LARGE-SCALE BIOLOGY ARTICLE

The Biotrophic Development of Ustilago maydis Studied byRNA-Seq AnalysisOPEN

Daniel Lanver,a,1,2 André N. Müller,a,1,3 Petra Happel,a Gabriel Schweizer,a Fabian B. Haas,b Marek Franitza,c

Clément Pellegrin,a Stefanie Reissmann,a Janine Altmüller,c,d Stefan A. Rensing,b,e and Regine Kahmanna,4

aMax-Planck-Institut für Terrestrische Mikrobiologie, Abteilung Organismische Interaktionen, 35043 Marburg, Germanyb Philipps Universität Marburg, Fb17 Biologie, AG Zellbiologie der Pflanzen, 35043 Marburg, GermanycCologne Center for Genomics, University of Cologne, 50931 Cologne, GermanydCenter for Molecular Medicine Cologne, University of Cologne, 50931 Cologne, GermanyeBIOSS Centre for Biological Signalling Studies, University of Freiburg, 79104 Freiburg, Germany

ORCID IDs: 0000-0002-8679-4598 (G.S.); 0000-0002-7711-5282 (F.B.H.); 0000-0002-6591-655X (C.P.); 0000-0002-0225-873X(S.A.R.); 0000-0001-7779-7837 (R.K.)

The maize 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-typestrains. 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 virulencemodules rather than being controlled by nutrient availability. We show that oligopeptide transporters likely involved innitrogen assimilation are important virulence factors. By measuring the intramodular connectivity of transcription factors, weidentified the potential drivers for the virulence modules. While known components of the b-mating type cascade emerged asinducers for the plant surface and biotrophy module, we identified a set of yet uncharacterized transcription factors as likelyresponsible for expression of the tumor module. We demonstrate a crucial role for leaf tumor formation and effector geneexpression for one of these transcription factors.

INTRODUCTION

Plant pathogenic fungi have adopted discrete lifestyles in in-teraction with their hosts ranging from biotrophy, where the plantneeds to be kept alive to sustain fungal growth, to necrotrophy,where infected plant tissue is actively killed and the fungus feedson the dead material. In between these extremes are hemi-biotrophs, which initially establish a biotrophic phase and then atsome point switch to necrotrophic development. To promote therespective colonization strategy pathogens secrete a large ar-senal of effector proteins. In recent years the study of lifestyletransitions in plant pathogenic fungi by time resolved tran-scriptome analyses through RNA-seq provided deep insights intothe 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; Dobonet al., 2016; Thatcher et al., 2016; Copley et al., 2017; Wang et al.,2017; Zeng et al., 2017; Massonnet et al., 2018). These and otherstudies (Toruño et al., 2016) have shown that different sets ofeffectors are synthesized and presumably neededduring discretedevelopmental stages in fungal pathogens.The biotrophic fungus Ustilago maydis causes smut disease in

maize (Zea mays). This system has advanced to a model for bi-otrophic pathogens mainly because of the ease by which fungalgenes can be manipulated through reverse genetic techniques(Kämper, 2004; Dean et al., 2012; Schuster et al., 2016). InU.maydisand relatedsmut fungi, theprocessofplant colonizationis intimately coupled with sexual development. Plant colonizationis initiated when haploid cells of a compatible mating type rec-ognize each other on the leaf surface via a pheromone-receptorsystem(Bölkeretal.,1992), fuse, anddevelopadikaryoticfilament.Filament formation and subsequent pathogenic development arecontrolled by the heterodimeric bE/bW (bEast/bWest) homeo-domain transcription factor, which is formed after cells with dif-ferent alleles of the b locus have fused (Gillissen et al., 1992). Thedikaryon is cell cycle arrested (Castanheira et al., 2014) and is ableto invade the plant via a specialized infection structure, the ap-pressorium. Appressoria allow direct invasion of epidermal plant

1 These authors contributed equally to this work.2 Current address: Bayer CropScience Biologics, Metkenberg 6,23970 Wismar, Germany.3 Current address: GSK Vaccines, Emil-von-Behring-Strasse 76, 35041Marburg, Germany.4 Address correspondence to kahmann@mpi-marburg.mpg.de.The author responsible for distribution of materials integral to the findingspresented in this article in accordance with the policy described in theInstructions for Authors (www.plantcell.org) is: Regine Kahmann(kahmann@mpi-marburg.mpg.de).OPENArticles can be viewed without a subscription.www.plantcell.org/cgi/doi/10.1105/tpc.17.00764

The Plant Cell, Vol. 30: 300–323, February 2018, www.plantcell.org ã 2018 ASPB.

cells in a process that is likely aided by plant cell wall-degradingenzymes and plant cell wall loosening. During this stage, thefungus remains intracellular and becomes completely encased bythe plasma membrane of the host, forming a tight and extendedinteraction zone. After penetration, the cell cycle block is releasedand mitotic growth of the dikaryotic form is observed, usingcharacteristic clamp-like structures for sorting the nuclei (Schereret al., 2006). After reaching the mesophyll layer and the veins,U. maydis cells grow along and inside of the veins, presumably toforagenutrients.During thisstage, discreteplant cell typesenlargeand resume mitotic divisions (Matei et al., 2018), leading to theformation of tumors, the most conspicuous signs of maize smutdisease. In these tumors, extracellular hyphae form large ag-gregates in cavities between plant tumor cells, their dikaryoticnuclei fuse, and massive proliferation ensues followed by hyphalfragmentation and spore formation (Vollmeister et al., 2012; Mateiand Doehlemann, 2016; Tollot et al., 2016; Lanver et al., 2017;Redkar et al., 2017; Snetselaar and McCann, 2017). This entiredevelopmental cycle strictly depends on the plant and remainsbiotrophic throughout.

U. maydis is predicted to encode 476 secreted proteins ofwhich 215 lack any known structural or functional domains,preventing conclusions to be drawn about their molecularfunctions (Schuster et al., 2017).Many of these potential effectorgenes reside in clusters in the genome (Kämper et al., 2006;Schirawski et al., 2010), are expressed specifically in tumortissue compared with axenic culture conditions (Kämper et al.,2006), and contribute to virulence (Kämper et al., 2006; Mülleret al., 2008; Schirawski et al., 2010; Schilling et al., 2014;Stirnberg and Djamei, 2016). So far, the molecular function ofonly five U. maydis effectors has been elucidated (Djamei et al.,2011; Hemetsberger et al., 2012; Mueller et al., 2013; Tanakaet al., 2014; Redkar et al., 2015).

The bE/bW transcription factor triggers a regulatory cascadeincluding several transcription factor genes as well as 38 po-tential effector genes (Heimel et al., 2010a, 2010b). The ex-pression 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 ofU. maydis up to the stage of appressorium formation can bemimicked by stimulation with hydroxy fatty acids and exposingcells to a hydrophobic surface (Mendoza-Mendoza et al., 2009).An array study of these stages revealed that two transmembraneproteins, the U. maydis homologs of Sho1p (synthetic highosmolarity sensitive) and Msb2p (multicopy suppressor ofa budding defect) from Saccharomyces cerevisiae, are specifi-cally responsible for plant surface cue-induced expression of41 potential effectors (Lanver et al., 2014). In addition, twotranscription factors,hdp2 (homeodomain transcription factor 2)and biz1 (b-dependent zinc finger protein), which belong to therbf1-induced genes (Heimel et al., 2010b) and have critical rolesin virulence (Flor-Parra et al., 2006; Lanver et al., 2014), aretranscriptionally induced by Sho1 and Msb2 providing indirectevidence that these transcription factors may induce the ex-pression of early effectors. After induction of tumors, the WOPRtranscription factor Ros1 (regulator of sporogenesis 1) inducesnuclear fusion, subsequent proliferation, and initiates sporeproduction (Tollot et al., 2016). Ros1 also emerged as an

important regulator of effector gene expression. Seventy genesencoding secreted proteins require Ros1 for full expression and128 genes encoding secreted proteins are downregulated byRos1. It is speculated that U. maydis can afford the down-regulation of so many effectors, including some of the essentialeffectors expressed early during pathogenic development be-cause Ros1 is also required for the generation of a mucilaginousmatrix which may shield hyphae from plant defense molecules(Tollot et al., 2016).The transcriptional response of maize plants during infection

withU.maydishasbeenstudied indetail usinggenome-widearrayanalysis. These studies have revealed thatU.maydis triggers earlyplant defense responses when on the leaf surface, presumablythrough the perception of microbe-associated molecular pat-terns. These responses are subsequently suppressed during theearly colonization stages, likely through the action of early ef-fectors (Doehlemann et al., 2008b). U. maydis also inducestranscription of plant cell death suppressors like cystatins andBax-inhibitor 1, induces jasmonate signaling, and prevents thetransition from sink to source leaves (Doehlemann et al., 2008b).All these studies were performed with the engineered solo-pathogenic haploid strain SG200 (Kämper et al., 2006) that caninfect plants without a mating partner. The transcriptome of theU. maydis SG200 strain as well as of SG200 mutants duringpathogenic development has been investigated in several studiesusing array technology (Kämper et al., 2006; Zheng et al., 2008;Skibbe et al., 2010; Gao et al., 2013; Schuler et al., 2015; Rabeet al., 2016). A few studies also analyzed the transcriptome ofcompatible 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 duringcolonization were so far restricted to stages of biotrophic de-velopment where significant amounts of fungal biomass haveaccumulated. A comprehensive, time-resolved transcriptionalprofiling of the plant-associated stages of theU. maydis life cycleis lacking so far. We have recently noted that SG200 behavesdifferently from compatible haploid strains during the late stagesof pathogenic development. While fungal biomass significantlyincreases after karyogamy in infectionswith compatible wild-typestrains (Tollot et al., 2016), fungal biomass of SG200 decreasedat the corresponding time points (S. Tanaka, P. Erchinger,S. Krombach, and R. Kahmann, personal communication). In thisstudy, we have therefore performed an RNA-seq analysis ofcompatiblehaploidU.maydisstrainsstartingat12hafter infectionof maize seedlings, i.e., when cells have mated, switched tofilamentous growth, and begun to form appressoria, to 12 dpostinfection (dpi) when tumors contain mature spores. This of-fered an unprecedented view of the changes in the fungal tran-scriptome associatedwith the passage through the biotrophic lifecycle. Deep sequencing enabled us to resolve also the very earlystages of infection where fungal biomass is low and fungaltranscripts are heavily underrepresented. We focus our attentionon fungal metabolism, nutritional strategies, secreted effectors,and regulatory networks. Based on uncovering discrete geneexpression waves successively following the developmentalstages of U. maydis, we expect this data set to become a highlyvaluable resource for future studies in this biotrophic fungalmodelas well as related host-pathogen systems.

