Unraveling the transcriptional features and gene expression networks of pathogenic and saprotrophic Ophiostoma species during the infection of Ulmus americana

| Open Peer Review | Genetics and Molecular Biology | Research Article Unraveling the transcriptional features and gene expression networks of pathogenic and saprotrophic Ophiostoma species during the infection of Ulmus americana Thais C. de Oliveira,1,2 Nastasia J. Freyria,3 Jorge Luis Sarmiento-Villamil,1,2,4 Ilga Porth,1,2 Philippe Tanguay,5 Louis Bernier1,2 AUTHOR AFFILIATIONS See affiliation list on p. 20. ABSTRACT American elm (Ulmus americana), highly prized for its ornamental value, has suffered two successive outbreaks of Dutch elm disease (DED) caused by ascomy cete fungi belonging to the genus Ophiostoma. To identify the genes linked to the pathogenicity of different species and lineages of Ophiostoma, we inoculated 2-year-old U. americana saplings with six strains representing three species of DED fungi, and one strain of the saprotroph Ophiostoma quercus. Differential expression analyses were performed following RNA sequencing of fungal transcripts recovered at 3and 10-days post-infection. Based on a total of 8,640 Ophiostoma genes, we observed a difference in fungal gene expression depending on the strain inoculated and the time of incubation in host tissue. Some genes overexpressed in the more virulent strains of Ophiostoma encode hydrolases that possibly act synergistically. A mutant of Ophiostoma novo-ulmi in which the gene encoding the ogf1 transcription factor had been deleted did not produce transcripts for the gene encoding the hydrophobin cerato-ulmin and was less virulent. Weighted gene correlation network analyses identified several candidate pathogenicity genes distributed among 13 modules of interconnected genes. IMPORTANCE Ophiostoma is a genus of cosmopolitan fungi that belongs to the family Ophiostomataceae and includes the pathogens responsible for two devastating pandemics of Dutch elm disease (DED). As the mechanisms of action of DED agents remain unclear, we carried out the first comparative transcriptomic study including representative strains of the three Ophiostoma species causing DED, along with the phylogenetically close saprotrophic species Ophiostoma quercus. Statistical analyses of the fungal transcriptomes recovered at 3 and 10 days following infection of Ulmus americana saplings highlighted several candidate genes associated with virulence and host-pathogen interactions wherein each strain showed a distinct transcriptome. The results of this research underscore the importance of investigating the transcriptional behavior of different fungal taxa to understand their pathogenicity and virulence in relation to the timeline of infection. KEYWORDS transcriptome, differential gene expression, Ophiostoma, pathogenicity, host-pathogen interaction, Dutch elm disease Human activities have resulted in a dramatic increase in the global movement of fungal tree pathogens, which have spread into both natural forests and tree plantations on several continents, hereby threatening biodiversity and causing important economic losses (1–4). Ophiostoma ulmi and Ophiostoma novo-ulmi (Ascomy cota, Ophiostomatales) are the causal agents of the highly destructive Dutch elm disease (DED), which impacted field and urban elm populations worldwide (5). Infection of a susceptible individual rapidly leads to vascular wilt, dieback, and death of the host (5). February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-23 1 Editor Lindsey Price Burbank, USDA - San Joaquin Valley Agricultural Sciences Center, Parlier, California, USA Address correspondence to Thais C. de Oliveira, thais.campos-de[email protected], or Louis Bernier, [email protected]. The authors declare no conflict of interest. See the funding table on p. 20. Received 17 October 2023 Accepted 8 December 2023 Published 17 January 2024 Copyright © 2024 de Oliveira et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. Elm bark beetles belonging to genera Scolytus and Hylurgopinus vector the pathogen and allow it to penetrate the host vascular system when they feed on healthy elms (6, 7). Six taxa, including three distinct species, are recognized in the DED fungi. The moderately aggressive O. ulmi (OU) was responsible for the first pandemic, which began in the early 1900s (8) and persisted until the 1950s to the 1980s depending on the location. The second, ongoing pandemic is caused by the highly aggressive O. novo-ulmi (1, 9), which progressively replaced O. ulmi in most areas and includes two subspecies designated novo-ulmi (ONU) and americana (AME) (9, 10). Two distinct genetic lineages, AME1 and AME2, have been reported in subsp. americana (11–13). A third species, Ophiostoma himal-ulmi (OHU), was recovered from symptomless Ulmus wallichiana in the Indian Himalayas and shown to be pathogenic toward European elm varieties; it is thus considered a DED fungus (14). All DED fungi exhibit yeast-mycelium dimorphism and the in vitro response of this trait to external stimuli varies according to individual strain (15, 16). Furthermore, physiological, molecular, and genomic data have shown the occurrence of O. ulmi-type DNA introgression into the O. novo-ulmi genome (13, 17–19), as well as the emergence of hybrid swarms resulting from sexual crosses between subsp. novo-ulmi and americana individuals (20). The DED fungi belong to a group of closely related species (21), which also includes saprobes causing sapstain. One of them, Ophiostoma quercus (OQ), has a cosmopolitan distribution (22–24) and was suggested to be the species from which the DED fungi evolved following interspecific hybridization and/or secondary speciation (25). Some O. quercus mutants transformed with the O. novo-ulmi gene encoding the hydrophobin cerato-ulmin (CU) were reported to be pathogenic to Commelin elm (26). The meteoric growth of genomic sequencing technologies in the last decades (27) offers unprecedented opportunities for investigating further the biology of plant pathogens and their interactions with their hosts. For instance, high-quality reference genomes have been obtained for fungal wilt pathogens, including Fusarium oxysporum f. sp. lycopersici (28), Verticillium albo-atrum and V. dahliae (29), and the DED fungi O. ulmi (30) and O. novo-ulmi (31). The availability of a fully assembled and well-annotated genome for O. novo-ulmi subsp. novo-ulmi (32) has facilitated genome-wide analyses of population dynamics of DED fungi (13) and transcriptomic profiling of the yeast-myce lium growth dynamics of O. novo-ulmi in vitro (33, 34). Comparative in silico analyses also showed that a fujikurin-like OpPKS8 biosynthetic gene cluster found in O. ulmi and O. novo-ulmi was likely acquired through horizontal gene transfer from a taxon belonging to a different family in the Ascomycota and might contribute to the pathogenicity of the DED fungi (35). Studies of DED fungi based on the -omics in planta lag behind those carried out in vitro or in silico. Pioneering works on elm-O. novo-ulmi interactions (36, 37) allowed the recovery of transcripts for less than 200 fungal genes and were therefore not very informative regarding the biology of the pathogen. Nigg et al. (38) presented the first comprehensive analysis of transcriptomes of O. novo-ulmi subsp. americana following inoculation of one susceptible and one resistant (Valley Forge) clone of Ulmus americana. In the work described herein, we compared the transcriptomes of seven Ophiostoma strains, which had been inoculated to U. americana saplings. The strains represented all taxa of DED fungi, along with the saprobe O. quercus and one mutant of O. novo-ulmi subsp. novo-ulmi in which gene ogf1 had been deleted (∆Ogf1). Gene ogf1 encodes a putative homolog of the Magnaporthe oryzae GPF1 fungal-specific Zn2Cys6 transcrip tion factor, which is required for normal growth, conidia germination, appressorium formation, and virulence (39–41). We observed marked differences among the Ophios toma transcriptomes and identified several candidate genes that might be involved in the parasitic fitness of the DED pathogens. Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-23 2 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. RESULTS External symptoms in American elm saplings inoculated with Ophiostoma spp. At 3 days post-inoculation (dpi), low levels of leaf wilting were observed in most saplings inoculated with ONU or ∆Ogf1, whereas at 10 dpi, moderate levels of wilting were observed in saplings inoculated with OU, ONU, AME2, or ∆Ogf1 (Table S1.1). In addi tional tests of the effect of inoculum concentration on the virulence of wild-type (WT) strain ONU and its Δogf1 mutant, both strains induced similar levels of leaf symptoms (including wilting, yellowing, browning, or abscission) when inoculated at higher doses (50,000 or 100,000 cells), whereas strain ∆Ogf1 showed a partial (25%) but significant (P < 0.05) reduction in virulence when saplings were inoculated at lower doses (3.12 × 103 or 1.25 × 104 cells; Tables S1.2 to S1.4). Mutant ∆Ogf1 was also significantly less virulent (P < 0.05) than strain ONU on Golden Delicious apples, based on the diameter of necroses developing around the inoculation point (Table S1.5). Overview of the Ophiostoma spp. transcriptomes in planta In total, 2 TB of data were collected after sequencing, processing, and mapping of 68 RNA samples recovered from U. americana saplings inoculated with a strain of Ophiostoma or injected with sterile water (Table S2). Since not all genomes and functional annotations were publicly available for the fungal taxa assayed in this work, we used the well-charac terized O. novo-ulmi subsp. novo-ulmi strain H327 genome sequence consisting of 8,640 genes (32) as our model for mapping and analyzing all fungal transcripts. The Ophios toma transcriptomes included 9,268,611 raw reads at 3 dpi and 7,073,182 raw reads at 10 dpi (Table S2). A total of ca. 280,000 uniquely mapped reads were obtained for each fungal strain (Table S2). The numbers of reads for all ONU H327 orthologs in Ophiostoma spp. are shown in Table S3. Fungal transcripts recovered from water-injected elms were considered to represent conserved genes of fungal endophytes of U. americana (see Discussion) and therefore removed from further analyses [principal component analysis (PCA), Heatmap, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Carbohydrate-Active enZYmes (CAZy), and Pathogen-Host Interactions database (PHI-base)]. Depending on the strain, the top 10 most-expressed genes were represented by an expression level of 3 to 3,263, with lower numbers recorded for OQ (Table S4). Gene OnuG5955 encoding an alcohol oxidase was the most highly expressed gene in all strains except OQ. This gene was one of 90 O. novo-ulmi genes expressed only in planta in previous transcriptomics studies (38). Transcripts for 82 of the remaining 89 genes were detected with counts per million > 4 (cpm > 4) in our data set (Table S5). Genes from DED-causing Ophiostoma species usually exhibited comparable expression values, with a few notable exceptions, such as OnuG7311 (protein TOXD), which was more expressed in ONU, OnuG5670 (exopolygalacturonase) not detected in OHU, and OnuG4739 (flavincontaining monooxygenase) more highly expressed in OU. Gene OnuG1537 was not expressed (cpm < 4) in mutant ∆Ogf1 (Table S3). Differential expression analysis The differential expression analysis of transcriptomes at 3 and 10 dpi (Fig. 1A) showed different patterns among Ophiostoma strains. Strains OU, AME2, and OHU had intermedi ate to high total numbers of differentially expressed genes (DEGs) (n = 324–603) and these were more abundant at 3 dpi than at 10 dpi. Strains ∆Ogf1 and AME1 had moderately high numbers of DEGs (n = 333 and 353, respectively) and these were observed in higher numbers at 10 dpi than at 3 dpi. A similar type of distribution was observed in OQ but, in this case, the total number of DEGs was low (n = 114). In the case of ONU, the 204 DEGs were distributed roughly equally between the 3and 10-dpi subsets. The 12 DEGs at 3 dpi recorded in water-injected elms were considered to represent conserved genes of fungal endophytes. Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-23 3 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. Principal component analysis of the transcription data set for the seven Ophiostoma strains clearly distinguished OQ from all specimens of DED fungi (Fig. 2). In general, data obtained at 3 dpi tended to be clustered toward the positive side of PC2, as was the case FIG 1 Differentially expressed Ophiostoma spp. (OU, ONU, AME1, AME2, OHU, OQ, and ∆Ogf1) genes during interaction with Ulmus americana. (A) MA plot of all Ophiostoma genes for which transcripts were detected including genes that were overexpressed at 3 and 10 dpi. Read counts were normalized using DESeq2 package in R with log2FC > 2 and scatter plot visualization. Fungal transcripts recorded in water-injected elms represent conserved genes of fungal endophytes. (B) Gene ontology terms for the top 18 biological processes that were significantly enriched in Ophiostoma (OU, ONU, AME1, AME2, OHU, OQ, and ∆Ogf1) colonizing U. americana at 3 dpi (left)and 10 dpi (right). Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-23 4 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. for all replicates for strains OU, AME2, and OHU. Although strains ONU and ∆Ogf1 differed only by a mutation at a single locus, their transcription patterns tended to be distinct from each other. When DEGs were compared among O. novo-ulmi strains, the Venn diagram analysis yielded contrasting results (Fig. S1). At 3 dpi, strains ∆Ogf1 and AME2 shared the highest number of DEGs (n = 30), followed by the AME1-AME2 pair (n = 29), whereas only 12 DEGs were common to strain ∆Ogf1 and its wild-type ONU parental. At 10 dpi, the number of DEGs shared by ∆Ogf1 and AME2 dropped to only six, whereas ∆Ogf1 and AME1 now shared the highest number of DEGs (n = 38). We conducted comparative heatmap analyses of the top 50 DEGs in OU and ONU at 3 and 10 dpi, following variance stabilizing transformation (Fig. S2). At 3 dpi, we identified four DEGs (OnuG0653, OnuG3998, OnuG8505, and OnuG8458) encoding small unknown proteins of 322, 222, 283, and 296 amino acids, respectively, whereas a single DEG (OnuG5230) for a small unknown protein of 76 aa was observed at 10 dpi. These five genes were upregulated in OU. Furthermore, four of the eight genes in the OpKS8 putative fujikurin-like cluster (OnuG 7305, 7306, 7311, and 7312; see Table S6 for annotation) were upregulated in ONU at 10 dpi (Fig. S2). These genes, along with OnuG7310, were downregulated in mutant ∆Ogf1 at 10 dpi compared to ONU (Fig. S2). In addition, the ∆Ogf1 mutant did not express the OnuG4296 gene encoding cerato-ulmin. FIG 2 Principal component analysis of the complete transcription data set for the seven Ophiostoma spp. strains (OU, ONU, AME1, AME2, OHU, OQ, and ∆Ogf1) inoculated to Ulmus americana saplings. The symbol for AME2 at 10 dpi is hidden behind the symbol for AME1 at 10 dpi, and the symbol for OU at 3 dpi is hidden behind the symbol for ∆Ogf1 at 3 dpi. Each group is represented as the average position of all samples within the group and the standard errors of each PC as the error bars. Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-23 5 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. GO enrichment analysis Comparative analysis of GO terms for “biological processes” that were significantly enriched showed that “carbohydrate metabolic process,” “metabolic process,” and “transport” were the terms that included the highest numbers of DEGs at 3 dpi (Fig. 1B). Strains AME2, OU, and OHU accounted for most DEGs in “carbohydrate metabolic process” (n = 33, 15, and 14, respectively), “metabolic process” (21, 16, and 16, respec tively), and “transport” (15, 12, and 13, respectively) at 3 dpi. In contrast, at 10 dpi, the numbers of AME2 and OU DEGs linked to “carbohydrate metabolism” dropped drastically to four and six, respectively. Moreover, at 10 dpi, the three terms with the most DEGs were “metabolic process,” “electron transport,” and “transport.” Most DEGs in “metabolic process” originated from OU (n = 23), ∆Ogf1 (n = 16), and ONU and AME1 (n = 15 each). Top DEG contributors to “electron transport” were AME1, ∆Ogf1, and OU (19, 14, and 12, respectively), whereas most DEGs in “transport” were found in AME1 (n = 11), AME2 (n = 7), and OU and OHU (n = 6 each). In the case of saprobe OQ, the highest numbers of DEGs were recorded at 10 dpi in “metabolic process” and “transport,” with five DEGs each. Analysis