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Early transcriptomic response of the mycoparasite Sphaerodes mycoparasitica to the mycotoxigenic Fusarium graminearum 3-ADON, the cause of Fusarium head blight


Mycoparasites are an assemblage of biotrophic and necrotrophic fungi that occur on plant pathogenic fungal hosts. Biotrophic mycoparasites are often overlooked in transcriptomic-based biocontrol studies. Sphaerodes mycoparasitica (S.m.) is a specific biotrophic mycoparasite of plant pathogenic Fusarium graminearum (F.g.), a devastating Fusarium head blight (FHB) disease in small-grain cereals. To understand the biotrophic mycoparasitism comprehensively, we performed Illumina RNA-Seq transcriptomic study on the fungus–fungus interaction in vitro. The aim is to identify the transcript-level mechanism related to the biotrophic S.m. mycoparasitism, particularly its ability to effectively control the F.g. 3-ADON chemotype. A shift in the transcriptomic profile of the mycoparasite was triggered in response to its interaction with F.g. during recognition (1.5 days) and colonization (3.5 days) steps. RNA-Seq analysis revealed ~ 30% of annotated transcripts with "function unknown". Further, 14 differentially expressed genes functionally linked to the biotrophic mycoparasitism were validated by quantitative real-time PCR (qPCR). The gene expression patterns of the filamentous haemagglutinin/adhesin/attachment factor as well as cell wall-degrading glucanases and chitinases were upregulated by host interaction. Besides, mycoparasitism-associated antioxidant resistance genes encoding ATP-binding cassette (ABC) transporter(s) and glutathione synthetase(s) were upregulated. However, the thioredoxin reductase was downregulated which infers that this antioxidant gene can be used as a resistance marker to assess S.m. antifungal and antimycotoxigenic activities. The interactive transcriptome of S. mycoparasitica provides new insights into specific mycoparasitism and will contribute to future research in controlling FHB.

Graphic Abstract


Advances in DNA/RNA-sequencing technologies have provided an opportunity to accelerate research on the beneficial microbiome and pathobiota of plants. Fusarium graminearum [teleomorph, Gibberella zeae (Schwabe) Petch] (F.g.) is the principal causal agent of Fusarium head blight (FHB or scab) disease, resulting in severe losses of small cereal crop yields worldwide (Karlsson et al. 2021; Powell and Vujanovic 2021). F. graminearum actively produces various secondary metabolites, and F.g. 3-ADON is a particularly virulent chemotype producing abundant mycotoxins, such as zearalenone (ZEN), deoxynivalenol (DON/vomitoxin), its acetyl-derivatives, and aurofusarin (AUR) (Spanic et al. 2020; Birr et al 2021). Despite the economic importance of F.g. 3-ADON, effective FHB control and DON detoxification methods are yet to be discovered.

Recent discoveries of fungal antagonists, including mycoparasitic fungi and mycoparasitism-related genes, have provided a basis for controlling Fusarium and improving phytoprotection (Moreno-Ruiz 2021; Kim et al. 2021). Indeed, emerging new biocontrol approaches are expected to control diseases and improve breeding programs to increase crop yields and generate more microbe-optimized crops (Trivedi et al. 2017; Vujanovic 2021). Biocontrol is seen as a promising tool to reduce the use of synthetic chemicals in staple crops while controlling FHB (Legrand et al. 2017). Hence, the integrative research based on the mycoparasite-driven control of FHB and associated toxins could generate new insights. In particular, the suppression of the early growth and reproduction of F.g. 3-ADON is a key focus.