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RESULTS AND DISCUSSION

To analyze the transcriptional changes during plant-associatedstages of U. maydis, maize seedlings of the variety Early GoldenBantam were infected with the compatible U. maydis wild-typestrains FB1 andFB2 (Banuett andHerskowitz, 1989), and infectedtissuewascollectedover aperiod of 12d (Figure 1A). The samplesrepresented the following developmental stages of U. maydis:filaments and appressoria in the prepenetration phase (0.5 dpi),penetrating appressoria (1 dpi), dikaryotic biotrophic hyphae withclamp connections (2 dpi), proliferating hyphae and aggregatedhyphae (4 and 6 dpi), fragmented hyphae (8 dpi), and maturespores (12 dpi; Figure 1A). For each time point, three biologicalreplicatesweregenerated.Asanadditional reference,we includedFB1 and FB2 grown exponentially in YEPSL medium.

By Illumina sequencing of mRNA libraries, we created in totalmore than two billion paired-end reads from all samples(Supplemental DataSet 1). Prior tomapping them to theU.maydisgenome, readsmapping to themaize genomewere filtered. Thesedata are not discussed here but have been deposited in the NCBIGene Expression Omnibus (Edgar et al., 2002) and are accessiblethroughGEOSeries accession number GSE103876. For the earlytime points of 0.5 dpi and 1 dpi in particular, reads mapping tothe fungal genome amounted to <0.5%, i.e., were heavily un-derrepresented (Figure 1B; Supplemental Data Set 1). In the totalpaired-end reads from all samples, the sequencing depths of the0.5and1dpi sampleshasbeenadjusted to reach;500 thousand-read counts that mapped to the fungal genome and in the re-maining samples to reach about one million or more fungal readcounts (Supplemental Data Set 1). In each sample, >75% of the6766 U. maydis genes were represented with more than 10 frag-ments per kilobase of exonpermillion fungal reads, indicating thatwe have efficiently detected fungal gene expression across alltime points. Correlated with the increasing fungal biomass, thefungal reads steadily increased during later time points andreached;10%at12dpi (Figures1Band1C).Weobserveda linearcorrelation between the number of fungal transcripts and increasein fungal biomass (Supplemental Figure 1). However, the steepincrease in fungal transcripts from 1 to 2 dpi (Figure 1B), that isstatistically significant (Supplemental Data Set 2), was not ac-companied by a comparable change in fungal biomass (Figure1C). This suggests that the transcriptional activity of U. maydiscells after recovery fromcell cycle arrest (2dpi) is higher thanat thestage before penetration or during later biotrophic development.

To enable comparisons between samples, all read counts werenormalized by DESeq2 (differential expression analysis for se-quencecount data2) (Loveet al., 2014) (Supplemental DataSet 3).Toassessvariability among thesamples,weperformedaprincipalcomponent analysis (Figure 2A). The three biological replicatesformed distinct clusters, indicating time point-specific expressionpatterns and an acceptable variation between replicates at any ofthe chosen time points. To analyze differential gene expression,we compared expression in all 28 possible pairs of the eightdifferent time points. This analysis revealed that in total5759 genes (85% of all U. maydis genes) were differentially ex-pressed (log2 fold change > 0.5 and adjusted P value < 0.01) in atleast one of the 28 comparisons (Figure 2B; Supplemental DataSet 4). To validate the expression data, we randomly picked nine

genesexpressedatdifferent levelsandperformedRT-qPCRusingall 24 generated RNA samples. We observed a strong linearcorrelation (r = 0.90) between the RT-qPCR and RNA-seq data(Supplemental Figure 2). To further strengthen the bioinformaticsanalysis, EdgeR was used as an alternative tool to normalize thedata and to identify differentially expressed genes. This analysisyielded results comparable to theDESeq2analysis (SupplementalData Set 5). In the following analyses, we refer to the DESeq2results.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 culturesample, whichmay be unrelated to the conditions on or inside theplant with respect to nutrient availability, were excluded from theanalysis, 4586 genes (68% of the U. maydis genes) remaineddifferentially expressed (Figure 2B). Such a high proportion ofgenesshowingdifferential expressionduring theplant-associateddevelopmental stages distinguishes U. maydis from other path-ogenic fungiwheresimilarRNA-seqstudieshavebeenperformed.InZymoseptoria tritici,Colletotrichumhigginsianum, andPucciniastriiformis f. sp tritici, 28%, 44%, and 50% of the genes weredifferentially expressed, respectively (O’Connell et al., 2012;Ruddet al., 2015; Dobon et al., 2016). We consider it likely thatthe coupling between pathogenic and sexual development inU. maydis and the associated morphological changes contributeto this high percentage of differentially expressedU.maydisgenes.For the other three examples given, asexual reproduction cycleswere studied. However, rather than assuming that the discretelydifferent lifestyles of these pathogens contribute to the per-centage in differentially expressed genes, variation in se-quencing depth and numbers of analyzed samples in thedifferent studies cannot be excluded as cause.Overall, the largenumber of differentially expressed U. maydis genes under-scores the comprehensiveness of our analysis and indicatesa complex transcriptional regulation during all stages of biotrophicdevelopment.In the next step of the analysis, we used the expression data of

all stages to perform a weighted gene coexpression networkanalysis (WGCNA;Supplemental Figure 3). This analysis identifiesmodules of coexpressed genes and represents the modules bytheir centrally located genes, referred to as module eigengenes(Zhang and Horvath, 2005; Langfelder and Horvath, 2008). Weidentified 14 modules that were color-coded and ranged in sizefrom 36 genes (blue module) to 1231 genes (yellow module;Supplemental Data Set 6). The expression profiles of the re-spective module eigengenes are depicted in Figure 2C. Some ofthe modules reflect distinct stages during fungal development.The red module was expressed solely on the plant surface (0.5–1dpi). The light-green module was expressed during penetrationand early biotrophic development (1–2 dpi) and ceased after-wards. The magenta module was strongly induced from 0.5 to2 dpi and expression was largely maintained up to 12 dpi. Thismodule therefore correlated with the establishment and mainte-nance of biotrophy. The cyan module was induced after estab-lishment of biotrophy just at the onset of tumor induction (2–4dpi),and expression stayed high also at later time points. Thus, thecyan module represented a tumor module. The blue module wasspecific for spore development (8–12 dpi).

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To confirm that the observed expression patterns are nota product of the specific bioinformatics tool, we performedk-mean clustering of the gene expression data, sorting the genesintosixclusters.Fiveof theseclusterswerehighlycorrelated (>0.9)with a distinct module of the coexpression obtained by WGCNA

(Supplemental Data Set 7). These included the three virulencemodules red, magenta, and cyan, as well as the yellow and light-cyan module. One k-mean cluster correlated with two, the dark-green and the burlywood module, which are highly correlatedmodules (Supplemental Figure 3B). There was thus a substantial

Figure 1. Changes in the Amount of Fungal Transcripts and Fungal Biomass during Infection.

(A)Schematic viewof cross sections ofU.maydis infectedmaize leaves illustrating the stages of fungal development aswell as plant tumor formation at thetimepoints analyzed byRNA-seq-based transcriptional profiling.U.maydis infection is not synchronized, and each sample thus contains fungal transcriptsfrom different developmental stages. Green, plant leaf tissue; brown, vascular tissue; orange, fungal cytoplasm; gray, empty fungal hyphae separated bysepta; beige, plant tumor cells; rose, matrix; ornamented in black, fungal spores.(B)Amountof fungal transcriptsbasedon theRNA-seqanalysis. Foreach timepoint (0.5, 1, 2, 4, 6, 8, and12dpi) the ratioof readsuniquelymapped to theU.maydis genome relative to the total number of uniquelymapped reads (U.maydis andmaize) was determined. Error bars denote SD of three biologicalreplicates.(C) Fungal biomass determination based on the amount of genomic DNA. A qPCR with plant-specific (GAPDH) and fungus-specific (ppi) primers wasperformed using the same infected plantmaterial that was used for the RNA-seq analysis. Data points givemean ratios of fungal DNA to plant DNA (22DCt).Error bars denote SD of three biological replicates.

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Figure 2. Assessment of the RNA-Seq Data Set of U. maydis during Infection.

(A)Principal componentanalysis ofRNA-seqdata. The replicatesofeachdevelopmental stageofU.maydis (axenic, 0.5, 1, 2, 4, 6, 8, and12dpi) formdistinctclusters.(B)The expression data of the eight analyzed developmental stages ofU.maydis (axenic, 0.5, 1, 2, 4, 6, 8, and 12 dpi) was the basis to extract all 28 possiblecontrasts. Genes with a log2 fold change > 0.5 and adjusted P value < 0.01 were considered differentially expressed. Gray triangles depict the number ofgenesexpressedathigher levels at thestagesdenotedby thehorizontal labels, andyellow trianglesdepict thenumberof genesexpressedathigher levelsatthe vertically labeled stages. In total, 5759 of the 6766 U. maydis genes were differentially expressed.(C)Modules of coexpressed genes during pathogenic development of U. maydis. The RNA-seq expression data set was subjected to WGCNA to detectmodules of coexpressed genes. Each graph shows the expression of the module eigengene, which can be considered as the representative gene of therespective coexpression module. The vertical axes indicate log2 expression values relative to the mean expression across all stages. The horizontal axesindicate thestages, i.e., axenic (ax), 0.5, 1, 2, 4, 6, 8, and12dpi. Error bars indicate SDof threebiological replicates. Themodules arenamedaccording to theircolor, and the number of genes residing in each module is given in parentheses.

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overlap between the respective gene sets obtained by WGCNAand k-mean clustering with overlap coefficients ranging from 0.7to 1.0 (Supplemental Data Set 7). All subsequent analyses refer tothe modules obtained by WGCNA.

To generate a concise picture of the biological processes as-sociated with pathogenic development, each module was sub-jected to an enrichment analysis for Gene Ontology (GO) terms(Supplemental Data Set 8) (Ashburner et al., 2000; The GeneOntology Consortium, 2017). We visualized the respective en-riched gene sets in aweighted similarity network, which facilitatedthe identification of the predominant processes in any givenmodule (Figure 3; Supplemental Figure 4). In the sections thatfollow, we make use of these functionally annotated modules togain a better understanding of the virulence strategies adopted byU. maydis.

