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LARGE-SCALE BIOLOGY

The Biotrophic Development of Ustilago maydis Studied by RNAseq Analysis

Daniel Lanver1a*, André N. Müller1b*, Petra Happel1, Gabriel Schweizer1, Fabian B. Haas2,Marek Franitza3, Clément Pellegrin1, Stefanie Reissmann1, Janine Altmüller3,4, Stefan A. Rensing2,5 and Regine Kahmann1Ɨ

1Max-Planck-Institut für Terrestrische Mikrobiologie, Abt. Organismische Interaktionen, Karl-von-Frisch Straße 10, 35043 Marburg, Germany 2Philipps Universität Marburg, Fb17 Biologie, AG Zellbiologie der Pflanzen, Karl-von-Frisch-Straße 8, 35043 Marburg, Germany 3 Cologne Center for Genomics (CCG), University of Cologne, Weyertal 115b, 50931 Cologne, Germany. 4 Center for Molecular Medicine Cologne (CMMC), University of Cologne, 50931 Cologne, Germany. 5 BIOSS Centre for Biological Signalling Studies, University of Freiburg, Freiburg, Germany a Present address: Bayer CropScience Biologics GmbH, Metkenberg 6, 23970 Wismar, Germany b Present address: RWTH Aachen University, BIO3: Plant Physiology, Plant Biochemistry and Molecular Biology, Worringer Weg 1, 52074 Aachen, Germany * These authors contributed equally to this work

Ɨ Address correspondence to kahmann@mpi-marburg.mpg.de Dr. Regine Kahmann Max-Planck-Institut für terrestrische Mikrobiologie, Abt. Organismische Interaktionen Karl-von-Frisch-Straße 10 35043 Marburg, Germany Phone: +49-6421-178501 Fax: +49-6421-178509 Email: kahmann@mpi-marburg.mpg.de

Short title: U. maydis biotrophic transcriptome

One-sentence summary: A study of U. maydis gene expression provides unexpected new leads concerning fungal nutrition, defense suppression and tumor induction during plant colonization.

The author responsible for distribution of materials integral to the findings presented in this article in accordance with the policy described in the Instructions for Authors (www.plantcell.org) is: Regine Kahmann (kahmann@mpi-marburg.mpg.de).

Plant Cell Advance Publication. Published on January 25, 2018, doi:10.1105/tpc.17.00764

©2018 American Society of Plant Biologists. All Rights Reserved

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Abstract

The corn smut fungus Ustilago maydis is a model organism for elucidating host colonization strategies of biotrophic fungi. Here we performed an in depth transcriptional profiling of the entire plant-associated development of U. maydis wild-type strains. In our analysis we focused on fungal metabolism, nutritional strategies, secreted effectors and regulatory networks. Secreted proteins were enriched in three distinct expression modules corresponding to stages on the plant surface, establishment of biotrophy and induction of tumors. These modules are likely the key determinants for U. maydis virulence. With respect to nutrient utilization, we observed that expression of several nutrient transporters was tied to these virulence modules rather than being controlled by nutrient availability. We show that oligopeptide transporters likely involved in nitrogen assimilation are important virulence factors. By measuring the intramodular connectivity of transcription factors, we identified the potential drivers for the virulence modules. While known components of the b-mating type cascade emerged as inducers for the plant surface and biotrophy module, we identified a set of yet uncharacterized transcription factors as likely responsible for expression of the tumor module. We demonstrate a crucial role for leaf tumor formation and effector gene expression for one of these transcription factors.

Key words

RNA-seq, plant pathogen, fungi, virulence, effectors, nutrient utilization, transcriptional regulation

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Introduction

Plant pathogenic fungi have adopted discrete lifestyles in interaction with their hosts ranging

from biotrophy, where the plant needs to be kept alive to sustain fungal growth, to necrotrophy,

where infected plant tissue is actively killed and the fungus feeds on the dead material. In

between these extremes are hemibiotrophs, which initially establish a biotrophic phase and then

at some point switch to necrotrophic development. To promote the respective colonization

strategy pathogens secrete a large arsenal of effector proteins. In recent years the study of life-

style transitions in plant pathogenic fungi by time resolved transcriptome analyses through

RNAseq provided deep insights into the processes associated with stages of fungal

development on and inside the host (Kawahara et al., 2012; O'Connell et al., 2012; Hacquard et

al., 2013; Jupe et al., 2013; Dong et al., 2015; Fondevilla et al., 2015; Kong et al., 2015; Rudd et

al., 2015; Dobon et al., 2016; Thatcher et al., 2016; Copley et al., 2017; Wang et al., 2017; Zeng

et al., 2017; Massonnet et al., 2018). These and other studies (Toruno et al., 2016) have shown

that different sets of effectors are synthesized and presumably needed during discrete

developmental stages in fungal pathogens.

The biotrophic fungus Ustilago maydis causes smut disease in maize (Zea mays). This system

has advanced to a model for biotrophic pathogens mainly because of the ease by which fungal

genes can be manipulated through reverse genetic techniques (Kamper, 2004; Dean et al.,

2012; Schuster et al., 2016). In U. maydis and related smut fungi the process of plant

colonization is intimately coupled with sexual development. Plant colonization is initiated when

haploid cells of a compatible mating type recognize each other on the leaf surface via a

pheromone-receptor system (Bolker et al., 1992), fuse and develop a dikaryotic filament.

Filament formation and subsequent pathogenic development are controlled by the heterodimeric

bE/bW (bEast/bWest) homeodomain transcription factor, which is formed after cells with different

alleles of the b locus have fused (Gillissen et al., 1992). The dikaryon is cell cycle arrested

(Castanheira et al., 2014) and is able to invade the plant via a specialized infection structure, the

appressorium. Appressoria allow direct invasion of epidermal plant cells in a process which is

likely aided by plant cell wall degrading enzymes and plant cell wall loosening. During this stage

the fungus remains intracellular and becomes completely encased by the plasma membrane of

the host, forming a tight and extended interaction zone. After penetration, the cell cycle block is

released and mitotic growth of the dikaryotic form is observed, using characteristic clamp-like

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structures for sorting the nuclei (Scherer et al., 2006). After reaching the mesophyll layer and the

veins, U. maydis cells grow along and inside of the veins, presumably to forage nutrients. During

this stage discrete plant cell types enlarge and resume mitotic divisions (Matei, 2016), leading to

the formation of tumors, the most conspicuous signs of corn smut disease. In these tumors

extracellular hyphae form large aggregates in cavities between plant tumor cells, their dikaryotic

nuclei fuse and massive proliferation ensues followed by hyphal fragmentation and spore

formation (Vollmeister et al., 2012; Matei and Doehlemann, 2016; Tollot et al., 2016; Lanver et

al., 2017; Redkar et al., 2017; Snetselaar and McCann, 2017). This entire developmental cycle

strictly depends on the plant and remains biotrophic throughout.

U. maydis is predicted to encode 476 secreted proteins of which 215 lack any known structural

or functional domains, preventing conclusions to be drawn about their molecular functions

(Schuster et al., 2017). Many of these potential effector genes reside in clusters in the genome

(Kamper et al., 2006; Schirawski et al., 2010), are expressed specifically in tumor tissue

compared to axenic culture conditions (Kamper et al., 2006) and contribute to virulence (Kamper

et al., 2006; Muller et al., 2008; Schirawski et al., 2010; Schilling et al., 2014; Stirnberg and

Djamei, 2016). So far the molecular function of only five U. maydis effectors has been elucidated

(Djamei et al., 2011; Hemetsberger et al., 2012; Mueller et al., 2013; Tanaka et al., 2014; Redkar

et al., 2015).

The bE/bW transcription factor triggers a regulatory cascade including several transcription

factor genes as well as 38 potential effector genes (Heimel et al., 2010a; Heimel et al., 2010b).

The expression of the majority of these genes further requires the bE/bW-regulated transcription

factor rbf1 (regulator of b-filament; Heimel et al., 2010b). Early infection-related development of

U. maydis up to the stage of appressorium formation can be mimicked by stimulation with

hydroxy fatty acids and exposing cells to a hydrophobic surface (Mendoza-Mendoza et al.,

2009). An array study of these stages revealed that two transmembrane proteins, the U. maydis

homologs of Sho1p (psynthetic high osmolarity sensitive) and Msb2p (multicopy suppressor of a

budding defect) from Saccharomyces cerevisiae, are specifically responsible for plant surface

cue-induced expression of 41 potential effectors (Lanver et al., 2014). In addition, two

transcription factors, hdp2 (homeodomain transcription factor 2) and biz1(b-dependent zinc

finger protein), which belong to the rbf1-induced genes (Heimel et al., 2010b) and have critical

roles in virulence (Flor-Parra et al., 2006; Lanver et al., 2014), are transcriptionally induced by

Sho1 and Msb2 providing indirect evidence that these transcription factors may induce the

expression of early effectors. After induction of tumors, the WOPR transcription factor Ros1

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(regulator of sporogenesis 1) induces nuclear fusion, subsequent proliferation and initiates spore

production (Tollot et al., 2016). Ros1 also emerged as an important regulator of effector gene

expression. Seventy genes encoding secreted proteins require Ros1 for full expression and 128

genes encoding secreted proteins are downregulated by Ros1. It is speculated that U. maydis

can afford the downregulation of so many effectors, including some of the essential effectors

expressed early during pathogenic development because Ros1 is also required for the

generation of a mucilaginous matrix which may shield hyphae from plant defense molecules

(Tollot et al., 2016).

The transcriptional response of maize plants during infection with U. maydis has been studied in

detail using genome-wide array analysis. These studies have revealed that U. maydis triggers

early plant defense responses when on the leaf surface, presumably through the perception of

microbe-associated molecular patterns (MAMPs). These responses are subsequently

suppressed during the early colonization stages, likely through the action of early effectors

(Doehlemann et al., 2008b). U. maydis also induces transcription of plant cell death suppressors

like cystatins and Bax-inhibitor 1, induces jasmonate signaling and prevents the transition from

sink to source leaves (Doehlemann et al., 2008b). All these studies were performed with the

engineered solopathogenic haploid strain SG200 (Kamper et al., 2006) that can infect plants

without a mating partner. The transcriptome of the U. maydis SG200 strain as well as of SG200

mutants during pathogenic development has been investigated in several studies using array

technology (Kamper et al., 2006; Zheng et al., 2008; Skibbe et al., 2010; Gao et al., 2013;

Schuler et al., 2015; Rabe et al., 2016). A few studies also analyzed the transcriptome of

compatible wild-type strains at discrete stages after infection (Wahl et al., 2010; Zahiri et al.,

2010; Tollot et al., 2016). However, most global analyses of the U. maydis transcriptome during

colonization were so far restricted to stages of biotrophic development where significant

amounts of fungal biomass have accumulated. A comprehensive, time-resolved transcriptional

profiling of the plant-associated stages of the U. maydis life cycle was lacking so far. We have

recently noted that SG200 behaves differently from compatible haploid strains during the late

stages of pathogenic development. While fungal biomass significantly increases after karyogamy

in infections with compatible wild-type strains (Tollot et al., 2016), fungal biomass of SG200

decreased at the corresponding time points (S. Tanaka, P. Erchinger, S. Krombach and R. K.,

personal communication). In this study we have therefore performed an RNAseq analysis of

compatible haploid U. maydis strains starting at 12 h after infection of Z. mays seedlings, i.e.

when cells have mated, switched to filamentous growth and begun to form appressoria, to 12

days post infection (dpi) when tumors contain mature spores. This offered an unprecedented

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view of the changes in the fungal transcriptome associated with the passage through the

biotrophic life cycle. Deep sequencing enabled us to resolve also the very early stages of

infection where fungal biomass is low and fungal transcripts are heavily under-represented. We

focus our attention on fungal metabolism, nutritional strategies, secreted effectors and regulatory

networks. Based on uncovering discrete gene expression waves successively following the

developmental stages of U. maydis, we expect this data set to become a highly valuable

resource for future studies in this biotrophic fungal model as well as related host–pathogen

systems.