of functional annotations for the top 20 GO terms in “molecular function” and “biological process” and all 15 terms detected in “cellular components” confirmed trends observed for global differential expression: the highest numbers of DEGs at 3 dpi were recorded in strains OU, AME2, and OHU, whereas more DEGs were observed in strains ∆Ofg1, AME1, OQ, and, to a lesser extent, ONU at 10 dpi (Fig. 3). Furthermore, the global gene expression pattern of the AME2 strain was more similar to that of the OU than to the phylogenetically closer AME1 strain. In “molecular function,” we observed high numbers of DEGs linked to “oxidoreductase activity” (for instance, 26 DEGs in OU at 10 dpi and 26 DEGs in AME2 at 3 dpi) and to “hydrolase activity” (notably 35 DEGs in AME2 at 3 dpi). At 3 dpi, DEGs linked to “zinc ion binding,” “ATP binding,” “DNA binding,” and “nucleic acid binding” terms were markedly more abundant in OU and AME2. No DEGs linked to “cellulase activity” were detected in OQ, OU, and ONU, although they were observed in mutant ∆Ogf1 at 10 dpi. The latter strain had no DEGs linked to the term “transcription factor activity.” In “biological processes” term, mutant ∆Ogf1 was once again characterized by the absence of DEGs for the term “transcription.” In “cellular components,” we observed more genes upregulated linked to the term “membrane” at 3 dpi (OU, AME2, and OHU), “integral to membrane” (OU 3 dpi, AME1 10 dpi, AME2 3 and 10 dpi, and OHU 10 dpi), “nucleus” at 3 dpi (OU and AME2), and “intracellular” at 3 dpi (OU and AME2). KEGG analyses When KEGG pathway annotations were subjected to PCA (Fig. 4A), the component 1 axis separated OQ from all other Ophiostoma samples, whereas component 2 separated transcriptomes at 3 dpi from those at 10 dpi. According to PC1, the 3-dpi data set was more homogenous than the 10-dpi data set in which transcriptomes of ΔOgf1 and OHU had diverged from the others. More detailed analyses of Ophiostoma DEGs (Fig. 4B) showed more upregulated genes linked to “biosynthesis of secondary metabolites” at 3 dpi, except in AME1 and OQ where no upregulated gene was observed. Strains OU, AME2, and OHU presented more upregulated genes at 3 dpi compared to other strains. Analysis of CAZyme-encoding genes A PCA of all Ophiostoma gene reads with orthologs in the CAZy database confirmed that gene expression in OQ differed strongly from that in other Ophiostoma species and lineages (Fig. 5A). Expression patterns of lineage AME1 were also well separated from those of other Ophiostoma strains investigated. Within each strain, DEG data at 3 and 10 dpi generally clustered, and this was particularly evident in ONU and AME2. The notable exception was OHU in which data at 3 dpi overlapped with 10 dpi data for OU. We documented for the first time the possible action of hydrolases linked to β-glucose Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-23 6 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. FIG 3 Functional annotation analysis based on the Gene Ontology of genes expressed at 3 and 10 dpi in Ophiostoma spp. strains (OU, ONU, ∆Ogf1, AME1, AME2, OHU, and OQ) inoculated to Ulmus americana. Heatmap analysis based on the number of genes in the top 20 GO terms in “molecular function” (in green) and “biological processes” (in blue) and all genes contained in “cellular components” (in yellow). Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-23 7 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. digestion (Fig. 5B) in some of the DED fungi. We thus observed that gene OnuG0547 (GH6) was DEG at 3 dpi in OU and AME2, and DEG at 10 dpi in AME1 and OHU; genes OnuG0647 and OnuG5654 (GH7) were both DEG at 3 dpi in AME2, whereas the former was DEG at 10 dpi in OHU; and finally, gene OnuG0647 (GH45) was DEG at 3 dpi in ∆Ogf1 and DEG at 10 dpi in AME1 and AME2. Overall, AME2 had the largest number of DEGs for CAZymes (n = 56), with most of them (n = 48) observed at 3 dpi. Furthermore, 39 of the 56 DEGs encoded a protein expected to have a signal peptide (32). On the other hand, AME1 had 21 DEGs for CAZymes, including 18 DEGs at 10 dpi. The saprobe OQ had the FIG 4 Functional annotation analysis based on the Kyoto Encyclopedia of Genes and Genomes of genes expressed at 3 and 10 dpi in Ophiostoma spp. strains (OU, ONU, ∆Ogf1, AME1, AME2, OHU, and OQ) inoculated to Ulmus americana. (A) Principal component analysis of the predicted functional features in KEGG. Each group is represented as the average position of all samples within the group and the standard errors of each PC as the error bars. (B) Number of representative KEGG DEGs among Ophiostoma taxa and strains. Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-23 8 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. lowest number of DEGs (n = 6), split evenly between 3 and 10 dpi. Three of the proteins encoded by OQ DEGs were expected to include a signal peptide. FIG 5 Functional annotation analysis based on the Carbohydrate-Active enZYmes database of genes expressed at 3 and 10 dpi in Ophiostoma spp. strains (OU, ONU, ∆Ogf1, AME1, AME2, OHU, and OQ) inoculated to Ulmus americana. (A) PCA analysis of Ophiostoma CAZyme-encoding genes. Each group is represented as the average position of all samples within the group and the standard errors of each PC as the error bars. (B) Circular heatmap for differentially expressed genes (Log2FC > 2) divided into different families: carbohydrate esterases (CE), glycoside hydrolases (GH), carbohydrate-binding modules (CBM), glycosyl transferases (GT), and polysaccharide lyases (PL). The initials are followed by the gene number represented. DEGs at 3 dpi have a positive FC value and are shown in green; DEGs at 10 dpi have a negative FC value and are shown in yellow. Genes marked with a blue star code for a CAZyme with a signal peptide. Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-23 9 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. correlated with ONU. The hub gene of module Turquoise (OnuGp2723) encodes profilin, a small actin-binding protein that is conserved among eukaryotic organisms (67) and has also been detected in viruses and cyanobacteria (68, 69). Although profilin is involved in many pathogenic interactions (67), it has never been associated with a fungal plant disease and this warrants further investigations. Module Purple (Fig. 8A) contained all eight genes of the OpPKS8 cluster (OnuG7305– OnuG7312) encoding a putative fujikurin-like toxin (35). The first neighborhood connection between the OpPKS8 cluster and the hub gene OnuG7303 suggests these physically close elements interact with each other. OnuG7303, along with OnuG7306 from the OpPKS8 cluster, encode a putative sterigmatocystin 8-O-methyltransferase responsible for the biosynthesis of aflatoxin type B1 and B2 toxins (70). Gene OnuG7303 was expressed when O. novo-ulmi subsp. americana MH75-4O colonized susceptible U. americana but not resistant (Valley Forge) elm (38). Should biochemical analyses confirm that DED fungi produce fujikurinand aflatoxin-like toxins in vitro and in planta, the contribution of these molecules to virulence could be verified by functional analysis through the production of null mutants for genes OnuG7303, OnuG7606, and others to account for the genetic plasticity of fungal genomes (71). The Blue module, positively correlated with ∆Ogf1, was the second largest mod ule and included several genes encoding transcription factors: OnuG1809 (PHI: 1560), OnuG3441, OnuG1220 (PHI: 1914), OnuG5671, OnuG5928 (PHI: 1796), OnuG3294 (PHI: 1710), OnuG0079 (PHI: 1354), and OnuG5077.1. As transcripts for these genes were more abundant in strain ΔOgf1 than in other strains (Table S3), this implies that the deletion of ogf1 possibly stimulated the upregulation of other transcription factors whose role in the pathogenicity of O. novo-ulmi should be investigated by knockout of the genes that encode them. In the Green module positively correlated with the moderately aggressive O. ulmi, the hub gene (OnuGp6492) was an ortholog of zap1 encoding a zinc-responsive transcrip tional regulator protein (Fig. 8B). zap1 controls zinc uptake and regulates the transcrip tion of the zrt1 and zrt2 genes in response to zinc availability in Saccharomyces cerevisiae (72). The regulation of zinc acquisition by zap1 is fundamental for fungal pathogenesis in mammalian