supply, pH, and culture conditions (Han et al. 2003, Bayram and Braus

2012). Accordingly, within the scope of this work, MAT1-1-1 was shown to directly regulate

a number of genes assigned to iron transport and iron acquisition. Most importantly, sidD,

encoding a non-ribosomal siderophore-peptide synthetase important for biosynthesis of the

intracellular siderophore triacetylfusarinine C (TAFC) (Schrettl et al. 2007), was identified as

a specific MAT1-1-1 target gene. It has previously been described that fungal intracellular

siderophores are essential for asexual and sexual reproduction (Johnson 2008). For example,

deletion of sidC, a gene involved in biosynthesis of the intracellular siderophore ferricrocin,

V. DISCUSSION 28

resulted in a delayed germination of conidia, reduced production of asexual spores, as well as

blocked sexual development in A. nidulans (Eisendle et al. 2003, Eisendle et al. 2006).

Furthermore, in A. fumigatus, deletion of sidC revealed that intracellular siderophores are

required for conidial germ-tube formation, asexual sporulation, and conidial catalase A

activity (Schrettl et al. 2007), and in the heterothallic ascomycete Cochliobolus

heterostrophus, deletion of the siderophore-encoding nps2 resulted in defective ascus and

ascospore development (Oide et al. 2007). Besides the observation that MAT1-1-1 is involved

in regulation of iron metabolism, identification of meaB, encoding a TF involved in the

regulation of nitrogen-dependent gene expression in A. nidulans (Wong et al. 2007), as a

specific MAT1-1-1 target gene, established a connection between MAT protein regulatory

functions and nitrogen utilization in P. chrysogenum.

It has been hypothesized by Ádám et al. (2011) that MAT genes are functionally retained even

during the asexual part of the life cycle and the apparent absence of a sexual phase,

presumably because of their positive selective impact on important processes unrelated to

sexual development. Accordingly, a direct connection between MAT1-1-1 and regulation of

penicillin biosynthesis, which was shown to be significantly down-regulated in a ∆MAT1-1-1

strain compared to wild type, has been demonstrated (Böhm et al. 2013). Within this work, a

possible explanation for this observation was provided by the identification of MAT1-1-1

target genes encoding a cyanide hydratase/nitrilase as well as an enzyme catalyzing the

chemical reaction of L-homocysteine to L-methionine. As both encoded enzymes are likely to

influence biosynthesis of L-cysteine, they might have a direct impact on penicillin

biosynthesis, which starts with the formation of a tripeptide based on L-cysteine, L-valine,

and L-α-aminoadipic acid (Figure 4).

Although validation of the overall biological significance of large ChIP-seq datasets is

generally a challenging task, comprehensive qRT-PCR analyses and DNA-binding studies

provided experimental evidence for – at least selected – MAT1-1-1 target genes, covering the

functional categories mentioned above. Furthermore, functional characterization of the new

MAT1-1-1 target gene artA, encoding a protein that was shown to be necessary for

conidiospores germination, provided experimental evidence for MAT1-1-1 downstream

factors being involved in processes others than mating. All in all, findings presented within

this work are strongly supporting the hypothesis that MAT1-1-1 functions on a genome-wide

level are more far ranging than expected. This hypothesis is in accordance with previous

genome-wide gene-expression analyses in various euascomycetes, revealing that the number

V. DISCUSSION 29

Figure 4: Schematic overview of penicillin biosynthesis. During the first step of penicillin biosynthesis L-α-aminoadipic acid,

L-cysteine, and L-valine are condensed into a tripeptide. The condensation reaction is catalyzed by the enzyme δ-(L-α-

aminoadipyl)-L-cysteinyl-D-valine synthetase (ACVS), a non-ribosomal peptide synthetase. The second step involves the

oxidative conversion of linear ACV into the bicyclic intermediate isopenicillin N by isopenicillin N synthase (IPNS). Finally,

the α-aminoadipyl side-chain of isopenicillin N is removed and exchanged by a phenylacetyl side-chain through the enzyme

acyl-coenzyme A:isopenicillin N acyltransferase (Brakhage et al. 2004). (kindly provided by Dr. S. Bloemendal)

of MAT-regulated genes is rather high (Table 5). For example, a total of 2,421 genes are

expressed in a MAT1-1-1-dependent manner in P. chrysogenum (Böhm et al. 2013).

Nevertheless, when including corresponding data from hemiascomycetes, a striking

discrepancy between MAT-dependent gene expression in euascomycetes and

hemiascomycetes can be observed. For example, ChIP-chip analysis in S. cerevisiae haploid

a–cells and α–cells, as well as diploid a/α–cells identified a total of six a-specific genes (asgs),

five α-specific genes (αsgs), and 19 a/α-specific genes (Galgoczy et al. 2004). Remarkably,

with the exception of one αsg, all of these genes were shown to be related to some aspect of

mating, such as pheromone signaling, mating-cassette recombination, pheromone-induced

cell-cycle arrest, and agglutination. Based on this observation, one can assume that MAT TF

target genes in hemiascomycetes are restricted to genes relevant for mating, whereas

MAT-mediated regulation beyond sexual development might be a common feature in

euascomycetes. Accordingly, deletion of the MAT1-2-1 gene in Fusarium verticillioides was

shown to lead to a drastic reduction in carotenoid production, paralleled with a severe

decrease in photo-induced expression of genes encoding key enzymes of the carotenoid

biosynthesis pathway (Ádám et al. 2011). Moreover, MAT1-1-1 and MAT1-2-1 deletion

mutants were shown to be reduced in virulence although none of the MAT locus genes was

important for plant infection, indicating that MAT1-1-1 and MAT1-2-1 genes may play a

host-specific role in colonization of corn stalks (Zheng et al. 2013). Another interesting

example for MAT TFs acting outside the sexual life cycle can be found in the basidiomycete

and human fungal pathogen Cryptococcus neoformans. Here, the heterodimer of MAT TFs

Sxi2a and Sxi1α (Sex inducer 2a / Sex inducer 1α) was shown to be involved in regulation of

several well-studied virulence genes (Mead et al. 2014).

V. DISCUSSION 30

Table 5: MAT-dependent gene expression in euascomycetes and hemiascomycetes

Species Experimental approach Number of regulated genes Reference

Euascomycetes

Aspergillus fumigatus RNA-seq ∆MAT1-1/∆MAT1-2 vs WT

214 (MAT1-1) 729 (MAT1-2)

S. Krappmann, personal communication

Aspergillus oryzae Microarray after idiomorph replacement

596 (MAT1-1) 559 (MAT1-2) (only downregulated)

(Wada et al. 2012)

Fusarium graminearum Reverse Northern and Microarray ∆MAT1-2 vs WT

171 (only downregulated)

(Lee et al. 2006)

Fusarium verticillioides Microarray ∆MAT1-2-1 vs WT 248 (Keszthelyi et al. 2007)

Neurospora crassa Microarray during asexual development

44, 61, and 469 (MatA)

a

233, 159, and 744 (Mata)

a

(only upregulated in comparison to opposing mating type)

(Wang et al. 2012)

Penicillium chrysogenum Microarray ∆MAT1-1-1 vs. WT 2,421 (Böhm et al. 2013)

Podospora anserina Microarray mat+ vs mat− 167 (Bidard et al. 2011)

Sordaria macrospora Microarray ∆Smta-1 vs WT 107 (Pöggeler et al. 2006)

Sordaria macrospora Cross-species microarray analysis of ΔSmtA-1 and ΔSmtA-2

978 (SmtA-1) 854 (SmtA-2)

(Klix et al. 2010)

Hemiascomycetes


ChIP-chip MATα2 6

(Galgoczy et al. 2004) ChIP-chip MATα1 5

ChIP-chip MATa1-MATα2 19

Candida albicans Microarray analysis with strains of different MAT loci configuration

2 (MATα1) 2 (MATa2)

(Tsong et al. 2003)

Lachancea kluyveri ChIP-chip MATa2 9 (Baker et al. 2012)

Schizosaccharomyces pombe

Microarray on genes whose induction by Ste11p overexpression is cell type-dependent

12 (MAT1-M) 4 (MAT1-P)

(Mata and Bahler 2006)

a) after 36, 60, and 96h of cultivation

1.2 Rewiring of MAT-regulated transcriptional networks

Within this work, the MAT1-1-1 DNA-binding motif “CTATTGAG”, designated MAT1.1,

was identified. Interestingly, MAT1.1 shows close similarity to the cis-regulatory sequence

“TCATTGAT”, which has previously been described for αsgs in S. cerevisiae (Hagen et al.

1993, Baker et al. 2011). Furthermore, a high degree of conservation of MAT1.1 within

promoter sequences of αsgs in the euascomycetes A. fumigatus, A. nidulans, F. graminearum,

T. reesei, and N. crassa was demonstrated, whereas only moderate conservation was observed

in promoter regions from S. cerevisiae, and as good as no conservation was documented

within promoter regions from C. albicans. Taken together, these findings are in accordance

with recent studies demonstrating the conservation of the αsg cis-regulatory sequence from

S. cerevisiae and the filamentous fungus Uncinocarpus reesei. Moving of an αsg

cis-regulatory sequence from U. reesei into S. cerevisiae resulted in efficient activation of

V. DISCUSSION 31

reporter-gene expression by the S. cerevisiae Matα1 protein, whereas only weak activation

was documented in C. albicans (Baker et al. 2011). Based on this observation, it has been

hypothesized that S. cerevisiae, C. albicans, and filamentous ascomycetes may share a

common ancestor with an α-domain MAT protein DNA-binding specificity similar to that of

the modern S. cerevisiae Matα1 protein. Accordingly, DNA-binding specificity of the protein

would have changed so extensively that its cis-regulatory sequence appears different even in

related species, such as S. cerevisiae and C. albicans. However, as Matα1 still controls the

same core set of mating-related genes in various fungi, Matα1 and its DNA-recognition site

seem to have evolved together, preserving the protein-DNA interaction but significantly

changing its molecular details (Baker et al. 2011). Comparable results were described in

studies focusing on the evolution of gene regulation by the highly conserved transcriptional

regulator Mcm1 (Tuch et al. 2008). Mcm1 is a founding member of the MADS-box family of

TFs and an essential protein involved in regulation of diverse cellular and developmental

processes, such as the cell cycle, osmotic regulation, and arginine metabolism in yeast (Shore

and Sharrocks 1995, Carr et al. 2004). Furthermore, S. cerevisiae Mcm1 is necessary for

cell-type-specific transcription and pheromone response, and plays a central role in the

formation of repressor and activator complexes in cooperation with the MAT proteins

MATα1 and MATα2 (Mead et al. 2002, Pachkov et al. 2007). A direct interaction between

Mcm1 and Matα1 was shown to be essential for activation of the expression of yeast αsgs

(Bender and Sprague 1987, Jarvis et al. 1989, Carr et al. 2004). Surprisingly, comparison of

genes regulated by Mcm1 in the yeasts S. cerevisiae, C. albicans, and Kluyveromyces lactis

revealed substantial differences. Most importantly, new Mcm1-cofactor interactions were

shown to have evolved along different branches of the yeast lineage, whereas the core

Mcm1-cofactor interactions associated with cell cycle and mating remained the same. For

example, C. albicans Mcm1 was shown to bind to a new DNA-binding motif within the

promoter region of genes involved in the white-opaque phenotypic switch, necessary for

adaption within a human host (Kvaal et al. 1997). As the phenotypic switch has been only

described in C. albicans and two related pathogenic species, Candida tropicalis and

Candida dubliniensis (Pujol et al. 2004, Xie et al. 2012), one can assume that the adaption of

Mcm1 DNA-binding properties was crucial to this development.

A comparable scenario, where a MAT-encoded TF acquired additional regulatory features, is

also conceivable for P. chrysogenum MAT1-1-1. Here, expansion of MAT1-1-1 regulatory

functions during the asexual part of the life cycle, resulting in an involvement in various

cellular and developmental processes, such as formation of asexual conidiospores, amino

V. DISCUSSION 32

acid, iron, and secondary metabolism, might have been promoted by a slow decline in sexual

fertility within the species as a whole (Dyer and Paoletti 2005). This “slow decline”

hypothesis is in accordance with the observation that not all P. chrysogenum crosses produce

cleistothecia with ascospores and the same efficiency (Böhm et al. 2013). Furthermore, as no

information is available about the extent of sexual fertility within natural P. chrysogenum

populations, it is imaginable that the adaption of MAT1-1-1 regulatory functions to

non-mating-related processes might be an evolutionary step towards preference of an asexual

life style. A comparable evolutionary progress has recently been documented for

S. cerevisiae, where the elimination of the expression of 23 mating-related genes resulted in a

2 % growth-rate advantage in sterile mutants compared to wild type (Lang et al. 2009).

Evidently, further research will be necessary to elucidate the mechanisms leading to rewiring

of the GRN governed by MAT1-1-1. In this context, various mechanisms, ranging from

mutation or recombination of promoter sequences that might have brought additional genes

under control of MAT1-1-1, to changes in MAT1-1-1 itself, which may have altered its

binding specificity, activity, or interaction with other factors, have to be taken into account.

Although specificity and functionality of the DNA-binding motif MAT1.1 has been verified

within the scope of this work, the exact mechanisms underlying MAT1-1-1-DNA interactions

remained unclear. However, comparison of MAT1.1 to known DNA-binding motif sequences

present in the JASPAR core (2014) databases revealed noticeable similarities to known

DNA-binding consensus sequences from fungi and vertebrates. Within this context, a

significant overrepresentation of DNA-binding motifs specific for homeobox- and HMG-box

domain proteins from both fungi and vertebrates was observed. While homeobox-domain TFs

are renowned for their involvement in regulation of development in higher eukaryotes (Lewis

1978, Svingen and Koopman 2007, Mukherjee et al. 2009, Hay and Tsiantis 2010, Mallo et al.

2010), they have also been shown to fulfill vital regulatory functions during fungal

development and differentiation, e.g. in the form of MAT proteins from basidiomycetes

(Casselton and Olesnicky 1998, Yan et al. 2007, Haber 2012), during philaide development

and conidiogenesis in Fusarium species (Zheng et al. 2012), and as regulators of hyphal

morphology and microconidogenesis in Podospora anserina (Arnaise et al. 2001).

HMG-domain proteins are eukaryotic DNA-binding proteins, which are characterized by a

functional HMG-box, a conserved motif containing approximately 80 amino acids arranged in

a distinctive L-shaped three-α-helical fold (Read et al. 1993). Prominent examples for

HMG-domain proteins are MAT TFs from various fungi (Glass et al. 1990, Idnurm et al.

2008, Martin et al. 2010, Böhm et al. 2015), as well as the SOX (SRY-type HMG-box)

V. DISCUSSION 33

proteins, including SRY (sex-determining region Y), a crucial factor involved in mammalian

male sex determination (Gubbay et al. 1990, Giese et al. 1994, Werner et al. 1995). It has for

long been recognized that fungal MAT loci share structural and functional features of the

mammalian X and Y sex determination system (Kronstad and Staben 1997, Fraser et al. 2004,

Idnurm et al. 2008) and evidence for this assumption has recently been provided by Czaja et

al. (2014). It was demonstrated that the human SRY protein is able to functionally replace the

MAT protein MatA and drive sexual development in A. nidulans (Czaja et al. 2014).

Although little is known about fungal TFs and their evolutionary relatedness to TFs in other

eukaryotes (Shelest 2008), the observed similarities in DNA-binding consensus sequences of

MAT1-1-1 and various homeobox- and HMG-domain proteins from vertebrates and fungi can

be taken as a hint to similar DNA-binding properties. This is in accordance with recent work

providing evidence for the hypothesis that extant α-box genes originated from an ancestral

HMG gene, and that the α-domain should be able to bind DNA in a manner similar to

canonical HMG domains (Martin et al. 2010). Further support for this theory comes from the

observation that MATα1 is able to bend DNA (Carr et al. 2004), which is an important feature

of members of the HMG family of regulatory proteins (Grosschedl et al. 1994, Bianchi and

Agresti 2005, Malarkey and Churchill 2012).

Taken together, it appears that MAT-regulated GRNs have undergone drastic reorganization,

resulting in the presence of TFBSs in the promoters of – at a first glance – unrelated target

genes that are bound and controlled by highly conserved transcriptional regulators in different

fungi. Overall, this hypothesis fits the general assumption that transcriptional network

rewiring, allowing new regulatory patterns of existing gene products, is a key mechanism by

which organismal complexity arises in evolution (Carroll 2000, Levine and Tjian 2003, Tsong

et al. 2003, Lavoie et al. 2009, Booth et al. 2010, Li and Johnson 2010). Future research will

be necessary in order to determine exactly which changes in MAT1-1-1 and its corresponding

DNA-binding site were necessary to allow the enormous expansion in MAT1-1-1 regulatory

functions described within this work and when these facets of MAT1-1-1 functions were

acquired during evolution. Moreover, additional experiments will be needed to elucidate the

exact mechanism of MAT1-1-1 DNA-binding and its transcriptional regulatory activity.

1.3 A new MAT1-1-1 working model

Based on data presented within this work, a new model of MAT1-1-1 action can be proposed.

As shown in Figure 5, two levels of MAT1-1-1 regulatory functions can be distinguished: [a]

V. DISCUSSION 34

highly conserved, mating-related functions and [b] so far undescribed, non-mating-related

functions.

a) Highly conserved, mating-related MAT1-1-1 functions:

The expression of genes necessary for sexual development in P. chrysogenum, such as

those coding for the α-pheromone precursor PcPpg1, the α-pheromone processing

endoprotease Kex1, and the a-pheromone receptor PcPre1, is induced by MAT1-1-1.

However, none of the identified MAT1-1-1 target genes assigned to sexual

development, except for kex1, shows MAT1-1-1-dependent changes in expression

profiles at early developmental stages in a ΔMAT1-1-1 strain, suggesting that these

are, up to a certain point, independent of MAT1-1-1. It is conceivable that binding of a

so far uncharacterized a-pheromone to the respective a-pheromone receptor, encoded

by Pcpre1, initiates a positive feedback loop resulting in an increased expression of

MAT1-1-1-dependent genes.

b) New, non-mating-related MAT1-1-1 functions:

Transcriptional control mediated by MAT1-1-1 is not restricted to genes encoding

highly conserved key elements of sexual reproduction but also affects a considerable

number of non-mating genes. These include genes related to asexual development,

morphogenesis, secondary, amino acid, and iron metabolism, which can be used to

explain the previously described phenotypic characteristics of MAT1-1-1

overexpression and deletion strains (Böhm et al. 2013). In contradiction to

mating-related genes, expression levels of many non-mating-related genes were shown

to be affected in both MAT1-1-1-deletion and overexpression background.

V. DISCUSSION 35

Figure 5: Schematic representation of MAT1-1-1 regulatory functions. Two levels of MAT1-1-1 regulatory functions can be

distinguished. One the one hand, MAT1-1-1 acts as the main regulator of sexual development in P. chrysogenum

MAT1-1/α-cells (blue). Here, MAT1-1-1 induces the expression of key elements of sexual reproduction, such as Pcpre1,

encoding the a-pheromone receptor, and Pcppg1, encoding the α-pheromone. On the other hand, MAT1-1-1 regulates

genes with functions beyond the sexual part of the life cycle, e.g. those involved in asexual development and

morphogenesis, as well as secondary, iron, and amino acid metabolism. Presumably, binding of the (so far uncharacterized)

a-pheromone, produced by MAT1-2/a-cells (red), to the a-pheromone receptor of α-cells initiates a positive feedback loop

(marked by +), which leads to an increased expression of MAT1-1-1-regulated genes.