Results and Discussion

To analyze the transcriptional changes during plant-associated stages of U. maydis, maize

seedlings of the variety Early Golden Bantam were infected with the compatible U. maydis wild-

type strains FB1 and FB2 (Banuett and Herskowitz, 1989), and infected tissue was collected

over a period of 12 days (Figure 1A). The samples represented the following developmental

stages of U. maydis: filaments and appressoria in the pre-penetration phase (0.5 days post

infection, dpi), penetrating appressoria (1 dpi), dikaryotic biotrophic hyphae with clamp

connections (2 dpi), proliferating hyphae and aggregated hyphae (4 dpi and 6 dpi), fragmented

hyphae (8 dpi) and mature spores (12 dpi; Figure 1A). For each time point, three biological

replicates were generated. As an additional reference, we included FB1 and FB2 grown

exponentially in YEPSL medium.

By Illumina sequencing of mRNA libraries, we created in total more than two billion paired end

reads from all samples (Supplemental Data Set 1). Prior to mapping them to the U. maydis

genome, reads mapping to the maize genome were filtered. These data are not discussed here

but have been deposited in the NCBI Gene Expression Omnibus (Edgar et al., 2002) and are

accessible through GEO Series accession number GSE103876. For the early time points of 0.5

dpi and 1 dpi in particular, reads mapping to the fungal genome amounted to <0.5%, i.e. were

heavily under-represented (Figure 1B and Supplemental Data Set 1). In the total paired-end

reads from all samples, the sequencing depths of the 0.5 and 1 dpi samples has been adjusted

to reach about 500 thousand-read counts that mapped to the fungal genome and in the

remaining samples to reach about one million or more fungal read counts (Supplemental Data

Set 1). In each sample >75% of the 6766 U. maydis genes were represented with more than 10

fragments per kilobase of exon per million (FPKM) fungal reads, indicating that we have

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efficiently detected fungal gene expression across all time points. Correlated with the increasing

fungal biomass, the fungal reads steadily increased during later time points and reached

approximately 10% at 12 dpi (Figure 1B and 1C). We observed a linear correlation between the

number of fungal transcripts and increase in fungal biomass (Supplemental Figure 1). However,

the steep increase in fungal transcripts from 1 dpi to 2 dpi (Figure 1B), that is statistically

significant (Supplemental Data Set 2), was not accompanied by a comparable change in fungal

biomass (Figure 1C). This suggests that the transcriptional activity of U. maydis cells after

recovery from cell cycle arrest (2 dpi) is higher than at the stage before penetration or during

later biotrophic development.

To enable comparisons between samples, all read counts were normalized by DESeq2

(differential expression analysis for sequence count data 2) (Love et al., 2014) (Supplemental

Data Set 3). To assess variability among the samples we performed a principal component

(PCA) analysis (Figure 2A). The three biological replicates formed distinct clusters, indicating

time point-specific expression patterns and an acceptable variation between replicates at any of

the chosen time points. To analyze differential gene expression, we compared expression in all

28 possible pairs of the 8 different time points. This analysis revealed that in total 5759 genes

(85% of all U. maydis genes) were differentially expressed (log2 fold change > 0.5 and adjusted

pvalue < 0.01) in at least one of the 28 comparisons (Figure 2B, Supplemental Data Set 4). To

validate the expression data, we randomly picked nine genes expressed at different levels and

performed RT-qPCR (reverse transcription quantitative PCR) using all 24 generated RNA

samples. We observed a strong linear correlation (r = 0.90) between the RT-qPCR and RNAseq

data (Supplemental Figure 2). To further strengthen the bioinformatics analysis, EdgeR was

used as an alternative tool to normalize the data and to identify differentially expressed genes.

This analysis yielded results comparable to the DESeq2 analysis (Supplemental Data Set 5). In

the following analyses, we refer to the DESeq2 results.

The most dramatic changes in gene expression were observed in pairwise comparisons

including axenic culture conditions (Figure 2B). However, even if comparisons with the axenic

culture sample, which may be unrelated to the conditions on or inside the plant with respect to

nutrient availability, were excluded from the analysis, 4586 genes (68% of the U. maydis genes)

remained differentially expressed (Figure 2B). Such a high proportion of genes showing

differential expression during the plant-associated developmental stages distinguishes U.

maydis from other pathogenic fungi where similar RNAseq studies have been performed. In

Zymoseptoria tritici, Colletotrichum higginsianum and Puccinia striiformis f. sp. tritici 28%, 44%

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and 50% of the genes were differentially expressed, respectively (O'Connell et al., 2012; Rudd

et al., 2015; Dobon et al., 2016). We consider it likely that the coupling between pathogenic and

sexual development in U. maydis and the associated morphological changes contribute to this

high percentage of differentially expressed U. maydis genes. For the other three examples

given, asexual reproduction cycles were studied. However, rather than assuming that the

discretely different life styles of these pathogens contribute to the percentage in differentially

expressed genes, variation in sequencing depth and numbers of analyzed samples in the

different studies cannot be excluded as cause. Overall, the large number of differentially

expressed U. maydis genes underscores the comprehensiveness of our analysis and indicates a

complex transcriptional regulation during all stages of biotrophic development.

In the next step of the analysis, we used the expression data of all stages to perform a weighted

gene co-expression network analysis (WGCNA; Supplemental Figure 3). This analysis identifies

modules of co-expressed genes and represents the modules by their centrally located genes,

referred to as module eigengenes (Zhang and Horvath, 2005; Langfelder and Horvath, 2008).

We identified 14 modules which were color-coded and ranged in size from 36 genes (blue

module) to 1231 genes (yellow module; Supplemental Data Set 6). The expression profiles of

the respective module eigengenes are depicted in Figure 2C. Some of the modules reflect

distinct stages during fungal development. The red module was expressed solely on the plant

surface (0.5–1 dpi). The light-green module was expressed during penetration and early

biotrophic development (1–2 dpi) and ceased afterwards. The magenta module was strongly

induced from 0.5–2 dpi and expression was largely maintained up to 12 dpi. This module

therefore correlated with the establishment and maintenance of biotrophy. The cyan module was

induced after establishment of biotrophy just at the onset of tumor induction (2–4 dpi), and

expression stayed high also at later time points. Thus, the cyan module represented a tumor

module. The blue module was specific for spore development (8–12 dpi).

To confirm that the observed expression patterns are not a product of the specific bioinformatics

tool, we performed k-mean clustering of the gene expression data, sorting the genes into six

clusters. Five of these clusters were highly correlated (> 0.9) with a distinct module of the co-

expression obtained by WGCNA (Supplemental Data Set 7). These included the three virulence

modules red, magenta and cyan, as well as the yellow and lightcyan module. One k-mean

cluster correlated with two, the darkgreen and the burlywood module, which are highly correlated

modules (Supplemental Figure 3B). There was thus a substantial overlap between the

respective gene sets obtained by WGCNA and k-mean clustering with overlap coefficients

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ranging from 0.7 to 1.0 (Supplemental Data Set 7). All subsequent analysis refer to the modules

obtained by WGCNA.

To generate a concise picture of the biological processes (BP) associated with pathogenic

development, each module was subjected to an enrichment analysis for gene ontology (GO)

terms (Supplemental Data Set 8) (Ashburner et al., 2000; The Gene Ontology, 2017). We

visualized the respective enriched gene sets in a weighted similarity network, which facilitated

the identification of the predominant processes in any given module (Figure 3 and Supplemental

Figure 4). In the sections that follow, we make use of these functionally annotated modules to

gain a better understanding of the virulence strategies adopted by U. maydis.

General changes in fungal metabolism throughout the infection cycle

Looking for modules which reflect high metabolic and cellular activity identified the yellow

module (Figure 3A, Supplemental Data Set 9). This module is enriched for genes involved in

translation, ribosome biogenesis, amino acid and nucleic acid biosynthesis, cell division, primary

metabolism and respiration. The highest expression level of the yellow module was observed

during U. maydis growth in axenic culture (Figure 2C), a condition in which the doubling time of a

cell was around two hours, which likely exceeds the growth rate in all plant-associated stages.

The yellow module shows lowest expression at 0.5 and 1dpi, i.e., the stages where mating

occurs and the cell cycle-arrested dikaryon is formed. At 2 dpi the module displays a second

expression peak, likely reflecting the release of the cell cycle block after penetration and early

biotrophic growth. This expression pattern resembles the response of starving cells to nutrient

repletion (Conway et al., 2012). From 2 dpi until 12 dpi, genes of the yellow module were

progressively downregulated (Figure 2C) and instead genes involved in protein catabolism and

autophagy were induced. Such genes are located in the light-cyan module (Figure 3B,

Supplemental Data Set 10), which is almost perfectly negatively correlated with the yellow

module (r = -0.99, Pearson correlation; Figure 2C). Autophagy is an important mechanism by

which eukaryotic cells degrade cytosolic macromolecules and recycle them for the synthesis of

new macromolecules or use them as energy source. In addition to induction of autophagy,

expression of genes involved in lipid transport and lipid metabolism increased after 2 dpi. The

respective genes are located in the dark-green module (Figure 3C, Supplemental Data Sets 8

and 9). These findings suggest that autophagy-mediated cellular recycling as well as

degradation of fatty acids become important during the later biotrophic interaction. Increased

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fatty acid metabolism has also been associated with the biotrophic growth of Z. tritici (Rudd et

al., 2015) and arbuscular mycorrhizal fungi. The latter were recently shown to take up lipids from

their host to sustain colonization (Rudd et al., 2015; Jiang et al., 2017; Keymer et al., 2017). The

synchronous upregulation of autophagy and lipid metabolism genes and downregulation of

ribosome biogenesis genes are typical expression patterns during slowed growth and during

starvation (Gasch et al., 2000). We also observed that nit2 (nitrogen catabolic enzyme regulatory

protein) and snf1 (sucrose nonfermenting 1), the nitrogen and carbon catabolite de-repressors

(Nadal et al., 2010; Horst et al., 2012), have increased transcript levels during tumor formation

compared to early biotrophic growth (Supplemental Data Sets 3 and 4), indicating that carbon

and nitrogen sources may be limiting. Previous studies indicated that tumor tissue is a strong

sink tissue with an efficient supply of organic nutrients from systemic source leaves (Billet and

Burnett, 1978; Doehlemann et al., 2008b; Horst et al., 2008; Horst et al., 2010). Metabolome

profiling of U. maydis infected tissue revealed that free hexoses and amino acids are highly

abundant in tumor tissue, and even reach the levels of juvenile sink tissue (Horst et al., 2010).

This latter finding seems to contradict our RNAseq-based assessment that U. maydis may be

starved for carbon and nitrogen in tumor tissue. However, the metabolome analysis (Horst et al.,

2010) was conducted with SG200, a haploid solopathogenic U. maydis strain. In contrast to

infections with compatible wild-type strains in which fungal biomass continuously increases

within tumor tissue (Figure 1C) (Tollot et al., 2016), SG200 does not increase its biomass late

during infection (S. Tanaka, P. Erchinger, S. Krombach and R. K., personal communication). We

speculate that the free hexoses and amino acids detected in SG200-induced tumor tissue (Horst

et al., 2010) may not accumulate to the same extent in tumors induced by wild-type strains

because they may be take up to support the continuous fungal proliferation (Billet and Burnett,

1978; Doehlemann et al., 2008b; Horst et al., 2008; Horst et al., 2010). The proliferation within

tumors of U. maydis wild-type strains, that are most likely diploid at that stage (Tollot et al.,

2016), may therefore resemble chemostat growth, in which cells grow slowly due to nutrient

limitation but, due to a constant nutrient flow towards tumor tissue, are not starving for essential

nutrients. A detailed metabolic profiling of tumors induced by a mixture of compatible U. maydis

wild-type strains will have to be done to monitor the dynamics of the available nutrients. It will be

interesting to see how this can then be linked to the transcriptome data presented here which

indicate nutrient-limited fungal growth within tumors. We consider it likely that nutrient limitation

could also contribute to the induction of the developmental program for aggregate formation and

teliospore formation inside the tumors.