hosts (73). For instance, zap1 regulates zinc homeostasis and modulates virulence in Cryptococcus gattii, one of the causal agents of cryptococcosis (74). zap1 was DEG in OU at 3 dpi (log2FC = 3.91), whereas zrt1 was DEG in OU at 10 dpi (log2FC = −4.27). In ONU, however, the log2FC values for zap1, zrt1, and zrt2 were low (0.48, 0.001, and 0.54, respectively). This suggests that the investigation of a possible link between zinc acquisition and virulence in DED fungi should include the production and analysis of null mutants in both O. ulmi and O. novo-ulmi. Furthermore, WGCNA showed that zap1 was directly linked to several other genes (Fig. 8B). These included OnuG4429 encoding a calpain (calcium-activated neutral proteinase), a protein involved in fungal development and pathogenicity (75, 76). zap1 was also directly linked to OnuG2192 encoding an Hsp70-like protein, which, in Cryptococcus neoformans, influences the interaction between the pathogen and its host (75). Hsp70 may also play a dual role during infection since it can act as an effector molecule inducing nitric oxide (NO) production by epithelial cells or, conversely, as an immunosuppressive molecule reducing NO production by macrophages (76, 77). In plants, the inhibition of NO synthesis compromises the hypersensitive disease resistance response of Arabidopsis leaves to Pseudomonas syringae infection, thereby promoting disease and bacterial growth (78). The role of NO was also shown in soybean cells where reactive oxygen intermediates induce genes for the synthesis of protective natural products (79). In the present work, zap1 and hsp70-like genes were upregulated (log2FC = 3.91 and 3.35, respectively) only in the moderately virulent O. ulmi at 3 dpi, suggesting they play a role as virulence modulators during the earlier phase of disease development. The Greenyellow module, which was also positively correlated with OU, included four genes that were DEG at 10 dpi: OnuG1485 (PHI: 112), OnuG3416, OnuG6502, and OnuG6506 (PHI: 438). OnuG1485 encodes a monooxygenase, whereas the predicted Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-2316 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. product of OnuG6506 is a pisatin demethylase (PDA). Pisatin is a plant phytoalexin produced in response to microbial attack. However, pisatin can be biotransformed by PDA into a molecule less harmful to fungal pathogens (80). The Black and Pink modules were positively correlated with OHU and contained only genes that were upregulated at 3 dpi (Table S6). Four genes in the pink module have orthologs in PHI-base, including OnuG1398 (PHI: 2022), OnuG3407 (PHI: 58), and OnuG3720 (PHI: 2022), which are involved in the synthesis of melanin, an amorphous polymer that increases the ability of fungi to survive in a hostile environment (81) and is a virulence factor in pathogenic fungi (82, 83). The fourth gene, OnuG1395 (PHI: 2921), is linked to iron absorption. The deletion of the ortholog of this gene in Colletotri chum graminicola resulted in a reduction in virulence (84). Additional Ophiostoma genes of interest with orthologs in the PHI-base might be present in the Magenta module associated with AME2. For instance, deletion or disruption of orthologs of OnuG1084 (PHI:423), OnuG366 (PHI:2020), OnuG5175 (PHI:504), OnuG7194 (PHI:881), or OnuG4969 (PHI:2292) led to reduced virulence in various species (85–89), whereas inactivation of the ortholog of OnuG5266 (PHI:2177) in M. oryzae enhanced conidiation (90). Gene clustering by WGCNA also highlighted genes whose expression differed markedly according to time. Thus, OnuG4204, one of two genes predicted to encode tetraspanins in ONU (32), was the hub gene in the Brown module that was positively correlated with global expression at 3 dpi (Fig. 7; Fig. S4). Tetraspanins are membrane proteins that act as organizers of membrane-signaling complexes required for the pathogenicity of plant pathogenic ascomycetes (91). OnuG4204 had a direct connection with another hub gene, OnuG2833, which codes for calcium channel subunit cch1, the only high-affinity calcium channel in the plasma membrane of fungal cells (85). Expression of this gene is linked to cell stress signaling (92). OnuG4204 and OnuG2833 were both DEGs at 3 dpi in strain OHU (log2FC = 2.30 and 2.64, respectively), thereby suggesting concerted activation in these genes in OHU. A comparison of the 50 genes with the most variable expression in OU and ONU (Fig. S2) showed that several genes encoding relatively small unknown proteins were upregulated in the less virulent species O. ulmi either early (OnuG0653, OnuG3998, OnuG8505, and OnuG8458; Green module) or late (OnuG5230; Turquoise module) in disease development. These DEGs were found almost only in OU except for OnuG8505 (DEG at 10 dpi in AME2 with Log2FC = −2.43) and OnuG5230 (DEG at 10 dpi in AME1 with Log2FC = −2.05, AME2 with Log2FC = −2.88, and OHU with Log2FC = −2.77). Although the predicted products of these genes lack a signal peptide, they may nevertheless represent effectors secreted by non-conventional processes (86) and, therefore, their role in pathogenicity should be investigated. Conclusions This work compared for the first time the transcriptomes of different Ophiostoma taxa inoculated to American elm. Overall, we observed taxon-specific transcriptome profiles, with transcriptomes of the saprotroph O. quercus significantly different from transcrip tomes of DED pathogens. For the pathogenic taxa analyzed, the transcriptome variations were not associated with phylogenetic relationships. Gene network analyses showed the diversity and complexity of gene interactions among strains, which is further exemplified by a significant rewiring of gene interactions by the deletion of the gene encoding a homolog of the GPF1 transcription factor. In fact, a mutation in O. novo-ulmi subsp. novo-ulmi at this single locus silenced the gene encoding cerato-ulmin, downregulated several genes in the OpKS8 cluster, and upregulated a subset of genes encoding other transcription factors. This resulted in a moderate, yet significant decrease in virulence. Our study provides a spectrum of candidate pathogenicity genes in the DED fungi, which should be further examined by functional genetic studies. Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-2317 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. MATERIALS AND METHODS Plant material and growth conditions On the 22 January 2020, dormant, 2-year-old saplings of Ulmus americana grown from seeds collected on the Laval University campus (Québec City) were taken indoors, allowed to thaw for 1 day, and transferred to a growth chamber kept at 10°C without light. Over the next 2 weeks, growth conditions were progressively brought to the following conditions: 16 h light at 24°C (±2) and 8 h darkness at 18°C (±2), with 60% relative humidity and light intensity at 725 lux. Saplings were inoculated 28–30 days after bud break once leaves were fully expanded. Inoculation procedure and sampling of elms Prior to inoculation, seven strains of Ophiostoma spp. were recovered from −80°C storage at the Centre d’Étude de la Forêt collection (http://www.cef-cfr.ca/index.php?n=CEF.Col lections). Strains included the moderately aggressive O. ulmi Q412T-O (OU), the highly aggressive O. novo-ulmi subsp. novo-ulmi H327-O (ONU), O. novo-ulmi subsp. americana lineage 1 DDS100 (AME1), O. novo-ulmi subsp. americana lineage 2 DDS154 (AME2), O. himal-ulmi HP30 (OHU), the saprobe O. quercus AU5-1 (OQ), as well as O. novo-ulmi subsp. novo-ulmi ∆13–15 (∆Ogf1) in which gene OnuG1537 encoding a homolog of transcription factor GPF1 had been deleted by targeted mutagenesis of the highly aggressive strain H327-OΔmus52 using the procedure developed by Sarmiento-Villamil et al. (87). Yeast cells were obtained by incubating strains in liquid Ophiostoma minimal medium (88) on a rotary shaker (130 rpm) at 21°C for 4 days. Fungal cultures were filtered through eight layers of cheesecloth and centrifuged for 5 min at 5,000 rpm, and yeast cells were resuspended in sterile distilled water at a concentration of 4 × 106 cells mL−1. Saplings used in the transcriptomic analysis of the elm-Ophiostoma interactome were inoculated on 20 March 2020 by injecting 25 µL of Ophiostoma yeast cell suspension into each of three holes (total inoculum = 3 × 104 