The development of highly reliable transcriptomic technologies (Ziegenhain et al. 2017) using cell and tissue-based in vitro assays has opened a new chapter of research on biocontrol agent–phytopathogen interactomes (Nygren et al. 2018). Clarifying the molecular principles of mycoparasite–Fusarium interactions at the gene expression level may provide insights into the mechanisms underlying mycoparasitism. Sphaerodes mycoparasitica (S.m.) SMCD 2220–01, a specific ascomycetous mycoparasite on Fusaria (Vujanovic and Goh 2009), shows a diphasic mycoparasitic lifestyle and an ability to rapidly adapt during the mitosporic stage (Vujanovic and Kim 2018) to efficiently control the pathogenic and mycotoxin-producing Fusarium hosts (Kim and Vujanovic 2016; Vujanovic and Goh 2010). In addition to the biocontrol effect, this mycopathogen effectively reduces AUR mycotoxin production in red-pigmented Fusaria by down-regulating AUR gene expression (Vujanovic et al. 2017; Vujanovic and Goh 2011). Although the mycoparasite degrades DON, 3-ADON, 15-ADON, and ZEN (Kim and Vujanovic 2017) and inhibits their production in F. graminearum 3-ADON (Vujanovic and Chau 2012; Vujanovic et al. 2012), the mechanism underlying mycoparasitism at the transcriptomic level is still unknown (Vujanovic 2021). In addition to transcripts associated with host recognition, attachment, and infection, several other differentially expressed transcripts during mycoparasitism are still unknown, suggesting that further research is needed to understand the biological processes contributing to effective biocontrol (Kim et al. 2021; Zhao et al. 2020). An important gap of transcriptomic knowledge exists about specific-biotrophic mycoparasitism, while the expression trends of some loci may greatly differ between S. mycoparasitica and other mycoparasitic taxa (Alfiky and Weisskopf 2021; Gupta et al. 2014). Hence, an improved understanding of the mycoparasitism of S. mycoparasitica on F. graminearum could be the basis for the development of more efficient Fusarium biocontrol methods.

Recently, Kim and Vujanovic (2018) discovered important changes in mycoparasite–Fusarium host interfaces, as revealed by water contact angle and atomic force microscopy (Kim and Vujanovic 2018). In this study, we hypothesized that shifts in functional transcripts exist on the exogenous (interspecific) host interface between S.m. and F.g. 3-ADON. To date, a reliable RNA-seq library has not been established to evaluate the biotrophic mycoparasitism. Hence, the aim of this research was to relate the biotrophic mycoparasitic behavior of S.m., particularly its ability to effectively control F.g. 3-ADON, to its capacity to regulate gene expression during the early recognition (1.5 d) and colonization (3.5 days) events in the process of specific mycoparasitism. The interface-interaction was evaluated by NGS-RNA sequencing technology, with superior performance and sensitivity to discover variation in modified interactive transcripts (Schwartz and Motorin 2017; Tsang et al 2021). To confirm the expression profile obtained by RNA-Seq, 14 functional and randomly selected genes were validated in the passive and active mycoparasite response to the F.g. 3-ADON host defense by qRT-PCR.


Fungal growth and treatment

The specific mycoparasite Sphaerodes mycoparasitica Vujan. (SMCD 2220–01) and the phytopathogenic and mycotoxigenic host Fusarium graminearum Schwabe 3-ADON (SMCD 2243) were used. For general transcriptomic profiling of the mycoparasite during biotrophic mycoparasitism or to induce mycoparasitism-related gene expression, the mycoparasite was co-cultured on top of the host with a monofilament fabric, nylon mesh with 30 µm openings (SEFAR NITEX 03–48/31; Sefar Inc., Depew, NY, USA) placed between the mycoparasite and the host. The used growth medium was potato glucose agar (PGA; Sigma-Aldrich, St. Louis, MO, USA) and co-cultures were incubated at 23 °C. The mycelia of the interactive mycoparasite were collected at both incubation time (1.5 and 3.5 days) used for RNA extraction. The method used in this study allowed to obtain a mycelium of the mycoparasite from co-cultures during an active mycoparasite–host interaction. The control samples consist of mycoparasite’s hyphae grown on the fabric tissue without the host.

RNA extraction, cDNA library construction and Illumina sequencing

Four different mycelia samples with six biological replicates were subjected to RNA extraction using the Aurum™ Total RNA Mini Kit (Bio-Rad, Hercules, CA, USA) according to the manufacturer’s instructions. The quality and quantity of the extracted total RNA was checked on 1% agarose gels followed by the NanoDrop 2000c spectrophotometer (Thermo Scientific, Wilmington, DE, USA) resulting in all samples having a 260/280 nm ratio of 1.9–2.1 and a 260/230 nm ratio of 2.0–2.3. To validate RNA quality, the RNA integrity number (RIN) was measured using the Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). All samples showing RIN values above 8 were used for further poly(A) + RNA purification and cDNA library synthesis using the TrueSeq Stranded mRNA Sample Prep Kit (Illumina, San Diego, CA, USA), conducted at the McGill University and Génome Québec Innovation Center (Montréal, Québec). The libraries were multiplexed with Illumina barcodes and each lane was sequenced using the Illumina HiSeq 4000 (100 bp PE reads) based on the massively parallel sequencing protocol (Xiao et al. 2013).