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Nitrogen Transporters

The utilization of complex nitrogen sources in fungi is regulated by specific transcription factors,

which de-repress the expression of genes needed for the degradation and uptake of various

nitrogen compounds in situations where the most favourable nitrogen sources ammonia and

glutamine are scarce (Marzluf, 1997). In many plant pathogenic fungi, including U. maydis,

mutants of the nitrogen catabolite de-repressors (nit2 in U. maydis) show reduced virulence

(Pellier et al., 2003; Divon et al., 2006; Thomma et al., 2006; Divon and Fluhr, 2007; Kim and

Woloshuk, 2008; Horst et al., 2012). The importance of nitrogen availability in biotrophic

associations is further corroborated by the observation that nitrogen fertilizers generally increase

the susceptibility of plants to biotrophs, whereas they decrease the susceptibility of plants to

necrotrophs (Snoeijers et al., 2000; Dordas, 2008; Ballini et al., 2013). Indeed, U. maydis is

known to grow on various nitrogen sources and has the ability to generate all proteinogenic

amino acids (Holliday, 1961; McCann and Snetselaar, 2008).

To obtain more insights into nutrient assimilation during biotrophic growth, we searched for

transporters that are highly induced in the plant environment compared to growth in YEPSL

medium (2 dpi vs axenic). The top five induced transporters were two putative urea permeases

dur3-1 (UMAG_02625) and dur3-2 (UMAG_06253), two putative oligopeptide transporters (OPT)

opt2 (UMAG_11057) and opt4 (UMAG_02387), and the candidate methylammonium permease

(MEP) ump2 (UMAG_05889) (Supplemental Data Set 11, Figures 4A and 4D). In fungi, related

transporter families are required for nitrogen utilization from peptides, urea and ammonium,

respectively (ElBerry et al., 1993; Lorenz and Heitman, 1998; Hauser et al., 2001; Abreu et al.,

2010; Hartmann et al., 2011; Navarathna et al., 2011; Dunkel et al., 2013), while OPTs can also

mediate sulfur utilization by taking up glutathione (Bourbouloux et al., 2000). All five transporters

are located in the magenta expression module (Figure 2C), and are thus linked to biotrophy

(Supplemental Data Set 11).

Besides the two urea permeases dur3-1 and dur3-2, which are highly induced during biotrophic

development, U. maydis possesses dur3-3 (UMAG_04577), a likely third urea permease. While

dur3-1 and dur3-2 are not induced under nitrogen depletion (Horst et al., 2012; Sanchez-

Arreguin et al., 2017), dur3-3 expression depends on nit2 under nitrogen starvation conditions

(Horst et al., 2012). In line with this observation, our expression analysis placed both dur3-3 and

nit2 into the light-cyan expression module likely involved in the response to limiting nutrients

(Figure 2C).

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To study the contribution of all three urea transporters to virulence and to exclude redundancy,

we made use of the recently established CRISPR-Cas9 system in U. maydis (Schuster et al.,

2016; Schuster et al., 2017) and generated frame shift mutations near the 5´ends of the

respective three genes in haploid FB1 and FB2 strains. The resulting dur3-1,2,3 triple mutants

were affected during growth on medium with urea as sole nitrogen source (Figure 4B), but they

were not affected in virulence (Figure 4C), suggesting that urea uptake is not important for

biotrophic development. However, we did not investigate the effects on virulence when plants

are grown on nitrogen-poor soil and we therefore cannot exclude that the urea transporters

become virulence factors when the overall nitrogen supply of the plant is lower.

The high affinity ammonium transporter Ump2 has been characterized previously (Smith et al.,

2003). Besides mediating ammonium acquisition, this transporter has a signaling function and

initiates filamentous growth under nitrogen starvation (Smith et al., 2003). The ump2 gene is

partially subject to nitrogen catabolite repression, i.e., is regulated by Nit2 (Horst et al., 2012).

The high expression of ump2 during early biotrophic growth, which we observed here

(Supplemental Data Set 11), suggests additional regulation by plant signals. While ump2

mutants were unaffected in virulence (Smith et al., 2003), an ump1 (with a defect in the gene

encoding a low affinity ammonium transporter) ump2 double mutant was severely reduced in

virulence (M. Perlin, personal communication). Our expression analysis thus reinforces the

importance of ammonium uptake and its regulation for biotrophic development of U. maydis.

From the seven U. maydis peptide transporters, none was demonstrated to be regulated by Nit2

in response to nitrogen starvation (Horst et al., 2012). According to our transcriptional profiling,

three oligopeptide transporters (OPTs), opt2, opt4, and opt3 (UMAG_05968), were highly

induced during biotrophic development (Figure 4D). opt5 (UMAG_10896) and opt6

(UMAG_04347) are placed in the dark-green module (Figure 2C) based on their expression

pattern (Figure 4D), and may therefore be coupled to nutritional regulation (see previous

section), while opt1 (UMAG_10908) was constitutively expressed (Figure 4D). U. maydis

possesses only one member of the dipeptide/tripeptide transporter (PTR) family, ptr2

(UMAG_06138), and this gene also showed little variation in expression during fungal

development (Figure 4D). To study whether the biotrophy-coupled induction of OPT transporters

is important for virulence, we introduced frame-shift mutations by CRISPR-Cas9 in the 5´

regions of opt2, opt3 and opt4 genes in the FB1 and FB2 strain backgrounds. Plant infections

with compatible mixtures of the respective triple mutants revealed that these transporters are

important for virulence. While mating and filament formation were not affected (Figure 4E),

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severe disease symptoms such as heavy tumors and plant death were drastically reduced in

infections with these mutants (Figure 4F). The virulence defect of the triple mutant could be

completely restored by introducing single copies of all three opt genes into the ip locus of the

triple mutant strains (Figure 4F). Full complementation makes it unlikely that truncated gene

products were produced due to the CRISPR-Cas9-induced frame-shifts, which could cause the

attenuated virulence in the triple mutant. The full restoration of virulence by complementation

also makes off-target mutations of Cas9 with unwanted side effects highly unlikely. The virulence

function of the oligopeptide transporters suggests that peptides produced from extracellular

proteins are important nutrient sources for U. maydis during biotrophic growth. It has been

previously demonstrated in the yeast Candida albicans that synchronous production of aspartic

proteases and OPTs enables growth on proteins as sole nitrogen source (Martinez and

Ljungdahl, 2005). Interestingly, two secreted aspartic proteases from U. maydis (UMAG_05097

and UMAG_12330) are located in the magenta module (Figure 2C) and are thus co-expressed

with the highly induced OPTs. Given the specific expression pattern of the described subset of

OPTs and aspartic proteases, we propose that extracellular proteolysis and subsequent uptake

of peptides may be intrinsically tied to the plant-associated developmental program of U. maydis.

With respect to utilization of carbohydrates, the uptake of sucrose follows a similar scheme

(Wahl et al., 2010): The high-affinity sucrose transporter srt1 (UMAG_02374), an important

virulence factor of U. maydis, is transcriptionally induced specifically during pathogenic

development, but not by the presence of sucrose under axenic culture conditions or under

carbon starvation (Wahl et al., 2010). The trigger for induction during the biotrophic phase is

unknown, but srt1 as well as the nitrogen-related transporters opt2, opt4, dur3-1, dur3-2 and

ump2, are all induced by the plant surface cues hydrophobicity and 16-hydroxy hexadecanoic

acid (Lanver et al., 2014). This corroborates that the early developmental stages like

filamentation and appressorium formation on the plant surface prepare U. maydis not only with

respect to effectors that can suppress plant defenses, but also with respect to nutrient utilization

systems important for growth inside the plant environment. In the wheat stripe rust fungus, OPT

transporters have been shown to be highly expressed in haustoria, the biotrophic fungal feeding

structures (Garnica et al., 2013). Interestingly, an OPT of the hemibiotroph C. gloeosporioides

was identified as an auxin-induced gene (Chague et al., 2009). In the U. maydis-maize system,

auxin levels are highly induced during infection (Turian and Hamilton, 1960; Basse et al., 1996;

Reineke et al., 2008) and it will be interesting to test whether auxin controls opt expression also

in U. maydis.

14

Overall, our data suggest that nitrogen utilization in U. maydis has a high level of complexity

involving nutrient level dependent as well as independent regulation. Both utilization modes

contribute to virulence and it may be this two-pronged strategy that allows U. maydis to

proliferate efficiently inside the infected plant tissue.

The machinery for protein secretion

From the GO-enrichment analysis (Figure 3) we found the light-green module (Figure 2C)

enriched in various processes related to protein secretion, e.g. ER to Golgi trafficking, vesicular

transport, and N-glycosylation (Figure 3; Supplemental Data Set 8 and 10). Genes of the

unfolded protein response (UPR) also mainly reside in the light-green module (Supplemental

Data Set 10). The light-green secretion machinery module is induced during penetration of the

plant surface (1 dpi) and peaks at 2 dpi (Figure 2C). A similar expression pattern was observed

for many genes encoding secreted proteins (discussed below), and in all likelihood this reflects

the high demand for the secretion machinery during plant colonization. The observed strong

increase in expression of the light-green module from 1 dpi to 2 dpi is challenging to interpret.

Since the 1 dpi time point represents a mixture of mostly filamentous hyphae on the leaf surface

and only a small percentage of hyphae that have developed appressoria and have penetrated

(Figure 1A), it is conceivable that the actual expression levels of the secretion machinery and the

secretome components in the few penetrated hyphae are as high as the levels observed in

branching mycelium at 2 dpi. The lower overall expression values at 1 dpi might thus simply

reflect that only a small percentage of the inoculum has mated and penetrated. This

interpretation is in line with previous studies showing that penetrated hyphae activate the

unfolded protein response (UPR) through the Cib1 (Clp1 interacting bZip1) transcription factor

(Heimel et al., 2013). The UPR affects secretion as well as expression of effectors (Heimel et al.,

2013; Hampel et al., 2016; Lo Presti et al., 2016).

In contrast to N-glycosylation, components of the O-glycosylation pathway are most strongly

expressed in axenic culture as well as on the plant surface (yellow and burlywood module,

Figure 2C and Supplemental Data Set 10). Both processes, O-mannosylation and N-

glycosylation, are necessary for virulence of U. maydis (Fernandez-Alvarez et al., 2009;

Fernandez-Alvarez et al., 2013). Previous studies could trace back the virulence defect of

mutants in the O-mannosylation pathway to a failure in appressoria formation, mainly explained

by the reduced glycosylation of the plant surface cue sensing receptor Msb2 (Fernandez-Alvarez

15

et al., 2012). By contrast, mutants defective in components of the N-glycosylation pathway are

able to penetrate the plant, but induce strong defense responses, e.g. an oxidative burst,

indicative of a malfunction of effectors (Schirawski et al., 2005; Fernandez-Alvarez et al., 2013).

Our expression data strongly support the model that O-mannosylation is particularly important

for early fungal development on the plant surface, while N-glycosylation is mainly necessary

inside the plant tissue to produce functional effectors. For M. oryzae it has been demonstrated

that N-glycosylation of an effector is necessary for its virulence promoting function (Chen et al.,

2014). For U. maydis effectors this has yet to be demonstrated.

Development-associated changes of the secretome

The U. maydis genome contains 467 genes encoding putatively secreted proteins (Schuster et

al., 2017). 215 of the predicted secreted proteins are lacking any predicted structural or

functional domain (Schuster et al., 2017) and some of those proteins have been previously

found to act as important virulence effectors (Lanver et al., 2017). Secreted proteins are

distributed over 12 of the 14 expression modules (Figure 5, Supplemental Data Set 12), i.e.,

their expression occurs in waves following the characteristic expression profile of the respective

module during the course of an infection.This suggests that certain groups of secreted proteins

are only needed during specific periods of an infection cycle. A search for modules in which

secreted proteins are significantly overrepresented identified only three modules: the red module

(p-value 5.71E-5, Fisher exact test), the magenta module (p-value 8.46E-23, Fisher exact test)

and the cyan module (p-value 3.42E-10, Fisher exact test; Figure 2C and Supplemental Data

Set 13), i.e. the modules specific for the plant surface, biotrophic development and tumor

formation, respectively. These three modules have in common that they are almost completely

off during the axenic culture condition (Figure 5). Thus, they likely represent specific virulence

modules.