V. DISCUSSION 36

2. ChIP-seq analyses with PcVelA

Members of the velvet family of proteins act as key regulators of secondary metabolism and

differentiation processes. They are characterized by the presence of the so-called velvet

domain, which is widely distributed within the fungal kingdom (Gerke and Braus 2014).

Remarkably, velvet proteins have not been verified in fungi lacking SM gene clusters, such as

the yeasts S. cerevisiae and C. albicans (Bayram and Braus 2012).

The founding member of the velvet family, VeA (Velvet A), was firstly described as a

light-dependent regulator in A. nidulans (Käfer 1965). Since then, functional characterization

of VeA and its homologs in various filamentous fungi confirmed its involvement in terms of

sexual and asexual development, morphogenesis, virulence, and secondary metabolism. For

example, deletion of veA in A. nidulans leads to defects in the formation of sexual fruiting

bodies and abolishes sterigmatocystin production, whereas overexpression results in

constitutive formation of cleistothecia, independent of light conditions (Kim et al. 2002, Kato

et al. 2003, Calvo 2008). In F. fujikuroi, FfVel1 acts simultaneously as a positive and negative

regulator of secondary metabolism, and deletion mutants are characterized by an aberrant

formation of conidiospores and reduced virulence (Wiemann et al. 2010). Similarly, Fgve1

deletion mutants in F. graminearum are characterized by hyperbranching of the mycelium,

suppression of aerial hyphae formation, reduced hydrophobicity of the mycelium, as well as

reduced sporulation and virulence (Merhej et al. 2012). In P. chrysogenum, deletion of PcvelA

leads to reduced production of penicillin, together with light-independent formation of

conidiospores, dichotomous branching of hyphae, and increased pellet formation in shaking

cultures (Hoff et al. 2010). Similarly, deletion of AcveA in A. chrysogenum leads to reduced

production of cephalosporin, early hyphal fragmentation, and hyperbranching of hyphal tips

(Dreyer et al. 2007).

Besides VeA, three other members of the velvet family, namely VelB, VelC, and VosA, have

been identified, which are able to form multi-subunit protein complexes together with VeA

and the putative S-adenosyl-L-methionine (SAM)-dependent methyltransferase LaeA (Hoff et

al. 2010, Sarikaya-Bayram et al. 2010, Bayram and Braus 2012, Kopke et al. 2013). One of

the best studied functions of LaeA is its involvement in regulation of SM gene-cluster

expression (Bok and Keller 2004, Kale et al. 2008, Kosalková et al. 2009, Sarikaya-Bayram et

al. 2010, Wiemann et al. 2010, Karimi-Aghcheh et al. 2013). For example, transcriptional

profiling in A. fumigatus revealed that expression of 13 out of 22 SM gene clusters is

significantly reduced in the ΔlaeA background (Perrin et al. 2007). Correspondingly, in

V. DISCUSSION 37

P. chrysogenum, deletion of PclaeA resulted in a significant reduction in penicillin

biosynthesis and conidiospore formation (Hoff et al. 2010). Besides its involvement in

regulation of secondary metabolism, LaeA was shown to affect virulence in numerous

pathogenic fungi (Bok et al. 2005, Sugui et al. 2007, Kale et al. 2008, Amaike and Keller

2009, Wiemann et al. 2010, Wu et al. 2012, López-Berges et al. 2013).

Comprehensive characterization of the velvet proteins and LaeA in A. nidulans and

P. chrysogenum enabled the establishment of working models for velvet complex-mediated

regulation, which are described in detail in Bayram et al. (2012) and Kopke et al. (2013). In

brief, in A. nidulans, VeA and VelB are transported into the nucleus under dark conditions,

where interaction with LaeA leads to the formation of a heterotrimeric VelB-VeA-LaeA

complex that controls sexual development and secondary metabolism (Bayram et al. 2008).

Moreover, LaeA-dependent shuffling of VelB between VelB-VeA-LaeA and a second

complex, VelB-VosA, has been described. In the absence of light, VelB-VosA was shown to

repress asexual differentiation and regulate biogenesis of trehalose, a compound necessary for

long-term viability of fungal spores (d'Enfert and Fontaine 1997, Elbein et al. 2003, Sarikaya-

Bayram et al. 2010). In P. chrysogenum, all velvet subunits, including PcLaeA, have been

shown to interact with one or more other subunits (Hoff et al. 2010, Kopke et al. 2013).

However, it has not been solved if sub-complexes are formed at distinct time points or as a

function of developmental stages. Phenotypic characterization of a set of single- and

double-deletion mutants revealed that PcVelA, together with PcLaeA and PcVelC activates

penicillin biosynthesis, whereas PcVelB represses this process. Moreover, PcVelB and

PcVosA were shown to promote conidiation, while PcVelC has an inhibitory effect (Hoff et

al. 2010, Kopke et al. 2013).

2.1 PcVelA acts as a transcriptional regulator on DNA level

In order to advance our general understanding of velvet complex-mediated regulatory

functions in P. chrysogenum, PcVelA ChIP-seq analyses were performed (see section IV).

467 statistically significant PcVelA DNA-binding sites, corresponding to 631 putative direct

PcVelA target genes, were identified. Based on the fact that PcVelA regulatory functions

were generally thought to be restricted to protein level, this number of direct PcVelA

DNA-binding sites appeared surprisingly high. However, previous studies already indicated

that VeA greatly influences overall gene expression levels in various fungi. For example,

microarray analyses in P. chrysogenum revealed that a total of 13.6 % of all nuclear genes is

expressed in a PcVelA-dependent manner (Hoff et al. 2010). Moreover, RNA-seq analyses in

V. DISCUSSION 38

A. fumigatus and A. nidulans demonstrated that a total of 32 % and 26 % of all protein-coding

genes are differentially regulated in a ∆veA strain compared to wild type (Lind et al. 2015).

A large number of high-affinity PcVelA target regions were found to be located next to genes

known to be involved in processes that are affected by velvet proteins. For example, a total of

at least 14 genes related to conidiation and development as well as 16 genes that could be

assigned to secondary metabolism were identified. Interestingly, data from PcVelA ChIP-seq

analyses showed overlap to previous ChIP-chip analyses of other velvet components in

A. nidulans. Here, specific binding of VosA to the promoter sequences of brlA, coding for a

master regulator of conidiogenesis (Adams et al. 1988), and treA, associated with trehalose

biosynthesis, was demonstrated (Ahmed et al. 2013). Taken together, these observations not

only suggest that data obtained from ChIP-seq analysis are indeed of high biological

relevance, but also provide the first experimental evidence for PcVelA acting as a direct

transcriptional regulator on DNA level, possibly even as a TF. All in all, data presented within

this work provide evidence for a hypothesis made by Ni and Yu almost ten years ago, which

implies that the velvet proteins might be acting as global transcriptional regulators,

representing a new fungus-specific class of TFs (Ni and Yu 2007).

One of the best described features of VeA is its involvement in regulation of conidiation in

various fungi (Kim et al. 2002, Kato et al. 2003, Calvo 2008, Tuch et al. 2008, Hoff et al.

2010, Wiemann et al. 2010, Merhej et al. 2012, Kopke et al. 2013). While almost nothing is

known about the molecular details of light-dependent formation of asexual conidiospores in

P. chrysogenum, our current understanding of these mechanisms in A. nidulans is

comparatively profound. In general, conidiation is thought to be regulated by the master

regulator BrlA, whose expression is dependent on a number of genes, including the fluffy

genes fluG and flbA-E (Adams et al. 1988, Lee and Adams 1994, Etxebeste et al. 2008, Garzia

et al. 2010, Arratia-Quijada et al. 2012, Oiartzabal-Arano et al. 2015). It was hypothesized

that the light-sensing FphA-LreA-LreB photoreceptor complex might be signaling FlbB and

FlbC, which in turn bind to the brlA promoter region to activate its expression. Alternatively,

the photoreceptor complex itself might bind to the promoter of brlA in a mechanism that

involves FlbB and FlbC (Ruger-Herreros et al. 2011). Another working model assumes direct

involvement of VeA. Based on the fact that a direct interaction between FphA and VeA as

well as light-dependent shuttling of VeA between nucleus and cytoplasm can be observed

(Stinnett et al. 2007, Purschwitz et al. 2009), it was hypothesized that light signals are

transmitted to photoreceptors, which in turn control VeA activity through direct

V. DISCUSSION 39

protein-protein interaction (Bayram et al. 2008). VeA in turn would interact with additional

downstream factors, such as LaeA, to orchestrate light-dependent development and

biosynthesis of SMs (Bayram et al. 2008, Sarikaya-Bayram et al. 2010). Interestingly, not

only brlA but also flbC, encoding a C2H2 zinc-finger protein, and flbD, encoding a Myb-like

DNA-binding protein (Wieser and Adams 1995, Arratia-Quijada et al. 2012), have been

identified as specific PcVelA target genes in ChIP-seq analyses presented within this work. In

contradiction to the working models supposed by Ruger-Herreros et al. (2011) and Bayram et

al. (2008), this finding points to a direct involvement of PcVelA in regulation of conidiation

on DNA level. Nevertheless, it remains unclear whether PcVelA binding to promoter regions

of its target genes is mediated by PcVelA alone, possibly acting as a downstream factor of the

photoreceptor complex, or if DNA-binding of PcVelA is dependent on the interaction with

additional proteins, such as the photoreceptors FphA, LreA/LreB and/or the velvet proteins.

Besides target genes that are related to regulatory pathways known to be affected by the

velvet complex, ChIP-seq identified a significant number of direct PcVelA target genes that

have never been related to PcVelA or any other component of the velvet complex before.

Among these targets, a total of at least six TF-encoding genes and seven genes coding for

putative methyltransferases were identified. This observation might indicate that overall

PcVelA regulatory functions are dependent on additional downstream factors with direct

impact on transcriptional regulation, either as TFs or as modifiers of epigenetic marks.

Moreover, this observation is in line with current research, which demonstrated a close link

between VeA and various putative methyltransferases in a number of filamentous

ascomycetes (Jiang et al. 2011, Bi et al. 2013, Connolly et al. 2013b, Palmer et al. 2013,

Sarikaya-Bayram et al. 2014). Based on this finding, as well as on data obtained from

ChIP-seq and downstream analyses, PcLlmA (LaeA-like methyltransferase A), a putative

SAM-dependent methyltransferase, encoded by a new PcVelA target gene, was identified as

the most promising candidate for further characterization (see section V.2.2).

Besides identification of putative direct PcVelA target genes, ChIP-seq data were also used

for the de novo prediction of the PcVelA DNA-binding consensus sequence

“AACCTTGGAA” (PcVelA.M1), which was shown to specifically mediate protein-DNA

binding in vitro. This observation is consistent with the recent finding that two other velvet

proteins from A. nidulans, namely VosA and the VosA-VelB heterodimer, are able to bind

DNA in a sequence-specific manner (Ahmed et al. 2013). However, despite the fact that

PcVelA and A. nidulans VosA share a number of specific target genes, PcVelA.M1 displayed

V. DISCUSSION 40

only moderate similarity to the described VosA binding site “CTGGCCAAGGC”. Hence, it is

likely that even though both proteins bind to different DNA-consensus elements, they share

some fundamental regulatory features. Interestingly, comparison of PcVelA.M1 to

DNA-binding motifs present in the JASPAR core (2014) databases revealed significant

overlap to DNA-binding consensus sequences of NR2F1 and NR2E3, both involved in the

regulation of the development of the visual system in humans (Kobayashi et al. 1999, Milam

et al. 2002, Bosch et al. 2014). It was shown, that mutations in NR2E3 are associated with the

enhanced S-cone syndrome, characterized by night blindness, varying degrees of color vision,

and retinal degeneration in humans (Haider et al. 2000). Remarkably, this phenotype

somehow resembles those of PcvelA/veA deletion mutants in P. chrysogenum and A. nidulans.

Here, deletion of veA/PcvelA results in impaired light-sensing abilities, leading to the

formation of conidiospores in the absence of light, whereas conidiogenesis in the wild type is

light-dependent (Käfer 1965, Mooney and Yager 1990, Hoff et al. 2010).

Taken together, a regulatory function of PcVelA on DNA level instead or simultaneously to a

regulatory function on protein level has to be considered. Moreover, a direct involvement of

PcVelA in light-dependent gene regulation has to be assumed. Further experiments will be

necessary in order to fully elucidate DNA-binding properties of PcVelA and to understand the

dynamics underlying PcVelA-dependent regulation of target gene expression in cooperation

with other proteins.

2.2 The putative SAM-dependent methyltransferase PcLlmA is a direct interaction partner of PcVelA

ChIP-seq, qRT-PCR and microarray analyses, as well as DNA-binding studies

unambiguously identified PcllmA as a direct PcVelA-target gene, coding for a putative

SAM-dependent methyltransferase with noticeable similarity to PcLaeA. Furthermore, yeast

two-hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) analyses confirmed

direct interaction between PcLlmA and PcVelA, as well as nuclear localization of the

PcVelA-PcLlmA heterodimer. Interestingly, interaction between PcLlmA and the velvet

complex appeared to be restricted to PcVelA, as it has previously been shown for the

interaction between the velvet complex and the putative SAM-dependent methyltransferase

PcLaeA (Kopke et al. 2013). It is a recent observation that interaction with various putative

methyltransferases, others than LaeA, seems to be a characteristic feature of VeA. For

example, a reverse genetics screen in A. nidulans identified LlmF (LaeA-like

methyltransferase F), an interaction partner of VeA and a negative regulator of sexual

V. DISCUSSION 41

development and secondary metabolism (Palmer et al. 2013). Furthermore, methyltransferases

VipC (velvet interacting protein C) and VapB (VipC associated protein B were shown to

directly interact with VeA in the nucleus to promote asexual development or, together with

the membrane protein VapA, at the plasma membrane to support sexual development

(Sarikaya-Bayram et al. 2014). Moreover, using a Y2H approach, F. graminearum FgVeA

was shown to interact with a total of six putative methyltransferases that show sequence

homologies to FgLaeA1 (Jiang et al. 2011). As it has been demonstrated for PcVelA-PcLlmA,

interactions between PcVelA/VeA and other methyltransferases, such as PcLaeA (Hoff et al.

2010), as well as A. nidulans LlmF, VipC and VapB (Palmer et al. 2013, Sarikaya-Bayram et

al. 2014) are restricted to the nucleus. However, it remains unclear how the interaction

between VeA and the growing number of methyltransferases is mediated on a structural level.

It was suggested that VeA should have an affinity domain for methyltransferases or a tertiary

domain providing interaction with methyltransferases (Bayram et al. 2008, Sarikaya-Bayram

et al. 2015) but experimental evidence is needed to verify these hypotheses and to elucidate

the functional output of these interactions in more detail.

Besides its direct interaction with PcVelA, PcLlmA was shown to carry the

methyltransferase-specific sequence motifs I-III (Kagan and Clarke 1994), and therefore to

fulfill the requirements for epigenetic methyltransferase activity. Similar functions have

already been hypothesized for LaeA and VapB but experimental evidence for an involvement

in genome-wide chromatin remodeling is still lacking (Reyes-Dominguez et al. 2010,

Sarikaya-Bayram et al. 2014). Against this background it will be highly interesting to see if

PcLlmA is able to exert epigenetic methyltransferase activity, which would make it one of the

most promising PcVelA interaction partners identified so far. However, when speculating

about PcLaeA, VapB, and even PcLlmA functions on a molecular level, it has to be

mentioned that biological functions of SAM-dependent methyltransferases are versatile. They

catalyze the transfer of methyl groups from SAM to a large variety of acceptor substrates,

ranging from small metabolites to bio-macromolecules, including DNA, proteins and SMs

(Martin and McMillan 2002, Jiang et al. 2011, Struck et al. 2012). On the one hand, this

marks them as interesting candidates for application in biotechnology (Struck et al. 2012),

but, on the other hand, this emphasizes why numerous other functions besides those involved

in epigenetic modification of chromatin have to be taken into account. Above that, occasional

examples for proteins, which incorporate the core SAM-dependent methyltransferase fold but

do not exert any quantifiable methyltransferase activity, can be found in the literature (Dong

et al. 2001).

V. DISCUSSION 42

2.3 An expanded model of PcVelA regulatory functions

Starting from the aforementioned observations, an expanded version of the current model of

PcVelA regulatory functions can be hypothesized (Figure 6). On the one hand, PcVelA acts

on the protein level as one of the core components of the velvet complex and, most likely, as

an interaction partner of the light sensing FphA-LreA-LreB complex. On the other hand,

PcVelA operates as a genome-wide transcriptional regulator on DNA level, possibly in

cooperation with other proteins, such as the velvet components or the FphA-LreA-LreB

photoreceptor complex. Examples from the literature demonstrate that these functions must

not be mutually exclusive. For instance, the metabolic enzyme IMPDH, which controls the

cellular guanidine nucleotide pool was shown to be also a DNA-binding transcriptional

repressor involved in regulation of histone genes and E2f, a key driver of cell proliferation in

Drosophila (Kozhevnikova et al. 2012). Furthermore, as the putative SAM-dependent

methyltransferase PcLlmA was identified as a direct interaction partner of PcVelA, a third

level of PcVelA regulatory functions, dependent on interacting methyltransferases, others

than PcLaeA, has to be assumed.

Figure 6: Three levels of PcVelA regulatory functions. (1) PcVelA acts as one of the core components of the velvet complex,

which is likely to form different sub-complexes in order to mediate control of development, morphology, and secondary

metabolism. (2) Based on data obtained from ChIP-seq and follow-on analyses, PcVelA acts as a regulatory protein on

DNA-level, probably even as a TF. It is conceivable that DNA-binding is dependent on interaction with other proteins, such

as the velvet components or the FphA-LreA-LreB photoreceptor complex. (3) PcVelA directly interacts with putative

methyltransferases, others than PcLaeA. For example, the putative SAM-dependent methyltransferase PcLlmA was

identified as a direct interaction partner and downstream factor of PcVelA. (modified from a model provided by

Dr. S. Bloemendal)

V. DISCUSSION 43

3. Overall analysis of ChIP-seq data

3.1 Genome-wide TF binding beyond direct target-gene control

Using ChIP-seq analysis, a total of 243 and 467 specific DNA-binding sites have been

identified for MAT1-1-1 and PcVelA, respectively. All of these binding sites passed a high

statistical threshold and were present in at least two independent biological replicates.

Overall, the observed DNA-binding patterns matched the characteristic features of TF

DNA-binding in most eukaryotes. For example, a total of 79.4 % of MAT1-1-1 and 78.9 % of

PcVelA DNA-binding regions was found to be located within intergenic regions, matching

the general observation that regulatory genomic regions targeted by TFs are primarily found

within intergenic or intronic DNA (Stergachis et al. 2013). Furthermore, the distance of peak

summits and transcription start sites (TSSs) of neighboring genes was found to be within a

range of 200-500 nt for MAT1-1-1 and 100-600 nt for PcVelA. This finding is consistent with

the observation that, depending on TF identity, expression of target genes in S. cerevisiae

reaches maximal values when the TFBS is either within 150 bp (short-range regulation),

150-300 bp (mid-range regulation), or 300-500 bp (long-range regulation) from the start

codon, and that most regulatory DNA sequences for a given gene fall within a few hundred bp

from its TSS (Nguyen and D'Haeseleer 2006, Lin et al. 2010).