De novo transcriptome assembly

Transcriptome de novo assembly from RNA-Seq data was performed at the Canadian Centre for Computational Genomics based on a general pipeline described previously (Grabherr et al. 2011; Haas et al. 2013) using the Trinity assembly software suite ( Raw reads were trimmed from the 3′ end with a Phred score threshold of 30. Illumina sequencing adapters were removed, and all reads were required to have a length of at least 50 bp. Trimmomatic tool was used for quality trimming and adapter clipping (Bolger et al. 2014). The clean reads were utilized for de novo transcriptome assembly using the Trinity assembler (Grabherr et al. 2011) consisting of three modules: Inchworm, Chrysalis, and Butterfly. Then, Bowtie2 and eXpress generated the length normalized counts (transcripts per million, TPM).

Functional annotation of transcriptome

BlastX alignment and annotation with an E-value cut-off set to 10–5 were performed against the National Center for Biotechnology and Information (NCBI) non-redundant protein database (nr), UniProtKB/Swiss-Prot (SwissProt) database (, and Uniref90 ( (Apweiler et al. 2004). Further functional annotation was achieved using the Trinotate pipeline (, including Hmmer v.3.1b1 ( and PFAM (Finn et al. 2015) for protein domain identification, SignalP v.4.1 ( (Petersen et al. 2011). The Tmhmm v.2.0c ( was employed for protein signal peptide and transmembrane domain prediction, while the EMBL UniProt eggNOG/GO pathway databases ( was used to perform the functional enrichment analyses (Powell et al. 2011).

Quantitative real-time PCR for validation of specific genes

The expression levels of S.m. transcripts that were differentially expressed at different incubation times with and without host interactions were calculated by TPM. A TPM ratio > 2 between the control and mycoparasitism conditions at each incubation time was used to identify gene expression differences. To validate the reliability of the transcriptome of S.m., 14 up- or down-regulated transcripts were randomly selected as mycoparasitism-associated candidates and their expression levels were quantified with and without interactions with the host at each incubation time by quantitative real-time PCR (qPCR), with the internal reference gene 18S rRNA for normalization (Xu et al. 2018). The primer pairs (Table 1) for the genes of interest were designed using Primer3 (Koressaar and Remm 2007; Untergasser et al. 2012). Prior to qPCR, the specificity of the designed primers was checked by conventional PCR with the following program: 94 °C for 2 min; 35 cycles of 94 °C for 30 s, 60 °C for 30 s, and 72 °C for 30 s; followed by 72 °C for 10 min. For qPCR assays, the total RNAs of the four samples were used to synthesize cDNAs using the iScript™ cDNA Synthesis Kit (Bio-Rad, Hercules, CA, USA) and the cDNAs were subjected to the qPCR assays using iTaq Universal SYBR® Green Supermix (Bio-Rad) and the MiniOpticon Real-time PCR Detection System with CFX Manager™ (Bio-Rad) in a 20 μl reaction system following manufacturer’s instructions. Quantitative PCR was performed with three replicates with the following program: 95 °C for 30 s; 40 cycles of 95 °C for 10 s and 60 °C for 30 s. After the reaction, fluorescence values were monitored every 0.5 °C from 65 °C to 95 °C to check for non-specific amplification. The relative expression levels of the transcripts were calculated using the 2–∆∆Ct method (Livak and Schmittgen 2001).