The secreted proteins of the red module (Figure 2C) predominantly have predicted hydrolytic

functions (Figure 5; Supplemental Data Set 12) and contain amongst others cutinases, lipases,

and peptidases (Supplemental Data Set 12). Thus, the red module harbors key enzymes

potentially involved in the degradation of the cuticle. It is conceivable that some of the secreted

hydrolases also have antimicrobial activity like a putative lysozyme (UMAG_06218). In this

context it may thus not be a coincidence that this module also contains genes from secondary

metabolite clusters like mannosylerythritol lipid (MEL) and ustilagic acid (UA) biosynthesis

16

(Supplemental Data Set 14) (Teichmann et al., 2007). MELs are highly potent surface-active

substances (Fluharty and O'Brien, 1969) while UA has broad antimicrobial activity (Haskins and

Thorn, 1951) and was shown to aid against microbial competitors like the necrotrophic fungus

Botrytis cinerea in co-infection experiments (Teichmann et al., 2007).

After penetrating the cuticle, the fungal hyphae need to breach the polysaccharide plant cell wall,

and most likely this is accomplished by the secretion of plant cell wall degrading enzymes

(PCWDEs) (Schirawski et al., 2005; Lanver et al., 2014) of which U. maydis is predicted to

encode 40 (Figure 5; Supplemental Data Set 12) (Lo Presti et al., 2015). Notably, PCWDEs

were not enriched in the red module but in the light-cyan module (Figure 2C), a module which is

otherwise rather scarce in secreted proteins (Figure 5; Supplemental Data Set 12). We

implicated the light-cyan module in the response to nutrient limitation during growth on the leaf

surface as well as during late biotrophic interaction (see previous sections), suggesting that most

PCWDEs mediate utilization of carbon from plant cell walls at these stages. The expression

pattern of the PCWDEs also correlated with the colonization strategy of U. maydis, in which

plant cell walls must be penetrated at the beginning of the infection to enable initial intracellular

growth, and plant cell walls must be loosened again later during infection to allow enlargement of

cells during tumor formation. The expression data do not allow discrimination between a role for

these enzymes in nutrition and/or breaching plant cell walls. Previous studies in other plant

pathogenic fungi suggest a direct link between carbon utilization and plant cell wall penetration

(Tonukari et al., 2000; Ospina-Giraldo et al., 2003). In U. maydis this needs to be followed up by

functional studies of candidate genes.

Interestingly, our expression data show that few PCWDEs are uncoupled from nutritional

regulation and are instead tied to biotrophic development (i.e., they are found in the magenta

module). In this group of PCWDEs are all three U. maydis GH45 cellulases, five potential

hemicellulases, including two arabinofuranosidases, and the sole predicted pectin lyase

(Supplemental Data Set 12). While virulence functions of the GH45 family and the pectin lyase

were so far not demonstrated (Schauwecker et al., 1995; Doehlemann et al., 2008a; Lanver et

al., 2014), the arabinofuranosidase UMAG_01829 contributes to virulence (Lanver et al., 2014;

Schilling et al., 2014). Our expression data therefore suggest that also carbon supply from

complex sources, e.g., plant cell walls, might be ensured via a two-pronged strategy consisting

of nutrient level-dependent and independent regulation.

17

The magenta module (Figure 2C) predominantly contains genes encoding secreted proteins

lacking known functional domains (p-value 2.79E-8, Fisher exact test; Supplemental Data Sets

12 and 13) (Schuster et al., 2017).This module is the only module overrepresented for core

effectors lacking known domains which are conserved in five sequenced smut fungi (Schuster et

al., 2017) (Supplemental Data Sets 12 and 13): of the 24 core effector families (plus 30

putatively paralogous genes in U. maydis), the magenta module harbours 28 genes including

pep1(protein essential during penetration 1),pit2 (protein important for tumor 2) and stp1 (stop

after penetration 1), the three effectors known to be essential for establishing biotrophy

(Supplemental Data Set 12 and 13) (Doehlemann et al., 2009; Schipper, 2009; Doehlemann et

al., 2011; Mueller et al., 2013). We therefore speculate that these core effectors, which represent

14 different effector families (Schuster et al., 2017), may contain the key determinants for

establishing biotrophy in smut fungi. Another effector found in the magenta module but not

belonging to the core effectors is see1 (seedling efficient effector 1). This effector has been

shown to contribute to the reactivation of plant DNA synthesis in leaves, which is crucial for

tumor formation (Redkar et al., 2015). The early expression peak of see1 observed at 2 dpi

(Supplemental Data Set 12) suggests that this activation of plant DNA synthesis either has to

happen only in a narrow time window or that maintaining replication within tumors is controlled

also by other factors. The fact that expression of genes encoding secreted proteins in the

magenta module is decreasing during later stages of infection (Figure 5) is puzzling, given the

immunity suppressing function of several of these effectors (Doehlemann et al., 2009; Schipper,

2009; Mueller et al., 2013). To shed light on the underlying mechanism, we have generated two

haploid U. maydis strains in which promoters from two of the effectors from the magenta

module, pep1 and stp1, were fused to gfp. In addition, these strains expressed cytoplasmic

mCherry constitutively under the control of the actin promoter. These strains were crossed with

untagged compatible haploid strains and the developmental stage was analyzed when

downregulation of the module takes place. At this time point, fungal aggregates are the

predominant structures in the infected tissue (Tollot et al., 2016). While mCherry was rather

evenly distributed in cells forming the aggregate, pep1 and stp1 promoter activity was mainly

restricted to cells at the surface of the aggregates (Figure 6). To exclude that this spatial

expression pattern is specific for the gfp reporter, we also placed gfp under the control of the

actin promoter, and found the signal rather evenly distributed within the aggregates

(Supplemental Figure 5). This illustrates that while the majority of cells in the aggregates have

ceased to express pep1 and stp1, fungal cells that are in direct contact with the infected plant

tissue maintain high expression levels of these genes, which in all likelihood is sufficient to

18

downregulate plant defenses. How this heterogeneity in effector gene expression is achieved

and to which extent the production of matrix provides additional protection is currently unknown.

The cyan module is induced after establishment of biotrophy (2 dpi) which coincides with the

development of tumors and fungal aggregates. Like the magenta module, the cyan module is

enriched for secreted proteins lacking functional annotation (p-value 1.92E-7, Fisher exact test;

Supplemental Data Set 13).The cyan and magenta modules together contain 153 of the 215

secreted proteins lacking functional signatures (Figure 5 and Supplemental Data Set 12). None

of the candidate effector proteins belonging to the cyan module have been characterized so far.

Due to their tumor-specific expression profile, we hypothesize that effectors in the cyan module

might directly be involved in inducing the plant cell developmental switch to tumor cells. It is

interesting that the cyan module also contains a non-ribosomal peptide synthase

(UMAG_10543), as well as three of the six U. maydis polyketide synthases (UMAG_10532,

UMAG_06414, UMAG_06418; Supplemental Data Set 14). While UMAG_06414 is involved in

spore melanin biosynthesis (Islamovic et al., 2015), the functions of the other polyketide

synthases remain to be elucidated. The third wave of secreted proteins is thus accompanied by

active secondary metabolism. It is not clear whether the yet unknown secondary metabolites

produced are used for communication with the plant or represent molecules needed for

development of U. maydis.

Taken together, the transcriptome reveals the modular expression of putatively secreted proteins

while U. maydis is on the plant surface, during biotrophic development and during tumor

formation. Consecutive waves of effector gene expression linked to the transition from the

biotrophic lifestyle to necrotrophy have also been observed in the hemibiotrophic fungi C.

higgensianum, Colletotrichum graminicola, Z. tritici and Leptosphaeria maculans (Kleemann et

al., 2012; O'Connell et al., 2012; Mirzadi Gohari et al., 2015; Gervais et al., 2016). In C.

higgensianum the induced transcriptome of early infection stages is dominated by genes for

secondary metabolism, leading to the speculation also here that they may actually function in

host manipulation (O'Connell et al., 2012). In the biotrophic fungi Blumeria graminis f. sp. hordei,

Blumeria graminis f. sp. tritici and P. striiformis f. sp. tritici effector expression waves have also

been described (Hacquard et al., 2013; Dobon et al., 2016; Zeng et al., 2017), reinforcing the

idea that the need for certain effectors changes during host colonization and that this is a

conserved feature of different pathosystems.

19

Identification of potential transcriptional regulators of effector waves

To shed more light into the regulation of genes encoding secreted proteins, we visualized a

weighted co-expression network of all differentially regulated genes encoding secreted proteins

and all differentially regulated predicted transcription factors which are connected to at least one

of these (Figure 7A and 7B). We then looked for transcription factors which have strongest

connectivity to the respective modules in which secreted proteins reside. Such intramodular hub

genes are likely the key drivers of a given module (Mason et al., 2009). Through this analysis we

detected three transcription factor genes with strong connectivity (> 0.9) to the red module: the

homeodomain protein UMAG_10544, the TEA/ATTS transcription factor UMAG_02835 and the

bHLH transcription factor UMAG_11235 (Figure 7B and 7C; Supplemental Data Set 15). We

also detected rbf1 (UMAG_03172), the central transcriptional regulator downstream of the

bE/bW complex (Heimel et al., 2010b), with a high intramodular connectivity of 0.9 (Figure 7B

and 7C). Rbf1 is responsible for the bE/bW-induced filamentation and cell cycle arrest (Scherer

et al., 2006; Heimel et al., 2010b). The previously observed downregulation of rbf1 after

penetration has been suggested to be a prerequisite to resume the cell cycle after entering the

plant (Heimel et al., 2010a). In line with our prediction of rbf1 as potential driver of the red

module, the previously identified rbf1-induced genes (Heimel et al., 2010b) are highly

overrepresented in the red module (Supplemental Data Set 13). To what extent the other three

transcription factors (UMAG_10544, UMAG_02835 and UMAG_11235, Figure 7C) detected

here drive expression of the red module needs to be investigated.

By the same type of analysis we found hdp2 (UMAG_04928) (Heimel et al., 2010b), biz1

(UMAG_02549) (Flor-Parra et al., 2006) and the two mating type genes bE (UMAG_12052) and

bW (UMAG_00578) (Gillissen et al., 1992) to be the transcription factors with strongest

connectivity to the magenta module, i.e., the second wave of effectors (Figure 7B and

Supplemental Data Set 15). Those transcription factors have previously been suggested to be

the main inducers of early effectors (Heimel et al., 2010b; Lanver et al., 2014). Furthermore, our

analysis suggests UMAG_11658, UMAG_00501, mzr1 (mig2-5 zinc finger regulator1,

UMAG_05804) (Zheng et al., 2008) and UMAG_02104 (Figure 7C) as being involved in early

effector gene expression. They all have a reasonably strong connectivity to the magenta module

of > 0.9 (Figure 7C). Consistently, mzr1 has been demonstrated to be involved in the expression

of several effector genes (Zheng et al., 2008), which we now place in the magenta module

(Supplemental Data Sets 12). However, in contrast to hdp2 and biz1 (Flor-Parra et al., 2006;

20

Heimel et al., 2010b; Lanver et al., 2014), mzr1 is not a major virulence factor (Zheng et al.,

2008), most likely because biz1 and hdp2 can compensate for the lack of mzr1 during infection.

Genes for which induced expression has been observed after artificial overexpression of rbf1

(Heimel et al., 2010b) are not only found in the red module as discussed above, but also in the

magenta module. The latter group includes hdp2, biz1 and 11 potential effectors (Supplemental

Data Sets 6 and 12). In our analysis of intramodular connectivity, which relies solely on the co-

expression of transcription factors and their targets under natural expression conditions, we

were thus unable to identify the contribution of rbf1 to expression of the magenta module.

Overall our analysis delivered those transcription factors which were expected to control early

effectors, and this made us confident that the analysis would also yield promising candidate

transcriptional regulators for the third expression wave of effectors, i.e. the cyan module.

For the cyan module, three potential drivers were identified, UMAG_05601, UMAG_02765 and

UMAG_04778, which have intramodular connectivities of 0.98, 0.97 and 0.96, respectively

(Figure 7B and 7C and Supplemental Data Set 15). In addition, several other transcription

factors (UMAG_05721, UMAG_11138, fox1 (forkhead box 1, UMAG_01523) (Zahiri et al., 2010),

UMAG_06257, UMAG_06308, UMAG_01456) showed intramodular connectivity at slightly lower

values of between 0.9 and 0.93 (Figure 7B and 7C and Supplemental Data Set 15) and were

thus also candidates for transcriptional regulators of the cyan module. Except for fox1 (Zahiri et

al., 2010), none of these transcription factors had so far been functionally analyzed. fox1

mutants displayed reduced virulence, and transcriptional profiling of this mutant revealed a set of

29 potential effector genes which required fox1 for full expression (Zahiri et al., 2010). These

putative fox1 target genes are significantly overrepresented in the cyan module (Supplemental

Data Set 13). This shows that fox1 contributes to the regulation of the cyan module, but

according to our data and the intramodular connectivity analysis (Figure 7B and 7C) fox1 may

not be the main driver of the third wave of effector gene expression.