cells) drilled with a 3/32 bit (89) at ca. 20 cm up the main stem. Non-inoculated control saplings were injected with sterile distilled water. Inoculation/injection holes were covered with Parafilm (Bemis Co., Neenah, WI, USA). Each treatment was applied to four saplings distributed randomly within four different blocks. Each block also included one sapling that received no treatment, for a total of 68 saplings. Because of crown size expansion between bud break and inoculation, saplings had to be incubated in two growth chambers set according to the parameters described above. Block 1 was placed in one growth chamber, whereas blocks 2–4 were placed in the other growth chamber. Stem samples for transcriptomic analyses were collected at 0-, 3-, and 10-days post-inoculation. External foliar symptoms (wilting, yellowing, or browning of leaves) were noted at 0, 3, and 10 dpi, and treatments were rated on a scale from 0 (no symptomatic leaves) to 5 (>90% symptomatic leaves) (Table S1.1). Saplings that had been treated with Ophiostoma or distilled water were sampled ca. 2 cm above the section of the stem that had been inoculated or injected. The stem of untreated (0 dpi) saplings was sampled at a similar height. For each time point, four destructive biological replicates were generated. Stem samples were quickly frozen in liquid nitrogen and stored at −80°C. The virulence of mutant ∆Ogf1 and its H327∆mus52 parental strain was also assessed on Golden Delicious apples and three additional sets of U. americana saplings. Apples (eight biological replicates per treatment) were inoculated with plugs of mycelium and the diameter of necroses was measured at 14 dpi, as previously described (93). The first set of inoculated saplings (20 March 2020, eight biological replicates per treatment, 1 × 105 cells inoculated per sapling) was placed in the same growth chamber as block 1 saplings used in the transcriptomics study. Development of external foliar symptoms was monitored over 14 days, and treatments were rated on a scale from 0 (no symptoms) to 4 (>75% symptomatic leaves) (Table S1.2). The second and third sets of saplings were inoculated on 25 June 2020 and 16 June 2021, respectively, as part of larger-scale testing of O. novo-ulmi insertional mutants. In the 2020 trial (Table S1.3), saplings (10 Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-2318 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. biological replicates per treatment) were inoculated with 5 × 104 cells, whereas saplings (eight biological replicates per treatment) inoculated in 2021 (Table S1.4A) received lower doses of inoculum (1.25 × 103 or 3.12 × 103 cells). Inoculated and water-injected controls were maintained in a greenhouse compartment as previously described (87) and external symptoms were assessed over 21 days (2020) or 35 days (2021). Percent symptomatic leaves (including leaves that had fallen off) were recorded for each sapling, and the mean proportion of symptomatic leaves was calculated for each treatment. Development of O. novo-ulmi in the xylem of saplings inoculated in 2021 (Table S1.4B) was evaluated by reisolating from tissue around the inoculation point, at mid-stem, and in the distal portion of the stem. Strains of O. novo-ulmi were identified morphologically among cultures that developed on a semi-selective medium containing cycloheximide, streptomycin sulfate, and chloramphenicol (53, 94). RNA extraction and sequencing Stem samples were ground with a Mixer mill 400, set at 30 seconds, frequency 30. The entire grinding process was carried out in the presence of liquid nitrogen, and ground samples were immediately stored at −80°C. Total RNA was extracted by the cetrimonium bromide (CTAB) protocol (92) and quantified in a Thermo Scientific Nanodrop 1000 Spectrophotometer (RRID: SCR_016517). The integrity of RNA extractions was checked in a Bioanalyzer RNA 6000 Nano assay. All samples were diluted to 35 ng µL−1 in a final volume of 30 µL, sent to Centre d’Expertise et de Services, Génome Québec (Montréal, QC, Canada) and sequenced on the Illumina HiSeq 1000 platform. Sequence data processing, mapping, and annotation Raw sequence data quality was visualized using FastQC v.0.11.9 (95). All samples were filtered with Trimmomatic v.0.36 (paired-end mode; minimum length 100 pb) (96). For the analysis of fungal transcriptomes, all samples were mapped and aligned onto the O. novo-ulmi H327 genome (32) with STAR read mapper v.2.7.8a (97) using default parameters. Mapped reads were annotated according to the O. novo-ulmi H327 functional annotation (32) based on KEGG (98), GO (99), CAZy (100), and PHI-base (101). Differentially expressed genes were selected when comparing transcript levels at 3 and 10 dpi of each fungal organism (or water control). Gene Ontology term enrichment analyses were performed on sets of overexpressed fungal genes at 3 and 10 dpi as described previously (33). Statistical analyses All statistical analyses were performed using R Studio v.1.4.1106. To enable the repre sentation of differential expression analyses between samples, all read counts were normalized with DESeq2 package in R (102) with a fold change of two (|log2FC| > 2) and FDR < 0.05 using scatter plot visualization as previously described (103). Variabil ity among fungal transcriptomes was assessed by PCA and computed using the rda() function in R from Vegan package. Heatmap of clustered results was based on normal ized and transformed read counts with the vst() function in DESeq2 package in R. Venn diagrams were produced with the Bioinformatics & Evolutionary Genomics platform (http://bioinformatics.psb.ugent.be/webtools/Venn/). Gene transcription patterns in the different Ophiostoma species, lineages, and genotypes were also investigated by WGCNA using the WGCNA package from R (42). The gene matrix was constructed with a threshold of 12. All modules were hierarchically clustered based on topological overlap matrix similarity. Interaction networks within selected gene modules were identified using Cytoscape v3.9.0 (104). Statistical analyses of all inoculations in elms and Golden Delicious apples with Ophiostoma spp. were performed using Past4 v.4.03 (Table S1). Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-2319 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. ACKNOWLEDGMENTS The authors thank Isabelle Giguère, André Gagné, and Jean-Guy Catford for technical support. This research was funded by Genome Canada, Genome British Columbia, and Génome Québec within the framework of project bioSAFE (Biosurveillance of Alien Forest Enemies, Project number 10106), and by The Natural Sciences and Engineering Research Council of Canada (NSERC Discovery Grant RGPIN-2018-06607). T.C.O. and L.B. conceived and designed the experiments. T.C.O., N.J.F., I.P., P.T., and L.B. performed transcriptional analysis curation and comparative analyses. T.C.O. and N.J.F. performed bioinformatics analyses. T.C.O., J.L.S.V., I.P., P.T., and L.B. contributed reagents, materials, and analysis tools. T.C.O. and L.B. wrote the manuscript. All authors read and approved the final manuscript. AUTHOR AFFILIATIONS 1Institut de Biologie Intégrative et des Systèmes, Université Laval, Québec, Quebec, Canada 2Centre d’étude de la Forêt, Faculté de foresterie, de géographie et de géomatique, Université Laval, Québec, Quebec, Canada 3Department of Natural Resource Sciences, McGill University, St. Anne-de-Bellevue, Quebec, Quebec, Canada 4Instituto de Hortofruticultura Subtropical y Mediterránea, Consejo Superior de Investigaciones Científicas-Universidad de Málaga (IHSM-CSIC-UMA), Estación Experi mental “La Mayora”, Málaga, Spain 5Canadian Forest Service, Natural Resources Canada, Laurentian Forestry Centre, Québec, Quebec, Canada AUTHOR ORCIDs Thais C. de Oliveira http://orcid.org/0000-0002-7565-7563 Louis Bernier http://orcid.org/0000-0002-1789-8190 FUNDING Funder Grant(s) Author(s) Genome Canada (GC) 10106 Ilga Porth Louis Bernier Philippe Tanguay Genome British Columbia (Genome BC) 10106 Ilga Porth Louis Bernier Philippe Tanguay Génome Québec (GQ) 10106 Ilga Porth Louis Bernier Philippe Tanguay Gouvernement du Canada | Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN-2018-06607 Louis Bernier AUTHOR CONTRIBUTIONS Thais C. de Oliveira, Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review and editing | Nastasia J. Freyria, Data curation, For mal analysis, Investigation, Methodology, Software, Visualization, Writing – review and editing | Jorge Luis Sarmiento-Villamil, Conceptualization, Data curation, Formal analysis, Methodology, Writing – review and editing | Philippe Tanguay, Conceptualization, Data Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-2320 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. curation, Funding acquisition, Investigation, Writing – review and editing | Louis Bernier, Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Resources, Supervision, Validation, Writing – review and editing. DATA AVAILABILITY The transcript sequences are available in GenBank under the accession number PRJNA856292, ID 856292. ADDITIONAL FILES The following material is available online. Supplemental Material Supplemental Figures S1 to S4 (Spectrum03694-23-S0001.pdf). Supplementary figures. Supplemental Tables S1 to S6 (Spectrum03694-23-S0002.xlsx). Supplemental tables. Open Peer Review PEER REVIEW HISTORY (review-history.pdf). An accounting of the reviewer comments and feedback. REFERENCES 1. Brasier CM. 1991. Ophiostoma novo-ulmi sp. nov., causative agent of current Dutch elm disease pandemics. Mycopathol 115:151–161. https: //doi.org/10.1007/BF00462219 2. Oerke EC. 2006. Crop losses to pests. J Agric Sci 144:31–43. https://doi. org/10.1017/S0021859605005708 3. Lo Presti L, Lanver D, Schweizer G, Tanaka S, Liang L, Tollot M, Zuccaro A, Reissmann S, Kahmann R. 2015. Fungal effectors and plant susceptibility. Annu Rev Plant Biol 66:513–545. https://doi.org/10.1146/ annurev-arplant-043014-114623 4. Santini A, Liebhold A, Migliorini D, Woodward S. 2018. Tracing the role of human civilization in the globalization of plant pathogens. ISME J 12:647–652. https://doi.org/10.1038/s41396-017-0013-9 5. Bernier L. 2022. Chapter 16 - Dutch elm disease, p 291–309. In Asiegbu FO, Kovalchuk A (ed), For Microbiol. Academic Press. 6. Chapman JW. 1910. The introduction of a European Scolytid (the smaller elm bark-beetle, Scolytus multistriatus Marsh) into Massachu setts. Psyche: A J Entomology 17:63–68. https://doi.org/10.1155/1910/ 76129 7. Santini A, Faccoli M. 2015. Dutch elm disease and elm bark beetles: a century of association. iForest Biogeo For 8:126–134. https://doi.org/10. 3832/ifor1231-008 8. Schwarz M. 1922. Das zweigsterben der ulmen, trauerweiden und pfirsichbäume. Meded Phytopathol Lab 5:1–74. 9. Brasier CM. 1979. Dual origin of recent Dutch elm disease outbreaks in Europe. Nature 281:78–80. https://doi.org/10.1038/281078a0 10. Brasier CM. 1986. Comparison of pathogencity and cultural characteris tics in the EAN and NAN aggressive subgroups of Ophiostoma ulmi. Trans Br Mycol 87:1–13. https://doi.org/10.1016/S0007-1536(86)800018 11. Brasier CM, Kirk SA. 2000. Survival of clones of NAN Ophiostoma novoulmi around its probable centre of appearance in North America. Mycol Res 104:1322–1332. https://doi.org/10.1017/S0953756200002732 12. Solla A, Dacasa MC, Nasmith C, Hubbes M, Gil L. 2008. Analysis of Spanish populations of Ophiostoma ulmi and O. novo-ulmi using phenotypic characteristics and RAPD markers. Plant Pathol 57:33–44. https://doi.org/10.1111/j.1365-3059.2007.01692.x 13. Hessenauer P, Fijarczyk A, Martin H, Prunier J, Charron G, Chapuis J, Bernier L, Tanguay P, Hamelin RC, Landry CR. 2020. Hybridization and Introgression drive genome evolution of Dutch elm disease pathogens. Nat Ecol Evol 4:626–638. https://doi.org/10.1038/s41559-020-1133-6 14. Brasier CM, Mehrotra MD. 1995. Ophiostoma himal-ulmi sp. nov., a new species of Dutch elm disease fungus endemic to the Himalayas. Mycol Res 99:205–215. https://doi.org/10.1016/S0953-7562(09)80887-3 15. Naruzawa ES, Bernier L. 2014. Control of yeast-mycelium dimorphism in vitro in Dutch elm disease fungi by manipulation of specific external stimuli. Fungal Biol 118:872–884. https://doi.org/10.1016/j.funbio.2014. 07.006 16. Wedge MÈ, Naruzawa ES, Nigg M, Bernier L. 2016. Diversity in yeastmycelium dimorphism response of the Dutch elm disease pathogens: the inoculum size effect. Can J Microbiol 62:525–529. https://doi.org/ 10.1139/cjm-2015-0795 17. Brasier C, Franceschini S, Forster J, Kirk S. 2021. Enhanced outcrossing, directional selection and transgressive segregation drive evolution of novel phenotypes in hybrid swarms of the Dutch elm disease pathogen Ophiostoma novo-ulmi. J Fungi (Basel) 7:452. https://doi.org/10.3390/ jof7060452 18. Et-Touil A, Brasier CM, Bernier L. 1999. Localization of a pathogenicity gene in Ophiostoma novo-ulmi and evidence that it may be intro gressed from O. ulmi. Mol Plant Microbe Interact 12:6–15. https://doi. org/10.1094/MPMI.1999.12.1.6 19. Et-Touil A, Dusabenyagasani M, Bouvet GF, Brasier CM, Bernier L. 2019. Ophiostoma ulmi DNA naturally introgressed into an isolate of Ophiostoma novo-ulmi is clustered around pathogenicity and mating type Loci. Phytoprotection 99:1–11. https://doi.org/10.7202/1059303ar 20. Brasier CM, Kirk SA. 2010. Rapid emergence of hybrids between the two subspecies of Ophiostoma novo ‐ ulmi with a high level of pathogenic fitness. Plant Pathol 59:186–199. https://doi.org/10.1111/j.1365-3059. 2009.02157.x 21. De BeerZW, Wingfield MJ. 2013. Emerging lineages in the Ophiostoma tales, p 21–46. In Seifert KA, De Beer ZW, Wingfield MJ (ed), The Ophiostomatoid fungi: Expanding frontiers. CBS, Utrecht. 22. Brasier CM, Kirk SA. 1993. Sibling species within Ophiostoma piceae. Mycol Res 97:811–816. https://doi.org/10.1016/S0953-7562(09)81156-8 23. Harrington TC, McNew D, Steimel J, Hofstra D, Farrell R. 2001. Phylogeny and taxonomy of the Ophiostoma piceae complex and the Dutch elm disease fungi. Mycologia 93:111–136. https://doi.org/10. 1080/00275514.2001.12061284 24. Taerum SJ, de Beer ZW, Marincowitz S, Jankowiak R, Wingfield MJ. 2018. Ophiostoma quercus: an unusually diverse and globally widespread Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-2321 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. tree-infecting fungus. Fungal Biol 122:900–910. https://doi.org/10. 1016/j.funbio.2018.05.005 25. Brasier CM. 1990. China and the origins of Dutch elm disease: an appraisal. Plant Pathol 39:5–16. https://doi.org/10.1111/j.1365-3059. 1990.tb02470.x 26. Del Sorbo G, Scala F, Parrella G, Lorito M, Comparini C, Ruocco M, Scala A. 2000. Functional expression of the gene cu, encoding the phytotoxic hydrophobin cerato-ulmin, enables Ophiostoma quercus, a nonpatho gen on elm, to cause symptoms of Dutch elm disease. Mol Plant Microbe Interact 13:43–53. https://doi.org/10.1094/MPMI.2000.13.1.43 27. Shendure J, Balasubramanian S, Church GM, Gilbert W, Rogers J, Schloss JA, Waterston RH. 2017. DNA sequencing at 40: past, present and future. Nature 550:345–353. https://doi.org/10.1038/nature24286 28. Ma LJ, van der Does HC, Borkovich KA, Coleman JJ, Daboussi MJ, Di Pietro A, Dufresne M, Freitag M, Grabherr M, Henrissat B, et al. 2010. Comparative genomics reveals mobile pathogenicity chromosomes in Fusarium. Nature 464:367–373. https://doi.org/10.1038/nature08850 29. Klosterman SJ, Subbarao KV, Kang S, Veronese P, Gold SE, Thomma BPHJ, Chen Z, Henrissat B, Lee Y-H, Park J, et al. 2011. Comparative genomics yields insights into niche adaptation of plant vascular wilt pathogens. PLoS Pathog 7:e1002137. https://doi.org/10.1371/journal. ppat.1002137 30. Khoshraftar S, Hung S, Khan S, Gong Y, Tyagi V, Parkinson J, Sain M, Moses AM, Christendat D. 2013. Sequencing and annotation of the Ophiostoma ulmi genome. BMC Genomics 14:162. https://doi.org/10. 1186/1471-2164-14-162 31. Forgetta V, Leveque G, Dias J, Grove D, Lyons R, Genik S, Wright C, Singh S, Peterson N, Zianni M, Kieleczawa J, Steen R, Perera A, Bintzler D, Adams S, Hintz W, Jacobi V, Bernier L, Levesque R, Dewar K. 2013. Sequencing of the Dutch elm disease fungus genome using the Roche/454 GS-FLX titanium system in a comparison of multiple genomics core facilities. J Biomol Tech 24:39–49. https://doi.org/10. 