General Changes in Fungal Metabolism throughout theInfection Cycle

Looking for modules that reflect high metabolic and cellular ac-tivity identified the yellow module (Figure 3A; Supplemental DataSet 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 highestexpression level of the yellow module was observed duringU.maydisgrowth inaxenicculture (Figure2C), acondition inwhichthedoubling timeof a cellwasaround2h,which likely exceeds thegrowth rate in all plant-associated stages. The yellow moduleshows lowest expression at 0.5 and 1 dpi, i.e., the stages wheremating occurs and the cell cycle-arrested dikaryon is formed. At2 dpi, the module displays a second expression peak, likely re-flecting the release of the cell cycle block after penetration andearly biotrophic growth. This expression pattern resembles theresponse of starving cells to nutrient repletion (Conway et al.,2012). From 2 dpi until 12 dpi, genes of the yellow module wereprogressively downregulated (Figure 2C), and instead genesinvolved 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 negativelycorrelatedwith the yellowmodule (r =20.99, Pearson correlation;Figure 2C). Autophagy is an important mechanism by which eu-karyotic cells degrade cytosolic macromolecules and recyclethem for the synthesis of new macromolecules or use them asenergy source. In addition to induction of autophagy, expressionof genes involved in lipid transport and lipidmetabolism increasedafter 2 dpi. The respective genes are located in the dark-greenmodule (Figure 3C; Supplemental Data Sets 8 and 9). Thesefindings suggest that autophagy-mediated cellular recycling aswell as degradation of fatty acids become important during thelater biotrophic interaction. Increased fatty acid metabolism hasalso been associated with the biotrophic growth of Z. tritici (Ruddet al., 2015) and arbuscular mycorrhizal fungi. The latter wererecently shown to take up lipids from their host to sustain colo-nization (Rudd et al., 2015; Jiang et al., 2017; Keymer et al., 2017).The synchronous upregulation of autophagy and lipidmetabolismgenes and downregulation of ribosome biogenesis genes aretypical expression patterns during slowed growth and duringstarvation (Gasch et al., 2000). We also observed that nit2

(nitrogen catabolic enzyme regulatory protein) and snf1 (sucrosenonfermenting1), thenitrogenandcarboncatabolitederepressors(Nadal et al., 2010; Horst et al., 2012), have increased transcriptlevels during tumor formation compared with early biotrophicgrowth (Supplemental Data Sets 3 and 4), indicating that carbonand nitrogen sources may be limiting. Previous studies indicatedthat tumor tissue is a strong sink tissue with an efficient supply oforganic nutrients from systemic source leaves (Billet and Burnett,1978; Doehlemann et al., 2008b; Horst et al., 2008, 2010). Me-tabolome profiling of U. maydis infected tissue revealed that freehexoses and amino acids are highly abundant in tumor tissue andeven reach the levels of juvenile sink tissue (Horst et al., 2010). Thislatter finding seems to contradict our RNA-seq-based assess-ment that U. maydis may be starved for carbon and nitrogen intumor tissue. However, the metabolome analysis (Horst et al.,2010) was conducted with SG200, a haploid solopathogenicU. maydis strain. In contrast to infections with compatible wild-typestrains inwhich fungalbiomasscontinuously increaseswithintumor tissue (Figure 1C) (Tollot et al., 2016), SG200 does notincrease its biomass late during infection (S. Tanaka, P. Erchinger,S. Krombach, and R. Kahmann, personal communication). Wespeculate that the free hexoses and amino acids detected inSG200-induced tumor tissue (Horst et al., 2010) may not accu-mulate to the same extent in tumors induced by wild-type strainsbecause they may be taken up to support the continuous fungalproliferation (Billet and Burnett, 1978; Doehlemann et al., 2008b;Horst et al., 2008, 2010). The proliferation within tumors ofU.maydiswild-typestrains that aremost likelydiploid at that stage(Tollot et al., 2016) may therefore resemble chemostat growth, inwhich cells grow slowly due to nutrient limitation but, due toa constant nutrient flow toward tumor tissue, are not starving foressential nutrients. A detailed metabolic profiling of tumors in-duced by a mixture of compatible U. maydiswild-type strains willhave tobedone tomonitor thedynamics of theavailable nutrients.It will be interesting to see how this can then be linked to thetranscriptome data presented here, which indicate nutrient-limitedfungal growth within tumors. We consider it likely that nutrientlimitation could also contribute to the induction of the developmentalprogram for aggregate formation and teliospore formation inside thetumors.

Nitrogen Transporters

The utilization of complex nitrogen sources in fungi is regulated byspecific transcription factors, which derepress the expression ofgenes needed for the degradation and uptake of various nitrogencompounds in situations where the most favorable nitrogensources ammonia and glutamine are scarce (Marzluf, 1997). Inmany plant pathogenic fungi, including U. maydis, mutants of thenitrogen catabolite derepressors (nit2 inU.maydis) show reducedvirulence (Pellier et al., 2003; Divon et al., 2006; Thomma et al.,2006;DivonandFluhr, 2007;KimandWoloshuk,2008;Horst etal.,2012). The importance of nitrogen availability in biotrophic as-sociations is further corroborated by the observation that nitrogenfertilizers generally increase the susceptibility of plants to bio-trophs, whereas they decrease the susceptibility of plants tonecrotrophs (Snoeijers et al., 2000; Dordas, 2008; Ballini et al.,2013). Indeed, U. maydis is known to grow on various nitrogen

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Figure 3. Biological Processes Enriched in Selected Coexpression Modules.

GOenrichment analysis for theyellow (A), light-cyan (B), dark-green (C), red (D),magenta (E), cyan (F), light-green (G), andblue (H)modules.Onlybiologicalprocess termswere considered in the analysis. Each significantly enriched gene set (hypergeometric P value < 0.005) is represented by a node. Node sizesare proportional to the number of geneswithin the respective gene set, and the edges indicate overlappingmember genes. Highly similar gene sets tend to

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sources and has the ability to generate all proteinogenic aminoacids (Holliday, 1961; McCann and Snetselaar, 2008).

To obtainmore insights into nutrient assimilation during biotrophicgrowth, we searched for transporters that are highly induced in theplant environment compared with growth in YEPSL medium (2 dpiversus axenic). The top five induced transporters were two putativeurea permeases dur3-1 (UMAG_02625) and dur3-2 (UMAG_06253),two putative oligopeptide transporters (OPTs) opt2 (UMAG_11057)and opt4 (UMAG_02387), and the candidate methylammoniumpermease (MEP) ump2 (UMAG_05889) (Supplemental Data Set 11;Figures 4A and 4D). In fungi, related transporter families are requiredfor nitrogen utilization from peptides, urea and ammonium, re-spectively (ElBerry et al., 1993; Lorenz and Heitman, 1998; Hauseret al., 2001; Abreu et al., 2010; Hartmann et al., 2011; Navarathnaet al., 2011; Dunkel et al., 2013), while OPTs can also mediate sulfurutilization by taking up glutathione (Bourbouloux et al., 2000). All fivetransporters are located in the magenta expression module (Figure2C) and are thus linked to biotrophy (Supplemental Data Set 11).

Besides the two urea permeases dur3-1 and dur3-2, which arehighly induced during biotrophic development, U. maydis pos-sesses dur3-3 (UMAG_04577), a likely third urea permease.Whiledur3-1anddur3-2arenot inducedunder nitrogendepletion (Horstet al., 2012; Sánchez-Arreguin et al., 2017), dur3-3 expressiondepends on nit2 under nitrogen starvation conditions (Horst et al.,2012). In linewith this observation, our expression analysis placedboth dur3-3 and nit2 into the light-cyan expression module likelyinvolved in the response to limiting nutrients (Figure 2C).

To study the contribution of all three urea transporters to vir-ulence and to exclude redundancy, we made use of the recentlyestablished CRISPR-Cas9 system in U. maydis (Schuster et al.,2016, 2017) and generated frameshift mutations near the 59endsof the respective three genes in haploid FB1 and FB2 strains. Theresultingdur3-1,2,3 triplemutantswere affected during growth onmedium with urea as sole nitrogen source (Figure 4B), but theywere not affected in virulence (Figure 4C), suggesting that ureauptake is not important for biotrophic development. However, wedid not investigate the effects on virulence when plants are grownon nitrogen-poor soil and we therefore cannot exclude that theurea transporters become virulence factors when the overall ni-trogen supply of the plant is lower.

The high affinity ammonium transporter Ump2 has beencharacterized previously (Smith et al., 2003). Besides mediatingammonium acquisition, this transporter has a signaling functionand initiates filamentous growth under nitrogen starvation (Smithet al., 2003). The ump2 gene is partially subject to nitrogen ca-tabolite repression, i.e., is regulatedbyNit2 (Horst etal., 2012). Thehigh expression of ump2during early biotrophic growth, whichweobserved here (Supplemental Data Set 11), suggests additionalregulation by plant signals. While ump2mutants were unaffectedin virulence (Smith et al., 2003), an ump1 (with a defect in the geneencoding a low affinity ammonium transporter) ump2 double

mutant was severely reduced in virulence (M. Perlin, personalcommunication). Our expression analysis thus reinforces theimportance of ammonium uptake and its regulation for biotrophicdevelopment of U. maydis.From the seven U. maydis peptide transporters, none was

demonstrated to be regulated by Nit2 in response to nitrogenstarvation (Horst et al., 2012). According to our transcriptionalprofiling, three OPTs, opt2, opt4, and opt3 (UMAG_05968), werehighly 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) andmay therefore be coupled to nutritional regulation(see previous section), while opt1 (UMAG_10908) was constitu-tively expressed (Figure 4D). U. maydis possesses only onemember of the dipeptide/tripeptide transporter (PTR) family, ptr2(UMAG_06138), and this gene also showed little variation in ex-pressionduring fungal development (Figure 4D). To studywhetherthe biotrophy-coupled induction of OPT transporters is importantfor virulence, we introduced frameshift mutations by CRISPR-Cas9 in the59 regionsofopt2,opt3, andopt4genes in theFB1andFB2 strain backgrounds. Plant infections with compatible mix-tures of the respective triple mutants revealed that these trans-porters are important for virulence. While mating and filamentformationwerenot affected (Figure4E), severediseasesymptomssuch as heavy tumors and plant death were drastically reduced ininfections with these mutants (Figure 4F). The virulence defect ofthe triple mutant could be completely restored by introducingsingle copies of all three opt genes into the ip locus of the triplemutant strains (Figure 4F). Full complementationmakes it unlikelythat truncated gene products were produced due to the CRISPR-Cas9-induced frameshifts, which could cause the attenuatedvirulence in the triple mutant. The full restoration of virulence bycomplementation also makes off-target mutations of Cas9 withunwanted side effects highly unlikely. The virulence function of theoligopeptide transporters suggests that peptides produced fromextracellular proteins are important nutrient sources forU.maydisduring biotrophic growth. It has been previously demonstrated inthe yeast Candida albicans that synchronous production of as-partic proteases and OPTs enables growth on proteins as solenitrogen source (Martínez and Ljungdahl, 2005). Interestingly, twosecreted aspartic proteases from U. maydis (UMAG_05097 andUMAG_12330) are located in themagentamodule (Figure 2C) andare thus coexpressed with the highly induced OPTs. Given thespecific expression pattern of the described subset of OPTs andaspartic proteases, we propose that extracellular proteolysis andsubsequent uptake of peptides may be intrinsically tied to theplant-associated developmental program of U. maydis. With re-spect toutilizationofcarbohydrates, theuptakeof sucrose followsa similar scheme (Wahl et al., 2010): The high-affinity sucrosetransporter srt1 (UMAG_02374), an important virulence factorof U. maydis, is transcriptionally induced specifically during

Figure 3. (continued).

form clusters, which weremanually circled and labeled with appropriate summarizing terms. Gene sets that have no overlap with other enriched GO termsare shown in the rightmost corner and are not labeleddespite twoexceptions, one in (D) (secondarymetabolism) andone in (F) (N-linkedglycosylation). SeeSupplemental Data Set 8 to retrieve all GO terms of the enriched gene sets.