To analyze a possible contribution of the yet un-characterized potential regulators of the cyan

module to virulence and tumor formation, we picked the APSES type transcription factor gene

UMAG_04778 (Figure 7C and 8A) and generated a targeted knock-out mutant in the compatible

FB1 and FB2 strains. Interestingly, the introduced frame-shift mutation in UMAG_04778 caused

a strong reduction of virulence, with tumor formation in leaves being completely abolished

(Figure 8B). We therefore named the gene nlt1 (no leaf tumors 1). The virulence defect of the

mutants was almost fully complemented by introduction of a single copy of nlt1 into the ip locus

21

of the respective mutant strains (Figure 8B). This links the mutant phenotype to the inactivation

of the nlt1 gene and makes additional off-target effects of Cas9 unlikely. While chlorotic spots

observed at 4 dpi were comparable after infections with nlt1 mutants, wild-type and

complemented strains, the latter two had induced leaf tumors at 8 dpi, while the nlt1 mutant

failed to do so (Figure 8C). Even at 12 dpi when tumors induced by wild-type and complemented

strains had reached their maximum size and started to turn black due to spore formation, no leaf

tumors were detected in infections with the nlt1 mutants (Figure 8B; Supplemental Figure 6A).

Strong anthocyanin formation in leaves infected with nlt1 mutants (Figure 8C) showed that the

mutants were able to successfully establish a biotrophic interaction. Anthocyanin induction

requires expression of the tin2 (tumor inducing 2) effector gene (Tanaka et al., 2014), a gene

placed in the magenta module (Supplemental Data Set 12). nlt1 mutants displayed two

additional phenotypes rarely seen in wild-type infections: death of the 4th or 5th leaf only in

otherwise viable plants in about 25% of the cases (Figure 8B, Supplemental Figure 6A) and late

spore-filled tumors (detected later than 8 dpi) restricted to the base of the stem in about 8% of

the infected plants (Figure 8B; Supplemental Figure 6B), sometimes associated with death of the

4th or 5th leaf. The presence of basal stem tumors in nlt1 mutant infections indicates that the

ability to induce tumors is not completely abolished and can occur in meristematic stem tissue.

We speculate that the dead leaf phenotype and the appearance of late stem tumors may be

connected, i.e., formation of the basal stem tumors may affect the nutrient supply to the 4th or 5th

leaf.

The ability of the nlt1 mutants to establish a biotrophic interaction is in line with our expectations,

i.e., the third effector wave, which we consider being controlled by nlt1, temporally follows the

establishment of biotrophy mediated by the second effector wave. To verify that nlt1 contributes

to the induction of the third wave of effectors, we measured expression of six potential effectors

of the cyan module during plant colonization (2 and 4 dpi) in the nlt1 mutants and compared this

with the expression in wild-type infections. We found that of 4 of the 6 cyan genes chosen were

highly dependent on nlt1 for induction (Figure 9). As a negative control, we included in this

analysis also six effectors of the magenta module, and none of these genes required nlt1 for

expression (Figure 9). These data demonstrate that nlt1 is indeed a driver of the cyan module.

The observation that two of the tested effectors of the cyan module did not show any

dependence of nlt1 suggests a sharp division of labor between nlt1 and the other transcription

factors in the cyan module. Thus, nlt1 likely drives the expression of a specific subset of

effectors in this module. The two Zn2Cys6 proteins UMAG_05601 and UMAG_02765, which are

highly connected to the cyan module (Figure 7C), are interesting candidates for the expression

22

of other subsets of genes of the cyan module. Such a proposed division of labor may also hold

true for transcription factors of the magenta module and this requires experimental clarification.

Recently, the central regulator of spore formation in U. maydis, ros1 (UMAG_05853), has been

identified (Tollot et al., 2016). Ros1 not only induces spore formation but also regulates many

effector genes (Tollot et al., 2016). We identified Ros1 as being part of the cyan module with a

medium high intramodular connectivity of 0.82 (Supplemetal Table 15). Indeed, Ros1-induced

genes are significantly over-represented in the cyan module (p-value = 3.83E-21, Fisher exact

test; Supplemental Data Set 13). However, a substantial number of genes of the cyan module

are also repressed by Ros1 (Supplemental Data Sets 12 and 13). We therefore conclude that

Ros1 may to some extent contribute to the expression of the third wave of effectors, but is

clearly not a driver of this module. We also searched for modules in which Ros1-repressed

genes were significantly over-represented and Ros1-induced genes were significantly under-

represented. Two modules, red and magenta, fulfilled these criteria (p-value < 0.001, Fisher

exact test; Supplemental Data Set 13). Both modules have in common that they are expressed

early upon contact with the plant, corroborating the previous finding that Ros1 is a repressor of

effector genes required early during pathogenic development (Tollot et al., 2016).

Our approach of using intramodular connectivity to identify regulators of effector gene

expression has identified three sets of transcription factors likely responsible for the plant-

associated expression of the secretome. The first and second sets of transcription factors largely

consist of components of the b-cascade. This corroborates previous studies that emphasized the

impact of the b-cascade for early pathogenic development and the establishment of biotrophy

(Brachmann et al., 2001; Heimel et al., 2010b; Lanver et al., 2014). The third set of transcription

factors that we identified here consists mainly of yet un-characterized genes. It will be an

interesting future task to study the contribution of these transcription factors to the expression of

the third wave of effectors, which is expected to fulfill virulence functions after the early

biotrophic phases have been established. Studying in detail which effector groups are regulated

by these transcription factors and linking this information with the physiology of plants infected

by the respective transcription factor mutants could be a key to identifying novel, host-

manipulating fungal strategies that go beyond suppression of the plant immune system.

This large-scale transcriptome analysis has provided a detailed temporal view of gene

expression in U. maydis throughout its biotrophic life cycle. The analysis has allowed us to

formulate novel hypotheses concerning fungal nutrition in the plant environment, and to visualize

the deployment of certain groups of secreted effectors as well as connected transcription factors

23

during discrete stages of colonization. This is expected to fuel, stimulate and direct future

functional studies of the identified U. maydis genes, as well as provide a new resource for

comparative studies in related fungal pathogens.

24

Methods

Bacterial and fungal strains and growth conditions

The Escherichia coli strain Top10 (Life technologies) was used for cloning purposes. U. maydis

strains used in this study are listed in Supplemental Data Set 16. They are derived from haploid

strains FB1 and FB2 (Banuett and Herskowitz, 1989). DNA from the solopathogenic strain

SG200, which was derived from FB1 (Kamper et al., 2006) was used for gene ampflications. U.

maydis strains were grown in liquid YEPSL (0.4% yeast extract, 0.4% peptone, 2% sucrose) at

28°C on a rotary shaker at 200 rpm. To assess the ability of compatible strains to mate and form

dikaryotic hyphae, FB1 and FB2 wild-type strains and the respective mutants were grown in

YEPSL to an OD600 of approximately 1, harvested by centrifugation (1700g, 10 min at room

temperature), and resuspended in water to an OD600 of 1. Compatible strains were mixed in a

1:1 ratio and 4 µl of this mixture was spotted on potato dextrose plates containing 1% (w/v)

activated charcoal. The plates were incubated at room temperature for 48 hours. Strains which

have mated produce dikaryotic hyphae, visible as white mycelium against the black background.

To test growth of haploid U. maydis strains on different nitrogen sources, FB1 and FB2 wild-type

strains and the respective dur3-1,2,3 triple mutants were grown in YEPSL to an OD600 of

approximately 1, harvested by centrifugation (1700g, 10 min at room temperature), washed two

times with water and resuspended in water to an OD600 = 1. Serial dilutions (OD600=1 to 10-4)

were prepared in water and 2.5 µl of each dilution was spotted on minimal medium plates

(Holliday, 1961), supplemented with 2% glucose and either 10 mM ammonium or 10 mM or 1

mM urea. The plates were incubated for three days at 28°C.

Plasmid and strain construction

All strains generated in this study, plasmids, primers and double stranded DNA fragments are

listed in Supplemental Data Sets 16 to 19. To generate U. maydis mutants, the recently

established CRISPR-Cas9 multiplex system was used (Schuster et al., 2017). The non-

integrative, self-replicating backbone plasmids of this system, pDL272 and pMS73, contain cas9

either under the control of the strong constitutive otef promoter (pDL272) or under control of the

even stronger hsp70 promoter (pMS73). All target sequences for the guideRNA constructs were

25

designed using the E-CRISP tool (www.e-crisp.org) (Heigwer et al., 2014) with medium

stringency settings. pDL272 and pMS73 contain one copy of the U6 promoter for fusion with the

first guide RNA construct. Additional guide RNA constructs are then fused to distinct tRNA

promoters, and all components are finally cloned into one single plasmid (Schuster et al.,

2017).This yielded the plasmids described below.

To generate the plasmid pDL286 for inactivation of the three opt genes, UMAG_11057,

UMAG_05968 and UMAG_02387, the respective double stranded DNA fragments fDL12, fDL13

and fDL14 listed in Supplemental Data Set 18 were synthesized (gBLOCKs from IDT; Coralville,

USA) and cloned into Acc65I-linearized pDL272 using isothermal assembly (Gibson et al.,

2009). To generate the plasmid pDL287 for inactivation of the three urea transporters

UMAG_02625, UMAG_04577 and UMAG_06253, the respective double stranded DNA

fragments fDL15, fDL16 and fDL17 were synthesized and cloned into Acc65I-linearized pMS73.

As the resulting plasmid proved inefficient for inactivation of all three genes except in strain

PH72, a second plasmid, pPH22 was generated which is identical to pDL287 but contains new

target sequences for the inactivation of UMAG_02625 and UMAG_06253. To this end fPH1 and

fPH2 were generated by PCR using oPH163 and oPH160 as primers on gBLOCK fDL15, and

oPH161 and oPH162 as primers on gBLOCK fDL16 (Supplemental Data Sets 17 and 18).

pPH22 was then assembled by Gibson cloning using fPH1, fPH2 , fDL17 and Acc65I-linearized

pMS73. To generate the plasmid pPH20 for inactivation of the transcription factor gene

UMAG_04778, the double stranded fragment fPH3 listed in Supplemental Data Set 18 was

generated by PCR with primers oPH137 and oPH138 using gBLOCK fDL17 as template. This

fragment was cloned via Gibson assembly into Acc65I-linearized pMS73 to yield the final

plasmid.

For complementation analysis we made use of the genome integrative p123 plasmid (Aichinger

et al., 2003). To generate the plasmid for complementation of the opt triple mutant, plasmid

pPH19 was constructed. UMAG_11057 was amplified from genomic DNA of SG200 using

primers oPH131 and oPH132, UMAG_02387 was amplified using primers oPH133 and oPH134,

and UMAG_05968 was amplified using primers oPH135 and oPH136. The three fragments were

inserted into the backbone of p123 cleaved with Acc65I and EcoRV. In pPH19 all three genes

carry their native promoters and termination regions. The plasmid was linearized with SspI prior

to transformation in U. maydis.

26

To generate the plasmid for complementation of the nlt1 mutant, pPH23 was generated. To this

end the nlt1gene was amplified from SG200 DNA with primers oPH164 and oPH165. The

amplified fragment was Gibson assembled into the backbone of p123 cleaved with Acc65I and

EcoRV. In pPH23, nlt1 is driven by its native promoter and termination region. The plasmid was

linearized with PsrI prior to transformation.