Biological significance of ChIP-seq datasets was verified using ChIP-PCR, in order to rule

out bias from bioinformatics analysis, as well as microarray and qRT-PCR analyses for the

identification of specific MAT1-1-1 and PcVelA target genes. Comparison of ChIP-seq data

to expression values from previous microarray analysis in ΔMAT1-1-1 and ΔPcvelA strains

revealed that 29.9 % and 18.9 % of genes showing 5’-3’ orientation with regard to

neighboring peak regions are expressed in a MAT1-1-1- and PcVelA-dependent manner,

respectively. This observation is in line with previous works, analyzing TF binding in relation

to changes in expression profiles of neighboring genes and demonstrating that 58 % of genes

whose promoter region is bound by a TF are true regulatory targets of the respective factor in

S. cerevisiae, whereas this is true for only 10-25 % of putative target genes in Drosophila and

mammalian systems (Gao et al. 2004, Sandmann et al. 2006, Jakobsen et al. 2007, Vokes et

al. 2008). Correspondingly, a comprehensive TF knock-down analysis of 59 TFs and

chromatin modifiers in a human lymphoblastoid cell line, revealed that 46.4 % to 99.1 % of

all analyzed TF binding events are likely to be non-functional, as no association between

knock down of the respective factor and changes in expression levels could be documented

(Cusanovich et al. 2014).

V. DISCUSSION 44

Based on the growing amount of data from genome-wide TF DNA-binding studies (Table 6),

it is a common observation that TFs vary greatly in their number of genomic binding sites.

This suggests that TF binding events can significantly exceed the number of

conceivable or even possible direct target genes. Accordingly, starting from the observation

that genome-wide TF binding is not necessarily equivalent to expressional regulation of

adjacent genes, three types of TF binding events can be distinguished: [1] specific, functional

binding to cis-regulatory regions with a direct impact on gene regulation, [2] specific but

non-functional binding, and [3] non-specific, non-functional binding (when functionality is

defined as transcriptional regulation) (Todeschini et al. 2014). It is generally accepted, and

might also be true for MAT1-1-1 and PcVelA TFBSs identified within this work, that

although the most highly bound regions in ChIP-seq analyses are known functional targets,

many of the thousands of regions bound at much lower levels may represent specific or

non-specific, non-functional interactions. Support for this hypothesis has been provided by

recent studies in Drosophila, analyzing DNA-binding patterns of 21 TFs involved in embryo

development (MacArthur et al. 2009, Fisher et al. 2012). Here, it was demonstrated that

high-affinity binding of TFs is more likely to occur within close proximity of genes showing

transcriptional regulation, whereas low-affinity binding generally occurs in regions not

regulated by the respective factor. Consistently, analyses focusing on the conservation of TF

Table 6: Numbers of TFBSs from selected ChIP-seq experiments

Species Transcription factor Reported number of TFBSs Reference

Anabaena sp. All3953 142 (Picossi et al. 2015)

Arabidopsis thaliana KAN1 4,183 (Merelo et al. 2013)

Aspergillus fumigatus SrbA 111 (Chung et al. 2014)

Caenorhabditis elegans PHA-4 4,350/4,808b (Zhong et al. 2010)

Caenorhabditis elegans Tra-1 184 (Berkseth et al. 2013)

Candida parapsilosis Efg1 931 (Connolly et al. 2013a)

Fusarium graminearum Tri6 198 (Nasmith et al. 2011)

Human NRSF 1,946 (Johnson et al. 2007)

Human STAT1 11,004/41,582c (Robertson et al. 2007)

Human TdIF1 1,274 (Koiwai et al. 2015)

Mouse MyoD 25,956/59,267a (Cao et al. 2010)

Neurospora crassa WCC 287 (Hurley et al. 2014)

Penicillium chrysogenum MAT1-1-1 243 This work (section III)

Penicillium chrysogenum PcVelA 467 This work (section IV)

Saccharomyces cerevisiae Pho7 1,676 (Carter-O'Connell et al. 2012)

Solanum lycopersicum ASR1 225 (Ricardi et al. 2014)

Sordaria macrospora PRO1 215 E. Steffens, personal communication

Zebrafish Gli2a 93/122e (Wang et al. 2013)

Zebrafish Zic3 3,209/2,088d (Winata et al. 2013)

a)binding sites at two different statistical cutoffs

b)

binding sites in embryos and L1 larvae c)

binding sites in un-stimulated and interferon-γ-stimulated cells d)

binding sites after 8 and 24 hours post-fertilization e)

binding sites at the 5 and 15 somitic stage

V. DISCUSSION 45

binding and gene expression patterns within different Drosophila species demonstrated that

TFBSs producing strong peaks are more likely to be conserved across species than those

characterized by weak signals in ChIP-seq analyses (He et al. 2011, Paris et al. 2013).

However, as signal strengths obtained from ChIP assays usually represent mean values of the

corresponding signals across millions of cells, caution should be exercised when trying to

draw conclusions about the biological relevance, functionality, or binding-affinity of a

DNA-binding protein from ChIP-seq signal strength alone (Slattery et al. 2014).

In contradiction to the assumption that the majority of low-affinity TFBSs identified in

large-scale TF-binding studies is likely to serve no apparent biological purpose, Tanay (2006)

hypothesized that TF binding to these sites might contribute to gene expression at levels that

are low but sufficient enough to allow evolutionary conservation. Another model aiming at

explaining the apparently contradictory DNA-binding patterns of many TFs proceeds on the

assumption that genome-wide TF binding at non-regulatory sites might serve as a reservoir

for TFs, sequestering them in a manner comparable to other biological buffering systems, and

ensuring an optimal amount of available TF in the nucleus (MacQuarrie et al. 2011). Other

explanations for the existence of apparently non-functional TFBSs include the possibility of

TFs exerting their regulatory influence on genes over large genomic distances by distal

elements like enhancers or silencers (most possibly mediated by chromatin looping), as well

as TF-mediated induction of changes in chromatin and nuclear structure, and the evolution of

new GRNs (Cao et al. 2010, MacQuarrie et al. 2011, Zaret and Carroll 2011, Weingarten-

Gabbay and Segal 2014).

3.2 MAT1-1-1 and PcVelA bind DNA via specific DNA-consensus sequences

Starting from peak regions identified in ChIP-seq analyses, DNA-binding motif consensus

sequences were predicted and verified for both, MAT1-1-1 and PcVelA. Remarkably, no

evidence was found for the necessity of both motifs to be orientated in a definite direction in

order to drive expression of neighboring target genes. This finding is in agreement with

previous work in S. cerevisiae, revealing that activity of only 6 out of 75 (8 %) analyzed TFs

is dependent on the orientation of the corresponding TFBS (Sharon et al. 2012).

When focusing on the overall distribution of DNA-binding motifs MAT1.1 and PcVelA.M1,

it was striking that some of the most significant DNA-binding regions were found to carry

noticeably high numbers of the corresponding DNA-binding consensus sequences.

Interestingly, this observation is fits the recent discovery that functional binding of human

V. DISCUSSION 46

TFs is enriched in genomic regions that carry large numbers of specific TFBSs, at sites with

predicted higher binding affinity, and at sites that are clustered within genomic regions

annotated as enhancers (Cusanovich et al. 2014). A possible explanation for this observation

has been provided by a large-scale bioinformatics analysis of more than 950 TF-binding

motifs, leading to the conclusion that clustering of DNA-binding motifs is necessary to target

TFs to their specific binding sites in eukaryotic genomes (Wunderlich and Mirny 2009).

Fundamental for this hypothesis was the observation that eukaryotic TFs typically recognize

short sequences of 10-12 nt, whereas bacterial TFs tend to recognize extended DNA sites of

~ 23 nt, which is enough to ensure specificity in small genomes (Wunderlich and Mirny 2009,

Stewart et al. 2012).

Although DNA-binding motifs were present in the majority of high-affinity MAT1-1-1 and

PcVelA binding-sites, a significant proportion of peaks lacked clearly identifiable copies of

MAT1.1 and PcVelA.M1, respectively. Accordingly, at least one copy of the respective

consensus sequences was identified in 83.1 % of MAT1-1-1 peak regions (p ≤ 0.01) and

46.5 % of PcVelA peak regions (p ≤ 0.001). This is a common observation: although TF

DNA-binding regions identified in ChIP-seq analyses are typically enriched in the consensus

motif for the TF in question, a significant proportion of peaks lack it (Robertson et al. 2007,

Li et al. 2008, Rabinovich et al. 2008, Valouev et al. 2008, Berkseth et al. 2013). Possible

explanations for this observation can be found in recent studies providing evidence for the

highly combinatorial nature of TF binding in eukaryotes. For example, it has been

demonstrated that TFs not only interact with DNA via a consensus sequence but also

recognize divergent sequences, and that TF regulatory functions involve interactions with

other TFs binding to neighboring DNA and chromatin sites, as well as binding to chromatin

without directly contacting DNA (Li et al. 2007, Brent et al. 2008, Badis et al. 2009, Georges

et al. 2010, Todeschini et al. 2014). For example, expression of amdS, encoding an

acedamidase enzyme that is vital for A. nidulans carbon and nitrogen metabolism, was shown

to be regulated by at least six different TFs, which directly bind within a ~ 200 nt upstream

region of the gene (Hynes 1994). Combinatorial binding can be mediated by direct

protein-protein interactions, leading to the formation of homodimers, heterodimers, or large

transcriptional complexes, as well as indirectly by co-binding of the same DNA-sequence

(Walhout 2006, Amoutzias et al. 2008, Ravasi et al. 2010). Dimerization of TFs has been

shown to be linked to several benefits, such as an increase in the binding affinity and effective

length of the recognized DNA sites, which in turn increases binding specificity and decreases

the probability of random occurrence of the DNA-binding motif within the genome (Georges

V. DISCUSSION 47

et al. 2010). Furthermore, for some TFs it has been shown that they are able to assume

divergent functions when interacting with different partners. For example, TF SREBP1, an

important regulator of cholesterol and fatty acid metabolism in humans, has been shown to act

in distinct functional pathways when interacting with its binding partners NFY and SP1 (Reed

et al. 2008).

Based on findings presented within this work and in the literature, formation of homo- and

heterodimers might indeed be a regulatory feature of P. chrysogenum MAT1-1-1 and PcVelA.

For example, various direct interaction partners of PcVelA, including the velvet proteins

PcVelB, PcVelC, PcVosA and PcVelA itself, along with the putative methyltransferases

PcLaeA and PcLlmA, have been identified using Y2H and BiFC approaches in

P. chrysogenum (Hoff et al. 2010, Kopke et al. 2013). Moreover, recent crystal structure

analysis in A. nidulans provided evidence for an involvement of the velvet domain in the

dimerization of different velvet-domain proteins (Ahmed et al. 2013), and, based on the fact

that different combinations of dimerization exist in velvet proteins, their dimerization

properties have been described to resemble those of the bZIP family of TFs (Sarikaya-Bayram

et al. 2015). For MAT1-1-1 the situation turns out to be more vague, as dimerization was

shown to be a common feature in MAT proteins (Dranginis 1990, Bruhn et al. 1992, Ho et al.

1994, Nolting and Pöggeler 2006), but no experimental evidence has been provided for

interactions between MAT1-1-1 and any other proteins in P. chrysogenum until now. It will

be a challenging task to further analyze the molecular details and dynamics of MAT1-1-1 and

PcVelA DNA-binding properties in terms of putative interactions with other proteins, acting

as cooperative binding partners on DNA-level or as direct interaction partners on protein

level.

3.3 Concluding remarks

Taken together, data obtained from MAT1-1-1 and PcVelA ChIP-seq analyses in

P. chrysogenum share both the strengths and weaknesses of many other datasets from

ChIP-seq experiments, performed in a variety of organisms and tissues so far. While on the

one hand, ChIP-seq represents the most powerful tool for genome-wide binding profiling of

DNA-binding proteins and epigenetic marks, it is, on the other hand, a challenging task to

fully elucidate the biological meaning of raw sequencing data. This difficulty becomes

especially obvious, when it comes to the discrimination between functional and apparently

non-functional DNA-binding sites. Although integration of additional information obtained

from expression profiling or mapping of euchromatic and/or heterochromatic genomic regions

V. DISCUSSION 48

can help to shed light onto the genome-wide regulatory functions of DNA-binding proteins, it

is still not sufficient to fully elucidate the whole complexity of GRNs. It will be a major task

for the future to reconsider the traditional relationship between TF binding and gene

regulation, as well as to analyze new aspects of transcriptional regulation, such as the role of

widespread TF binding outside direct target-gene control and dependencies between TF

binding and the three-dimensional structure of chromatin, in more detail.

When focusing on ChIP-seq analyses performed within the scope of this work, it has to be

regarded that the presented experimental workflow was designed to depict a preferably

complete picture of as much MAT1-1-1- and PcVelA-specific DNA-binding regions as

possible. Therefore, DNA-binding profiles were analyzed independent of culture conditions,

developmental stages, or external stimuli and in MAT1-1-1 and PcvelA overexpression

background, respectively. Although this approach turned out to be well suited to get an

overview of the genome-wide regulatory functions of both proteins, it does not consider the

fact that some TFs can occupy diverse sets of binding sites, depending on the developmental

stage or condition considered. An example for this was provided by ChIP-seq analysis of the

erythroid Kruppel-like factor (EKLF) in mice, which revealed highly divergent binding

patterns in erythroid progenitor cells and more differentiated erythroblasts (Pilon et al. 2011).

Furthermore, it has to be taken into account that some binding events may require co-factors

that are only present after specific stimuli in order to become functional. For example, the

non-DNA-binding transcriptional co-activator Met4 enables the regulatory

centromere-binding factor Cbf1–Met4 heterodimer to recognize an extended DNA-binding

motif, mediating effective expression of the sulfur metabolism genes in S. cerevisiae (Siggers

et al. 2011). Hence, further studies will be needed in order to fully elucidate the GRNs

governed by MAT1-1-1 and PcVelA as a function of developmental stages, physiological

culture conditions, or environmental factors.

VI. SUMMARY 49

VI. SUMMARY

P. chrysogenum is the only industrial producer of the β-lactam antibiotic penicillin, the most

commonly used drug in the treatment of bacterial infections. In order to identify new starting

points for further optimization of high-production strains, it is necessary to obtain a more

comprehensive knowledge of GRNs controlling morphogenesis and secondary metabolism in

P. chrysogenum. Within this context, functional characterization of TFs, which orchestrate

gene expression control on the molecular level, is of major importance.

Within the scope of this work, the ChIP-seq technology, which is regarded as the most

powerful tool for the analysis of protein-DNA interactions on a genome-wide scale, was

successfully adapted for application in P. chrysogenum. A well-established experimental

pipeline for sample preparation, ChIP-seq data analysis, and follow-on experiments, including

validation of ChIP-seq data, identification of specific target genes, as well as prediction and

validation of DNA-binding consensus sequences, is now available. Furthermore,

comprehensive ChIP-seq analyses and follow-on experiments revealed important new insights

into the regulatory properties of the MAT α-domain TF MAT1-1-1 and the velvet protein

PcVelA. Most importantly, data presented within this work provide the first experimental

evidence for a direct involvement of MAT1-1-1 in transcriptional regulation of numerous

target genes beyond sexual development. Moreover, it was shown that extensive rewiring of

MAT controlled transcriptional networks must have occurred in euascomycetes compared to

hemiascomycetes. With regard to PcVelA, the most important finding presented within this

work is that its regulatory properties are not restricted to protein-protein interactions with

other components of the velvet complex, but instead involve regulatory functions on DNA

level, probably even as a TF. Moreover, a new downstream factor and direct interaction

partner of PcVelA, the putative SAM-dependent methyltransferase PcLlmA, was identified.

This finding points to a third level of PcVelA regulatory functions, which involves

interactions with putative methyltransferases others than the velvet-interacting putative

methyltransferase PcLaeA.

Taken together, data from ChIP-seq and follow-on analyses not only enabled new insights

into MAT1-1-1 and PcVelA regulatory functions, but also provide a versatile basis for further

analysis of GRNs controlling morphogenesis and secondary metabolism in the

biotechnologically highly relevant ascomycete P. chrysogenum.

VII. ZUSAMMENFASSUNG 50

VII. ZUSAMMENFASSUNG

P. chrysogenum ist der einzige industriell genutzte Produzent des β-Laktam-Antibiotikums

Penicillin, welches weltweit am häufigsten zur Behandlung bakterieller Infektionen eingesetzt

wird. Für die weitere Optimierung industrieller Hochleistungs-Produktionsstämme sind

fundierte Kenntnisse der transkriptionellen Regulation der Morphogenese und

Sekundärmetabolit-Biosynthese in P. chrysogenum unerlässlich. Insbesondere die

funktionelle Charakterisierung von Transkriptionsfaktoren, welche die Genregulation auf

molekularer Ebene steuern, ist hierbei von besonderer Bedeutung.

Im Rahmen der vorliegenden Arbeit wurde die ChIP-seq Technologie, welche zurzeit als die

vielversprechendste Methode zur Genom-weiten Analyse von Protein-DNA-Interaktionen

angesehen wird, erfolgreich für die Anwendung in P. chrysogenum adaptiert. Es steht nun ein

etabliertes Protokoll zur Verfügung, welches sämtliche Schritte einer ChIP-seq-Analyse und

nachfolgender Kontrollexperimente abdeckt. Des Weiteren wurden umfassende

ChIP-seq-Analysen durchgeführt, welche neue Einblicke in die regulatorischen Eigenschaften

des Kreuzungstyp-Transkriptionsfaktors MAT1-1-1 und des Velvet-Proteins PcVelA

lieferten. Es konnte gezeigt werden, dass MAT1-1-1 an der Regulation zahlreicher Gene

beteiligt ist, welche nicht unmittelbar mit der sexuellen Entwicklung des Pilzes in Verbindung

stehen. Darüber hinaus ergaben sich wichtige Hinweise auf grundlegende Umstrukturierungen

von Kreuzungstyp-regulierten Genregulationsnetzwerken in Euascomyzeten im Vergleich mit

Hemiascomyzeten. Für PcVelA konnte des Weiteren gezeigt werden, dass die regulatorischen

Eigenschaften des Proteins nicht allein auf Protein-Protein-Interaktionen mit Komponenten

des Velvet-Komplexes beschränkt sind, sondern auch regulatorische Funktionen auf

DNA-Ebene, möglicherweise sogar als Transkriptionsfaktor, umfassen. Außerdem konnte ein

neuer Downstream-Faktor und direkter Interaktionspartner von PcVelA, die putative

SAM-abhängige Methyltransferase PcLlmA, identifiziert werden. Diese Beobachtung deutet

auf eine weitere Ebene von PcVelA-vermittelter Regulation hin, welche auf der Interaktion

mit anderen Methyltransferasen neben der als Komponente des Velvet-Komplexes

beschriebenen putativen Methyltransferase PcLaeA beruht.