U. maydis Biotrophic Transcriptome 307

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 to the mean expressionacross all stages.(B)U.maydisdur3 transportersare important for nitrogenutilization fromurea.Serial dilutionsofFB1andFB2wild-typestrainsand the respectivedur3-1,2,3triplemutants inFB1 (PH72andPH109) andFB2 (PH110andPH112)werespottedonminimalmediumwithammoniumorureaassolenitrogensource in theindicated final concentrations.(C)The indicatedmixturesof compatible strainswere injected intomaize seedlingsandsymptomswere scored12dafter infectionaccording toseverity; thecolor code for each category is given below. Three independent experiments were performed and the average values are expressed as percentage of thetotal number of infected plants (n), which is given above each column.(D)Theheatmapshowstheexpressionprofileof theU.maydispeptide transporters; log2expressionvaluesareshownrelative to themeanexpressionacrossall stages.(E) The indicated mixtures of compatible haploid strains were spotted on charcoal-containing agar plates. FB1 and FB2 are wild-type strains, DL755 andPH158 are compatible opt2,3,4 mutants, and PH89 and PH167 are opt2,3,4mutants simultaneously complemented with wild-type opt2,3,4 genes. Theoccurrence of white mycelium indicates the formation of dikaryotic hyphae.(F) The indicatedmixtures of compatible strains were injected intomaize seedlings and symptomswere scored 12 d after infection according to severity asdescribed in (C). For (C) and (F), the gene name followed by “em” indicates that the respective gene was inactivated by CRISPR-Cas9. The gene namefollowed by “-C” indicates that a single copy of the respective gene was introduced into the indicated strains to test for complementation. Please note thatdead plants, which represent the most severe symptom category, are a result of the artificial virulence assay that is based on young maize seedlings anda high inoculum. U. maydis is a strict biotroph and does not kill plants under natural conditions.

308 The Plant Cell

pathogenic development, but not by the presence of sucrose underaxenic culture conditions or under carbon starvation (Wahl et al.,2010). The trigger for induction during the biotrophic phase is un-known, but srt1 as well as the nitrogen-related transporters opt2,opt4, dur3-1, dur3-2, and ump2 are all induced by the plant surfacecues hydrophobicity and 16-hydroxy hexadecanoic acid (Lanveret al., 2014). This corroborates that the early developmental stageslike filamentation and appressorium formation on the plant surfaceprepareU.maydisnotonlywith respecttoeffectors thatcansuppressplant defenses, but also with respect to nutrient utilization systemsimportant for growth inside theplant environment. In thewheat striperust fungus, OPT transporters have been shown to be highly ex-pressed in haustoria, the biotrophic fungal feeding structures(Garnica et al., 2013). Interestingly, an OPT of the hemibiotrophColletotrichum gloeosporioides was identified as an auxin-inducedgene (Chagué et al., 2009). In the U. maydis-maize system, auxinlevels are highly inducedduring infection (TurianandHamilton, 1960;Basseetal.,1996;Reinekeetal.,2008),and itwillbe interesting to testwhether auxin controls opt expression also in U. maydis.

Overall, our data suggest that nitrogen utilization in U. maydishasahigh level of complexity involvingnutrient level dependent aswell as independent regulation. Both utilization modes contributeto virulence and it may be this two-pronged strategy that allowsU. 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-greenmodule (Figure 2C) enriched in various processes related toproteinsecretion, e.g.,ER-to-Golgi trafficking, vesicular transport,andN-glycosylation (Figure 3; Supplemental Data Sets 8 and 10).Genesof theunfoldedprotein response (UPR)alsomainly reside inthe light-green module (Supplemental Data Set 10). The light-green secretion machinery module is induced during penetrationof the plant surface (1 dpi) and peaks at 2 dpi (Figure 2C). A similarexpression pattern was observed for many genes encoding se-creted proteins (discussedbelow), and in all likelihood this reflectsthe high demand for the secretion machinery during plant colo-nization. The observed strong increase in expression of the light-green module from 1 to 2 dpi is challenging to interpret. Since the1dpi timepoint represents amixtureofmostlyfilamentoushyphaeon the leaf surfaceandonlyasmallpercentageofhyphae thathavedeveloped appressoria and have penetrated (Figure 1A), it isconceivable that the actual expression levels of the secretionmachinery and the secretome components in the few penetratedhyphae are as high as the levels observed in branching myceliumat 2 dpi. The lower overall expression values at 1 dpi might thussimply reflect that only a small percentage of the inoculum hasmated and penetrated. This interpretation is in line with previousstudies showing that penetratedhyphaeactivate theUPR throughtheCib1 (Clp1 interactingbZip1) transcription factor (Heimel et al.,2013). TheUPRaffectssecretionaswell asexpressionofeffectors(Heimel et al., 2013; Hampel et al., 2016; Lo Presti et al., 2016).

Incontrast toN-glycosylation,componentsof theO-glycosylationpathway aremost strongly expressed in axenic culture aswell as onthe plant surface (yellow and burlywood module; Figure 2C;Supplemental Data Set 10). Both processes, O-mannosylationand N-glycosylation, are necessary for virulence of U. maydis

(Fernández-Álvarezet al., 2009, 2013). Previous studiescould traceback the virulence defect of mutants in the O-mannosylationpathway to a failure in appressoria formation, mainly explained bythe reducedglycosylationof the plant surface cue sensing receptorMsb2 (Fernández-Álvarez et al., 2012). By contrast, mutants de-fective in components of the N-glycosylation pathway are able topenetrate the plant, but induce strong defense responses, e.g., anoxidative burst, indicative of a malfunction of effectors (Schirawskiet al., 2005; Fernández-Álvarez et al., 2013). Our expression datastrongly support the model that O-mannosylation is particularlyimportant for early fungal development on the plant surface, whileN-glycosylation is mainly necessary inside the plant tissue toproduce functional effectors. For Magnaporthe oryzae it has beendemonstrated thatN-glycosylationofaneffector isnecessary for itsvirulence promoting function (Chen et al., 2014). For U. maydiseffectors, this has yet to be demonstrated.

Development-Associated Changes of the Secretome

The U. maydis genome contains 467 genes encoding putativelysecreted proteins (Schuster et al., 2017), and 215 of the predictedsecreted proteins are lacking any predicted structural or functionaldomain (Schuster et al., 2017). Some of those proteins have beenpreviously found to act as important virulence effectors (Lanver et al.,2017). Secreted proteins are distributed over 12 of the 14 expressionmodules (Figure 5; Supplemental Data Set 12), i.e., their expressionoccurs in waves following the characteristic expression profile of therespective module during the course of an infection. This suggeststhat certain groups of secreted proteins are only needed duringspecific periods of an infection cycle. A search for modules in whichsecreted proteins are significantly overrepresented identified onlythreemodules: theredmodule (Pvalue5.71E-5,Fisherexact test), themagenta module (P value 8.46E-23, Fisher exact test), and the cyanmodule (Pvalue3.42E-10,Fisherexact test;Figure2C;SupplementalDataSet13), i.e., themodulesspecificfor theplantsurface,biotrophicdevelopment and tumor formation, respectively. These three mod-ules have in common that they are almost completely off during theaxenicculturecondition (Figure5). Thus, they likely representspecificvirulence modules.The secreted proteins of the red module (Figure 2C) predominantly

have predicted hydrolytic functions (Figure 5; Supplemental Data Set12) and contain among others cutinases, lipases, and peptidases(Supplemental Data Set 12). Thus, the red module harbors key en-zymes potentially involved in the degradation of the cuticle. It isconceivable that some of the secreted hydrolases also have antimi-crobial activity likeaputative lysozyme (UMAG_06218). In this context,it may thus not be a coincidence that thismodule also contains genesfrom secondary metabolite clusters like mannosylerythritol lipid andustilagic acid biosynthesis (Supplemental Data Set 14) (Teichmannet al., 2007). Mannosylerythritol lipids are highly potent surface-activesubstances (FluhartyandO’Brien,1969),whileustilagicacidhasbroadantimicrobial activity (Haskins and Thorn, 1951) andwas shown to aidagainst microbial competitors like the necrotrophic fungus Botrytiscinerea in coinfection 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 ac-complished by the secretion of plant cell wall degrading enzymes(PCWDEs) (Schirawski et al., 2005; Lanver et al., 2014) of which

U. maydis Biotrophic Transcriptome 309

U.maydis is predicted to encode 40 (Figure 5; Supplemental DataSet12) (LoPresti et al., 2015).Notably,PCWDEswerenotenrichedin the redmodule but in the light-cyanmodule (Figure 2C), amodulethat is otherwise rather scarce in secreted proteins (Figure 5;Supplemental Data Set 12).We implicated the light-cyanmodule inthe response to nutrient limitation during growth on the leaf surfaceaswell as during late biotrophic interaction (see previous sections),suggesting that most PCWDEs mediate utilization of carbon fromplant cell walls at these stages. The expression pattern of thePCWDEs also correlated with the colonization strategy of U.maydis, inwhichplantcellwallsmustbepenetratedat thebeginningof the infection to enable initial intracellular growth, and plant cellwalls must be loosened again later during infection to allow

enlargement of cells during tumor formation. The expression datado not allow discrimination between a role for these enzymes innutrition and/or breaching plant cell walls. Previous studies in otherplant pathogenic fungi suggest a direct link between carbon utili-zation andplant cell wall penetration (Tonukari et al., 2000;Ospina-Giraldo et al., 2003). In U. maydis, this needs to be followed up byfunctional studies of candidate genes.Interestingly, our expression data show that few PCWDEs are

uncoupled from nutritional regulation and are instead tied tobiotrophic development (i.e., they are found in the magentamodule). In this group of PCWDEs are all three U. maydis GH45cellulases, five potential hemicellulases, including two arabino-furanosidases, and the sole predicted pectin lyase (Supplemental

Figure 5. Expression of the U. maydis Secretome.

The heat map shows expression of differentially expressed genes encoding putative secreted proteins. For each module indicated on the left, genes werehierarchically clustered,and log2expressionvaluesare visualized relative to themeanexpressionacrossall stages.Modulesarecoloredaccording toFigure2C.Blackbars on the right indicate for eachgene thepresenceof knownsignatures (basedon InterPro scan), thepredicted hydrolytic capabilities, andmorespecifically the predicted ability to degrade the plant cell wall (PCWDE).