For the generation of U. maydis strains carrying fluorescence reporter constructs we constructed

the following integrative plasmids based on the backbone of p123. To place mcherry under the

control of the actin promoter we amplified 2.0 kb of the promoter of the U. maydis actin gene

(UMAG_11232) using primers oDL575 and oDL576. The product was digested with HindIII and

NcoI and subjected to a three fragment ligation with a 0.9 kb NcoI/NsiI mcherry fragment from

pMF5-15g (plasmid collection of M. Feldbrügge; www.mikrobiologie.hhu.de/ustilago-community)

and the HindIII/NsiI digested p123 plasmid backbone yielding pPact-mcherry (pDL252). The

mcherry fragment of pPact-mcherry was replaced with a 0.9 kb NcoI/NsiI gfp fragment from

p123 to generate pPact-gfp (pDL289). To place gfp under the control of effector promoters, we

amplified 1.5 kb of the promoter of pep1 (UMAG_01987), and 0.6 kb of the promoter of stp1

(UMAG_02475) using primer pairs oDL746/oDL747 and oDL744/oDL745, respectively. In

addition, we amplified a gfp-Tnos fragment with the primers oDL742 and oDL743 using p123 as

template. The gfp-Tnos fragment, the respective effector promoter fragment, and the HindIII

linearized pPact-mcherry plasmid were Gibson assembled to yield pPact-mcherry-Ppep1-gfp

(pDL281) and pPact-mcherry-Pstp1-gfp (pDL290). The plasmids containing fluorescence

reporter constructs were linearized with AgeI prior to transformation.

Transformation of U. maydis was performed as described previously (Schulz et al., 1990), and

the transformants were selected on carboxin containing media (2 µg / ml). To identify strains

carrying Cas9-induced mutations, we followed the established screening protocol (Schuster et

al., 2017). The respective loci were amplified and sequenced with gene-specific primers

(Supplementary Table 14). The stable integration of p123-based plasmids was verified by

Southern blot as described previously (Loubradou et al., 2001). All complementation plasmids

were integrated in single copy into the U. maydis ip locus. Fluorescence reporter constructs

were integrated in multiple copies (> 3) into the ip locus.

Plant infections and collection of samples

For the transcriptional profiling, the compatible haploid strains FB1 and FB2 were grown

separately in YEPSL to an OD600 of 1.0. Cells were collected by centrifugation (5 min at 1250g at

27

room temperature) and resuspended in the original volume of sterile water. Compatible strains

were mixed in equal amounts prior to injection into six-day-old maize seedlings of the variety

Early Golden Bantam (UrbanFarmers, New York City, USA) as described previously (Tollot et

al., 2016). The plants were kept in growth chambers (Percival AR95-HIL) with a 16 h light (30

kLux) / 8 h dark cycle, with 28°C / 20°C and 40% / 60% humidity. To generate biological

replicates three plant infections were performed with independently cultivated fungal strains on

different days. For each replicate more than 100 plants were infected with a mixture of

compatible wild-type strains and the same number of plants was mock infected with water.

Samples of ten infected maize seedlings were collected at 0.5, 1, 2, 4, 6, 8, 10 and 12 days post

infection (dpi). For early time points, where symptoms are not yet visible (0.5, 1 and 2 dpi) 2 cm

sections of leaf blade of the 2nd and 3rd leaf were excised 0.5 cm below the infection holes.

These areas were shown by microscopy to be colonized at these time points. At later time points

(4, 6, 8 and 12 dpi) all visibly infected areas, including the leaf blades to approx. 1 cm below the

injection holes on the 2nd, 3rd and 4th leaf and the attached leaf sheets were harvested. At each

time point comparable parts of seedlings were harvested for the mock infected control plants.

For each sample the collected material from ten different plants was pooled, immediately frozen

in liquid nitrogen and stored at -80°C. For the axenic culture samples, FB1 and FB2 cells

separately grown in YEPSL to an OD600 of 1.0 in three independent biological replicates were

harvested by centrifugation (5 min at 3000 g), directly frozen in liquid nitrogen and stored at -

80°C until RNA extraction.

All virulence assays were performed in the greenhouse under controlled conditions with a light-

dark cycle of 28 °C for 14 hours and 20 °C for 10 hours. During the day phase, the illumination

intensity was at least 25 kLux - 30 kLux (with additional sunlight up to 90 kLux). U. maydis was

grown and injected into 6-day-old maize plants as described above. Except for nlt1 mutants the

OD600 of the injected suspension for virulence assays was adjusted to 1.0. To assess virulence

of nlt1 mutants and respective wild-type strains the OD600 was adjusted to 0.2. Virulence

symptoms were scored 12 days post infection using the disease rating scheme developed

previously (Kamper et al., 2006). When scoring disease symptoms of infections with nlt1

mutants, two new disease categories were introduced. Hatched was a category where plants

developed late tumors at the base of the stem only, sometimes associated with death of the 4th

or 5th leaf, as well as a category where only the 4th or 5th leaf died in otherwise healthy maize

seedlings (see Supplemental Figure 6 for examples).

28

RNA extraction and sequencing

Pooled plant material corresponding to individual samples was ground to a fine powder in liquid

nitrogen using a Retsch CryoMill (Retsch GmbH, Haan, Germany) with a 50 ml grinding beaker

and a 20 mm grinding ball. For each sample, the machine was precooled for 30 s followed by 60

s of grinding at 20 Hz. Approx. 500 mg of powder was resuspended in 1 ml TRIzol reagent (Life

Technologies) and total RNA was extracted according to the manufacturer’s recommendation.

For RNA extraction from U. maydis derived from axenic culture, glass beads (0.4–0.6 mm)

together with 1 ml of TRIzol reagent (Life Technologies) were added to the frozen cell pellet

followed by vigorous shaking (IKA Vibrax VXR, 2000 rpm, 10 min) and further processing. To

eliminate genomic DNA contamination, the Ambion Turbo DNA-free Kit (Life Technologies) was

used. The total RNA was further purified using the RNeasy Mini Kit (Qiagen, Hilden, Germany)

and subjected to quality control with an Agilent 2100 Bioanalyzer according to the instructions of

the Agilent RNA 6000 Nano Assay Protocol. Libraries were prepared using the Illumina®

TruSeq® stranded RNA sample preparation Kit and 1 µg of total RNA as input. After poly-A

selection (using poly-T oligo-attached magnetic beads), mRNA was purified and fragmented

using divalent cations under elevated temperature. The RNA fragments underwent reverse

transcription using random primers. This was followed by second strand cDNA synthesis with

DNA Polymerase I and RNase H. After end repair and A-tailing, indexing adapters were ligated.

The products were then purified and amplified (14 PCR cycles, 10 µl template) to create the final

cDNA libraries. After library validation and quantification (Agilent 2100 Bioanalyzer), up to 6

libraries were pooled. Pools were quantified using the Peqlab KAPA Library Quantification Kit

and the Applied Biosystems 7900HT Sequence Detection System. One pool per lane was

sequenced using an Illumina TruSeq PE Cluster Kit v3 and an Illumina TruSeq SBS Kit v3-HS

on an Illumina HiSeq 2000 sequencer with a paired- end (101x7x101 cycles) protocol. The raw

data have been deposited in the NCBI Gene Expression Omnibus (Edgar et al., 2002) and are

accessible through GEO Series accession number GSE103876

(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103876).

Read mapping, normalization and statistical analysis of differential gene expression

All reads were imported into CLC genomics workbench (v8.5) and quality-filtered to keep only

sequences with PhredScores higher than 20 (quality limit 0.01). The reads were trimmed for the

presence of TruSeq adapters and, according to a FastQC quality control, 12 nucleotides at the 5’

29

end were statically removed. Reads smaller than 50 nucleotides were discarded. The paired end

reads were merged to single reads, if the overlap was more than 10 nucleotides. Depending on

the sample, reads from different sequencing runs (but from the same Truseq libraries) were

pooled. For the axenic culture samples, the separately generated reads from FB1 and FB2

strains were pooled and analyzed in one batch. All reads were filtered against the annotated

maize genes (90% identity over 80% of the sequence) and all un-mapped reads were then

mapped against the annotated U. maydis genes (80% identity over 60% of the sequence). For

expression analysis, only uniquely mapping exon read counts were considered. Since highly

expressed genes make up a substantial fraction of the total library, causing the remaining genes

to be under-sampled and possibly making them falsely appear to be down-regulated in that

sample, it is important to normalize for the library composition. We chose to normalize our data

set using the relative log expression (RLE) method implemented in the DESeq2 package (v1.10)

(Love et al., 2014) in R (www.r-project.org). Alternatively we used the trimmed mean of M-values

(TMM) method implemented in the EdgeR package (v3.12) (Robinson et al., 2010; Robinson

and Oshlack, 2010). Both methods are very efficient in normalizing for library composition effects

(Dillies et al., 2013). Pairwise comparisons were made with EdgeR and DESeq2 using the

conditional maximum likelihood (qCML) method and the Cox-Reid profile-adjusted likelihood

(CR) method, respectively. In both analyses, genes with a log2 fold change > 0.5 and Benjamini

and Hochberg-adjusted pvalue < 0.01 were considered differentially expressed (Eisen et al.,

1998). All count data have been deposited in NCBI's Gene Expression Omnibus (Edgar et al.,

2002) and are accessible through GEO Series accession number GSE103876

(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103876). This also includes all count

data mapped to the Z. mays genome, which are not discussed here.

Co-expression analysis

For the unsupervised weighted gene co-expression analysis (WGCNA) analysis, only genes that

have at least for one time point over all three replicates a DESeq2-normalized read count of at

least 10 were considered. 6421 genes passed these filtering criteria. Log2-transformed DESeq2-

normalized counts then served as input for the network analysis. We used the function

blockwiseModules, implementetd in the R (www.r-project.org) WGCNA package (v1.51)

(Langfelder and Horvath, 2008), to create a signed network of a Pearson correlated matrix. All

genes were treated in a single block. The signed network ensured that only positive correlations

were considered. We have chosen a soft power threshold of 7 because this was the lowest

30

power needed to reach scale-free topology (R2 of = 0.92). Module detection was performed with

default settings (mergeCutHeight of 0.15 and enabled PAMstage). The minimal module size was

set to 25 genes. After module detection, highly correlated modules were merged using a

cutheight of 0.3. This step reduced the number of modules from 22 to 14 (Supplemental Figure

4). For each module, the expression profile of the module eigengene was calculated, which is

defined as the first principal component of the module’s expression data. For each gene, the

intramodular connectivity (kME) was calculated, which represents Pearson correlation of the

individual gene with the respective module eigengenes. To visualize weighted co-expression

networks, the topological overlap matrix was exported to Cytoscape (v3.3) (Shannon et al.,

2003) with an adjacency cutoff of < 0.2. k-mean clustering was carried out in MeV (v4.9)

(http://mev.tm4.org) using the same DESeq2 normalized and log2 transformed expression data

set that was used for the WGCNA analysis (see above). To determine a suitable number of

clusters we used the Figure of Merit (FOM) analysis, and chose to cluster the genes into six

clusters. The k-mean clusters were completed after 20 iterations using Pearson correlation as

distance measure. Average linkage hierarchical clustering of individual modules and gene sets

was performed with Cluster (v3.0) (Eisen et al., 1998) and heatmaps were visualized with Java

Treeview (v1.1.6).

Gene ontology term enrichment analysis

GO term enrichment analysis (Ashburner et al., 2000; The Gene Ontology, 2017) was performed

with the GOseq package (v1.22) (Young et al., 2010) in R (www.r-project.org) using

hypergeometric testing and an over-represented p-value cutoff of < 0.005. For the enrichment

analysis of the individual modules only genes were considered which had an intramodular

connectivity (kME) of greater than 0.5. Visualization of GO term networks was performed with

the Cytoscape Enrichment Map plugin (v2.1; www.baderlab.org/Software/EnrichmentMap)

(Merico et al., 2010) using a Jaccard coefficient cutoff of > 0.2. Clusters of gene sets were

manually marked and named according to their GO annotation using the Cytoscape Word Cloud

plugin (v3.1).