Zusammenfassend liefert die vorliegende Arbeit nicht nur wichtige neue Erkenntnisse über

die regulatorischen Funktionen von MAT1-1-1 und PcVelA, sondern stellt auch eine fundierte

Basis für die weitere Analyse genregulatorischer Netzwerke bereit, welche die Morphogenese

und den Sekundärmetabolismus in P. chrysogenum kontrollieren.

VIII. REFERENCES 51

VIII. REFERENCES

Ádám AL, García-Martínez J, Szücs EP, Avalos J, Hornok L (2011) The MAT1-2-1 mating-type gene upregulates photo-inducible carotenoid biosynthesis in Fusarium verticillioides. FEMS Microbiol Lett 318: 76-83

Adams TH, Boylan MT, Timberlake WE (1988) BrlA is necessary and sufficient to direct

conidiophore development in Aspergillus nidulans. Cell 54: 353-62 Ahmed YL, Gerke J, Park HS, Bayram Ö, Neumann P, Ni M, Dickmanns A, Kim SC, Yu JH,

Braus GH, Ficner R (2013) The velvet family of fungal regulators contains a DNA-binding domain structurally similar to NF-κB. PLoS Biol 11: e1001750

Albert I, Mavrich TN, Tomsho LP, Qi J, Zanton SJ, Schuster SC, Pugh BF (2007) Translational

and rotational settings of H2A.Z nucleosomes across the Saccharomyces cerevisiae genome. Nature 446: 572-6

Amaike S, Keller NP (2009) Distinct roles for VeA and LaeA in development and pathogenesis of

Aspergillus flavus. Eukaryot Cell 8: 1051-60 Ammerer G, Sprague GF, Jr., Bender A (1985) Control of yeast α-specific genes: evidence for two

blocks to expression in MATa/MATα diploids. Proc Natl Acad Sci U S A 82: 5855-9 Amoutzias GD, Robertson DL, Van de Peer Y, Oliver SG (2008) Choose your partners:

dimerization in eukaryotic transcription factors. Trends Biochem Sci 33: 220-9 Arnaise S, Zickler D, Poisier C, Debuchy R (2001) pah1: a homeobox gene involved in hyphal

morphology and microconidiogenesis in the filamentous ascomycete Podospora anserina. Mol Microbiol 39: 54-64

Arratia-Quijada J, Sanchez O, Scazzocchio C, Aguirre J (2012) FlbD, a Myb transcription factor

of Aspergillus nidulans, is uniquely involved in both asexual and sexual differentiation. Eukaryot Cell 11: 1132-42

Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K,

Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese

JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25-9

Astell CR, Ahlstrom-Jonasson L, Smith M, Tatchell K, Nasmyth KA, Hall BD (1981) The

sequence of the DNAs coding for the mating-type loci of Saccharomyces cerevisiae. Cell 27: 15-23

Audenaert K, Vanheule A, Hofte M, Haesaert G (2014) Deoxynivalenol: a major player in the

multifaceted response of Fusarium to its environment. Toxins 6: 1-19 Backus MP, Stauffer JF (1955) The production and selection of a family of strains in Penicillium

chrysogenum. Mycologia 47: 429-63 Badis G, Berger MF, Philippakis AA, Talukder S, Gehrke AR, Jaeger SA, Chan ET, Metzler G,

Vedenko A, Chen X, Kuznetsov H, Wang CF, Coburn D, Newburger DE, Morris Q,

Hughes TR, Bulyk ML (2009) Diversity and complexity in DNA recognition by transcription factors. Science 324: 1720-3

VIII. REFERENCES 52

Bailey T, Krajewski P, Ladunga I, Lefebvre C, Li Q, Liu T, Madrigal P, Taslim C, Zhang J

(2013) Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Comput Biol 9: e1003326

Baker CR, Tuch BB, Johnson AD (2011) Extensive DNA-binding specificity divergence of a

conserved transcription regulator. Proc Natl Acad Sci U S A 108: 7493-8 Barber MS, Giesecke U, Reichert A, Minas W (2004) Industrial enzymatic production of

cephalosporin-based β-lactams. Adv Biochem Eng Biotechnol 88: 179-215 Barreiro C, Martín JF, García-Estrada C (2012) Proteomics shows new faces for the old penicillin

producer Penicillium chrysogenum. J Biomed Biotechnol 2012: 1-15 Barski A, Cuddapah S, Cui K, Roh TY, Schones DE, Wang Z, Wei G, Chepelev I, Zhao K (2007)

High-resolution profiling of histone methylations in the human genome. Cell 129: 823-37 Bayram Ö, Krappmann S, Ni M, Bok JW, Helmstaedt K, Valerius O, Braus-Stromeyer S, Kwon

NJ, Keller NP, Yu JH, Braus GH (2008) VelB/VeA/LaeA complex coordinates light signal with fungal development and secondary metabolism. Science 320: 1504-6

Bayram Ö, Braus GH (2012) Coordination of secondary metabolism and development in fungi: the

velvet family of regulatory proteins. FEMS Microbiol Rev 36: 1-24 Bender A, Sprague GF, Jr. (1987) MAT α1 protein, a yeast transcription activator, binds

synergistically with a second protein to a set of cell-type-specific genes. Cell 50: 681-91 Bentley DR, Balasubramanian S, Swerdlow HP, Smith GP, Milton J, Brown CG, Hall KP, Evers

DJ, Barnes CL, Bignell HR, Boutell JM, Bryant J, Carter RJ, Keira Cheetham R, Cox

AJ, Ellis DJ, Flatbush MR, Gormley NA, Humphray SJ, Irving LJ, Karbelashvili MS,

Kirk SM, Li H, Liu X, Maisinger KS, Murray LJ, Obradovic B, Ost T, Parkinson ML,

Pratt MR, Rasolonjatovo IM, Reed MT, Rigatti R, Rodighiero C, Ross MT, Sabot A,

Sankar SV, Scally A, Schroth GP, Smith ME, Smith VP, Spiridou A, Torrance PE,

Tzonev SS, Vermaas EH, Walter K, Wu X, Zhang L, Alam MD, Anastasi C, Aniebo IC,

Bailey DM, Bancarz IR, Banerjee S, Barbour SG, Baybayan PA, Benoit VA, Benson KF,

Bevis C, Black PJ, Boodhun A, Brennan JS, Bridgham JA, Brown RC, Brown AA,

Buermann DH, Bundu AA, Burrows JC, Carter NP, Castillo N, Chiara ECM, Chang S,

Neil Cooley R, Crake NR, Dada OO, Diakoumakos KD, Dominguez-Fernandez B,

Earnshaw DJ, Egbujor UC, Elmore DW, Etchin SS, Ewan MR, Fedurco M, Fraser LJ,

Fuentes Fajardo KV, Scott Furey W, George D, Gietzen KJ, Goddard CP, Golda GS,

Granieri PA, Green DE, Gustafson DL, Hansen NF, Harnish K, Haudenschild CD,

Heyer NI, Hims MM, Ho JT, Horgan AM, Hoschler K, Hurwitz S, Ivanov DV, Johnson

MQ, James T, Huw Jones TA, Kang GD, Kerelska TH, Kersey AD, Khrebtukova I,

Kindwall AP, Kingsbury Z, Kokko-Gonzales PI, Kumar A, Laurent MA, Lawley CT,

Lee SE, Lee X, Liao AK, Loch JA, Lok M, Luo S, Mammen RM, Martin JW, McCauley

PG, McNitt P, Mehta P, Moon KW, Mullens JW, Newington T, Ning Z, Ling Ng B, Novo

SM, O'Neill MJ, Osborne MA, Osnowski A, Ostadan O, Paraschos LL, Pickering L, Pike

AC, Chris Pinkard D, Pliskin DP, Podhasky J, Quijano VJ, Raczy C, Rae VH, Rawlings

SR, Chiva Rodriguez A, Roe PM, Rogers J, Rogert Bacigalupo MC, Romanov N,

Romieu A, Roth RK, Rourke NJ, Ruediger ST, Rusman E, Sanches-Kuiper RM,

Schenker MR, Seoane JM, Shaw RJ, Shiver MK, Short SW, Sizto NL, Sluis JP, Smith

MA, Ernest Sohna Sohna J, Spence EJ, Stevens K, Sutton N, Szajkowski L, Tregidgo

CL, Turcatti G, Vandevondele S, Verhovsky Y, Virk SM, Wakelin S, Walcott GC, Wang

J, Worsley GJ, Yan J, Yau L, Zuerlein M, Mullikin JC, Hurles ME, McCooke NJ, West

JS, Oaks FL, Lundberg PL, Klenerman D, Durbin R, Smith AJ (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456: 53-9

VIII. REFERENCES 53

Berger SL (2007) The complex language of chromatin regulation during transcription. Nature 447: 407-12

Berkseth M, Ikegami K, Arur S, Lieb JD, Zarkower D (2013) TRA-1 ChIP-seq reveals regulators

of sexual differentiation and multilevel feedback in nematode sex determination. Proc Natl Acad Sci U S A 110: 16033-8

Bi Q, Wu D, Zhu X, Gillian Turgeon B (2013) Cochliobolus heterostrophus Llm1 - a Lae1-like

methyltransferase regulates T-toxin production, virulence, and development. Fungal Genet Biol 51: 21-33

Bianchi ME, Agresti A (2005) HMG proteins: dynamic players in gene regulation and differentiation.

Curr Opin Genet Dev 15: 496-506 Bidard F, Ait Benkhali J, Coppin E, Imbeaud S, Grognet P, Delacroix H, Debuchy R (2011)

Genome-wide gene expression profiling of fertilization competent mycelium in opposite mating types in the heterothallic fungus Podospora anserina. PLoS One 6: e21476

Blat Y, Kleckner N (1999) Cohesins bind to preferential sites along yeast chromosome III, with

differential regulation along arms versus the centric region. Cell 98: 249-59 Boeva V, Lermine A, Barette C, Guillouf C, Barillot E (2012) Nebula - a web-server for advanced

ChIP-seq data analysis. Bioinformatics 28: 2517-9 Böhm J, Hoff B, O'Gorman CM, Wolfers S, Klix V, Binger D, Zadra I, Kürnsteiner H, Pöggeler

S, Dyer PS, Kück U (2013) Sexual reproduction and mating-type-mediated strain development in the penicillin-producing fungus Penicillium chrysogenum. Proc Natl Acad Sci U S A 110: 1476-81

Böhm J, Dahlmann TA, Gümüşer H, Kück U (2015) A MAT1-2 wild-type strain from Penicillium

chrysogenum: functional mating-type locus characterization, genome sequencing and mating with an industrial penicillin-producing strain. Mol Microbiol 95: 859-74

Bok JW, Keller NP (2004) LaeA, a regulator of secondary metabolism in Aspergillus spp. Eukaryot

Cell 3: 527-35 Bok JW, Balajee SA, Marr KA, Andes D, Nielsen KF, Frisvad JC, Keller NP (2005) LaeA, a

regulator of morphogenetic fungal virulence factors. Eukaryot Cell 4: 1574-82 Booth LN, Tuch BB, Johnson AD (2010) Intercalation of a new tier of transcription regulation into

an ancient circuit. Nature 468: 959-63 Bosch DG, Boonstra FN, Gonzaga-Jauregui C, Xu M, de Ligt J, Jhangiani S, Wiszniewski W,

Muzny DM, Yntema HG, Pfundt R, Vissers LE, Spruijt L, Blokland EA, Chen CA,

Lewis RA, Tsai SY, Gibbs RA, Tsai MJ, Lupski JR, Zoghbi HY, Cremers FP, de Vries

BB, Schaaf CP (2014) NR2F1 mutations cause optic atrophy with intellectual disability. Am J Hum Genet 94: 303-9

Boyle AP, Davis S, Shulha HP, Meltzer P, Margulies EH, Weng Z, Furey TS, Crawford GE

(2008) High-resolution mapping and characterization of open chromatin across the genome. Cell 132: 311-22

Brakhage AA, Andrianopoulos A, Kato M, Steidl S, Davis MA, Tsukagoshi N, Hynes MJ (1999)

HAP-Like CCAAT-binding complexes in filamentous fungi: implications for biotechnology. Fungal Genet Biol 27: 243-52

VIII. REFERENCES 54

Brakhage AA, Spröte P, Al-Abdallah Q, Gehrke A, Plattner H, Tüncher A (2004) Regulation of penicillin biosynthesis in filamentous fungi. Adv Biochem Eng Biotechnol 88: 45-90

Brent MM, Anand R, Marmorstein R (2008) Structural basis for DNA recognition by FoxO1 and

its regulation by posttranslational modification. Structure 16: 1407-16 Bruhn L, Hwang-Shum JJ, Sprague GF, Jr. (1992) The N-terminal 96 residues of MCM1, a

regulator of cell type-specific genes in Saccharomyces cerevisiae, are sufficient for DNA binding, transcription activation, and interaction with α1. Mol Cell Biol 12: 3563-72

Bryant JM, Govin J, Zhang L, Donahue G, Pugh BF, Berger SL (2012) The linker histone plays a

dual role during gametogenesis in Saccharomyces cerevisiae. Mol Cell Biol 32: 2771-83 Burton JN, Liachko I, Dunham MJ, Shendure J (2014) Species-level deconvolution of

metagenome assemblies with Hi-C-based contact probability maps. G3 (Bethesda) 4: 1339-46 Cacho RA, Tang Y, Chooi YH (2015) Next-generation sequencing approach for connecting

secondary metabolites to biosynthetic gene clusters in fungi. Front Microbiol 5: 774 Calvo AM, Wilson RA, Bok JW, Keller NP (2002) Relationship between secondary metabolism and

fungal development. Microbiol Mol Biol Rev 66: 447-59 Calvo AM (2008) The VeA regulatory system and its role in morphological and chemical

development in fungi. Fungal Genet Biol 45: 1053-61 Cao Y, Yao Z, Sarkar D, Lawrence M, Sanchez GJ, Parker MH, MacQuarrie KL, Davison J,

Morgan MT, Ruzzo WL, Gentleman RC, Tapscott SJ (2010) Genome-wide MyoD binding in skeletal muscle cells: a potential for broad cellular reprogramming. Dev Cell 18: 662-74

Carr EA, Mead J, Vershon AK (2004) α1-induced DNA bending is required for transcriptional

activation by the Mcm1-α1 complex. Nucleic Acids Res 32: 2298-305 Carroll SB (2000) Endless forms: the evolution of gene regulation and morphological diversity. Cell

101: 577-80 Carter-O'Connell I, Peel MT, Wykoff DD, O'Shea EK (2012) Genome-wide characterization of the

phosphate starvation response in Schizosaccharomyces pombe. BMC Genomics 13: 697 Casselton LA, Olesnicky NS (1998) Molecular genetics of mating recognition in basidiomycete

fungi. Microbiol Mol Biol Rev 62: 55-70 Chakravarti R, Sahai V (2004) Compactin - a review. Appl Microbiol Biotechnol 64: 618-24 Charoensawan V, Wilson D, Teichmann SA (2010) Genomic repertoires of DNA-binding

transcription factors across the tree of life. Nucleic Acids Res 38: 7364-77 Chen H, Lin RJ, Xie W, Wilpitz D, Evans RM (1999) Regulation of hormone-induced histone

hyperacetylation and gene activation via acetylation of an acetylase. Cell 98: 675-86 Chooi YH, Cacho R, Tang Y (2010) Identification of the viridicatumtoxin and griseofulvin gene

clusters from Penicillium aethiopicum. Chem Biol 17: 483-94 Chu C, Qu K, Zhong FL, Artandi SE, Chang HY (2011) Genomic maps of long non-coding RNA

occupancy reveal principles of RNA-chromatin interactions. Mol Cell 44: 667-78

VIII. REFERENCES 55

Chung D, Barker BM, Carey CC, Merriman B, Werner ER, Lechner BE, Dhingra S, Cheng C,

Xu W, Blosser SJ, Morohashi K, Mazurie A, Mitchell TK, Haas H, Mitchell AP, Cramer

RA (2014) ChIP-seq and in vivo transcriptome analyses of the Aspergillus fumigatus SREBP SrbA reveals a new regulator of the fungal hypoxia response and virulence. PLoS Pathog 10: e1004487

Connolly LA, Riccombeni A, Grozer Z, Holland LM, Lynch DB, Andes DR, Gacser A, Butler G

(2013a) The APSES transcription factor Efg1 is a global regulator that controls morphogenesis and biofilm formation in Candida parapsilosis. Mol Microbiol 90: 36-53

Connolly LR, Smith KM, Freitag M (2013b) The Fusarium graminearum histone H3 K27

methyltransferase KMT6 regulates development and expression of secondary metabolite gene clusters. PLoS Genet 9: e1003916

Coppin E, Debuchy R, Arnaise S, Picard M (1997) Mating types and sexual development in

filamentous ascomycetes. Microbiol Mol Biol Rev 61: 411-28 Crawford GE, Holt IE, Whittle J, Webb BD, Tai D, Davis S, Margulies EH, Chen YD, Bernat

JA, Ginsburg D, Zhou DX, Luo SJ, Vasicek TJ, Daly MJ, Wolfsberg TG, Collins FS

(2006) Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS). Genome Res 16: 123-31

Cusanovich DA, Pavlovic B, Pritchard JK, Gilad Y (2014) The functional consequences of

variation in transcription factor binding. PLoS Genet 10: e1004226 Czaja W, Miller KY, Skinner MK, Miller BL (2014) Structural and functional conservation of

fungal MatA and human SRY sex-determining proteins. Nat Commun 5: 5434 d'Enfert C, Fontaine T (1997) Molecular characterization of the Aspergillus nidulans treA gene

encoding an acid trehalase required for growth on trehalose. Mol Microbiol 24: 203-16 de Wit E, de Laat W (2012) A decade of 3C technologies: insights into nuclear organization. Genes

Dev 26: 11-24 Dekker J, Marti-Renom MA, Mirny LA (2013) Exploring the three-dimensional organization of

genomes: interpreting chromatin interaction data. Nat Rev Genet 14: 390-403 Desai AN, Jere A (2012) Next-generation sequencing: ready for the clinics? Clin Genet 81: 503-10 Desjardins AE, Hohn TM, McCormick SP (1993) Trichothecene biosynthesis in fusarium species -

chemistry, genetics, and significance. Microbiol Rev 57: 595-604 Domínguez-Santos R, Martín JF, Kosalková K, Prieto C, Ullán RV, García-Estrada C (2012)

The regulatory factor PcRFX1 controls the expression of the three genes of β-lactam biosynthesis in Penicillium chrysogenum. Fungal Genet Biol 49: 866-81

Dong AP, Yoder JA, Zhang X, Zhou L, Bestor TH, Cheng XD (2001) Structure of human DNMT2,

an enigmatic DNA methyltransferase homolog that displays denaturant-resistant binding to DNA. Nucleic Acids Res 29: 439-48

Dostie J, Richmond TA, Arnaout RA, Selzer RR, Lee WL, Honan TA, Rubio ED, Krumm A,

Lamb J, Nusbaum C, Green RD, Dekker J (2006) Chromosome conformation capture carbon copy (5C): A massively parallel solution for mapping interactions between genomic elements. Genome Res 16: 1299-309

VIII. REFERENCES 56

Dostie J, Dekker J (2007) Mapping networks of physical interactions between genomic elements using 5C technology. Nat Protoc 2: 988-1002