310 The Plant Cell

Data Set 12).While virulence functions of the GH45 family and thepectin lyase were so far not demonstrated (Schauwecker et al.,1995; Doehlemann et al., 2008a; Lanver et al., 2014), the arabi-nofuranosidaseUMAG_01829 contributes to virulence (Lanver et al.,2014; Schilling et al., 2014). Our expression data therefore suggestthat carbon supply fromcomplex sources, e.g., plant cell walls,mightbe ensured via a two-pronged strategy consisting of nutrient level-dependent and independent regulation.

Themagentamodule (Figure 2C) predominantly containsgenesencoding secreted proteins lacking known functional domains (Pvalue 2.79E-8, Fisher exact test; Supplemental Data Sets 12 and13) (Schuster et al., 2017).This module is the only module over-represented for core effectors lacking known domains which areconserved in five sequenced smut fungi (Schuster et al., 2017)(Supplemental Data Sets 12 and 13): Of the 24 core effectorfamilies (plus 30 putatively paralogous genes in U. maydis), themagenta module harbors 28 genes including pep1 (protein es-sential during penetration1), pit2 (protein important for tumor2),and stp1 (stop after penetration1), the three effectors known to beessential for establishing biotrophy (Supplemental Data Sets12 and 13) (Doehlemann et al., 2009, 2011; Schipper, 2009;Mueller et al., 2013). We therefore speculate that these core ef-fectors, which represent 14 different effector families (Schusteret al., 2017), may contain the key determinants for establishingbiotrophy in smut fungi. Another effector found in the magentamodule but not belonging to the core effectors is see1 (seedlingefficient effector1). This effector has been shown to contribute tothe reactivation of plant DNA synthesis in leaves, which is crucialfor tumor formation (Redkar et al., 2015). The early expressionpeak of see1 observed at 2 dpi (Supplemental Data Set 12)suggests that this activation of plant DNA synthesis either has tohappen only in a narrow time window or that maintaining repli-cation within tumors is controlled also by other factors. The factthat expression of genes encoding secreted proteins in the ma-gentamodule is decreasing during later stages of infection (Figure5) is puzzling, given the immunity suppressing function of severalof these effectors (Doehlemann et al., 2009; Schipper, 2009;Mueller et al., 2013). To shed light on the underlying mechanism,we generated two haploid U. maydis strains in which promotersfrom two of the effectors from the magenta module, pep1 andstp1, were fused to gfp. In addition, these strains expressed cy-toplasmic mCherry constitutively under the control of the actinpromoter. These strains were crossed with untagged compatiblehaploid strains and the developmental stage was analyzed whendownregulation of the module takes place. At this time point,fungal aggregates are the predominant structures in the infectedtissue (Tollot et al., 2016). While mCherry was rather evenly dis-tributed in cells forming the aggregate, pep1 and stp1 promoteractivity was mainly restricted to cells at the surface of the ag-gregates (Figure 6). To exclude that this spatial expression patternis specific for thegfp reporter,wealsoplacedgfpunder thecontrolof theactinpromoter and found thesignal rather evenlydistributedwithin the aggregates (Supplemental Figure 5). This illustrates thatwhile the majority of cells in the aggregates have ceased to ex-press pep1 and stp1, fungal cells that are in direct contactwith theinfected plant tissue maintain high expression levels of thesegenes, which in all likelihood is sufficient to downregulate plantdefenses. How this heterogeneity in effector gene expression is

achieved and to which extent the production of matrix providesadditional protection is currently unknown.The cyan module is induced after establishment of biotrophy

(2 dpi), which coincides with the development of tumors and fungalaggregates. Like themagentamodule, the cyanmodule is enriched

Figure 6. Heterogenous Effector Gene Expression in Fungal Aggregates.

Maize seedlings were infected with mixtures of FB1 carrying the indicatedreporterconstructs (top)andFB2wild-typestrains.Sevendayspostinoculation,fungal aggregates within the tumor tissue were visualized by confocal mi-croscopy. The fluorescence of GFP andmCherry wasmonitored andmergedwith the respectivebright-field (BF) image.All imagesareprojectionsofmultiplez-stacks. GFP fluorescence indicative of effector gene expression was mainlydetected at the surface of the aggregates, while mCherry control fluorescencewas rather evenly distributed throughout the aggregates.

U. maydis Biotrophic Transcriptome 311

for secretedproteins lacking functional annotation (Pvalue1.92E-7,Fisherexact test;SupplementalDataSet13).Thecyanandmagentamodules together contain 153 of the 215 secreted proteins lackingfunctional signatures (Figure 5;Supplemental DataSet 12).Noneofthe candidate effector proteins belonging to the cyanmodule havebeen characterized so far. Due to their tumor-specific expressionprofile, we hypothesize that effectors in the cyan module mightdirectly be involved in inducing the plant cell developmental switchto tumor cells. It is interesting that the cyan module also containsa nonribosomal peptide synthase (UMAG_10543), as well asthree of the six U. maydis polyketide synthases (UMAG_10532,UMAG_06414, and 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 syn-thases remain to be elucidated. The thirdwaveof secreted proteinsis thusaccompaniedbyactive secondarymetabolism. It isnot clearwhether the yet unknown secondary metabolites produced areused for communication with the plant or represent moleculesneeded for development of U. maydis.

Taken together, the transcriptome reveals the modular ex-pression of putatively secreted proteins while U. maydis is on theplant surface, during biotrophic development and during tumorformation. Consecutive waves of effector gene expression linkedto the transition from the biotrophic lifestyle to necrotrophy havealso been observed in the hemibiotrophic fungi Colletotrichumhiggensianum, Colletotrichum graminicola, Z. tritici, and Lep-tosphaeria maculans (Kleemann et al., 2012; O’Connell et al.,2012; Mirzadi Gohari et al., 2015; Gervais et al., 2017). InC. higgensianum, the induced transcriptome of early infectionstages is dominated by genes for secondary metabolism, leadingto the speculation also here that theymay actually function in hostmanipulation (O’Connell et al., 2012). In the biotrophic fungiBlumeria graminis f. sp hordei, Blumeria graminis f. sp tritici, andP. striiformis f. sp tritici, effector expressionwaves have also beendescribed (Hacquard et al., 2013; Dobon et al., 2016; Zeng et al.,2017), reinforcing the idea that the need for certain effectorschanges during host colonization and that this is a conservedfeature of different pathosystems.

Identification of Potential Transcriptional Regulators ofEffector Waves

To shedmore light into the regulation of genes encoding secretedproteins, we visualized a weighted coexpression network of alldifferentially regulated genes encoding secreted proteins and alldifferentially regulated predicted transcription factors which areconnected to at least one of these (Figures 7A and 7B). We thenlooked for transcription factors that have the strongest connec-tivity to the respective modules in which secreted proteins reside.Such intramodular hub genes are likely the key drivers of a givenmodule (Mason et al., 2009). Through this analysis, we detectedthree transcription factor genes with strong connectivity (>0.9)to theredmodule: thehomeodomainproteinUMAG_10544, theTEA/ATTS transcription factorUMAG_02835, and the bHLH transcriptionfactorUMAG_11235 (Figures7Band7C;SupplementalDataSet15).We also detected rbf1 (UMAG_03172), the central transcriptionalregulator downstreamof the bE/bWcomplex (Heimel et al., 2010b),with a high intramodular connectivity of 0.9 (Figures 7B and 7C).

Rbf1 is responsible for the bE/bW-induced filamentation and cellcycle arrest (Scherer et al., 2006; Heimel et al., 2010b). The pre-viously observed downregulation of rbf1 after penetration has beensuggested tobeaprerequisite to resumethecellcycleafterenteringthe plant (Heimel et al., 2010a). In line with our prediction of rbf1 aspotential driver of the red module, the previously identified rbf1-induced genes (Heimel et al., 2010b) are highly overrepresented intheredmodule (SupplementalDataSet13).Towhatextent theotherthree transcription factors (UMAG_10544, UMAG_02835, andUMAG_11235; Figure 7C) detectedheredriveexpressionof the redmodule 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 factorswith strongest connectivity to themagentamodule, i.e., the secondwave of effectors (Figure 7B; Supplemental Data Set 15). Thosetranscription factorshavepreviouslybeensuggestedtobe themaininducers of early effectors (Heimel et al., 2010b; Lanver et al., 2014).Furthermore, our analysis suggestsUMAG_11658,UMAG_00501,mzr1 (mig2-5 zinc finger regulator1, UMAG_05804) (Zheng et al.,2008), and UMAG_02104 (Figure 7C) as being involved in earlyeffector gene expression. They all have a reasonably strong con-nectivity to the magenta module of >0.9 (Figure 7C). Consistently,mzr1 has been demonstrated to be involved in the expression ofseveral effector genes (Zheng et al., 2008), which we now place inthe magenta module (Supplemental Data Set 12). However, incontrast to hdp2 and biz1 (Flor-Parra et al., 2006; 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 cancompensate 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 onlyfound in the red module as discussed above, but also in themagenta module. The latter group includes hdp2, biz1, and11 potential effectors (Supplemental Data Sets 6 and 12). In ouranalysis of intramodular connectivity, which relies solely on thecoexpression of transcription factors and their targets undernatural expression conditions, wewere thus unable to identify thecontribution of rbf1 to expression of themagentamodule. Overall,our analysis delivered those transcription factors which wereexpected to control early effectors, and this made us confidentthat the analysis would also yield promising candidate tran-scriptional 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 haveintramodular connectivities of 0.98, 0.97, and 0.96, respectively(Figures 7B and 7C; Supplemental Data Set 15). In addition,several other transcription factors (UMAG_05721,UMAG_11138,fox1 [forkhead box1, UMAG_01523; Zahiri et al., 2010],UMAG_06257, UMAG_06308, and UMAG_01456) showed in-tramodular connectivityat slightly lower valuesofbetween0.9and0.93 (Figures7Band7C;SupplementalDataSet 15) andwere thusalso candidates for transcriptional regulators of the cyan module.Except for fox1 (Zahiri et al., 2010), none of these transcriptionfactors had so far been functionally analyzed. fox1 mutants dis-played reduced virulence, and transcriptional profiling of this

312 The Plant Cell

mutant revealed a set of 29 potential effector genes that requiredfox1 for full expression (Zahiri et al., 2010). These putative fox1target genes are significantly overrepresented in the cyanmodule(Supplemental Data Set 13). This shows that fox1 contributes tothe regulation of the cyan module, but according to our data andthe intramodular connectivity analysis (Figures 7B and 7C), fox1may not be the main driver of the third wave of effector geneexpression.