7171/jbt.12-2401-005 32. Comeau AM, Dufour J, Bouvet GF, Jacobi V, Nigg M, Henrissat B, Laroche J, Levesque RC, Bernier L. 2014. Functional annotation of the Ophiostoma novo-ulmi genome: insights into the phytopathogenicity of the fungal agent of Dutch elm disease. Genome Biol Evol 7:410–430. https://doi.org/10.1093/gbe/evu281 33. Nigg M, Laroche J, Landry CR, Bernier L. 2015. RNAseq analysis highlights specific transcriptome signatures of yeast and mycelial growth phases in the Dutch elm disease fungus Ophiostoma novo-ulmi. G3 (Bethesda) 5:2487–2495. https://doi.org/10.1534/g3.115.021022 34. Nigg M, Bernier L. 2016. From yeast to hypha: defining transcriptomic signatures of the morphological switch in the dimorphic fungal pathogen Ophiostoma novo-ulmi. BMC Genomics 17:920. https://doi. org/10.1186/s12864-016-3251-8 35. Sbaraini N, Andreis FC, Thompson CE, Guedes RLM, Junges Â, Campos T, Staats CC, Vainstein MH, Ribeiro de Vasconcelos AT, Schrank A. 2017. Genome-wide analysis of secondary metabolite gene clusters in Ophiostoma ulmi and Ophiostoma novo-ulmi reveals a fujikurin-like gene cluster with a putative role in infection. Front Microbiol 8:1063. https://doi.org/10.3389/fmicb.2017.01063 36. Aoun M, Jacobi V, Boyle B, Bernier L. 2010. Identification and monitoring of Ulmus americana transcripts during in vitro interactions with the Dutch elm disease pathogen Ophiostoma novo-ulmi. Physiol Mol Plant Pathol 74:254–266. https://doi.org/10.1016/j.pmpp.2010.04. 003 37. Perdiguero P, Venturas M, Cervera MT, Gil L, Collada C. 2015. Massive sequencing of Ulmus minor’s transcriptome provides new molecular tools for a genus under the constant threat of Dutch elm disease. Front Plant Sci 6:541. https://doi.org/10.3389/fpls.2015.00541 38. Nigg M, de Oliveira TC, Sarmiento-Villamil JL, de la Bastide PY, Hintz WE, Sherif SM, Shukla M, Bernier L, Saxena PK. 2022. Comparative analysis of transcriptomes of Ophiostoma novo-ulmi ssp. americana colonizing resistant or sensitive genotypes of American elm. J Fungi (Basel) 8:637. https://doi.org/10.3390/jof8060637 39. Chang PK, Ehrlich KC. 2013. Genome-wide analysis of the Zn (II) 2 Cys 6 zinc cluster-encoding gene family in Aspergillus flavus. Appl Microbiol Biotechnol 97:4289–4300. https://doi.org/10.1007/s00253-013-4865-2 40. Lu J, Cao H, Zhang L, Huang P, Lin F, Xu J-R. 2014. Systematic analysis of Zn2Cys6 transcription factors required for development and pathogenicity by high-throughput gene knockout in the rice blast fungus. PLoS Pathog 10:e1004432. https://doi.org/10.1371/journal. ppat.1004432 41. Motaung TE, Saitoh H, Tsilo TJ. 2017. Large-scale molecular genetic analysis in plant-pathogenic fungi: a decade of genome-wide functional analysis. Mol Plant Pathol 18:754–764. https://doi.org/10. 1111/mpp.12497 42. Langfelder P, Horvath S. 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinform 9:559. https://doi.org/10. 1186/1471-2105-9-559 43. Yu D, Lim J, Wang X, Liang F, Xiao G. 2017. Enhanced construction of gene regulatory networks using hub gene information. BMC Bioinform 18:186. https://doi.org/10.1186/s12859-017-1576-1 44. Fijarczyk A, Bernier L, Sakalidis ML, Medina-Mora CM, Porth I. 2022. Independent evolution has led to distinct genomic signatures in Dutch elm disease-causing fungi and other vascular wilts-causing fungal pathogens. J Fungi (Basel) 9:2. https://doi.org/10.3390/jof9010002 45. Fijarczyk A, Hessenauer P, Hamelin RC, Landry CR. 2022. Lifestyles shape genome size and gene content in fungal pathogens. bioRxiv. https://doi.org/10.1101/2022.08.24.505148 46. Jacobi WR, Koski RD, Harrington TC, Witcosky JJ. 2007. Association of Ophiostoma novo-ulmi with Scolytus schevyrewi (Scolytidae) in Colorado. Plant Dis 91:245–247. https://doi.org/10.1094/PDIS-91-30245 47. Hintz W, Pinchback M, de la Bastide P, Burgess S, Jacobi V, Hamelin R, Breuil C, Bernier L. 2011. Functional categorization of unique expressed sequence tags obtained from the yeast-like growth phase of the elm pathogen Ophiostoma novo-ulmi. BMC Genomics 12:431. https://doi. org/10.1186/1471-2164-12-431 48. Wang Z, Gerstein M, Snyder M. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63. https://doi.org/10.1038/ nrg2484 49. Nigg M, Bernier L. 2017. Large-scale genomic analyses of in vitro yeastmycelium dimorphism in human, insect and plant pathogenic fungi: from ESTs to RNAseq experiments. Fungal Bio Rev 31:131–142. https:// doi.org/10.1016/j.fbr.2017.04.001 50. Islam MT, Coutin JF, Shukla M, Dhaliwal AK, Nigg M, Bernier L, Sherif SM, Saxena PK. 2022. Deciphering the genome-wide transcriptomic changes during interactions of resistant and susceptible genotypes of american elm with Ophiostoma novo-ulmi. J Fungi (Basel) 8:120. https:// doi.org/10.3390/jof8020120 51. Witzell J, Martín JA. 2018. Endophytes and forest health, p 261–282. In Pirttilä AM, Frank AC (ed), Endophytes of forest trees: biology and applications. Springer International Publishing, Cham. 52. Macaya-Sanz D, Witzell J, Collada C, Gil L, Martín JA. 2023. Core endophytic mycobiome in Ulmus minor and its relation to Dutch elm disease resistance. Front Plant Sci 14:1125942. https://doi.org/10.3389/ fpls.2023.1125942 53. Brasier C. 1981. Laboratory investigation of Ceratocystis ulmi, p 76–79. In Stipes RJ, Campana RJ (ed), Compendium of elm diseases. American Phytopathological Society, St. Paul, MN. 54. Gow NAR, Gadd GM. 1995. The growing fungus, p 3–19. In The success of the hypha and mycelium. Springer Netherlands, Dordrecht. 55. Black B, Lee C, Horianopoulos LC, Jung WH, Kronstad JW. 2021. Respiring to infect: emerging links between mitochondria, the electron transport chain, and fungal pathogenesis. PLoS Pathog 17:e1009661. https://doi.org/10.1371/journal.ppat.1009661 56. Mäkelä MR, Marinović M, Nousiainen P, Liwanag AJM, Benoit I, Sipilä J, Hatakka A, de Vries RP, Hildén KS. 2015. Aromatic metabolism of filamentous fungi in relation to the presence of aromatic compounds in plant biomass. Adv Appl Microbiol 91:63–137. https://doi.org/10.1016/ bs.aambs.2014.12.001 57. Krishnan A, Convey P, Gonzalez-Rocha G, Alias SA. 2016. Production of extracellular hydrolase enzymes by fungi from King George Island. Polar Biol 39:65–76. https://doi.org/10.1007/s00300-014-1606-7 58. Kyu MT, Nishio S, Noda K, Dar B, Aye SS, Matsuda T. 2020. Predominant secretion of cellobiohydrolases and ENDO-Β-1,4-glucanases in nutrientlimited medium by Aspergillus spp. isolated from subtropical field. J Biochem 168:243–256. https://doi.org/10.1093/jb/mvaa049 Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-2322 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. 59. Henrissat B, Bairoch A. 1993. New families in the classification of glycosyl hydrolases based on amino acid sequence similarities. Biochem J 293 ( Pt 3):781–788. https://doi.org/10.1042/bj2930781 60. Segato F, Damásio ARL, de Lucas RC, Squina FM, Prade RA. 2014. Genomics review of holocellulose deconstruction by Aspergilli. Microbiol Mol Biol Rev 78:588–613. https://doi.org/10.1128/MMBR. 00019-14 61. Schülein M, Kauppinen MS, Lange L, Lassen SF, Andersen LN, Klysner S, Nielsen JB. 1998. Characterization of fungal cellulases for fiber modification, p 66–74. In Eriksson KEL, Paulo A (ed), Enzyme applica tions in fiber processing. Vol. 687. American Chemical Society, San Francisco. 62. Rahikainen J, Ceccherini S, Molinier M, Holopainen-Mantila U, Reza M, Väisänen S, Puranen T, Kruus K, Vuorinen T, Maloney T, Suurnäkki A, Grönqvist S. 2019. Effect of cellulase family and structure on modification of wood fibres at high consistency. Cellulose 26:5085– 5103. https://doi.org/10.1007/s10570-019-02424-x 63. Puranen T, Alapuranen M, Vehmaanperä J. 2014. Chapter 26 - trichoderma enzymes for textile Industries, p 351–362. In Gupta VK, Schmoll M, Herrera-Estrella A, Upadhyay RS, Druzhinina I, Tuohy MG (ed), Biotechnology and biology of Trichoderma. Elsevier, Amsterdam. 64. Sherif S, Jones AMP, Shukla MR, Saxena PK. 2014. Establishment of invasive and non-invasive reporter systems to investigate American elm-Ophiostoma novo-ulmi interactions. Fungal Genet Biol 71:32–41. https://doi.org/10.1016/j.fgb.2014.08.005 65. Boddy L. 2016. Pathogens of autotrophs, p 245–292. The fungi. Elsevier. 