Dowzer CE, Kelly JM (1989) Cloning of the creA gene from Aspergillus nidulans: a gene involved in

carbon catabolite repression. Curr Genet 15: 457-9 Dranginis AM (1990) Binding of yeast a1 and α-2 as a heterodimer to the operator DNA of a haploid-

specific gene. Nature 347: 682-5 Dreyer J, Eichhorn H, Friedlin E, Kürnsteiner H, Kück U (2007) A homologue of the Aspergillus

velvet gene regulates both cephalosporin C biosynthesis and hyphal fragmentation in Acremonium chrysogenum. Appl Environ Microbiol 73: 3412-22

Du RH, Li EG, Cao Y, Song YC, Tan RX (2011) Fumigaclavine C inhibits tumor necrosis factor α

production via suppression of toll-like receptor 4 and nuclear factor κB activation in macrophages. Life Sci 89: 235-40

Dyer PS, Paoletti M (2005) Reproduction in Aspergillus fumigatus: sexuality in a supposedly asexual

species? Med Mycol 43 Suppl 1: S7-14 Dyer PS, O'Gorman CM (2011) A fungal sexual revolution: Aspergillus and Penicillium show the

way. Curr Opin Microbiol 14: 649-54 Eid J, Fehr A, Gray J, Luong K, Lyle J, Otto G, Peluso P, Rank D, Baybayan P, Bettman B,

Bibillo A, Bjornson K, Chaudhuri B, Christians F, Cicero R, Clark S, Dalal R, Dewinter

A, Dixon J, Foquet M, Gaertner A, Hardenbol P, Heiner C, Hester K, Holden D, Kearns

G, Kong X, Kuse R, Lacroix Y, Lin S, Lundquist P, Ma C, Marks P, Maxham M,

Murphy D, Park I, Pham T, Phillips M, Roy J, Sebra R, Shen G, Sorenson J, Tomaney

A, Travers K, Trulson M, Vieceli J, Wegener J, Wu D, Yang A, Zaccarin D, Zhao P,

Zhong F, Korlach J, Turner S (2009) Real-time DNA sequencing from single polymerase molecules. Science 323: 133-8

Eisendle M, Oberegger H, Zadra I, Haas H (2003) The siderophore system is essential for viability

of Aspergillus nidulans: functional analysis of two genes encoding L-ornithine N 5-monooxygenase (sidA) and a non-ribosomal peptide synthetase (sidC). Mol Microbiol 49: 359-75

Eisendle M, Schrettl M, Kragl C, Müller D, Illmer P, Haas H (2006) The intracellular siderophore

ferricrocin is involved in iron storage, oxidative-stress resistance, germination, and sexual development in Aspergillus nidulans. Eukaryot Cell 5: 1596-603

Elander RP (2003) Industrial production of β-lactam antibiotics. Appl Microbiol Biotechnol 61: 385-

92 Elbein AD, Pan YT, Pastuszak I, Carroll D (2003) New insights on trehalose: a multifunctional

molecule. Glycobiology 13: 17R-27R Espeso EA, Tilburn J, Arst HN, Jr., Peñalva MA (1993) pH regulation is a major determinant in

expression of a fungal penicillin biosynthetic gene. EMBO J 12: 3947-56 Esteller M (2006) The necessity of a human epigenome project. Carcinogenesis 27: 1121-5 Etxebeste O, Ni M, Garzia A, Kwon NJ, Fischer R, Yu JH, Espeso EA, Ugalde U (2008) Basic-

zipper-type transcription factor FlbB controls asexual development in Aspergillus nidulans. Eukaryot Cell 7: 38-48

VIII. REFERENCES 57

Farnham PJ (2009) Insights from genomic profiling of transcription factors. Nat Rev Genet 10: 605-16

Fernandes M, Keller NP, Adams TH (1998) Sequence-specific binding by Aspergillus nidulans

AflR, a C6 zinc cluster protein regulating mycotoxin biosynthesis. Mol Microbiol 28: 1355-65 Fisher WW, Li JJ, Hammonds AS, Brown JB, Pfeiffer BD, Weiszmann R, MacArthur S,

Thomas S, Stamatoyannopoulos JA, Eisen MB, Bickel PJ, Biggin MD, Celniker SE

(2012) DNA regions bound at low occupancy by transcription factors do not drive patterned reporter gene expression in Drosophila. Proc Natl Acad Sci U S A 109: 21330-5

Fitzgerald DM, Bonocora RP, Wade JT (2014) Comprehensive mapping of the Escherichia coli

flagellar regulatory network. PLoS Genet 10: e1004649 Fleming A (1929) On the antibacterial action of cultures of a Penicillium, with special reference to

their use in the isolation of B. influenzae. British Journal of Experimental Pathology 10: 226-36

Flensburg C, Kinkel SA, Keniry A, Blewitt ME, Oshlack A (2014) A comparison of control

samples for ChIP-seq of histone modifications. Front Genet 5: 329 Francois P, Hernandez D, Schrenzel J (2007) Genome content determination in methicillin-resistant

Staphylococcus aureus. Future Microbiol 2: 187-98 Fraser JA, Diezmann S, Subaran RL, Allen A, Lengeler KB, Dietrich FS, Heitman J (2004)

Convergent evolution of chromosomal sex-determining regions in the animal and fungal kingdoms. PLoS Biol 2: e384

Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303:

799-805 Fullwood MJ, Liu MH, Pan YF, Liu J, Xu H, Mohamed YB, Orlov YL, Velkov S, Ho A, Mei PH,

Chew EG, Huang PY, Welboren WJ, Han Y, Ooi HS, Ariyaratne PN, Vega VB, Luo Y,

Tan PY, Choy PY, Wansa KD, Zhao B, Lim KS, Leow SC, Yow JS, Joseph R, Li H,

Desai KV, Thomsen JS, Lee YK, Karuturi RK, Herve T, Bourque G, Stunnenberg HG,

Ruan X, Cacheux-Rataboul V, Sung WK, Liu ET, Wei CL, Cheung E, Ruan Y (2009) An oestrogen-receptor-α-bound human chromatin interactome. Nature 462: 58-64

Furey TS (2012) ChIP-seq and beyond: new and improved methodologies to detect and characterize

protein-DNA interactions. Nat Rev Genet 13: 840-52 Galgoczy DJ, Cassidy-Stone A, Llinas M, O'Rourke SM, Herskowitz I, DeRisi JL, Johnson AD

(2004) Genomic dissection of the cell-type-specification circuit in Saccharomyces cerevisiae. Proc Natl Acad Sci U S A 101: 18069-74

Gao F, Foat BC, Bussemaker HJ (2004) Defining transcriptional networks through integrative

modeling of mRNA expression and transcription factor binding data. BMC Bioinformatics 5: 31

Garzia A, Etxebeste O, Herrero-García E, Ugalde U, Espeso EA (2010) The concerted action of

bZip and cMyb transcription factors FlbB and FlbD induces brlA expression and asexual development in Aspergillus nidulans. Mol Microbiol 75: 1314-24

Gaulton KJ, Nammo T, Pasquali L, Simon JM, Giresi PG, Fogarty MP, Panhuis TM,

Mieczkowski P, Secchi A, Bosco D, Berney T, Montanya E, Mohlke KL, Lieb JD, Ferrer

J (2010) A map of open chromatin in human pancreatic islets. Nat Genet 42: 255-9

VIII. REFERENCES 58

Georges AB, Benayoun BA, Caburet S, Veitia RA (2010) Generic binding sites, generic DNA-binding domains: where does specific promoter recognition come from? Faseb Journal 24: 346-56

Gerke J, Braus GH (2014) Manipulation of fungal development as source of novel secondary

metabolites for biotechnology. Appl Microbiol Biotechnol 98: 8443-55 Giese K, Pagel J, Grosschedl R (1994) Distinct DNA-binding properties of the high mobility group

domain of murine and human SRY sex-determining factors. Proc Natl Acad Sci U S A 91: 3368-72

Glass NL, Grotelueschen J, Metzenberg RL (1990) Neurospora crassa A mating-type region. Proc

Natl Acad Sci U S A 87: 4912-6 Green RE, Krause J, Briggs AW, Maricic T, Stenzel U, Kircher M, Patterson N, Li H, Zhai W,

Fritz MH, Hansen NF, Durand EY, Malaspinas AS, Jensen JD, Marques-Bonet T, Alkan

C, Prufer K, Meyer M, Burbano HA, Good JM, Schultz R, Aximu-Petri A, Butthof A,

Hober B, Hoffner B, Siegemund M, Weihmann A, Nusbaum C, Lander ES, Russ C,

Novod N, Affourtit J, Egholm M, Verna C, Rudan P, Brajkovic D, Kucan Z, Gusic I,

Doronichev VB, Golovanova LV, Lalueza-Fox C, de la Rasilla M, Fortea J, Rosas A,

Schmitz RW, Johnson PL, Eichler EE, Falush D, Birney E, Mullikin JC, Slatkin M,

Nielsen R, Kelso J, Lachmann M, Reich D, Paabo S (2010) A draft sequence of the Neandertal genome. Science 328: 710-22

Grosschedl R, Giese K, Pagel J (1994) HMG domain proteins: architectural elements in the assembly

of nucleoprotein structures. Trends Genet 10: 94-100 Gubbay J, Collignon J, Koopman P, Capel B, Economou A, Münsterberg A, Vivian N,

Goodfellow P, Lovellbadge R (1990) A gene mapping to the sex-determining region of the mouse Y-chromosome is a member of a novel family of embryonically expressed genes. Nature 346: 245-50

Guzmán-de-Peña D, Aguirre J, Ruiz-Herrera J (1998) Correlation between the regulation of

sterigmatocystin biosynthesis and asexual and sexual sporulation in Emericella nidulans. Antonie Van Leeuwenhoek 73: 199-205

Haber JE (2012) Mating-type genes and MAT switching in Saccharomyces cerevisiae. Genetics 191:

33-64 Hagen DC, Bruhn L, Westby CA, Sprague GF, Jr. (1993) Transcription of α-specific genes in

Saccharomyces cerevisiae: DNA sequence requirements for activity of the coregulator α1. Mol Cell Biol 13: 6866-75

Hahn S, Young ET (2011) Transcriptional regulation in Saccharomyces cerevisiae: transcription

factor regulation and function, mechanisms of initiation, and roles of activators and coactivators. Genetics 189: 705-36

Haider NB, Jacobson SG, Cideciyan AV, Swiderski R, Streb LM, Searby C, Beck G, Hockey R,

Hanna DB, Gorman S, Duhl D, Carmi R, Bennett J, Weleber RG, Fishman GA, Wright

AF, Stone EM, Sheffield VC (2000) Mutation of a nuclear receptor gene, NR2E3, causes enhanced S cone syndrome, a disorder of retinal cell fate. Nat Genet 24: 127-31

Han KH, Lee DB, Kim JH, Kim MS, Han KY, Kim WS, Park YS, Kim HB, Han DM (2003)

Environmental factors affecting development of Aspergillus nidulans. J Microbiol 41: 34-40

VIII. REFERENCES 59

Hay A, Tsiantis M (2010) KNOX genes: versatile regulators of plant development and diversity. Development 137: 3153-65

Haynes BC, Skowyra ML, Spencer SJ, Gish SR, Williams M, Held EP, Brent MR, Doering TL

(2011) Toward an integrated model of capsule regulation in Cryptococcus neoformans. PLoS Pathog 7: e1002411

He Q, Bardet AF, Patton B, Purvis J, Johnston J, Paulson A, Gogol M, Stark A, Zeitlinger J

(2011) High conservation of transcription factor binding and evidence for combinatorial regulation across six Drosophila species. Nat Genet 43: 414-20

Hecht A, Strahl-Bolsinger S, Grunstein M (1996) Spreading of transcriptional repressor SIR3 from

telomeric heterochromatin. Nature 383: 92-6 Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass

CK (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38: 576-89

Hicks JK, Yu JH, Keller NP, Adams TH (1997) Aspergillus sporulation and mycotoxin production

both require inactivation of the FadA G-α protein-dependent signaling pathway. Embo Journal 16: 4916-23

Hnisz D, Bardet AF, Nobile CJ, Petryshyn A, Glaser W, Schöck U, Stark A, Kuchler K (2012) A

histone deacetylase adjusts transcription kinetics at coding sequences during Candida albicans morphogenesis. PLoS Genet 8: e1003118

Ho CY, Adamson JG, Hodges RS, Smith M (1994) Heterodimerization of the yeast MATa1 and

MATα2 proteins is mediated by two leucine zipper-like coiled-coil motifs. EMBO J 13: 1403-13

Hoff B, Pöggeler S, Kück U (2008) Eighty years after its discovery, Fleming's Penicillium strain

discloses the secret of its sex. Eukaryot Cell 7: 465-70 Hoff B, Kamerewerd J, Sigl C, Mitterbauer R, Zadra I, Kürnsteiner H, Kück U (2010) Two

components of a velvet-like complex control hyphal morphogenesis, conidiophore development, and penicillin biosynthesis in Penicillium chrysogenum. Eukaryot Cell 9: 1236-50

Horn BW, Moore GG, Carbone I (2009a) Sexual reproduction in Aspergillus flavus. Mycologia

101: 423-9 Horn BW, Ramirez-Prado JH, Carbone I (2009b) Sexual reproduction and recombination in the

aflatoxin-producing fungus Aspergillus parasiticus. Fungal Genet Biol 46: 169-75 Hurley JM, Dasgupta A, Emerson JM, Zhou X, Ringelberg CS, Knabe N, Lipzen AM, Lindquist

EA, Daum CG, Barry KW, Grigoriev IV, Smith KM, Galagan JE, Bell-Pedersen D,

Freitag M, Cheng C, Loros JJ, Dunlap JC (2014) Analysis of clock-regulated genes in Neurospora reveals widespread posttranscriptional control of metabolic potential. Proc Natl Acad Sci U S A 111: 16995-7002

Hynes MJ (1975) Studies on the role of the areA gene in the regulation of nitrogen catabolism in

Aspergillus nidulans. Aust J Biol Sci 28: 301-13 Hynes MJ (1994) Regulatory circuits of the amdS gene of Aspergillus nidulans. Antonie Van

Leeuwenhoek 65: 179-82

VIII. REFERENCES 60

Idnurm A, Walton FJ, Floyd A, Heitman J (2008) Identification of the sex genes in an early diverged fungus. Nature 451: 193-6

Jain M, Fiddes IT, Miga KH, Olsen HE, Paten B, Akeson M (2015) Improved data analysis for the

MinION nanopore sequencer. Nat Methods 12: 351-6 Jakobsen JS, Braun M, Astorga J, Gustafson EH, Sandmann T, Karzynski M, Carlsson P,

Furlong EEM (2007) Temporal ChIP-on-chip reveals Biniou as a universal regulator of the visceral muscle transcriptional network. Gene Dev 21: 2448-60

Jarvis EE, Clark KL, Sprague GF, Jr. (1989) The yeast transcription activator PRTF, a homolog of

the mammalian serum response factor, is encoded by the MCM1 gene. Genes Dev 3: 936-45 Jekosch K, Kück U (2000) Glucose dependent transcriptional expression of the cre1 gene in

Acremonium chrysogenum strains showing different levels of cephalosporin C production. Curr Genet 37: 388-95

Jiang J, Liu X, Yin Y, Ma Z (2011) Involvement of a velvet protein FgVeA in the regulation of

asexual development, lipid and secondary metabolisms and virulence in Fusarium

graminearum. PLoS One 6: e28291 Johnson DS, Mortazavi A, Myers RM, Wold B (2007) Genome-wide mapping of in vivo protein-

DNA interactions. Science 316: 1497-502 Johnson L (2008) Iron and siderophores in fungal-host interactions. Mycol Res 112: 170-83 Jung YL, Luquette LJ, Ho JW, Ferrari F, Tolstorukov M, Minoda A, Issner R, Epstein CB,

Karpen GH, Kuroda MI, Park PJ (2014) Impact of sequencing depth in ChIP-seq experiments. Nucleic Acids Res 42: e74

Käfer E (1965) Origins of translocations in Aspergillus nidulans. Genetics 52: 217-32 Kagan RM, Clarke S (1994) Widespread occurrence of three sequence motifs in diverse S-

adenosylmethionine-dependent methyltransferases suggests a common structure for these enzymes. Arch Biochem Biophys 310: 417-27

Kale SP, Milde L, Trapp MK, Frisvad JC, Keller NP, Bok JW (2008) Requirement of LaeA for

secondary metabolism and sclerotial production in Aspergillus flavus. Fungal Genet Biol 45: 1422-9

Kamerewerd J, Zadra I, Kürnsteiner H, Kück U (2011) PcchiB1, encoding a class V chitinase, is

affected by PcVelA and PcLaeA, and is responsible for cell wall integrity in Penicillium

chrysogenum. Microbiology 157: 3036-48 Karimi-Aghcheh R, Bok JW, Phatale PA, Smith KM, Baker SE, Lichius A, Omann M, Zeilinger

S, Seiboth B, Rhee C, Keller NP, Freitag M, Kubicek CP (2013) Functional analyses of Trichoderma reesei LAE1 reveal conserved and contrasting roles of this regulator. G3 (Bethesda) 3: 369-78

Kato N, Brooks W, Calvo AM (2003) The expression of sterigmatocystin and penicillin genes in

Aspergillus nidulans is controlled by veA, a gene required for sexual development. Eukaryot Cell 2: 1178-86

Keller NP, Nesbitt C, Sarr B, Phillips TD, Burow GB (1997) pH regulation of sterigmatocystin and

aflatoxin biosynthesis in Aspergillus spp. Phytopathology 87: 643-8

VIII. REFERENCES 61

Keller NP, Turner G, Bennett JW (2005) Fungal secondary metabolism - from biochemistry to genomics. Nat Rev Microbiol 3: 937-47

Keszthelyi A, Jeney A, Kerényi Z, Mendes O, Waalwijk C, Hornok L (2007) Tagging target genes

of the MAT1-2-1 transcription factor in Fusarium verticillioides (Gibberella fujikuroi MP-A). Antonie Van Leeuwenhoek 91: 373-91

Kim H, Han K, Kim K, Han D, Jahng K, Chae K (2002) The veA gene activates sexual

development in Aspergillus nidulans. Fungal Genet Biol 37: 72-80 Kim H, Borkovich KA (2006) Pheromones are essential for male fertility and sufficient to direct

chemotropic polarized growth of trichogynes during mating in Neurospora crassa. Eukaryot Cell 5: 544-54

Kim M, Lee KH, Yoon SW, Kim BS, Chun J, Yi H (2013) Analytical tools and databases for

metagenomics in the next-generation sequencing era. Genomics Inform 11: 102-13 Kitzman JO, Snyder MW, Ventura M, Lewis AP, Qiu R, Simmons LE, Gammill HS, Rubens

CE, Santillan DA, Murray JC, Tabor HK, Bamshad MJ, Eichler EE, Shendure J (2012) Non-invasive whole-genome sequencing of a human fetus. Sci Transl Med 4: 137ra76

Klix V, Nowrousian M, Ringelberg C, Loros JJ, Dunlap JC, Pöggeler S (2010) Functional

characterization of MAT1-1-specific mating-type genes in the homothallic ascomycete Sordaria macrospora provides new insights into essential and nonessential sexual regulators. Eukaryot Cell 9: 894-905

Kobayashi M, Takezawa S, Hara K, Yu RT, Umesono Y, Agata K, Taniwaki M, Yasuda K,