To analyze a possible contribution of the yet uncharacterizedpotential regulators of the cyan module to virulence and tumorformation, we picked the APSES-type transcription factor geneUMAG_04778 (Figures 7C and 8A) and generated a targetedknockout mutant in the compatible FB1 and FB2 strains. In-terestingly, the introduced frameshift mutation in UMAG_04778caused a strong reduction of virulence, with tumor formation inleaves being completely abolished (Figure 8B). We thereforenamed the gene nlt1 (no leaf tumors1). The virulence defect of themutants was almost fully complemented by introduction ofa single copy of nlt1 into the ip locus of the respective mutantstrains (Figure 8B). This links the mutant phenotype to the in-activation of the nlt1 gene andmakes additional off-target effects

of Cas9 unlikely. While chlorotic spots observed at 4 dpi werecomparable after infections with nlt1 mutants, the wild type, andcomplemented strains, the latter two had induced leaf tumors at8 dpi, while the nlt1 mutant failed to do so (Figure 8C). Even at12 dpi when tumors induced by wild-type and complementedstrains had reached their maximum size and started to turn blackdue to spore formation, no leaf tumorswere detected in infectionswith the nlt1mutants (Figure 8B; Supplemental Figure 6A). Stronganthocyanin formation in leaves infectedwith nlt1mutants (Figure8C) showed that the mutants were able to successfully establisha biotrophic interaction. Anthocyanin induction requires expres-sion of the tin2 (tumor inducing 2) effector gene (Tanaka et al.,2014), a gene placed in the magenta module (Supplemental DataSet 12). nlt1 mutants displayed two additional phenotypes rarelyseen in wild-type infections: death of the 4th or 5th leaf only inotherwise viable plants in ;25% of the cases (Figure 8B;Supplemental Figure 6A) and late spore-filled tumors (detectedlater than 8 dpi) restricted to the base of the stem in ;8% of theinfected plants (Figure 8B; Supplemental Figure 6B), sometimesassociated with death of the 4th or 5th leaf. The presence of basalstem tumors in nlt1 mutant infections indicates that the ability to

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 differentially regulated transcriptionfactors (B)belonging to the respectivemodules inwhich thesecretedproteins reside. Theweightednetwork isbasedon the topological overlapmatrix of theexpression data, edges were included when the pairwise overlap was greater than 0.2, and genes are colored according to their modular membership.Selected transcription factors are labeled with their respective names.(C)Connectivity of transcription factors to the red (left panel), magenta (middle panel), and cyan module (right panel). Depicted are all transcription factorshaving an intramodular connectivity of greater than 0.9 to any of the three modules. Color intensity indicates connectivity strength.

U. maydis Biotrophic Transcriptome 313

induce tumors is not completely abolished and can occur inmeristematic stem tissue. We speculate that the dead leaf phe-notype and the appearance of late stem tumors may be con-nected, i.e., formation of the basal stem tumors may affect thenutrient supply to the 4th or 5th leaf.

The ability of the nlt1 mutants to establish a biotrophic in-teraction is in line with our expectations, i.e., the third effectorwave, which we consider being controlled by nlt1, temporallyfollows the establishment of biotrophy mediated by the secondeffectorwave. To verify that nlt1 contributes to the induction of the

Figure 8. Role of U. maydis nlt1 in Virulence.

(A)Theheatmapshows theexpressionprofileof theU.maydisnlt1gene; log2expressionvaluesarevisualized relative to themeanexpressionacrossall stages.(B)The indicatedmixtures of strainswere injected intomaize seedlings and symptomswere scored 12d after infection according to severity; the color codefor each category is given on the right. The nlt1 mutants were unable to induce tumors in leaves. nlt1em indicates that the nlt1 gene was inactivated byCRISPR-Cas9. nlt1-C indicates that a single copy of nlt1 was introduced into the indicated strains to test for complementation. Three independentexperiments were performed and the average values are expressed as a percentage of the total number of infected plants (n), which is given above eachcolumn.Pleasenote that deadplants,which represent themost severe symptomcategory, are the result of thevirulenceassay that isbasedonyoungmaizeseedlings and high cell density of the inoculum. U. maydis is a strict biotroph and does not kill plant tissue under natural conditions.(C) Representative leaves of infections with wild-type strains, nlt1 mutants, and the complemented strains at 4 and 8 dpi are shown. Examples of stemtumors and dead leaves observed in infections with nlt1 mutants are depicted in Supplemental Figure 6.

314 The Plant Cell

third wave of effectors, we measured expression of six potentialeffectors of thecyanmoduleduringplant colonization (2and4dpi)in the nlt1mutants and compared this with the expression in wild-type infections. We found that four of the six cyan genes chosenwere highly dependent on nlt1 for induction (Figure 9). As a neg-ative control, we included in this analysis also six effectors of themagenta module, and none of these genes required nlt1 for ex-pression (Figure 9). These data demonstrate that nlt1 is indeeda driver of the cyanmodule. The observation that twoof the testedeffectors of thecyanmoduledidnot showanydependenceonnlt1suggests a sharp division of labor between nlt1 and the othertranscription factors in thecyanmodule. Thus,nlt1 likelydrives theexpressionof a specificsubset of effectors in thismodule. The twoZn2Cys6 proteins UMAG_05601 and UMAG_02765, which arehighly connected to the cyan module (Figure 7C), are interestingcandidates for the expression of other subsets of genes of thecyanmodule. Suchaproposeddivisionof labormayalsohold truefor transcription factors of the magenta module and this requiresexperimental clarification.

Recently, the central regulator of spore formation in U. maydis,ros1 (UMAG_05853), has been identified (Tollot et al., 2016). Ros1not only induces spore formation but also regulatesmany effectorgenes (Tollot et al., 2016). We identified Ros1 as being part ofthe cyanmodule with a medium high intramodular connectivity of0.82 (Supplemental Table 15). Indeed, Ros1-induced genes are

significantly overrepresented in the cyanmodule (P value =3.83E-21, Fisher exact test; Supplemental Data Set 13). However,a substantial number of genes of the cyan module are also re-pressed by Ros1 (Supplemental Data Sets 12 and 13). Wetherefore conclude thatRos1may tosomeextent contribute to theexpression of the third wave of effectors, but is clearly not a driverof this module. We also searched for modules in which Ros1-repressed genes were significantly overrepresented and Ros1-induced geneswere significantly underrepresented. Twomodules,red and magenta, fulfilled these criteria (P value < 0.001, Fisherexact test; Supplemental Data Set 13). Both modules have incommon that they are expressed early upon contact with the plant,corroborating the previous finding that Ros1 is a repressor of ef-fector genes required early during pathogenic development (Tollotet al., 2016).Our approach of using intramodular connectivity to identify

regulators of effector geneexpressionhas identified three sets oftranscription factors likely responsible for the plant-associatedexpression of the secretome. The first and second sets oftranscription factors largely consist of components of the b-cascade. This corroborates previous studies that emphasizedthe impact of the b-cascade for early pathogenic developmentand the establishment of biotrophy (Brachmann et al., 2001;Heimel et al., 2010b; Lanver et al., 2014). The third set of tran-scription factors that we identified here consists mainly of yet

Figure 9. Regulation of Effector Gene Expression by nlt1.

The expression of selected effector genes during pathogenic development in crosses of FB1 and FB2 wild-type strains (circles) as well as in crosses ofcompatiblenlt1mutants (triangles)wasmeasuredviaRT-qPCR.Sixeffectorgenesof themagentamodule (leftmost twopanels) andsixpotential effectorsofthe 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 andrelative expression is depicted. Significant expression differences (P value < 0.01, Student’s t test) betweennlt1mutants andwild-type strains are indicatedwithanasterisk if applicable.ErrorbarsdenoteSDof threebiological replicates.Fourofsixgenesof thecyanmodule requirednlt1 forexpressionwhilenoneofthe six effector genes of the magenta module was regulated by nlt1.

U. maydis Biotrophic Transcriptome 315

uncharacterized genes. It will be an interesting future task tostudy the contribution of these transcription factors to the ex-pression of the third wave of effectors, which is expected to fulfillvirulence functions after the early biotrophic phases have beenestablished. Studying in detail which effector groups are regu-lated by these transcription factors and linking this informationwith the physiology of plants infected by the respective tran-scription factormutants could be a key to identifying novel, host-manipulating fungal strategies that go beyond suppression ofthe plant immune system.

This large-scale transcriptome analysis has provided a detailedtemporal view of gene expression in U. maydis throughout itsbiotrophic life cycle. The analysis has allowed us to formulatenovel hypotheses concerning fungal nutrition in the plant envi-ronment and to visualize the deployment of certain groups ofsecreted effectors as well as connected transcription factorsduring discrete stages of colonization. This is expected to fuel,stimulate, and direct future functional studies of the identifiedU.maydisgenes, aswell asprovideanewresource for comparativestudies in related fungal pathogens.

METHODS

Bacterial and Fungal Strains and Growth Conditions

The Escherichia coli strain Top10 (Life Technologies) was used for cloningpurposes. Ustilago maydis strains used in this study are listed inSupplemental Data Set 16. They are derived from haploid strains FB1 andFB2 (Banuett and Herskowitz, 1989). DNA from the solopathogenic strainSG200, which was derived from FB1 (Kämper et al., 2006), was used forgene amplifications. U. maydis strains were grown in liquid YEPSL (0.4%yeast extract, 0.4%peptone, and2%sucrose) at 28°Cona rotary shakerat200 rpm. To assess the ability of compatible strains to mate and formdikaryotic hyphae, FB1 and FB2 wild-type strains and the respectivemutants were grown in YEPSL to an OD600 of ;1, harvested by centrifu-gation (1700g, 10 min at room temperature), and resuspended in water toan OD600 of 1. Compatible strains were mixed in a 1:1 ratio and 4 mL of thismixture was spotted on potato dextrose plates containing 1% (w/v) ac-tivated charcoal. The plates were incubated at room temperature for 48 h.Strains which have mated produce dikaryotic hyphae, visible as whitemycelium against the black background. To test growth of haploidU. maydis strains on different nitrogen sources, FB1 and FB2 wild-typestrains and the respective dur3-1,2,3 triple mutants were grown in YEPSLto an OD600 of ;1, harvested by centrifugation (1700g, 10 min at roomtemperature), washed two times with water, and resuspended in water toanOD600 = 1. Serial dilutions (OD600 = 1 to 1024)were prepared inwater and2.5 mL of each dilution was spotted on minimal medium plates (Holliday,1961), supplemented with 2% glucose and either 10 mM ammonium or10 or 1 mM urea. The plates were incubated for 3 d at 28°C.