66. Yang C, Liu R, Pang J, Ren B, Zhou H, Wang G, Wang E, Liu J. 2021. Poaceae-specific cell wall-derived oligosaccharides activate plant immunity via OsCERK1 during Magnaporthe oryzae infection in rice. Nat Commun 12:2178. https://doi.org/10.1038/s41467-021-22456-x 67. Murk K, Ornaghi M, Schiweck J. 2021. Profilin isoforms in health and disease - all the same but different. Front Cell Dev Biol 9:681122. https:/ /doi.org/10.3389/fcell.2021.681122 68. Blasco R, Cole NB, Moss B. 1991. Sequence analysis, expression, and deletion of a vaccinia virus gene encoding a homolog of profilin, a eukaryotic actin-binding protein. J Virol 65:4598–4608. https://doi.org/ 10.1128/JVI.65.9.4598-4608.1991 69. Guljamow A, Jenke-Kodama H, Saumweber H, Quillardet P, Frangeul L, Castets AM, Bouchier C, Tandeau de Marsac N, Dittmann E. 2007. Horizontal gene transfer of two cytoskeletal elements from a eukaryote to a cyanobacterium. Curr Biol 17:R757–9. https://doi.org/10.1016/j. cub.2007.06.063 70. Ren S, Yang M, Yue Y, Ge F, Li Y, Guo X, Zhang J, Zhang F, Nie X, Wang S. 2018. Lysine succinylation contributes to aflatoxin production and pathogenicity in Aspergillus flavus. Mol Cell Proteomics 17:457–471. https://doi.org/10.1074/mcp.RA117.000393 71. Slepecky RA, Starmer WT. 2009. Phenotypic plasticity in fungi: a review with observations on Aureobasidium pullulans. Mycologia 101:823–832. https://doi.org/10.3852/08-197 72. Zhao H, Eide DJ. 1997. Zap1p, a metalloregulatory protein involved in zinc-responsive transcriptional regulation in Saccharomyces cerevisiae. Mol Cell Biol 17:5044–5052. https://doi.org/10.1128/MCB.17.9.5044 73. Staats CC, Kmetzsch L, Schrank A, Vainstein MH. 2013. Fungal zinc metabolism and its connections to virulence. Front Cell Infect Microbiol 3:65. https://doi.org/10.3389/fcimb.2013.00065 74. Schneider R de O, Fogaça N de S, Kmetzsch L, Schrank A, Vainstein MH, Staats CC, Nielsen K. 2012. Zap1 regulates zinc homeostasis and modulates virulence in Cryptococcus gattii. PLoS ONE 7:e43773. https:// doi.org/10.1371/journal.pone.0043773 75. Silveira CP, Piffer AC, Kmetzsch L, Fonseca FL, Soares DA, Staats CC, Rodrigues ML, Schrank A, Vainstein MH. 2013. The heat shock protein (Hsp) 70 of Cryptococcus neoformans is associated with the fungal cell surface and influences the interaction between yeast and host cells. Fungal Genet Biol 60:53–63. https://doi.org/10.1016/j.fgb.2013.08.005 76. Rossi GR, Cervi LA, García MM, Chiapello LS, Sastre DA, Masih DT. 1999. Involvement of nitric oxide in protecting mechanism during experi mental Cryptococcosis. Clin Immunol 90:256–265. https://doi.org/10. 1006/clim.1998.4639 77. Roy S, Sharma S, Sharma M, Aggarwal R, Bose M. 2004. Induction of nitric oxide release from the human alveolar epithelial cell line A549: an in vitro correlate of innate immune response to Mycobacterium tuberculosis. Immunol 112:471–480. https://doi.org/10.1046/j.13652567.2004.01905.x 78. Modolo LV, Augusto O, Almeida IMG, Pinto-Maglio CAF, Oliveira HC, Seligman K, Salgado I. 2006. Decreased arginine and nitrite levels in nitrate reductase-deficient Arabidopsis thaliana plants impair nitric oxide synthesis and the hypersensitive response to Pseudomonas syringae. Plant Sci 171:34–40. https://doi.org/10.1016/j.plantsci.2006.02. 010 79. Delledonne M, Xia Y, Dixon RA, Lamb C. 1998. Nitric oxide functions as a signal in plant disease resistance. Nature 394:585–588. https://doi. org/10.1038/29087 80. Milani NA, Lawrence DP, Arnold AE, VanEtten HD. 2012. Origin of pisatin demethylase (PDA) in the genus Fusarium. Fungal Genet Biol 49:933–942. https://doi.org/10.1016/j.fgb.2012.08.007 81. Nosanchuk JD, Stark RE, Casadevall A. 2015. Fungal melanin: what do we know about structure. Front Microbiol 6:1463. https://doi.org/10. 3389/fmicb.2015.01463 82. Langfelder K, Streibel M, Jahn B, Haase G, Brakhage AA. 2003. Biosynthesis of fungal melanins and their importance for human pathogenic fungi. Fungal Genet Biol 38:143–158. https://doi.org/10. 1016/s1087-1845(02)00526-1 83. Lee SH, Jang YB, Choi Y, Lee Y, Shin BN, Lee HS, Lee JS, Bahn YS. 2023. Adenylyl-sulfate kinase (Met14)-dependent cysteine and methionine biosynthesis pathways contribute distinctively to pathobiological processes in Cryptococcus neoformans. Microbiol Spectr 11:e0068523. https://doi.org/10.1128/spectrum.00685-23 84. Albarouki E, Deising HB. 2013. Infection structure-specific reductive iron assimilation is required for cell wall integrity and full virulence of the maize pathogen Colletotrichum graminicola. Mol Plant Microbe Interact 26:695–708. https://doi.org/10.1094/MPMI-01-13-0003-R 85. Hong MP, Vu K, Bautos JM, Tham R, Jamklang M, Uhrig JP, Gelli A. 2013. Activity of the calcium channel pore Cch1 is dependent on a modula tory region of the subunit Mid1 in Cryptococcus neoformans. Eukaryot Cell 12:142–150. https://doi.org/10.1128/EC.00130-12 86. Todd JNA, Carreón-Anguiano KG, Islas-Flores I, Canto-Canché B. 2022. Fungal effectoromics: a world in constant evolution. Int J Mol Sci 23:13433. https://doi.org/10.3390/ijms232113433 87. Sarmiento-Villamil JL, de Oliveira TC, Naruzawa ES, Bernier L. 2021. An efficient strategy for obtaining mutants by targeted gene deletion in Ophiostoma novo-ulmi. Front Microbiol 12:699783. https://doi.org/10. 3389/fmicb.2021.699783 88. Bernier L, Hubbes M. 1990. Mutations in Ophiostoma ulmi induced by N-methyl-N′-nitro-N-nitrosoguanidine. Can J Bot 68:225–231. https:// doi.org/10.1139/b90-031 89. Beier GL, Held BW, Giblin CP, Cavender‐Bares J, Blanchette RA. 2017. American elm cultivars: variation in compartmentalization of infection by Ophiostoma novo-ulmi and its effects on hydraulic conductivity. For Pathol 47. https://doi.org/10.1111/efp.12369 90. Wilson RA, Gibson RP, Quispe CF, Littlechild JA, Talbot NJ. 2010. An NADPH-dependent genetic switch regulates plant infection by the rice blast fungus. Proc Natl Acad Sci U S A 107:21902–21907. https://doi. org/10.1073/pnas.1006839107 91. Lambou K, Tharreau D, Kohler A, Sirven C, Marguerettaz M, Barbisan C, Sexton AC, Kellner EM, Martin F, Howlett BJ, Orbach MJ, Lebrun MH. 2008. Fungi have three tetraspanin families with distinct functions. BMC Genomics 9:63. https://doi.org/10.1186/1471-2164-9-63 92. Chang S, Puryear J, Cairney J. 1993. A simple and efficient method for isolating RNA from pine trees. Plant Mol Biol Rep 11:113–116. https:// doi.org/10.1007/BF02670468 93. Plourde KV, Bernier L. 2014. A rapid virulence assay for the Dutch elm disease fungus Ophiostoma novo-ulmi by inoculation of apple (Malus × domestica ‘Golden Delicious’) fruits. Plant Pathol 63:1078–1085. https:// doi.org/10.1111/ppa.12177 94. Harrington TC. 1981. Cycloheximide sensitivity as a taxonomic character in Ceratocystis. Mycologia 73:1123–1129. https://doi.org/10. 1080/00275514.1981.12021447 95. Andrews S, Andrews S, Krueger F, Segonds-Pichon A, Biggins L, Krueger C, Wingett S. 2010. FastQC: a quality control tool for high throughput sequence data. J Exp Med 370 Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-2323 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166. 96. Bolger AM, Lohse M, Usadel B. 2014. Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 30:2114–2120. https://doi.org/10.1093/bioinformatics/btu170 97. Dobin A, Gingeras TR. 2015. Mapping RNA-seq reads with STAR. Curr Protoc Bioinformatics 51:11. https://doi.org/10.1002/0471250953. bi1114s51 98. Kanehisa M, Goto S. 2000. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30. https://doi.org/10.1093/nar/28.1. 27 99. The Gene Ontology Consortium2008. The Gene Ontology project in 2008. Nucleic acids Res 36:D440–D444. https://doi.org/10.1093/nar/ gkm883 100. Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. 2009. The carbohydrate-active enzymes database (CAZy): an expert resource for glycogenomics. Nucleic Acids Res 37:D233–D238. https:// doi.org/10.1093/nar/gkn663 101. Urban M, Cuzick A, Seager J, Wood V, Rutherford K, Venkatesh SY, De Silva N, Martinez MC, Pedro H, Yates AD, Hassani-Pak K, HammondKosack KE. 2020. PHI-base: the pathogen–host interactions database. Nucleic Acids Res 48:D613–D620. https://doi.org/10.1093/nar/gkz904 102. Love MI, Huber W, Anders S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15:550. https://doi.org/10.1186/s13059-014-0550-8 103. Freyria NJ, Kuo A, Chovatia M, Johnson J, Lipzen A, Barry KW, Grigoriev IV, Lovejoy C. 2022. Salinity tolerance mechanisms of an arctic pelagophyte using comparative transcriptomic and gene expression analysis. Commun Biol 5:500. https://doi.org/10.1038/s42003-02203461-2 104. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. 2003. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13:2498–2504. https://doi.org/10.1101/gr.1239303 Research Article Microbiology Spectrum February 2024 Volume 12 Issue 2 10.1128/spectrum.03694-2324 Downloaded from https://journals.asm.org/journal/spectrum on 29 October 2024 by 150.214.58.166.