Umesono K (1999) Identification of a photoreceptor cell-specific nuclear receptor. Proc Natl Acad Sci U S A 96: 4814-9

Koiwai K, Kubota T, Watanabe N, Hori K, Koiwai O, Masai H (2015) Definition of the

transcription factor TdIF1 consensus-binding sequence through genome-wide mapping of its binding sites. Genes Cells 20: 242-54

Kopke K, Hoff B, Bloemendal S, Katschorowski A, Kamerewerd J, Kück U (2013) Members of

the Penicillium chrysogenum velvet complex play functionally opposing roles in the regulation of penicillin biosynthesis and conidiation. Eukaryot Cell 12: 299-310

Kosalková K, García-Estrada C, Ullán RV, Godio RP, Feltrer R, Teijeira F, Mauriz E, Martín

JF (2009) The global regulator LaeA controls penicillin biosynthesis, pigmentation and sporulation, but not roquefortine C synthesis in Penicillium chrysogenum. Biochimie 91: 214-25

Kozhevnikova EN, van der Knaap JA, Pindyurin AV, Ozgur Z, van Ijcken WFJ, Moshkin YM,

Verrijzer CP (2012) Metabolic enzyme IMPDH is also a transcription factor regulated by cellular state. Mol Cell 47: 133-9

Kronstad JW, Staben C (1997) Mating type in filamentous fungi. Annu Rev Genet 31: 245-76 Kück U, Böhm J (2013) Mating-type genes and cryptic sexuality as tools for genetically manipulating

industrial molds. Appl Microbiol Biotechnol 97: 9609-20 Kvaal CA, Srikantha T, Soll DR (1997) Mis-expression of the white-phase-specific gene WH11 in

the opaque phase of Candida albicans affects switching and virulence. Infection and Immunity 65: 4468-75

VIII. REFERENCES 62

Landt SG, Marinov GK, Kundaje A, Kheradpour P, Pauli F, Batzoglou S, Bernstein BE, Bickel

P, Brown JB, Cayting P, Chen Y, DeSalvo G, Epstein C, Fisher-Aylor KI, Euskirchen G,

Gerstein M, Gertz J, Hartemink AJ, Hoffman MM, Iyer VR, Jung YL, Karmakar S,

Kellis M, Kharchenko PV, Li Q, Liu T, Liu XS, Ma L, Milosavljevic A, Myers RM, Park

PJ, Pazin MJ, Perry MD, Raha D, Reddy TE, Rozowsky J, Shoresh N, Sidow A, Slattery

M, Stamatoyannopoulos JA, Tolstorukov MY, White KP, Xi S, Farnham PJ, Lieb JD,

Wold BJ, Snyder M (2012) ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res 22: 1813-31

Lang GI, Murray AW, Botstein D (2009) The cost of gene expression underlies a fitness trade-off in

yeast. Proc Natl Acad Sci U S A 106: 5755-60 Lavoie H, Hogues H, Whiteway M (2009) Rearrangements of the transcriptional regulatory networks

of metabolic pathways in fungi. Curr Opin Microbiol 12: 655-63 Lee BN, Adams TH (1994) Overexpression of flbA, an early regulator of Aspergillus asexual

sporulation, leads to activation of brlA and premature initiation of development. Mol Microbiol 14: 323-34

Lee SC, Ni M, Li W, Shertz C, Heitman J (2010) The evolution of sex: a perspective from the

fungal kingdom. Microbiol Mol Biol Rev 74: 298-340 Lee SH, Lee S, Choi D, Lee YW, Yun SH (2006) Identification of the down-regulated genes in a

mat1-2-deleted strain of Gibberella zeae, using cDNA subtraction and microarray analysis. Fungal Genet Biol 43: 295-310

Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, Hannett NM, Harbison CT,

Thompson CM, Simon I, Zeitlinger J, Jennings EG, Murray HL, Gordon DB, Ren B,

Wyrick JJ, Tagne JB, Volkert TL, Fraenkel E, Gifford DK, Young RA (2002) Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298: 799-804

Lefrançois P, Euskirchen GM, Auerbach RK, Rozowsky J, Gibson T, Yellman CM, Gerstein M,

Snyder M (2009) Efficient yeast ChIP-Seq using multiplex short-read DNA sequencing. BMC Genomics 10: 37

Levine M, Tjian R (2003) Transcription regulation and animal diversity. Nature 424: 147-51 Lewis EB (1978) A gene complex controlling segmentation in Drosophila. Nature 276: 565-70 Li B, Carey M, Workman JL (2007) The role of chromatin during transcription. Cell 128: 707-19 Li C, Chen C, Gao L, Yang S, Nguyen V, Shi X, Siminovitch K, Kohalmi SE, Huang S, Wu K,

Chen X, Cui Y (2015) The Arabidopsis SWI2/SNF2 chromatin remodeler BRAHMA regulates polycomb function during vegetative development and directly activates the flowering repressor gene SVP. PLoS Genet 11: e1004944

Li H, Johnson AD (2010) Evolution of transcription networks - lessons from yeasts. Curr Biol 20:

R746-53 Li R, Fan W, Tian G, Zhu H, He L, Cai J, Huang Q, Cai Q, Li B, Bai Y, Zhang Z, Zhang Y,

Wang W, Li J, Wei F, Li H, Jian M, Nielsen R, Li D, Gu W, Yang Z, Xuan Z, Ryder OA,

Leung FC, Zhou Y, Cao J, Sun X, Fu Y, Fang X, Guo X, Wang B, Hou R, Shen F, Mu B,

Ni P, Lin R, Qian W, Wang G, Yu C, Nie W, Wang J, Wu Z, Liang H, Min J, Wu Q,

Cheng S, Ruan J, Wang M, Shi Z, Wen M, Liu B, Ren X, Zheng H, Dong D, Cook K,

Shan G, Zhang H, Kosiol C, Xie X, Lu Z, Li Y, Steiner CC, Lam TT, Lin S, Zhang Q, Li

G, Tian J, Gong T, Liu H, Zhang D, Fang L, Ye C, Zhang J, Hu W, Xu A, Ren Y, Zhang

VIII. REFERENCES 63

G, Bruford MW, Li Q, Ma L, Guo Y, An N, Hu Y, Zheng Y, Shi Y, Li Z, Liu Q, Chen Y,

Zhao J, Qu N, Zhao S, Tian F, Wang X, Wang H, Xu L, Liu X, Vinar T, Wang Y, Lam

TW, Yiu SM, Liu S, Huang Y, Yang G, Jiang Z, Qin N, Li L, Bolund L, Kristiansen K,

Wong GK, Olson M, Zhang X, Li S, Yang H (2010) The sequence and de novo assembly of the giant panda genome. Nature 463: 311-7

Li XY, MacArthur S, Bourgon R, Nix D, Pollard DA, Iyer VN, Hechmer A, Simirenko L,

Stapleton M, Luengo Hendriks CL, Chu HC, Ogawa N, Inwood W, Sementchenko V,

Beaton A, Weiszmann R, Celniker SE, Knowles DW, Gingeras T, Speed TP, Eisen MB,

Biggin MD (2008) Transcription factors bind thousands of active and inactive regions in the Drosophila blastoderm. PLoS Biol 6: e27

Lieberman-Aiden E, van Berkum NL, Williams L, Imakaev M, Ragoczy T, Telling A, Amit I,

Lajoie BR, Sabo PJ, Dorschner MO, Sandstrom R, Bernstein B, Bender MA, Groudine

M, Gnirke A, Stamatoyannopoulos J, Mirny LA, Lander ES, Dekker J (2009) Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326: 289-93

Lin Z, Wu WS, Liang H, Woo Y, Li WH (2010) The spatial distribution of cis regulatory elements

in yeast promoters and its implications for transcriptional regulation. BMC Genomics 11: 581 Lind AL, Wisecaver JH, Smith TD, Feng X, Calvo AM, Rokas A (2015) Examining the evolution

of the regulatory circuit controlling secondary metabolism and development in the fungal genus Aspergillus. PLoS Genet 11: e1005096

Lister R, O'Malley RC, Tonti-Filippini J, Gregory BD, Berry CC, Millar AH, Ecker JR (2008)

Highly integrated single-base resolution maps of the epigenome in Arabidopsis. Cell 133: 523-36

Liu T, Ortiz JA, Taing L, Meyer CA, Lee B, Zhang Y, Shin H, Wong SS, Ma J, Lei Y, Pape UJ,

Poidinger M, Chen Y, Yeung K, Brown M, Turpaz Y, Liu XS (2011) Cistrome: an integrative platform for transcriptional regulation studies. Genome Biol 12: R83

López-Berges MS, Hera C, Sulyok M, Schäfer K, Capilla J, Guarro J, Di Pietro A (2013) The

velvet complex governs mycotoxin production and virulence of Fusarium oxysporum on plant and mammalian hosts. Mol Microbiol 87: 49-65

MacArthur S, Li XY, Li J, Brown JB, Chu HC, Zeng L, Grondona BP, Hechmer A, Simirenko

L, Keranen SV, Knowles DW, Stapleton M, Bickel P, Biggin MD, Eisen MB (2009) Developmental roles of 21 Drosophila transcription factors are determined by quantitative differences in binding to an overlapping set of thousands of genomic regions. Genome Biol 10: R80

Machanick P, Bailey TL (2011) MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics

27: 1696-7 MacQuarrie KL, Fong AP, Morse RH, Tapscott SJ (2011) Genome-wide transcription factor

binding: beyond direct target regulation. Trends Genet 27: 141-8 Magee BB, Magee PT (2000) Induction of mating in Candida albicans by construction of MTLa and

MTLα strains. Science 289: 310-3 Malarkey CS, Churchill MEA (2012) The high mobility group box: the ultimate utility player of a

cell. Trends Biochem Sci 37: 553-62

VIII. REFERENCES 64

Mallo M, Wellik DM, Deschamps J (2010) Hox genes and regional patterning of the vertebrate body plan. Dev Biol 344: 7-15

Manzoni M, Rollini M (2002) Biosynthesis and biotechnological production of statins by filamentous

fungi and application of these cholesterol-lowering drugs. Appl Microbiol Biotechnol 58: 555-64

Mardis ER (2008) The impact of next-generation sequencing technology on genetics. Trends Genet

24: 133-41 Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, Berka J, Braverman

MS, Chen YJ, Chen Z, Dewell SB, Du L, Fierro JM, Gomes XV, Godwin BC, He W,

Helgesen S, Ho CH, Irzyk GP, Jando SC, Alenquer ML, Jarvie TP, Jirage KB, Kim JB,

Knight JR, Lanza JR, Leamon JH, Lefkowitz SM, Lei M, Li J, Lohman KL, Lu H,

Makhijani VB, McDade KE, McKenna MP, Myers EW, Nickerson E, Nobile JR, Plant

R, Puc BP, Ronan MT, Roth GT, Sarkis GJ, Simons JF, Simpson JW, Srinivasan M,

Tartaro KR, Tomasz A, Vogt KA, Volkmer GA, Wang SH, Wang Y, Weiner MP, Yu P,

Begley RF, Rothberg JM (2005) Genome sequencing in microfabricated high-density picolitre reactors. Nature 437: 376-80

Martin JL, McMillan FM (2002) SAM (dependent) I AM: the S-adenosylmethionine-dependent

methyltransferase fold. Curr Opin Struct Biol 12: 783-93 Martin T, Lu SW, van Tilbeurgh H, Ripoll DR, Dixelius C, Turgeon BG, Debuchy R (2010)

Tracing the origin of the fungal α1 domain places its ancestor in the HMG-box superfamily: implication for fungal mating-type evolution. PLoS One 5: e15199

Mata J, Bahler J (2006) Global roles of Ste11p, cell type, and pheromone in the control of gene

expression during early sexual differentiation in fission yeast. Proc Natl Acad Sci U S A 103: 15517-22

Mathelier A, Zhao X, Zhang AW, Parcy F, Worsley-Hunt R, Arenillas DJ, Buchman S, Chen

CY, Chou A, Ienasescu H, Lim J, Shyr C, Tan G, Zhou M, Lenhard B, Sandelin A,

Wasserman WW (2014) JASPAR 2014: an extensively expanded and updated open-access database of transcription factor binding profiles. Nucleic Acids Res 42: D142-7

McGhee JD, Hippel PHV (1975a) Formaldehyde as a probe of DNA structure I. Reaction with

exocyclic amino-groups of DNA bases. Biochemistry 14: 1281-96 McGhee JD, Hippel PHV (1975b) Formaldehyde as a probe of DNA structure II. Reaction with

endocyclic imino groups of DNA bases. Biochemistry 14: 1297-303 McPherson JD (2014) A defining decade in DNA sequencing. Nat Methods 11: 1003-5 Mead J, Bruning AR, Gill MK, Steiner AM, Acton TB, Vershon AK (2002) Interactions of the

Mcm1 MADS box protein with cofactors that regulate mating in yeast. Mol Cell Biol 22: 4607-21

Mead ME, Stanton BC, Kruzel EK, Hull CM (2014) Targets of the Sex Inducer homeodomain

proteins are required for fungal development and virulence in Cryptococcus neoformans. Mol Microbiol 95: 804-18

Merelo P, Xie Y, Brand L, Ott F, Weigel D, Bowman JL, Heisler MG, Wenkel S (2013) Genome-

wide identification of KANADI1 target genes. PLoS One 8: e77341

VIII. REFERENCES 65

Merhej J, Urban M, Dufresne M, Hammond-Kosack KE, Richard-Forget F, Barreau C (2012) The velvet gene, Fgve1, affects fungal development and positively regulates trichothecene biosynthesis and pathogenicity in Fusarium graminearum. Mol Plant Pathol 13: 363-74

Merlini L, Dudin O, Martin SG (2013) Mate and fuse: how yeast cells do it. Open Biology 3:

130008 Metzenberg RL, Glass NL (1990) Mating type and mating strategies in Neurospora. Bioessays 12:

53-9 Metzker ML (2010) Sequencing technologies - the next generation. Nat Rev Genet 11: 31-46 Meyer CA, Liu XS (2014) Identifying and mitigating bias in next-generation sequencing methods for

chromatin biology. Nat Rev Genet 15: 709-21 Mihlan M, Homann V, Liu TWD, Tudzynski B (2003) AREA directly mediates nitrogen regulation

of gibberellin biosynthesis in Gibberella fujikuroi, but its activity is not affected by NMR. Mol Microbiol 47: 975-91

Milam AH, Rose L, Cideciyan AV, Barakat MR, Tang WX, Gupta N, Aleman TS, Wright AF,

Stone EM, Sheffield VC, Jacobson SG (2002) The nuclear receptor NR2E3 plays a role in human retinal photoreceptor differentiation and degeneration. Proc Natl Acad Sci U S A 99: 473-8

Mooney JL, Yager LN (1990) Light is required for conidiation in Aspergillus nidulans. Genes Dev 4:

1473-82 Morin RD, Bainbridge M, Fejes A, Hirst M, Krzywinski M, Pugh TJ, McDonald H, Varhol R,

Jones SJM, Marra MA (2008) Profiling the HeLa S3 transcriptome using randomly primed cDNA and massively parallel short-read sequencing. Biotechniques 45: 81-94

Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying

mammalian transcriptomes by RNA-Seq. Nat Methods 5: 621-8 Mukherjee K, Brocchieri L, Burglin TR (2009) A comprehensive classification and evolutionary

analysis of plant homeobox genes. Mol Biol Evol 26: 2775-94 Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, Snyder M (2008) The

transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320: 1344-9

Nakano M, Komatsu J, Matsuura S, Takashima K, Katsura S, Mizuno A (2003) Single-molecule

PCR using water-in-oil emulsion. J Biotechnol 102: 117-24 Nasmith CG, Walkowiak S, Wang L, Leung WW, Gong Y, Johnston A, Harris LJ, Guttman DS,

Subramaniam R (2011) Tri6 is a global transcription regulator in the phytopathogen Fusarium graminearum. PLoS Pathog 7: e1002266

Nelson PE, Desjardins AE, Plattner RD (1993) Fumonisins, mycotoxins produced by fusarium

species: biology, chemistry, and significance. Annu Rev Phytopathol 31: 233-52 Nepomnyashchaya YN, Artemov AV, Roumiantsev SA, Roumyantsev AG, Zhavoronkov A

(2013) Non-invasive prenatal diagnostics of aneuploidy using next-generation DNA sequencing technologies, and clinical considerations. Clin Chem Lab Med 51: 1141-54

VIII. REFERENCES 66

Nguyen DH, D'Haeseleer P (2006) Deciphering principles of transcription regulation in eukaryotic genomes. Mol Syst Biol 2: 2006.0012

Ni M, Yu JH (2007) A novel regulator couples sporogenesis and trehalose biogenesis in Aspergillus

nidulans. PLoS One 2: e970

Ni M, Feretzaki M, Sun S, Wang X, Heitman J (2011) Sex in fungi. Annu Rev Genet 45: 405-30 Niehaus EM, Kleigrewe K, Wiemann P, Studt L, Sieber CMK, Connolly LR, Freitag M,

Güldener U, Tudzynski B, Humpf HU (2013) Genetic manipulation of the Fusarium

fujikuroi fusarin gene cluster yields insight into the complex regulation and fusarin biosynthetic pathway. Chem Biol 20: 1055-66

Nolting N, Pöggeler S (2006) A MADS box protein interacts with a mating-type protein and is

required for fruiting body development in the homothallic ascomycete Sordaria macrospora. Eukaryot Cell 5: 1043-56

Noma K, Allis CD, Grewal SIS (2001) Transitions in distinct histone H3 methylation patterns at the

heterochromatin domain boundaries. Science 293: 1150-5 Nowrousian M, Stajich JE, Chu M, Engh I, Espagne E, Halliday K, Kamerewerd J, Kempken F,

Knab B, Kuo HC, Osiewacz HD, Pöggeler S, Read ND, Seiler S, Smith KM, Zickler D,

Kück U, Freitag M (2010) De novo assembly of a 40 Mb eukaryotic genome from short sequence reads: Sordaria macrospora, a model organism for fungal morphogenesis. PLoS Genet 6: e1000891

O'Gorman CM, Fuller H, Dyer PS (2009) Discovery of a sexual cycle in the opportunistic fungal

pathogen Aspergillus fumigatus. Nature 457: 471-4 Oiartzabal-Arano E, Garzia A, Gorostidi A, Ugalde U, Espeso EA, Etxebeste O (2015) Beyond

asexual development: modifications in the gene expression profile caused by the absence of the Aspergillus nidulans transcription factor FlbB. Genetics (doi: 10.1534/genetics.155.174342; Epub ahead of print)

Oide S, Krasnoff SB, Gibson DM, Turgeon BG (2007) Intracellular siderophores are essential for

ascomycete sexual development in heterothallic Cochliobolus heterostrophus and homothallic Gibberella zeae. Eukaryot Cell 6: 1339-53

Orlando V (2000) Mapping chromosomal proteins in vivo by formaldehyde-crosslinked-chromatin

immunoprecipitation. Trends Biochem Sci 25: 99-104 Pachkov M, Erb I, Molina N, van Nimwegen E (2007) SwissRegulon: a database of genome-wide

annotations of regulatory sites. Nucleic Acids Res 35: D127-31 Palmer JM, Keller NP (2010) Secondary metabolism in fungi: does chromosomal location matter?