Plasmid and Strain Construction

All strains generated in this study, plasmids, primers, and double-strandedDNA fragments, are listed in Supplemental Data Sets 16 to 19. To generateU. maydis mutants, the recently established CRISPR-Cas9 multiplexsystem was used (Schuster et al., 2017). The nonintegrative, self-repli-cating backbone plasmids of this system, pDL272 and pMS73, containcas9 either under the control of the strong constitutive otef promoter(pDL272)or under control of theevenstrongerhsp70promoter (pMS73).Alltarget sequences for the guide RNA constructs were designed using theE-CRISP tool (www.e-crisp.org) (Heigwer et al., 2014) with medium

stringency settings. pDL272 and pMS73 contain one copy of the U6promoter for fusion with the first guide RNA construct. Additional guideRNA constructs are then fused to distinct tRNA promoters, and all com-ponents 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 SupplementalData Set 18 were synthesized (gBLOCKs from IDT) and cloned into Acc65I-linearized pDL272 using isothermal assembly (Gibson et al., 2009). Togenerate the plasmid pDL287 for inactivation of the three urea transportersUMAG_02625, UMAG_04577, and UMAG_06253, the respective double-stranded DNA fragments fDL15, fDL16, and fDL17 were synthesized andcloned into Acc65I-linearized pMS73. As the resulting plasmid proved in-efficient for inactivation of all three genes except in strain PH72, a secondplasmid, pPH22was generated that is identical to pDL287but contains newtargetsequences for the inactivationofUMAG_02625andUMAG_06253.Tothis end, fPH1and fPH2weregeneratedbyPCRusing oPH163andoPH160as primers on gBLOCK fDL15, and oPH161 and oPH162 as primers ongBLOCK fDL16 (Supplemental Data Sets 17 and 18). pPH22 was then as-sembled byGibson cloning using fPH1, fPH2, fDL17, and Acc65I-linearizedpMS73. To generate the plasmid pPH20 for inactivation of the transcriptionfactor gene UMAG_04778, the double-stranded fragment fPH3 listed inSupplemental Data Set 18was generated by PCRwith primers oPH137 andoPH138 using gBLOCK fDL17 as template. This fragment was cloned viaGibson assembly into Acc65I-linearized pMS73 to yield the final plasmid.

For complementation analysis, wemade use of the genome integrativep123 plasmid (Aichinger et al., 2003). To generate the plasmid for com-plementation of the opt triple mutant, plasmid pPH19 was constructed.UMAG_11057 was amplified from genomic DNA of SG200 using primersoPH131 and oPH132,UMAG_02387was amplified using primers oPH133and oPH134, andUMAG_05968was amplified using primers oPH135 andoPH136. The three fragments were inserted into the backbone of p123cleavedwithAcc65I andEcoRV. In pPH19 all three genes carry their nativepromoters and termination regions. The plasmid was linearized with SspIprior to transformation in U. maydis.

Togenerate theplasmid for complementationof thenlt1mutant, pPH23was generated. To this end the nlt1gene was amplified from SG200 DNAwith primers oPH164 and oPH165. The amplified fragment was Gibsonassembled into the backbone of p123 cleaved with Acc65I and EcoRV. InpPH23, nlt1 is driven by its native promoter and termination region. Theplasmid was linearized with PsrI prior to transformation.

For the generation of U. maydis strains carrying fluorescence reporterconstructs,weconstructed the following integrativeplasmidsbasedon thebackbone of p123. To place mcherry under the control of the actin pro-moter, we amplified 2.0 kb of the promoter of the U. maydis actin gene(UMAG_11232) using primers oDL575 and oDL576. The product wasdigested with HindIII and NcoI and subjected to a three fragment ligationwith a 0.9-kb NcoI/NsiI mcherry fragment from pMF5-15g (plasmid col-lectionofM.Feldbrügge;www.mikrobiologie.hhu.de/ustilago-community)and the HindIII/NsiI-digested p123 plasmid backbone yielding pPact-mcherry (pDL252). Themcherry fragment of pPact-mcherry was replacedwith 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 am-plified 1.5 kb of the promoter of pep1 (UMAG_01987) and 0.6 kb of thepromoter of stp1 (UMAG_02475) using primer pairs oDL746/oDL747 andoDL744/oDL745, respectively. In addition, we amplified a gfp-Tnos frag-ment with the primers oDL742 and oDL743 using p123 as template. Thegfp-Tnos fragment, the respective effector promoter fragment, and theHindIII linearized pPact-mcherry plasmid were Gibson assembled to yieldpPact-mcherry-Ppep1-gfp (pDL281)andpPact-mcherry-Pstp1-gfp (pDL290).Theplasmids containing fluorescence reporter constructswere linearizedwithAgeI prior to transformation.

316 The Plant Cell

Transformation of U. maydis was performed as described previously(Schulz et al., 1990), and the transformants were selected on carboxincontaining media (2 mg/mL). To identify strains carrying Cas9-inducedmutations, we followed the established screening protocol (Schuster et al.,2017). The respective loci were amplified and sequenced with gene-specific primers (Supplemental Table 14). The stable integration of p123-based plasmids was verified by DNA gel blot as described previously(Loubradou et al., 2001). All complementation plasmids were integrated insingle copy into the U. maydis ip locus. Fluorescence reporter constructswere integrated in multiple copies (>3) into the ip locus.

Plant Infections and Collection of Samples

For the transcriptional profiling, the compatible haploid strains FB1 andFB2 were grown separately in YEPSL to an OD600 of 1.0. Cells were col-lected by centrifugation (5 min at 1250g at room temperature) and re-suspended in the original volume of sterile water. Compatible strains weremixed in equal amounts prior to injection into 6-d-old maize (Zea mays)seedlingsof the variety Early GoldenBantam (UrbanFarmers) as describedpreviously (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°Cand40%/60%humidity. Togeneratebiological replicates, threeplantinfections were performed with independently cultivated fungal strains ondifferent days. For each replicate, more than 100 plants were infected witha mixture of compatible wild-type strains and the same number of plantswas mock infected with water. Samples of 10 infected maize seedlingswerecollectedat0.5,1,2,4,6,8,10,and12dpi.Forearly timepoints,wheresymptomsarenotyet visible (0.5, 1,and2dpi) 2-cmsectionsof leafbladeofthe 2nd and 3rd leaves were excised 0.5 cm below the infection holes.These areas were shown by microscopy to be colonized at these timepoints. At later time points (4, 6, 8, and 12 dpi) all visibly infected areas,including the leaf blades to;1cmbelow the injectionholeson the2nd, 3rd,and 4th leaf and the attached leaf sheets were harvested. At each timepoint, comparable parts of seedlingswere harvested for themock infectedcontrol plants. For each sample, the collected material from 10 differentplants was pooled, immediately frozen in liquid nitrogen, and stored at280°C. For the axenic culture samples, FB1 and FB2 cells separatelygrown in YEPSL to an OD600 of 1.0 in three independent biological repli-cates were harvested by centrifugation (5 min at 3000g), directly frozen inliquid nitrogen, and stored at 280°C until RNA extraction.

All virulence assays were performed in the greenhouse under controlledconditions with a light-dark cycle of 28°C for 14 h and 20°C for 10 h. Duringthe day phase, the illumination intensity was at least 25 to 30 kLux (withadditional sunlightupto90kLux).U.maydiswasgrownand injected into6-d-oldmaizeplantsasdescribedabove.Except fornlt1mutants, theOD600of theinjected suspension for virulence assays was adjusted to 1.0. To assessvirulence of nlt1 mutants and respective wild-type strains the OD600 wasadjusted to 0.2. Virulence symptoms were scored 12 dpi using the diseaserating scheme developed previously (Kämper et al., 2006). When scoringdisease symptoms of infections with nlt1 mutants, two new disease cate-gorieswere introduced.Hatchedwasacategorywhereplantsdeveloped latetumors at the base of the stemonly, sometimes associatedwith death of the4th or 5th leaf, as well as a category where only the 4th or 5th leaf died inotherwisehealthymaizeseedlings (seeSupplementalFigure6 forexamples).

RNA Extraction and Sequencing

Pooled plant material corresponding to individual samples was ground toafinepowder in liquidnitrogenusingaRetschCryoMillwitha50-mLgrindingbeaker and a 20-mm grinding ball. For each sample, the machine wasprecooled for 30 s followed by 60 s of grinding at 20 Hz. Approximately500 mg of powder was resuspended in 1 mL TRIzol reagent (Life Tech-nologies) and total RNA was extracted according to the manufacturer’s

recommendation. For RNA extraction from U. maydis derived from axenicculture, glass beads (0.4–0.6 mm) together with 1 mL of TRIzol reagent (LifeTechnologies) were added to the frozen cell pellet followed by vigorousshaking (IKA Vibrax VXR, 2000 rpm, 10 min) and further processing. Toeliminate genomic DNA contamination, the Ambion Turbo DNA-free kit (LifeTechnologies)wasused.The totalRNAwas furtherpurifiedusing theRNeasyMini Kit (Qiagen) and subjected to quality control with an Agilent 2100 Bio-analyzer according to the instructions of the Agilent RNA 6000 Nano AssayProtocol. Libraries were prepared using the Illumina TruSeq stranded RNAsample preparationKit and 1 mg of total RNAas input. After poly(A) selection(using poly-T oligo-attached magnetic beads), mRNA was purified andfragmented using divalent cations under elevated temperature. The RNAfragments underwent reverse transcription using random primers. This wasfollowed by second strand cDNA synthesis with DNA Polymerase I andRNase H. After end repair and A-tailing, indexing adapters were ligated. Theproductswere thenpurifiedandamplified (14PCRcycles, 10mL template) tocreate the final cDNA libraries. After library validation and quantification(Agilent 2100 Bioanalyzer), up to six libraries were pooled. Pools werequantified using the Peqlab KAPA library quantification kit and the AppliedBiosystems 7900HT sequence detection system. One pool per lane wassequencedusingan IlluminaTruSeqPEClusterKit v3andan IlluminaTruSeqSBSKitv3-HSonanIlluminaHiSeq2000sequencerwithapaired-end(101373101cycles)protocol.Therawdatahavebeendeposited intheNCBIGeneExpression Omnibus (GEO; Edgar et al., 2002) and are accessible throughGEO Series accession number GSE103876 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103876).