Reverse transcription quantitative PCR and quantitative real-time PCR

31

For gene expression analysis via RT-qPCR, plants were infected with the indicated strains

adjusted to a cell density at OD600 of 1.0. Then, 1–2 µg of isolated total RNA was reverse-

transcribed with oligo dT primers using First-Strand Super Script III cDNA Synthesis Kit (Life

Technologies). Per RT-qPCR reaction, cDNA derived from 10–50 ng RNA was deployed. The RT-

qPCR was performed in a Bio-Rad iCycler using SYBR Green RT-qPCR SuperMix-UDG (Life

Technologies). Cycling conditions were 1 min 30 s at 95°C, followed by 45 cycles of 15 s at 95°C,

30 s at 60°C and 30 s at 72°C. Primers are listed in Supplemental Data Set 17. Two control genes

were used: the previously established peptidylprolyl isomerase (ppi) UMAG_03726 plus the rab7

GTPase UMAG_05511, which was according to the dataset created here one of the most

constantly expressed genes (coefficient of variation of 14.2). The arithmetic mean of the two

reference Ct values served as normalization. For the expression of fold changes of RT-qPCR

data, the 2-DDCt method was used (Livak and Schmittgen, 2001).

To determine fungal biomass, the same plant material that was used for the RNAseq analysis was

utilized. DNA was extracted from the ground powder using a phenol-based protocol (Hoffman and

Winston, 1987). Then, 150 ng total DNA was subjected to qPCR (quantitative real-time PCR)

analysis with plant-specific GAPDH (oDL833/oDL834) and fungal specific ppi primer pairs

(oPH231/oPH232) Supplemental Data Set 14), and the ratios of fungal DNA to plant DNA (2-ΔCt)

were calculated for each sample. This analysis allowed us to relate for each sample the number

of fungal transcripts to the amount of fungal DNA.

Microscopy

To visualize fungal aggregates in tumor tissue, 7 dpi tumors were collected and thin slices of the

tumors were prepared using a razor blade. The slices were analyzed by a TCS-SP5 confocal

microscope (Leica Microsystems). GFP was excited at 488 nm and emitted fluorescence was

detected at 495–530 nm. mCherry was excited at 561 nm and emission was detected at 580–

630 nm. Images were processed using LAS-AF software (Leica Microsystems).

Accession numbers, genome references and annotations

The Z. mays reference genome was downloaded from Ensembl

(http://plants.ensembl.org/info/website/ftp/index.html, Zea_mays.AGPv3.28.dna.toplevel). The U.

maydis reference genome was downloaded from NCBI

(www.ncbi.nlm.nih.gov/www.ncbi.nlm.nih.gov, accession numbers NC_026478.1 -

NC_026500.1, NW_011929455.1 - NW_011929458.1, NC_008368.1). Gene ontology categories

32

and InterPro motifs of U. maydis genes were extracted from the pedant database

(http://pedant.helmholtz-muenchen.de/). Membrane transport proteins were defined by filtering

the InterPro annotation of U. maydis for any of the following keywords: porter, porting, P-type

ATPase, facilitator, permease, channel, aquaporin. Secreted hydrolases were classified by

searching the InterPro descriptions of the 467 secreted proteins for the key words hydrolase,

protease, peptidase, nuclease, lipase, esterase, lyase, hydrolysis, glucanase and lytic.

Carbohydrate active enzymes (CAZymes) were categorized according to the CAZy database

(http://www.cazy.org/www.cazy.org). CAZymes potentially affecting the plant cell wall were

defined previously (Lo Presti et al., 2015). To identify and classify U. maydis transcription factors

we used a previously defined list of 82 InterPro signatures known to be involved in fungal

transcriptional regulation (Park et al., 2008), and supplemented this list with the InterPro

signatures IPR001092, IPR011598 representing bHLH and IPR018608 representing WOPR

transcription factors, respectively. The InterPro annotation of U. maydis was then filtered for the

presence of any of those 85 signatures. The RNA-sequencing data are available in the Gene

Expression Omnibus (GEO) database under the accession number GSE103876.

33

Supplemental Data

Supplemental Figure 1. Correlation between amount of fungal transcripts and fungal biomass

Supplemental Figure 2. Correlation between RNAseq and RT-qPCR derived expression data

Supplemental Figure 3. Detection of gene co-expression modules of U. maydis during pathogenic development

Supplemental Figure 4. Enriched biological processes in co-expression modules

Supplemental Figure 5. Expression of GFP under control of the actin promoter in fungal aggregates

Supplemental Figure 6. Disease categories specific to infections with nlt1 mutants

Supplemental Data Set 1: Overview of read counts mapping to U. maydis and Z. mays

Supplemental Data Set 2: Statistics of changes of fungal biomass based on DNA and

transcripts

Supplemental Data Set 3: DESeq2-normalized U. maydis read counts

Supplemental Data Set 4: Differentially expressed U. maydis genes throughout the infection

Supplemental Data Set 5: Overlap between differentially expressed gene sets identified by

DESeq2 and EdgeR

Supplemental Data Set 6: Gene co-expression modules of U. maydis and intramodular

connectivies of the genes

Supplemental Data Set 7: Overlap between modules detected by WGCNA and clusters

detected by k-mean clustering

Supplemental Data Set 8: GO-term enrichment analysis of co-expression modules

Supplemental Data Set 9: Expression of primary metabolism pathways

34

Supplemental Data Set 10: Expression of autophagy, glycosylation and ER-quality control

pathways

Supplemental Data Set 11: Expression of membrane transporters

Supplemental Data Set 12: Expression of the secretome

Supplemental Data Set 13: Enrichment of diverse gene sets in co-expression modules

Supplemental Data Set 14: Expression of secondary metabolism pathways

Supplemental Data Set 15: Intramodular connectivities of transcription factors with co-

expression modules

Supplemental Data Set 16: U. maydis strains used in this study

Supplemental Data Set 17: Oligonucleotides used in this study

Supplemental Data Set 18: Double stranded DNA fragments used in this study

Supplemental Data Set 19: Plasmids used in this study

Acknowledgements

We are grateful to Armin Djamei and Florian Raths for providing preliminary results on U. maydis

oligopeptide transporters. We thank Michael Bölker for sharing his interpretation of fungal growth

within tumors. We acknowledge Nicole Ludwig for helping with sampling of infected plant

material. We thank Kai Heimel for listing U. maydis UPR components and Gertrud Mannhaupt

and Ulrich Güldener for providing gene annotations. We enjoyed helpful discussions with Frank

Hochholdinger and Kristian K. Ullrich and all members of the Kahmann group. This work was

supported by generous funds from the Max Planck Society.

35

Author Contributions

R.K. conceived the project; A.M. planned and conducted plant infections and generated samples

for RNAseq analysis; M.F. and J.A. performed RNA sequencing; D.L. and G.S. performed data

analysis; F.B.H. and S.A.R. provided critical input in data analysis and quality control; P.H. and

D.L. generated mutants; P.H. performed RT-qPCR analyses and virulence assays; A.M. and

C.P. contributed to the finalization of the dataset; D.L. and R.K. led the study and wrote the

manuscript with help from S.R.

36

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A

time (dpi)

B C

0.5 dpi 1 dpi 2 dpi 4 dpi

6 dpi 8 dpi 12 dpi

biotrophicdevelopment in

tumor tissue

biotrophicdevelopment

Figure 1. Changes in the amount of fungal transcripts and fungal biomass during infection(A) Schematic view of cross-sections of U. maydis infected maize leaves illustrating the stages of fungal development aswell as plant tumor formation at the time points analyzed by RNAseq-based transcriptional profiling. U. maydis infection isnot synchronized, and each sample thus contains fungal transcripts from different developmental stages. dpi: days postinfection; plant leaf tissue: green; vascular tissue: brown; fungal cytoplasm: orange; empty fungal hyphae separated bysepta: grey; plant tumor cells: beige; matrix: rose; fungal spores: ornamented in black. (B) Amount of fungal transcriptsbased on the RNAseq analysis. For each time point (0.5, 1, 2, 4, 6, 8 and 12 dpi) the ratio of reads uniquely mapped to theU. maydis genome relative to the total number of uniquely mapped reads (U. maydis and Z. mays) was determined. Errorbars denote standard deviation of three biological replicates. (C) Fungal biomass determination based on the amount ofgenomic DNA. A qPCR with plant-specific (GAPDH) and fungus-specific (ppi) primers was performed using the sameinfected plant material that was used for the RNAseq analysis. Data points give mean ratios of fungal DNA to plant DNA(2-ΔCt). Error bars denote standard deviation of three biological replicates.

0

1

2

3

4

5

6

0 2 4 6 8 10 12

rela

tive

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alD

NA

time (dpi)

biotrophicdevelopment in

tumor tissue

biotrophicdevelopment

rela

tive

fung

altra

nscr

ipts

0 2 4 6 8 10 12

0.16

0

0.1

0.06

0.02

0.04

0.08

0.14

0.12

-0,6

-0,3

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ax 0.5 1 2 4 6 8 12

light-cyan (699)

-0,6

-0,3

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ax 0.5 1 2 4 6 8 12

red (398)

-0,6

-0,3

0,0

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burlywood (459)

-0,6

-0,3

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ax 0.5 1 2 4 6 8 12

dark-green (561)

-0,6

-0,3

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ax 0.5 1 2 4 6 8 12

beige (413)

-0,6

-0,3

0,0

0,3

0,6

ax 0.5 1 2 4 6 8 12

blue (36)

-0,6

-0,3

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ax 0.5 1 2 4 6 8 12

pink (302)

-0,6

-0,3

0,0

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ax 0.5 1 2 4 6 8 12

yellow (1231)

-0,6

-0,3

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light-green (297)

-0,6

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ax 0.5 1 2 4 6 8 12

cyan (795)

-0,6

-0,3

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ax 0.5 1 2 4 6 8 12

magenta (771)

-0,6

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ax 0.5 1 2 4 6 8 12

green (196)

-0,6

-0,3

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ax 0.5 1 2 4 6 8 12

green-yellow (144)

-0,6

-0,3

0,0

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ax 0.5 1 2 4 6 8 12

salmon (119)

PC1: 53% variance

PC

2: 2

2% v

aria

nce

0.5 1 2 4 86 12

dpi

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0.5

1

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8

6

dpi

A B

C

1930 1750 1711 1970 2008 2070 1543

934

708

224 1272 1020 988

900 817 786 840 654

899 1153 1504

76 334 681

108 511

369

7801359 1262 1621 1662 1672

63 1511 1070 1050 1012 710

1093 813 848 929 678

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67 375 681

59 401

213

1585 1704

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-40

-30

-20

-10

0

10

20

30

-60 -40 -20 0 20 40

axenic0.5 dpi1 dpi2 dpi4 dpi6 dpi8 dpi12 dpi

Figure 2. Quality assessment of the RNAseq dataset of U. maydis during infection(A) Principal component analysis (PCA) of RNAseq data. The replicates of each developmental stage of U. maydis (axenic, 0.5, 1, 2, 4, 6, 8 and 12 dpi) form distinct clusters. (B) The expression data of the eight analyzed developmental stages of U. maydis (axenic, 0.5, 1, 2, 4, 6, 8 and 12 dpi) was the basis to extract all 28 possible contrasts. Genes with a log2 fold change > 0.5 and adjusted p-value < 0.01 were considered differentially expressed. Grey triangles depict the number of genes expressed at higher levels at the stages denoted by the horizontal labels, and yellow triangles depict the number of genes expressed at higher levels at the vertically labeled stages. In total, 5759 of the 6766 U. maydis genes were differentially expressed. (C) Modules of co-expressed genes during pathogenic development of U. maydis. The RNAseq expression dataset was subjected to a weighted gene co-expression analysis (WGCNA) to detect modules of co-expressed genes. Each graph shows the expression of the module eigengene, which can be considered as the representative gene of the respective co-expression module. The vertical axes indicate log2 expression values relative to the mean expression across all stages. The horizontal axes indicate the stages, i.e. axenic (ax), 0.5, 1, 2, 4, 6, 8 and 12 dpi. Error bars indicate standard deviation of three biological replicates. The modules are named according to their color, and the number of genes residing in each module is given in parentheses.

chromosomesegregation

mitoticcheckpoint

proteasome

translation

protein complexdisassembly

chromosomecondensation

nucleartransport

ribosomebiogenesis

organelletargeting

mitochondrialimport

mitoticspindle

tRNAaminoacylation

amino acidbiosynthesis

purine & pyrimidinemetabolism

protontransport

aerobicrespiration

glucosemetabolism

DNA replication

telomereelongation

primarymetabolism

lipidtransport

alcoholmetabolism

acetatemetabolism

transcriptionalregulation

lipidmetabolism

signaltransduction

proteincatabolism

vesiculartransport

amino acidbiosynthesis

ER to golgi traffick

clathrin-mediated

endocytosis

ER import

N-linkedglycosylation

proteinprenylation

actinpolymerization

signaltransduction

phosphatidylinositolmetabolism

transmembranetransport

response tonitrogen levels unfolded protein

response

secondarymetabolism

polysaccharidecatabolism

gene set size

>500300-500200-300100-20050-100<50

A B

autophagy

C

D E F

N-linkedglycosylation

endosometransport

amino acid catabolism

ion transport

nitrateassimilation

lipidbiosynthesis

positive regulation ofcellular process

regulation ofacetylation

H

secondary metabolism

G

Figure 3. Biological processes enriched in selected co-expression modulesGene ontology enrichment analysis for the yellow (A), light-cyan (B), dark-green (C), red (D), magenta (E), cyan (F), light-green (G), and blue (H) module. Only biological process (BP) terms were considered in the analysis. Each significantly enriched gene set (hypergeometric p-value < 0.005) is represented by a node. Node sizes are proportional to the number of genes within the respective gene set, and the edges indicate overlapping member genes. Highly similar gene sets tend to form clusters, which were manually circled and labeled with appropriate summarizing terms. Gene sets that have no overlap with other enriched GO terms are shown in the rightmost corner and are not labeled despite two exceptions, one in D (secondary metabolism) and one in F (N-linked glycosylation). See Supplemental Data Set 8 to retrieve all GO terms of the enriched gene sets.