Curr Opin Microbiol 13: 431-6 Palmer JM, Theisen JM, Duran RM, Grayburn WS, Calvo AM, Keller NP (2013) Secondary

metabolism and development is mediated by LlmF control of VeA subcellular localization in Aspergillus nidulans. PLoS Genet 9: e1003193

Paoletti M, Seymour FA, Alcocer MJ, Kaur N, Calvo AM, Archer DB, Dyer PS (2007) Mating

type and the genetic basis of self-fertility in the model fungus Aspergillus nidulans. Curr Biol 17: 1384-9

VIII. REFERENCES 67

Paris M, Kaplan T, Li XY, Villalta JE, Lott SE, Eisen MB (2013) Extensive divergence of transcription factor binding in Drosophila embryos with highly conserved gene expression. PLoS Genet 9: e1003748

Park D, Lee Y, Bhupindersingh G, Iyer VR (2013) Widespread mis-interpretable ChIP-seq bias in

yeast. PLoS One 8: e83506 Park PJ (2009) ChIP-seq: advantages and challenges of a maturing technology. Nat Rev Genet 10:

669-80 Peñalva MA, Rowlands RT, Turner G (1998) The optimization of penicillin biosynthesis in fungi.

Trends Biotechnol 16: 483-9 Pepke S, Wold B, Mortazavi A (2009) Computation for ChIP-seq and RNA-seq studies. Nat

Methods 6: S22-32 Perrin RM, Fedorova ND, Bok JW, Cramer RA, Wortman JR, Kim HS, Nierman WC, Keller

NP (2007) Transcriptional regulation of chemical diversity in Aspergillus fumigatus by LaeA. PLoS Pathog 3: e50

Picossi S, Flores E, Herrero A (2015) The LysR-type transcription factor PacR is a global regulator

of photosynthetic carbon assimilation in Anabaena. Environ Microbiol (doi: 10.1111/1462-2920.12800; Epub ahead of print)

Pilon AM, Ajay SS, Kumar SA, Steiner LA, Cherukuri PF, Wincovitch S, Anderson SM,

Mullikin JC, Gallagher PG, Hardison RC, Margulies EH, Bodine DM (2011) Genome-wide ChIP-seq reveals a dramatic shift in the binding of the transcription factor erythroid Kruppel-like factor during erythrocyte differentiation. Blood 118: e139-48

Pöggeler S (2001) Mating-type genes for classical strain improvements of ascomycetes. Appl

Microbiol Biotechnol 56: 589-601 Pöggeler S, Nowrousian M, Ringelberg C, Loros JJ, Dunlap JC, Kück U (2006) Microarray and

real-time PCR analyses reveal mating type-dependent gene expression in a homothallic fungus. Mol Genet Genomics 275: 492-503

Pujol C, Daniels KJ, Lockhart SR, Srikantha T, Radke JB, Geiger J, Soll DR (2004) The closely

related species Candida albicans and Candida dubliniensis can mate. Eukaryot Cell 3: 1015-27

Purschwitz J, Müller S, Fischer R (2009) Mapping the interaction sites of Aspergillus nidulans

phytochrome FphA with the global regulator VeA and the White Collar protein LreB. Mol Genet Genomics 281: 35-42

Rabinovich A, Jin VX, Rabinovich R, Xu X, Farnham PJ (2008) E2F in vivo binding specificity:

comparison of consensus versus nonconsensus binding sites. Genome Res 18: 1763-77 Raper KB, Alexander DF, Coghill RD (1944) Penicillin: II. Natural variation and penicillin

production in Penicillium notatum and allied species. J Bacteriol 48: 639-59 Raper KB (1946) The development of improved penicillin-producing molds. Annals of the New York

Academy of Sciences 48: 41-56 Ravasi T, Suzuki H, Cannistraci CV, Katayama S, Bajic VB, Tan K, Akalin A, Schmeier S,

Kanamori-Katayama M, Bertin N, Carninci P, Daub CO, Forrest AR, Gough J,

Grimmond S, Han JH, Hashimoto T, Hide W, Hofmann O, Kamburov A, Kaur M,

VIII. REFERENCES 68

Kawaji H, Kubosaki A, Lassmann T, van Nimwegen E, MacPherson CR, Ogawa C,

Radovanovic A, Schwartz A, Teasdale RD, Tegner J, Lenhard B, Teichmann SA,

Arakawa T, Ninomiya N, Murakami K, Tagami M, Fukuda S, Imamura K, Kai C,

Ishihara R, Kitazume Y, Kawai J, Hume DA, Ideker T, Hayashizaki Y (2010) An atlas of combinatorial transcriptional regulation in mouse and man. Cell 140: 744-52

Read CM, Cary PD, Cranerobinson C, Driscoll PC, Norman DG (1993) Solution structure of a

DNA-binding domain from Hmg1. Nucleic Acids Res 21: 3427-36

Reed BD, Charos AE, Szekely AM, Weissman SM, Snyder M (2008) Genome-wide occupancy of SREBP1 and its partners NFY and SP1 reveals novel functional roles and combinatorial regulation of distinct classes of genes. PLoS Genet 4: e1000133

Regueira TB, Kildegaard KR, Hansen BG, Mortensen UH, Hertweck C, Nielsen J (2011)

Molecular basis for mycophenolic acid biosynthesis in Penicillium brevicompactum. Appl Environ Microbiol 77: 3035-43

Reiss J (1982) Development of Aspergillus parasiticus and formation of aflatoxin B1 under the

influence of conidiogenesis affecting compounds. Arch Microbiol 133: 236-8 Ren B, Robert F, Wyrick JJ, Aparicio O, Jennings EG, Simon I, Zeitlinger J, Schreiber J,

Hannett N, Kanin E, Volkert TL, Wilson CJ, Bell SP, Young RA (2000) Genome-wide location and function of DNA binding proteins. Science 290: 2306-9

Reyes-Dominguez Y, Bok JW, Berger H, Shwab EK, Basheer A, Gallmetzer A, Scazzocchio C,

Keller N, Strauss J (2010) Heterochromatic marks are associated with the repression of secondary metabolism clusters in Aspergillus nidulans. Mol Microbiol 76: 1376-86

Ricardi MM, González RM, Zhong SL, Domínguez PG, Duffy T, Turjanski PG, Salter JDS,

Alleva K, Carrari F, Giovannoni JJ, Estévez JM, Iusem ND (2014) Genome-wide data (ChIP-seq) enabled identification of cell wall-related and aquaporin genes as targets of tomato ASR1, a drought stress-responsive transcription factor. BMC Plant Biol 14: 29

Robertson G, Hirst M, Bainbridge M, Bilenky M, Zhao Y, Zeng T, Euskirchen G, Bernier B,

Varhol R, Delaney A, Thiessen N, Griffith OL, He A, Marra M, Snyder M, Jones S

(2007) Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat Methods 4: 651-7

Rodriguez R, Miller KM (2014) Unravelling the genomic targets of small molecules using high-

throughput sequencing. Nat Rev Genet 15: 783-96 Ropars J, López-Villavicencio M, Dupont J, Snirc A, Gillot G, Coton M, Jany JL, Coton E,

Giraud T (2014) Induction of sexual reproduction and genetic diversity in the cheese fungus Penicillium roqueforti. Evol Appl 7: 433-41

Rothberg JM, Hinz W, Rearick TM, Schultz J, Mileski W, Davey M, Leamon JH, Johnson K,

Milgrew MJ, Edwards M, Hoon J, Simons JF, Marran D, Myers JW, Davidson JF,

Branting A, Nobile JR, Puc BP, Light D, Clark TA, Huber M, Branciforte JT, Stoner IB,

Cawley SE, Lyons M, Fu Y, Homer N, Sedova M, Miao X, Reed B, Sabina J, Feierstein

E, Schorn M, Alanjary M, Dimalanta E, Dressman D, Kasinskas R, Sokolsky T, Fidanza

JA, Namsaraev E, McKernan KJ, Williams A, Roth GT, Bustillo J (2011) An integrated semiconductor device enabling non-optical genome sequencing. Nature 475: 348-52

Ruger-Herreros C, Rodríguez-Romero J, Fernández-Barranco R, Olmedo M, Fischer R,

Corrochano LM, Canovas D (2011) Regulation of conidiation by light in Aspergillus

nidulans. Genetics 188: 809-22

VIII. REFERENCES 69

Sandelin A, Alkema W, Engstrom P, Wasserman WW, Lenhard B (2004) JASPAR: an open-access database for eukaryotic transcription factor binding profiles. Nucleic Acids Res 32: D91-4

Sandmann T, Jensen LJ, Jakobsen JS, Karzynski MM, Eichenlaub MP, Bork P, Furlong EEM

(2006) A temporal map of transcription factor activity: Mef2 directly regulates at all stages of muscle target genes development. Dev Cell 10: 797-807

Sanford JR, Wang X, Mort M, Vanduyn N, Cooper DN, Mooney SD, Edenberg HJ, Liu Y (2009)

Splicing factor SFRS1 recognizes a functionally diverse landscape of RNA transcripts. Genome Res 19: 381-94

Sanger F, Nicklen S, Coulson AR (1977) DNA sequencing with chain-terminating inhibitors. Proc

Natl Acad Sci U S A 74: 5463-7 Sarikaya-Bayram Ö, Bayram Ö, Valerius O, Park HS, Irniger S, Gerke J, Ni M, Han KH, Yu

JH, Braus GH (2010) LaeA control of velvet family regulatory proteins for light-dependent development and fungal cell-type specificity. PLoS Genet 6: e1001226

Sarikaya-Bayram Ö, Bayram Ö, Feussner K, Kim JH, Kim HS, Kaever A, Feussner I, Chae KS,

Han DM, Han KH, Braus GH (2014) Membrane-bound methyltransferase complex VapA-VipC-VapB guides epigenetic control of fungal development. Dev Cell 29: 406-20

Sarikaya-Bayram Ö, Palmer JM, Keller N, Braus GH, Bayram Ö (2015) One Juliet and four

Romeos: VeA and its methyltransferases. Front Microbiol 6: 1 Sasaki T, Lynch KL, Mueller CV, Friedman S, Freitag M, Lewis ZA (2014) Heterochromatin

controls γH2A localization in Neurospora crassa. Eukaryot Cell 13: 990-1000 Scharf DH, Heinekamp T, Remme N, Hortschansky P, Brakhage AA, Hertweck C (2012)

Biosynthesis and function of gliotoxin in Aspergillus fumigatus. Appl Microbiol Biotechnol 93: 467-72

Schloss JA (2008) How to get genomes at one ten-thousandth the cost. Nat Biotechnol 26: 1113-5 Schmitt EK, Bunse A, Janus D, Hoff B, Friedlin E, Kürnsteiner H, Kück U (2004a) Winged helix

transcription factor CPCR1 is involved in regulation of β-lactam biosynthesis in the fungus Acremonium chrysogenum. Eukaryot Cell 3: 121-34

Schmitt EK, Hoff B, Kück U (2004b) AcFKH1, a novel member of the forkhead family, associates

with the RFX transcription factor CPCR1 in the cephalosporin C-producing fungus Acremonium chrysogenum. Gene 342: 269-81

Schneider GF, Dekker C (2012) DNA sequencing with nanopores. Nat Biotechnol 30: 326-8 Schrettl M, Bignell E, Kragl C, Sabiha Y, Loss O, Eisendle M, Wallner A, Arst HN, Haynes K,

Haas H (2007) Distinct roles for intra- and extracellular siderophores during Aspergillus

fumigatus infection. PLoS Pathog 3: 1195-207 Seiboth B, Karimi Aghcheh R, Phatale PA, Linke R, Hartl L, Sauer DG, Smith KM, Baker SE,

Freitag M, Kubicek CP (2012) The putative protein methyltransferase LAE1 controls cellulase gene expression in Trichoderma reesei. Mol Microbiol 84: 1150-64

Seidl V, Seibel C, Kubicek CP, Schmoll M (2009) Sexual development in the industrial workhorse

Trichoderma reesei. Proc Natl Acad Sci U S A 106: 13909-14

VIII. REFERENCES 70

Sharon E, Lubliner S, Segal E (2008) A feature-based approach to modeling protein-DNA interactions. PLoS Comput Biol 4: e1000154

Sharon E, Kalma Y, Sharp A, Raveh-Sadka T, Levo M, Zeevi D, Keren L, Yakhini Z,

Weinberger A, Segal E (2012) Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters. Nat Biotechnol 30: 521-30

Shelest E (2008) Transcription factors in fungi. FEMS Microbiol Lett 286: 145-51 Shendure J, Porreca GJ, Reppas NB, Lin X, McCutcheon JP, Rosenbaum AM, Wang MD,

Zhang K, Mitra RD, Church GM (2005) Accurate multiplex polony sequencing of an evolved bacterial genome. Science 309: 1728-32

Shendure J, Ji H (2008) Next-generation DNA sequencing. Nat Biotechnol 26: 1135-45 Shore P, Sharrocks AD (1995) The MADS-box family of transcription factors. Eur J Biochem 229:

1-13 Siggers T, Duyzend MH, Reddy J, Khan S, Bulyk ML (2011) Non-DNA-binding cofactors enhance

DNA-binding specificity of a transcriptional regulatory complex. Mol Syst Biol 7: 555 Simon MD, Wang CI, Kharchenko PV, West JA, Chapman BA, Alekseyenko AA, Borowsky

ML, Kuroda MI, Kingston RE (2011) The genomic binding sites of a non-coding RNA. Proc Natl Acad Sci U S A 108: 20497-502

Slattery M, Zhou T, Yang L, Dantas Machado AC, Gordan R, Rohs R (2014) Absence of a simple

code: how transcription factors read the genome. Trends Biochem Sci 39: 381-99 Smith AM, Heisler LE, St Onge RP, Farias-Hesson E, Wallace IM, Bodeau J, Harris AN, Perry

KM, Giaever G, Pourmand N, Nislow C (2010) Highly-multiplexed barcode sequencing: an efficient method for parallel analysis of pooled samples. Nucleic Acids Res 38: e142

Smith KM, Phatale PA, Sullivan CM, Pomraning KR, Freitag M (2011) Heterochromatin is

required for normal distribution of Neurospora crassa CenH3. Mol Cell Biol 31: 2528-42 Song L, Zhang Z, Grasfeder LL, Boyle AP, Giresi PG, Lee BK, Sheffield NC, Graf S, Huss M,

Keefe D, Liu Z, London D, McDaniell RM, Shibata Y, Showers KA, Simon JM, Vales T,

Wang T, Winter D, Clarke ND, Birney E, Iyer VR, Crawford GE, Lieb JD, Furey TS

(2011) Open chromatin defined by DNaseI and FAIRE identifies regulatory elements that shape cell-type identity. Genome Res 21: 1757-67

Soyer JL, Moller M, Schotanus K, Connolly LR, Galazka JM, Freitag M, Stukenbrock EH

(2015) Chromatin analyses of Zymoseptoria tritici: methods for chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq). Fungal Genet Biol (doi: doi:10.1016/j.fgb.2015.03.006; Epub ahead of print)

Specht T, Dahlmann TA, Zadra I, Kürnsteiner H, Kück U (2014) Complete sequencing and

chromosome-scale genome assembly of the industrial progenitor strain P2niaD18 from the penicillin producer Penicillium chrysogenum. Genome Announc 2: e00577-14

Stergachis AB, Haugen E, Shafer A, Fu W, Vernot B, Reynolds A, Raubitschek A, Ziegler S,

LeProust EM, Akey JM, Stamatoyannopoulos JA (2013) Exonic transcription factor binding directs codon choice and affects protein evolution. Science 342: 1367-72

Stewart AJ, Hannenhalli S, Plotkin JB (2012) Why transcription factor binding sites are ten

nucleotides long. Genetics 192: 973-85

VIII. REFERENCES 71

Stinnett SM, Espeso EA, Cobeno L, Araujo-Bazan L, Calvo AM (2007) Aspergillus nidulans VeA subcellular localization is dependent on the importin α-carrier and on light. Mol Microbiol 63: 242-55

Stormo GD, Zhao Y (2010) Determining the specificity of protein-DNA interactions. Nat Rev Genet

11: 751-60 Strahl-Bolsinger S, Hecht A, Luo K, Grunstein M (1997) SIR2 and SIR4 interactions differ in core

and extended telomeric heterochromatin in yeast. Genes Dev 11: 83-93 Struck AW, Thompson ML, Wong LS, Micklefield J (2012) S-adenosyl-methionine-dependent

methyltransferases: highly versatile enzymes in biocatalysis, biosynthesis and other biotechnological applications. Chembiochem 13: 2642-55

Studt L, Schmidt FJ, Jahn L, Sieber CM, Connolly LR, Niehaus EM, Freitag M, Humpf HU,

Tudzynski B (2013) Two histone deacetylases, FfHda1 and FfHda2, are important for Fusarium fujikuroi secondary metabolism and virulence. Appl Environ Microbiol 79: 7719-34

Suarez T, Peñalva MA (1996) Characterization of a Penicillium chrysogenum gene encoding a PacC

transcription factor and its binding sites in the divergent pcbAB-pcbC promoter of the penicillin biosynthetic cluster. Mol Microbiol 20: 529-40

Sugui JA, Pardo J, Chang YC, Müllbacher A, Zarember KA, Galvez EM, Brinster L, Zerfas P,

Gallin JI, Simon MM, Kwon-Chung KJ (2007) Role of laeA in the regulation of alb1, gliP, conidial morphology, and virulence in Aspergillus fumigatus. Eukaryot Cell 6: 1552-61

Survase SA, Kagliwal LD, Annapure US, Singhal RS (2011) Cyclosporin A - a review on

fermentative production, downstream processing and pharmacological applications. Biotechnol Adv 29: 418-35

Sutton P, Newcombe NR, Waring P, Müllbacher A (1994) In vivo immunosuppressive activity of

gliotoxin, a metabolite produced by human pathogenic fungi. Infect Immun 62: 1192-8 Svingen T, Koopman P (2007) Involvement of homeobox genes in mammalian sexual development.

Sex Dev 1: 12-23 Tanaka TU, Knapp D, Nasmyth K (1997) Loading of an Mcm protein onto DNA replication origins

is regulated by Cdc6p and CDKs. Cell 90: 649-60 Tanay A (2006) Extensive low-affinity transcriptional interactions in the yeast genome. Genome Res

16: 962-72 Tawfik DS, Griffiths AD (1998) Man-made cell-like compartments for molecular evolution. Nat

Biotechnol 16: 652-6 TePaske MR, Gloer JB, Wicklow DT, Dowd PF (1992) Aflavarin and β-Aflatrem - New anti-

insectan metabolites from the sclerotia of Aspergillus flavus. J Nat Prod 55: 1080-6 Terfehr D, Dahlmann TA, Specht T, Zadra I, Kürnsteiner H, Kück U (2014) Genome sequence

and annotation of Acremonium chrysogenum, producer of the β-lactam antibiotic Cephalosporin C. Genome Announc 2: e00948-14

Tfelt-Hansen PC, Koehler PJ (2008) History of the use of ergotamine and dihydroergotamine in

migraine from 1906 and onward. Cephalalgia 28: 877-86

VIII. REFERENCES 72

The ENCODE Project Consortium (2004) The ENCODE (ENCyclopedia Of DNA Elements) Project. Science 306: 636-40

The ENCODE Project Consortium (2012) An integrated encyclopedia of DNA elements in the

human genome. Nature 489: 57-74

The International Human Genome Sequencing Consortium (2004) Finishing the euchromatic sequence of the human genome. Nature 431: 931-45

Then Bergh K, Litzka O, Brakhage AA (1996) Identification of a major cis-acting DNA element

controlling the bidirectionally transcribed penicillin biosynthesis genes acvA (pcbAB) and ipnA (pcbC) of Aspergillus nidulans. J Bacteriol 178: 3908-16

Thürmer A (2014) Next Generation Sequencing in der mikrobiellen (Meta-)Genomforschung. BIOSpektrum 20: 168-71

Thurtle DM, Rine J (2014) The molecular topography of silenced chromatin in Saccharomyces

cerevisiae. Genes Dev 28: 245-58 Tilburn J, Sarkar S, Widdick DA, Espeso EA, Orejas M, Mungroo J, Peñalva MA, Arst HN, Jr.