Read Mapping, Normalization, and Statistical Analysis of DifferentialGene Expression

All reads were imported into CLC genomics workbench (v8.5) and quality-filtered to keep only sequences with PhredScores higher than 20 (qualitylimit 0.01). The reads were trimmed for the presence of TruSeq adaptersand, according to a FastQC quality control, 12 nucleotides at the 59 endwere statically removed. Reads smaller than 50 nucleotides were dis-carded. The paired end reads were merged to single reads, if the overlapwas more than 10 nucleotides. Depending on the sample, reads fromdifferent sequencing runs (but from the sameTruseq libraries)werepooled.For the axenic culture samples, the separately generated reads from FB1and FB2 strains were pooled and analyzed in one batch. All reads werefiltered against the annotated maize genes (90% identity over 80% of thesequence) and all unmapped reads were then mapped against the an-notated U. maydis genes (80% identity over 60% of the sequence). Forexpression analysis, only uniquely mapping exon read counts were con-sidered.Sincehighly expressedgenesmakeupasubstantial fractionof thetotal library, causing the remaininggenes tobeundersampledandpossiblymaking them falsely appear to be downregulated in that sample, it isimportant to normalize for the library composition. We chose to normalizeour data set using the relative log expression method implemented in theDESeq2 package (v1.10) (Love et al., 2014) in R (www.r-project.org). Al-ternatively,weused the trimmedmeanofM-valuesmethod implemented inthe EdgeR package (v3.12) (Robinson et al., 2010; Robinson and Oshlack,2010). Both methods are very efficient in normalizing for library compo-sition effects (Dillies et al., 2013). Pairwise comparisons were made withEdgeR and DESeq2 using the conditional maximum likelihood (qCML)methodand theCox-Reidprofile-adjusted likelihoodmethod, respectively.In both analyses, genes with a log2 fold change > 0.5 and Benjamini andHochberg-adjusted P value < 0.01 were considered differentially ex-pressed (Eisen et al., 1998). All count data have been deposited in NCBI’sGene Expression Omnibus (Edgar et al., 2002) and are accessible throughGEO Series accession number GSE103876 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103876). This also includes all countdata mapped to the maize genome, which are not discussed here.

U. maydis Biotrophic Transcriptome 317

Coexpression Analysis

For theunsupervisedWGCNA,onlygenesthathaveat least foronetimepointover all three replicates a DESeq2-normalized read count of at least 10wereconsidered. A total of 6421 genes passed these filtering criteria. Log2-transformedDESeq2-normalizedcounts thenservedas input for thenetworkanalysis. We used the function blockwiseModules, implemented 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 geneswere treated in a single block. The signed network ensured that only positivecorrelations were considered. We have chosen a soft power thresholdof 7 because thiswas the lowest power needed to reach scale-free topology(R2 = 0.92). Module detection was performed with default settings (merge-CutHeight of 0.15andenabledPAMstage). Theminimalmodule sizewas setto25genes.Aftermoduledetection, highly correlatedmodulesweremergedusingacutheightof0.3.Thisstep reduced thenumberofmodules from22to14 (Supplemental Figure 4). For each module, the expression profile of themodule eigengene was calculated, which is defined as the first principalcomponentof themodule’sexpressiondata.Foreachgene, the intramodularconnectivity (kME) was calculated, which represents Pearson correlation ofthe individual gene with the respective module eigengenes. To visualizeweighted coexpression networks, the topological overlap matrix was ex-ported toCytoscape (v3.3) (Shannon et al., 2003) with an adjacency cutoff of<0.2. The k-mean clustering was performed in MeV (v4.9) (http://mev.tm4.org) using the same DESeq2 normalized and log2 transformed expressiondata set that was used for theWGCNA (see above). To determine a suitablenumber of clusters, we used the figure ofmerit analysis and chose to clusterthe genes into six clusters. The k-mean clusters were completed after20 iterationsusingPearsoncorrelationasdistancemeasure.Average linkagehierarchical clustering of individual modules and gene sets was performedwithCluster (v3.0) (Eisenetal.,1998)andheatmapswerevisualizedwithJavaTreeview (v1.1.6).

GO Term Enrichment Analysis

GO term enrichment analysis (Ashburner et al., 2000; The Gene OntologyConsortium, 2017)was performedwith theGOseqpackage (v1.22) (Younget al., 2010) in R (www.r-project.org) using hypergeometric testing and anoverrepresented P value cutoff of < 0.005. For the enrichment analysis ofthe individual modules, only genes were considered that had an intra-modular connectivity (kME) of greater than 0.5. Visualization of GO termnetworks was performed with the Cytoscape Enrichment Map plug-in(v2.1; www.baderlab.org/Software/EnrichmentMap) (Merico et al., 2010)using a Jaccard coefficient cutoff of >0.2. Clusters of gene sets weremanually marked and named according to their GO annotation using theCytoscape Word Cloud plug-in (v3.1).

RT-qPCR and Quantitative Real-Time PCR

For gene expression analysis via RT-qPCR, plants were infected with theindicated strains adjusted to a cell density atOD600 of 1.0. Then, 1 to 2mgofisolated total RNAwas reverse-transcribedwith oligo(dT) primers using theFirst-Strand Super Script III cDNA Synthesis Kit (Life Technologies). PerRT-qPCR reaction, cDNAderived from10 to 50ngRNAwasdeployed. TheRT-qPCRwasperformed inaBio-Rad iCycler usingSYBRGreenRT-qPCRSuperMix-UDG (Life Technologies). Cycling conditions were 1 min 30 sat 95°C, followed by 45 cycles of 15 s at 95°C, 30 s at 60°C, and 30 s at72°C. Primers are listed in Supplemental Data Set 17. Two control geneswere used: the previously established peptidylprolyl isomerase (ppi )UMAG_03726 plus the rab7GTPaseUMAG_05511, which was accordingto the data set created here one of the most constantly expressed genes(coefficientof variationof14.2). Thearithmeticmeanof the two referenceCtvalues served as normalization. For the expression of fold changes ofRT-qPCRdata, the22DDCtmethodwasused (LivakandSchmittgen, 2001).

To determine fungal biomass, the same plantmaterial that was used forthe RNA-seq analysis was utilized. DNA was extracted from the groundpowder using a phenol-based protocol (Hoffman and Winston, 1987).Then,150ng totalDNAwassubjected toqPCR (quantitative real-timePCR)analysis with plant-specificGAPDH (oDL833/oDL834) and fungal-specificppi primer pairs (oPH231/oPH232; Supplemental Data Set 14), and theratios of fungal DNA to plant DNA (22DCt) were calculated for each sample.This analysis allowed us to relate for each sample the number of fungaltranscripts to the amount of fungal DNA.

Microscopy

To visualize fungal aggregates in tumor tissue, 7-dpi tumors were collected,and thinslicesof the tumorswerepreparedusinga razorblade.Thesliceswereanalyzedby a TCS-SP5 confocalmicroscope (LeicaMicrosystems). GFPwasexcited at 488 nm and emitted fluorescence was detected at 495 to 530 nm.mCherrywas excited at 561 nmand emissionwas detected at 580 to 630 nm.Images were processed using LAS-AF software (Leica Microsystems).

Accession Numbers, Genome References, and Annotations

Themaize reference genome was downloaded from Ensembl (http://plants.ensembl.org/info/website/ftp/index.html, Zea_mays.AGPv3.28.dna.top-level). TheU. 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, and NC_008368.1).GO categories and InterPro motifs of U. maydis genes were extracted fromthe pedant database (http://pedant.helmholtz-muenchen.de/). Membranetransport proteins were defined by filtering the InterPro annotation ofU.maydis for any of the following keywords: porter, porting, P-type ATPase,facilitator, permease, channel, and aquaporin. Secreted hydrolases wereclassifiedbysearching the InterProdescriptionsof the467secretedproteinsfor the keywords 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). CAZymes potentially affecting the plant cell wall were definedpreviously (LoPrestietal.,2015).To identifyandclassifyU.maydis transcriptionfactors,weusedapreviously defined list of 82 InterProsignatures known tobeinvolved in fungal transcriptional regulation (Park et al., 2008), and supple-mented this list with the InterPro signatures IPR001092, IPR011598 repre-senting bHLH, and IPR018608 representing WOPR transcription factors,respectively. The InterPro annotation of U. maydis was then filtered for thepresence of any of those 85 signatures. The RNA-seq data are available in theGEO database under accession number GSE103876.

Supplemental Data

Supplemental Figure 1. Correlation between amount of fungal tran-scripts and fungal biomass.

Supplemental Figure 2. Correlation between RNA-seq and RT-qPCRderived expression data.

Supplemental Figure 3. Detection of gene coexpression modules ofU. maydis during pathogenic development.

Supplemental Figure 4. Enriched biological processes in coexpres-sion modules.

Supplemental Figure 5. Expression of GFP under control of the actinpromoter in fungal aggregates.

Supplemental Figure 6. Disease categories specific to infections withnlt1 mutants.

Supplemental Data Set 1. Overview of read counts mapping to U. maydisand Z. mays.

318 The Plant Cell

Supplemental Data Set 2. Statistics of changes of fungal biomassbased on DNA and transcripts.

Supplemental Data Set 3. DESeq2-normalized U. maydis readcounts.

Supplemental Data Set 4. Differentially expressed U. maydis genesthroughout the infection.

Supplemental Data Set 5. Overlap between differentially expressedgene sets identified by DESeq2 and EdgeR.

Supplemental Data Set 6. Gene coexpression modules of U. maydisand intramodular connectivities of the genes.

Supplemental Data Set 7. Overlap between modules detected byWGCNA and clusters detected by k-mean clustering.

Supplemental Data Set 8. GO-term enrichment analysis of coex-pression modules.

Supplemental Data Set 9. Expression of primary metabolism pathways.

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 incoexpression modules.

Supplemental Data Set 14. Expression of secondary metabolismpathways.

Supplemental Data Set 15. Intramodular connectivities of transcrip-tion factors with coexpression 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 inthis study.

Supplemental Data Set 19. Plasmids used in this study.

ACKNOWLEDGMENTS

We thank Armin Djamei and Florian Raths for providing preliminary results onU. maydis oligopeptide transporters. We thank Michael Bölker for sharing hisinterpretation of fungal growth within tumors. We thank Nicole Ludwig forhelpingwith samplingof infectedplantmaterial.We thankKaiHeimel for listingU. maydis UPR components and Gertrud Mannhaupt and Ulrich Güldenerfor providing gene annotations. We enjoyed helpful discussions with FrankHochholdinger and Kristian K. Ullrich and all members of the Kahmann group.This work was supported by generous funds from the Max Planck Society.

AUTHOR CONTRIBUTIONS

R.K. conceived theproject. A.N.M. plannedandconductedplant infectionsand generated samples for RNA-seq analysis. M.F. and J.A. performedRNAsequencing.D.L. andG.S. performeddata analysis. F.B.H. andS.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 virulenceassays. A.N.M. and C.P. contributed to the finalization of the data set.D.L. and R.K. led the study and wrote the manuscript with help from S.R.

Received September 28, 2017; revised December 20, 2017; acceptedJanuary 24, 2018; published January 25, 2018.

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U. maydis Biotrophic Transcriptome 323

DOI 10.1105/tpc.17.00764; originally published online January 25, 2018; 2018;30;300-323Plant 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,

Studied by RNA-Seq AnalysisUstilago maydisThe Biotrophic Development of

 This information is current as of May 24, 2020

 

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References /content/30/2/300.full.html#ref-list-1

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