0

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60

80

100

FB1 x FB2 PH72 x 112 PH109 x 1100

20

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FB1xFB2 DL755 x PH158 PH89 x PH167

UMAG_02625 (dur3-1)

UMAG_06253 (dur3-2)

UMAG_04577 (dur3-3)

UMAG_11057 (opt2)

UMAG_02387 (opt4)

UMAG_05968 (opt3)

UMAG_10908 (opt1)

UMAG_10896 (opt5)

UMAG_04347 (opt6)

UMAG_06138 (ptr2)

-3 30

0.5

1 2 4 6 8 12axcentered log2 expression

0.5

1 2 4 6 8 12ax

sym

ptom

sof

plan

ts(%

)

sym

ptom

sof

plan

ts(%

)

B E

A D

FB1 x FB2

dur3-1emdur3-2emdur3-3em

PH109 x PH110PH72 x PH112 FB1 x FB2

opt2emopt3emopt4em

opt2em-opt2-Copt3em-opt3-Copt4em-opt4-C

n = 151 n = 144 n = 150 n = 117 n = 113 n = 115

DL755 x PH158 PH89 x PH167

no symptoms medium tumorschlorosis heavy tumors dead plantsligula swelling small tumors

FC

10 mM urea 1 mM urea

FB1 x FB2 DL755 x PH158 PH89 x PH167FB1

FB2PH110

PH112

PH72PH109

10 mM NH4+

Figure 4. Expression pattern and virulence function of nitrogen compound transporters(A) The heat-map shows the expression profiles of the U. maydis urea transporters; log2 expression values are visualized relative tothe mean expression across all stages. (B) U. maydis dur3 transporters are important for nitrogen utilization from urea. Serialdilutions of FB1 and FB2 wild-type strains and the respective dur3-1,2,3 triple mutants in FB1 (PH72 and PH109) and FB2 (PH110and PH112) were spotted on minimal medium with ammonium or urea as sole nitrogen source in the indicated final concentrations.(C) The indicated mixtures of compatible strains were injected into maize seedlings and symptoms were scored 12 days afterinfection according to severity; the color code for each category is given below. Three independent experiments were carried outand the average values are expressed as percentage of the total number of infected plants (n), which is given above each column.(D) The heat-map shows the expression profile of the U. maydis peptide transporters; log2 expression values are shown relative tothe mean expression across all stages. (E) The indicated mixtures of compatible haploid strains were spotted on charcoal-containing agar plates. FB1 and FB2 are wild-type strains, DL755 and PH158 are compatible opt2,3,4 mutants and PH89 andPH167 are opt2,3,4 mutants simultaneously complemented with wild-type opt2,3,4 genes. The occurrence of white myceliumindicates the formation of dikaryotic hyphae. (F) The indicated mixtures of compatible strains were injected into maize seedlings andsymptoms were scored 12 days after infection according to severity as described in (C). For (C) and (F): The gene name followedby em indicates that the respective gene was inactivated by CRISPR-Cas9. The gene name followed by -C indicates that a singlecopy of the respective gene was introduced into the indicated strains to test for complementation. Please note that dead plants,which represent the most severe symptom category, are a result of the artificial virulence assay that is based on young maizeseedlings and a high inoculum. U. maydis is a strict biotroph and does not kill plants under natural conditions.

mag

enta

axen

ic0.

51 2 4 6 8 12 si

gnat

ure

PC

WD

E

cyan

light

-gre

en

red

light

-cya

nda

rk-g

reen

axen

ic0.

51 2 4 6 8 12 si

gnat

ure

PC

WD

E

hydr

olas

e

hydr

olas

e

centered log2 expression

-3 30

mod

ule

mod

ule dpi

dpi

blue

beig

eye

llow

burly

woo

dpi

nk

Figure 5. Expression of the U. maydis secretomeThe heat-map shows expression of differentially expressed genes encoding putative secreted proteins. For each module indicatedon the left, genes were hierarchically clustered, and log2 expression values are visualized relative to the mean expression across allstages. Modules are colored according to Figure 2C. Black bars on the right indicate for each gene the presence of knownsignatures (based on InterPro scan), the predicted hydrolytic capabilities and more specifically the predicted ability to degrade theplant cell wall (PCWDE).

50 µm

Pactin:mcherry Ppep1:gfp Pactin:mcherry Pstp1:gfp

BFm

Che

rry

GFP

mer

ge

Figure 6. Heterogenous effector gene expression in fungal aggregatesMaize seedlings were infected with mixtures of FB1 carrying the indicated reporter constructs (top) and FB2 wild-type strains.Seven days post inoculation, fungal aggregates within the tumor tissue were visualized by confocal microscopy. Thefluorescence of GFP and mCherry was monitored and merged with the respective brightfield (BF) image. All images areprojections of multiple z-stacks. GFP fluorescence indicative of effector gene expression was mainly detected at the surface ofthe aggregates, while mCherry control fluorescence was rather evenly distributed throughout the aggregates.

A C

ID Name Type

0.93 0.30 -0.39 UMAG_10544 Homeobox0.92 0.41 -0.12 UMAG_02835 TEA/ATTS0.92 0.46 -0.11 UMAG_11235 bHLH 0.90 0.05 -0.61 UMAG_03172 rbf1 C2H20.90 0.38 -0.34 UMAG_04581 bHLH 0.34 0.98 0.74 UMAG_04928 hdp2 Homeobox0.47 0.98 0.57 UMAG_02549 biz1 C2H20.53 0.97 0.57 UMAG_12052 bE Homeobox0.45 0.97 0.65 UMAG_00578 bW Homeobox0.27 0.95 0.63 UMAG_11658 HMG0.32 0.95 0.63 UMAG_00501 CCHC0.07 0.94 0.86 UMAG_05804 mzr1 C2H20.58 0.92 0.47 UMAG_02104 Winged helix-0.37 0.64 0.98 UMAG_05601 Zn2Cys6-0.20 0.69 0.97 UMAG_02765 Zn2Cys6-0.29 0.72 0.96 UMAG_04778 nlt1 APSES-0.35 0.65 0.94 UMAG_05721 bHLH-0.24 0.69 0.93 UMAG_11138 C2H2-0.27 0.78 0.92 UMAG_01523 fox1 Forkhead-0.46 0.43 0.92 UMAG_06257 Zn2Cys6-0.15 0.71 0.92 UMAG_06308 Winged helix-0.09 0.78 0.91 UMAG_01456 Zn2Cys6

rbf1hdp2

biz1bEbW

mzr1

fox1

nlt1

B

Figure 7. Association of transcription factors with modules containing secreted proteins(A) and (B) Network of gene expression profiles of differentially regulated genes encoding secreted proteins (A) and differentiallyregulated transcription factors (B) belonging to the respective modules in which the secreted proteins reside. The weighted networkis based on the topological overlap matrix of the expression data, edges were included when the pairwise overlap was greater than0.2, and genes are colored according to their modular membership. Selected transcription factors are labeled with their respectivenames. (C) Connectivity of transcription factors to the red (left panel), magenta (middle panel) and cyan module (right panel).Depicted are all transcription factors having an intramodular connectivity of greater than 0.9 to any of the three modules. Colorintensity indicates connectivity strength.

0

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60

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FB1xFB2 DL755 x PH158 PH89 x PH167

-3 30

centered log2 expression

BA

C

0.5 1 2 4 6 8 12ax

sym

ptom

sof

plan

tsFB1 x FB2 PH133 x PH139PH75 x PH86

nlt1em nlt1em-nlt1-C

n = 138 n = 140 n = 136 no symptoms

medium tumors

heavy tumors

ligula swelling

small tumors

death of 4th - 5th leaf

chlorosis

tumors at baseof stem only

4 dp

i8

dpi

FB1 x FB2 PH133 x PH139PH75 x PH86

UMAG_4778 (nlt1)

dead plants

Figure 8. Role of U. maydis nlt1 in virulence(A) The heat-map shows the expression profile of the U. maydis nlt1 gene; log2 expression values are visualized relative to themean expression across all stages. (B) The indicated mixtures of strains were injected into maize seedlings and symptoms werescored 12 days after infection according to severity; the color code for each category is given on the right. The nlt1 mutants wereunable to induce tumors in leaves. nlt1em indicates that the nlt1 gene was inactivated by CRISPR-Cas9. nlt1-C indicates that asingle copy of nlt1 was introduced into the indicated strains to test for complementation. Three independent experiments werecarried out and the average values are expressed as a percentage of the total number of infected plants (n), which is given aboveeach column. Please note that dead plants, which represent the most severe symptom category, are the result of the virulenceassay that is based on young maize seedlings and high cell density of the inoculum. U. maydis is a strict biotroph and does not killplant tissue under natural conditions. (C) Representative leaves of infections with wild-type strains, nlt1 mutants, and thecomplemented strains at 4 and 8 dpi are shown. Examples of stem tumors and dead leaves observed in infections with nlt1 mutantsare depicted in Supplemental Figure 6.

02468

10

2dpi 4dpi

UMAG_02297

02468

10

2dpi 4dpi

UMAG_11415

0

4

8

12

16

2dpi 4dpi

UMAG_05929

02468

10

2dpi 4dpi

UMAG_05312

02468

10

2dpi 4dpi

UMAG_10555

02468

10

2dpi 4dpi

UMAG_06128

0

1

2

3

2dpi 4dpi

pit2

0

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3

2dpi 4dpi

cmu1

0

1

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eff1-4

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egl2

0

1

2

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2dpi 4dpi

pep1

0

1

2

3

2dpi 4dpi

stp1

wtnlt1em

**

**

Figure 9. Regulation of effector gene expression by nlt1The expression of selected effector genes during pathogenic development in crosses of FB1 and FB2 wild-type strains (circles)as well as in crosses of compatible nlt1 mutants (triangles) was measured via RT-qPCR. Six effector genes of the magentamodule (leftmost two panels) and six potential effectors of the cyan module (rightmost two panels) were tested. In each graph,the expression of the respective gene in the wild-type 2 dpi samples was set to 1 and relative expression is depicted. Significantexpression differences (p-value < 0.01, Student’s t-test) between nlt1 mutants and wild-type strains are indicated with anasterisk if applicable. Error bars denote standard deviation of three biological replicates. Four of six genes of the cyan modulerequired nlt1 for expression while none of the six effector genes of the magenta module was regulated by nlt1.

DOI 10.1105/tpc.17.00764; originally published online January 25, 2018;Plant Cell

Clément Pellegrin, Stefanie Reissmann, Janine Altmüller, Stefan A Rensing and Regine KahmannDaniel Lanver, André N. Müller, Petra Happel, Gabriel Schweizer, Fabian B. Haas, Marek Franitza,

The biotrophic development of Ustilago maydis studied by RNAseq analysis

 This information is current as of July 6, 2018

 

Supplemental Data

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