(1995) The Aspergillus PacC zinc finger transcription factor mediates regulation of both acid- and alkaline-expressed genes by ambient pH. EMBO J 14: 779-90

Todeschini AL, Georges A, Veitia RA (2014) Transcription factors: specific DNA binding and

specific gene regulation. Trends Genet 30: 211-9 Tompa M, Li N, Bailey TL, Church GM, De Moor B, Eskin E, Favorov AV, Frith MC, Fu Y,

Kent WJ, Makeev VJ, Mironov AA, Noble WS, Pavesi G, Pesole G, Regnier M, Simonis

N, Sinha S, Thijs G, van Helden J, Vandenbogaert M, Weng Z, Workman C, Ye C, Zhu

Z (2005) Assessing computational tools for the discovery of transcription factor binding sites. Nat Biotechnol 23: 137-44

Traeger S, Altegoer F, Freitag M, Gabaldon T, Kempken F, Kumar A, Marcet-Houben M,

Pöggeler S, Stajich JE, Nowrousian M (2013) The genome and development-dependent transcriptomes of Pyronema confluens: a window into fungal evolution. PLoS Genet 9: e1003820

Tsong AE, Miller MG, Raisner RM, Johnson AD (2003) Evolution of a combinatorial

transcriptional circuit: a case study in yeasts. Cell 115: 389-99 Tuch BB, Galgoczy DJ, Hernday AD, Li H, Johnson AD (2008) The evolution of combinatorial

gene regulation in fungi. PLoS Biol 6: e38 Tudzynski B (1999) Biosynthesis of gibberellins in Gibberella fujikuroi: biomolecular aspects. Appl

Microbiol Biotechnol 52: 298-310 Tudzynski B, Homann V, Feng B, Marzluf GA (1999a) Isolation, characterization and disruption of

the areA nitrogen regulatory gene of Gibberella fujikuroi. Mol Gen Genet 261: 106-14 Tudzynski P, Holter K, Correia T, Arntz C, Grammel N, Keller U (1999b) Evidence for an ergot

alkaloid gene cluster in Claviceps purpurea. Mol Gen Genet 261: 133-41 Turgeon BG, Yoder OC (2000) Proposed nomenclature for mating type genes of filamentous

ascomycetes. Fungal Genet Biol 31: 1-5

VIII. REFERENCES 73

Valouev A, Johnson DS, Sundquist A, Medina C, Anton E, Batzoglou S, Myers RM, Sidow A

(2008) Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods 5: 829-34

van den Berg MA, Albang R, Albermann K, Badger JH, Daran JM, Driessen AJM, Garcia-

Estrada C, Fedorova ND, Harris DM, Heijne WHM, Joardar V, Kiel JAKW, Kovalchuk

A, Martín JF, Nierman WC, Nijland JG, Pronk JT, Roubos JA, van der Klei IJ, van Peij

NNME, Veenhuis M, von Döhren H, Wagner C, Wortman J, Bovenberg RAL (2008) Genome sequencing and analysis of the filamentous fungus Penicillium chrysogenum. Nat Biotechnol 26: 1161-8

van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C (2014) Ten years of next-generation sequencing technology. Trends Genet 30: 418-26

Veiga T, Nijland JG, Driessen AJ, Bovenberg RA, Touw H, van den Berg MA, Pronk JT, Daran

JM (2012) Impact of velvet complex on transcriptome and penicillin G production in glucose-limited chemostat cultures of a β-lactam high-producing Penicillium chrysogenum strain. OMICS 16: 320-33

Vokes SA, Ji H, Wong WH, McMahon AP (2008) A genome-scale analysis of the cis-regulatory

circuitry underlying sonic hedgehog-mediated patterning of the mammalian limb. Genes Dev 22: 2651-63

Wada R, Maruyama J, Yamaguchi H, Yamamoto N, Wagu Y, Paoletti M, Archer DB, Dyer PS,

Kitamoto K (2012) Presence and functionality of mating type genes in the supposedly asexual filamentous fungus Aspergillus oryzae. Appl Environ Microbiol 78: 2819-29

Wal M, Pugh BF (2012) Genome-wide mapping of nucleosome positions in yeast using high-

resolution MNase ChIP-Seq. Methods Enzymol 513: 233-50 Walhout AJM (2006) Unraveling transcription regulatory networks by protein-DNA and protein-

protein interaction mapping. Genome Res 16: 1445-54 Wang X, Zhao Z, Muller J, Iyu A, Khng AJ, Guccione E, Ruan Y, Ingham PW (2013) Targeted

inactivation and identification of targets of the Gli2a transcription factor in the zebrafish. Biol Open 2: 1203-13

Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev

Genet 10: 57-63 Wang Z, Kin K, Lopez-Giraldez F, Johannesson H, Townsend JP (2012) Sex-specific gene

expression during asexual development of Neurospora crassa. Fungal Genet Biol 49: 533-43 Wang Z, Lopez-Giraldez F, Lehr N, Farré M, Common R, Trail F, Townsend JP (2014) Global

gene expression and focused knockout analysis reveals genes associated with fungal fruiting body development in Neurospora crassa. Eukaryot Cell 13: 154-69

Weingarten-Gabbay S, Segal E (2014) The grammar of transcriptional regulation. Hum Genet 133:

701-11 Weirauch MT, Cote A, Norel R, Annala M, Zhao Y, Riley TR, Saez-Rodriguez J, Cokelaer T,

Vedenko A, Talukder S, Bussemaker HJ, Morris QD, Bulyk ML, Stolovitzky G, Hughes

TR (2013) Evaluation of methods for modeling transcription factor sequence specificity. Nat Biotechnol 31: 126-34

VIII. REFERENCES 74

Werner MH, Huth JR, Gronenborn AM, Clore GM (1995) Molecular basis of human 46X,Y sex reversal revealed from the three-dimensional solution structure of the human SRY-DNA complex. Cell 81: 705-14

Werner T (2010) Next generation sequencing in functional genomics. Brief Bioinform 11: 499-511 Wiemann P, Brown DW, Kleigrewe K, Bok JW, Keller NP, Humpf HU, Tudzynski B (2010)

FfVel1 and FfLae1, components of a velvet-like complex in Fusarium fujikuroi, affect differentiation, secondary metabolism and virulence. Mol Microbiol 77: 972-94

Wiemann P, Sieber CM, von Bargen KW, Studt L, Niehaus EM, Espino JJ, Huss K, Michielse

CB, Albermann S, Wagner D, Bergner SV, Connolly LR, Fischer A, Reuter G,

Kleigrewe K, Bald T, Wingfield BD, Ophir R, Freeman S, Hippler M, Smith KM, Brown

DW, Proctor RH, Münsterkötter M, Freitag M, Humpf HU, Güldener U, Tudzynski B

(2013) Deciphering the cryptic genome: genome-wide analyses of the rice pathogen Fusarium

fujikuroi reveal complex regulation of secondary metabolism and novel metabolites. PLoS Pathog 9: e1003475

Wieser J, Adams TH (1995) flbD encodes a Myb-like DNA-binding protein that coordinates

initiation of Aspergillus nidulans conidiophore development. Genes Dev 9: 491-502 Wilbanks EG, Facciotti MT (2010) Evaluation of algorithm performance in ChIP-seq peak detection.

PLoS One 5: e11471 Wilhelm BT, Marguerat S, Watt S, Schubert F, Wood V, Goodhead I, Penkett CJ, Rogers J,

Bahler J (2008) Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature 453: 1239-43

Winata CL, Kondrychyn I, Kumar V, Srinivasan KG, Orlov Y, Ravishankar A, Prabhakar S,

Stanton LW, Korzh V, Mathavan S (2013) Genome wide analysis reveals Zic3 interaction with distal regulatory elements of stage specific developmental genes in zebrafish. PLoS Genet 9: e1003852

Wolfers S, Kamerewerd J, Nowrousian M, Sigl C, Zadra I, Kürnsteiner H, Kück U, Bloemendal

S (2015) Microarray hybridization analysis of light-dependent gene expression in Penicillium

chrysogenum identifies bZIP transcription factor PcAtfA. J Basic Microbiol 55: 480-9 Woloshuk CP, Foutz KR, Brewer JF, Bhatnagar D, Cleveland TE, Payne GA (1994) Molecular

characterization of aflR, a regulatory locus for aflatoxin biosynthesis. Appl Environ Microbiol 60: 2408-14

Won J, Kim TK (2006) Histone modifications and transcription factor binding on chromatin ChIP-

PCR assays. Methods Mol Biol 325: 273-83 Wong KH, Hynes MJ, Todd RB, Davis MA (2007) Transcriptional control of nmrA by the bZIP

transcription factor MeaB reveals a new level of nitrogen regulation in Aspergillus nidulans. Mol Microbiol 66: 534-51

Wu D, Oide S, Zhang N, Choi MY, Turgeon BG (2012) ChLae1 and ChVel1 regulate T-toxin

production, virulence, oxidative stress response, and development of the maize pathogen Cochliobolus heterostrophus. PLoS Pathog 8: e1002542

Wunderlich Z, Mirny LA (2009) Different gene regulation strategies revealed by analysis of binding

motifs. Trends Genet 25: 434-40

VIII. REFERENCES 75

Xie J, Du H, Guan GB, Tong YJ, Kourkoumpetis TK, Zhang LX, Bai FY, Huang GH (2012) N-acetylglucosamine induces white-to-opaque switching and mating in Candida tropicalis, poviding new Insights into adaptation and fungal sexual evolution. Eukaryot Cell 11: 773-82

Yan Z, Hull CM, Sun S, Heitman J, Xu J (2007) The mating type-specific homeodomain genes

SXI1α and SXI2a coordinately control uniparental mitochondrial inheritance in Cryptococcus

neoformans. Curr Genet 51: 187-95 Yang Y, Xie B, Yan J (2014) Application of next-generation sequencing technology in forensic

science. Genomics Proteomics Bioinformatics 12: 190-7 Yin W, Keller NP (2011) Transcriptional regulatory elements in fungal secondary metabolism. J

Microbiol 49: 329-39

Yu J, Chang PK, Ehrlich KC, Cary JW, Bhatnagar D, Cleveland TE, Payne GA, Linz JE,

Woloshuk CP, Bennett JW (2004) Clustered pathway genes in aflatoxin biosynthesis. Appl Environ Microbiol 70: 1253-62

Yu JH, Butchko RA, Fernandes M, Keller NP, Leonard TJ, Adams TH (1996) Conservation of

structure and function of the aflatoxin regulatory gene aflR from Aspergillus nidulans and A.

flavus. Curr Genet 29: 549-55 Zaret KS, Carroll JS (2011) Pioneer transcription factors: establishing competence for gene

expression. Genes Dev 25: 2227-41 Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM,

Brown M, Li W, Liu XS (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9: R137

Zhao Y, Ruan S, Pandey M, Stormo GD (2012) Improved models for transcription factor binding

site identification using non-independent interactions. Genetics 191: 781-90 Zheng Q, Hou R, Juanyu, Zhang, Ma J, Wu Z, Wang G, Wang C, Xu JR (2013) The MAT locus

genes play different roles in sexual reproduction and pathogenesis in Fusarium graminearum. PLoS One 8: e66980

Zheng WH, Zhao X, Xie QR, Huang QP, Zhang CK, Zhai HC, Xu LP, Lu GD, Shim WB, Wang

ZH (2012) A conserved homeobox transcription factor Htf1 is required for phialide development and conidiogenesis in Fusarium species. PLoS One 7: e45432

Zhong M, Niu W, Lu ZJ, Sarov M, Murray JI, Janette J, Raha D, Sheaffer KL, Lam HY,

Preston E, Slightham C, Hillier LW, Brock T, Agarwal A, Auerbach R, Hyman AA,

Gerstein M, Mango SE, Kim SK, Waterston RH, Reinke V, Snyder M (2010) Genome-wide identification of binding sites defines distinct functions for Caenorhabditis elegans PHA-4/FOXA in development and environmental response. PLoS Genet 6: e1000848

IX. EIGENANTEIL AN PUBLIKATIONEN 76

IX. EIGENANTEIL AN PUBLIKATIONEN

Genome-wide identification of target genes of a mating-type α-domain transcription factor

reveals functions beyond sexual development

Kordula Becker, Christina Beer, Michael Freitag, and Ulrich Kück (2015) Molecular Microbiology doi:10.1111/mmi.12987

Planung (P): 50 %

Experimentelle Durchführung (E): 90 %

Verfassen des Manuskripts (M): 60 %

New insights into PcVelA regulatory functions on a genome-wide scale reveal evidence for

methyltransferase PcLlmA acting as a downstream factor and direct interaction partner of

PcVelA in Penicillium chrysogenum

Kordula Becker, Sandra Bloemendal, and Ulrich Kück (2015) – prepared for submission –

Planung (P): 50 %

Experimentelle Durchführung (E): 60 %

Verfassen des Manuskripts (M): 70 %

X. CURRICULUM VITAE 77

X. CURRICULUM VITAE

Kordula Becker geb. 31. Mai 1987, Essen

Witteringstr. 1 45130 Essen

[email protected]

AUSBILDUNG

seit 07/2011 Promotionsstudium, Ruhr-Universität Bochum „Functional genomics provide new insights into regulation of morphogenesis and secondary metabolism in the industrial penicillin producer Penicillium chrysogenum“ angefertigt am Lehrstuhl für Allgemeine und Molekulare Botanik, Christian Doppler Labor für Biotechnologie der Pilze; Betreuer: Prof. Dr. U. Kück

04/2010 - 06/2011 Vorbereitung der Fast-Track Promotion, Ruhr-Universität Bochum

Lehrstuhl für Allgemeine und Molekulare Botanik, Christian Doppler Labor für Biotechnologie der Pilze; Betreuer: Prof. Dr. U. Kück

10/2006 - 09/2009 Bachelorstudium der Biologie, Ruhr-Universität Bochum

„Entwicklungsbiologie bei dem Ascomyceten Sordaria macrospora: Bioinformatorische und biochemische Charakterisierung von Interaktionspartnern des Entwicklungsproteins PRO22“ angefertigt am Lehrstuhl für Allgemeine und Molekulare Botanik; Betreuer: Prof. Dr. U. Kück

06/2006 Erwerb der Allgemeinen Hochschulreife, Maria-Wächtler Gymnasium, Essen

AUSLANDSAUFENTHALTE

09/2012 - 11/2012 Forschungsaufenthalt an der Oregon State University, Corvallis, USA

Etablierung und Anwendung der ChIP-seq Technologie in P. chrysogenum Department for Biochemistry and Biophysics; Betreuer: Prof. Dr. M. Freitag

10/2009 - 02/2010 ERASMUS-Studiensemester

Karl-Franzens Universität Graz und Technische Universität Graz (Österreich)

STIPENDIEN

seit 12/2012 Promotionsstipendium der Studienstiftung des Deutschen Volkes

10/2009 - 09/2011 Stipendium des Bildungsfonds der Ruhr-Universität Bochum

X. CURRICULUM VITAE 78

AUSZEICHNUNGEN

03/2014 Novozymes Poster Award

im Rahmen des „11th

International Aspergillus Satellite Meeting“, Sevilla, Spanien

09/2013 1. Posterpreis der Deutschen Gesellschaft für Genetik

anlässlich der Jahrestagung der Deutschen Gesellschaft für Genetik (GfG), Braunschweig

PUBLIKATIONEN

Becker K, Beer C, Freitag M, Kück U (2015) Genome-wide identification of target genes of a mating-type α-domain transcription factor reveals functions beyond sexual development. Mol Microbiol (in press; doi: 10.1111/mmi.12987)

Becker K, Bloemendal S, Kück U (2015) New insights into PcVelA regulatory functions on a genome-wide scale reveal evidence for methyltransferase PcLlmA acting as a downstream factor and direct interaction partner of PcVelA in Penicillium chrysogenum (prepared for submission)

Becker K, Böhm J, Dahlmann T, Kück U (2015) Sex und Penicillin-Biosynthese in Schimmelpilzen. Biospektrum (in press)

Marlinghaus L, Becker K, Korte M, Neumann S, Gatermann SG, Szabados F (2011) Construction and characterization of three knockout mutants of the fbl gene of Staphylococcus lugdunensis. APMIS 120: 108-116

KONGRESSBEITRÄGE

K. Becker, C. Beer, M. Freitag, U. Kück (2015) Genome-wide identification of target genes of a mating-type α-domain transcription factor reveals functions beyond sexual development. Abstracts, 28th Fungal Genetics Conference, Asilomar, CA, USA, Poster #201

K. Becker, M. Freitag, U. Kück (2014) Use of ChIP-seq technology for the functional characterization of the mating-type protein MAT1-1-1 from the industrial penicillin producer Penicillium chrysogenum. Abstracts, 12th European Conference on Fungal Genetics, ECFG12, Sevilla, Spain, Poster #250

K. Becker, M. Freitag, U. Kück (2013) Use of ChIP-seq technology for the functional characterization of two transcription factors from the industrial penicillin producer Penicillium chrysogenum. Abstracts, Annual Meeting of the German Genetics Society, Braunschweig, Poster #41

K. Becker, S. Bloemendal, U. Kück (2012) Genetic and molecular characterization of the Penicillium chrysogenum PcrsmA gene, encoding a homologue of the Aspergillus nidulans bZIP transcription factor RsmA. Abstracts, 11th European Conference on Fungal Genetics, ECFG11, Marburg, PR8.21

K. Becker, K. Kopke, B. Hoff, A. Katschorowski, S. Milbredt, J. Kamerewerd, S. Bloemendal, U. Kück (2011) The velvet-like complex in Penicillium chrysogenum participates in pathways controlling morphogenesis and penicillin production. Abstracts, Molecular Biology of Fungi, 10th VAAM-Symposium, Marburg, Poster #3

XI. ERKLÄRUNG 79

XI. ERKLÄRUNG

Hiermit erkläre ich, dass ich die Arbeit selbständig verfasst und bei keiner anderen Fakultät

eingereicht und dass ich keine anderen als die angegebenen Hilfsmittel verwendet habe. Es

handelt sich bei der heute von mir eingereichten Dissertation um sechs in Wort und Bild

völlig übereinstimmende Exemplare.

Weiterhin erkläre ich, dass digitale Abbildungen nur die originalen Daten enthalten und in

keinem Fall inhaltsverändernde Bildbearbeitung vorgenommen wurde.

Bochum, den

_______________________________

(Kordula Becker)

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