Table 1 Primers used in this study


COG functional classification

The functions of 15,346 transcripts were predicted and classified into 25 categories (resulting in the total number 16,054 since some of transcripts had assigned into more than one cluster) based on the Clusters of Orthologous Groups (COG) and EggNOG databases. As shown in Fig. 1, the largest cluster was ‘function unknown’ (4823, 30.04%), followed by ‘Post-translational modification, protein turnover, and chaperones’ (1246, 7.76%), ‘Signal transduction mechanisms’ (1230, 7.66%), ‘Translation, ribosomal structure and biogenesis’ (995, 6.20%), ‘Replication, recombination and repair’ (899, 5.60%), ‘Carbohydrate transport and metabolism’ (840, 5.23%), ‘Transcription’ (778, 4.85%), and ‘Amino acid transport and metabolism’ (706, 4.40%).

Fig. 1
figure 1

COG functional classification of transcripts of S. mycoparasitica. The functions of 15,346 transcripts were predicted and classified into 25 categories (indicated by the alphabetical notations) in descending order through the Clusters of Orthologous Groups (COG) classification

Other clusters, such as ‘Energy production and conversion’ (607, 3.78%), ‘Intracellular trafficking, secretion, and vesicular transport’ (447, 2.78%), ‘Inorganic ion transport and metabolism’ (446, 2.78%), ‘Cytoskeleton’ (413, 2.57%), ‘Lipid transport and metabolism’ (396, 2.47%), ‘Secondary metabolites biosynthesis, transport, and catabolism’ (344, 2.14%), ‘Cell cycle control, cell division, chromosome partitioning’ (317, 1.97%), ‘Coenzyme transport and metabolism’ (315, 1.96%), ‘Chromatin structure and dynamics’ (284, 1.77%), ‘Cell wall/membrane/envelope biogenesis’ (239, 1.49%), ‘Nucleotide transport and metabolism’ (232, 1.45%), and ‘General function prediction only’ (223, 1.39%) were less frequent.

Only a few transcripts (< 1% of the COG-annotated genes) were assigned to the clusters ‘Defense mechanisms’ (146, 0.91%), ‘RNA processing and modification’ (100, 0.62%), ‘Cell motility’ (20, 0.12%), ‘Nuclear structure’ (7, 0.04%), and ‘Extracellular structures’ (1, 0.01%).

Gene Ontology (GO) functional classification

The functions of the transcripts were predicted by a Gene Ontology (GO) analysis ( in three functional categories: biological process, cellular component, and molecular function. A total of 25,668 transcripts (total number of GO: 220,543; many of the transcripts had more than one GO term; only 1393 transcripts had one GO term) were matched with 27 terms in the biological process group (97,265, 44.10%), 19 terms in the cellular components group (66,695, 30.24%), and 16 terms in the molecular function group (56,583, 25.66%), as shown in Fig. 2.

Fig. 2
figure 2

Gene Ontology (GO) functional classification or categorization of S. mycoparasitica transcripts. Each annotated sequence or transcript was assigned to terms within the three major categories: biological process, cellular component, and molecular function

In the biological process (BP) category, ‘metabolic process’ (25,950, 26.68%) was the most dominant subcategory, followed by ‘regulation of biological process’ (11,594, 11.92%), ‘cellular process’ (9895, 10.17%), ‘localization’ (8389, 8.62%), ‘positive regulation of biological process’ (8122, 8.35%), ‘negative regulation of biological process’ (6336, 6.51%), and ‘developmental process’ (6319, 6.50%). The enriched subcategories were generally associated with the physiological and biological metabolism of the mycoparasite. For example, in the BP category, the terms ‘response to stimulus’ (5654, 5.81%), ‘cellular component organization or biogenesis’ (4059, 4.17%), ‘multicellular organismal process’ (2483, 2.55%), ‘reproductive process’ (1521, 1.56%), ‘biological regulation’ (1281, 1.32%), ‘biological adhesion’ (885, 0.91%), and ‘locomotion’ (877, 0.90%) may be related to the recognition, attachment, infection, and proliferation of the mycoparasite on the host. The fewest transcripts (13, 0.01%) were related to ‘detoxification’, which may be connected to the biodegradation of mycotoxins or secondary metabolites of Fusarium host.

In the cellular component (CC) category, the enriched terms included ‘cell part’ (15,905, 23.85%), ‘organelle’ (15,317, 22.97%), ‘membrane part’ (8354, 12.53%), ‘organelle part’ (7803, 11.70%), and membrane (6635, 9.95%). Unlike other parasitic fungi, S.m. is more closely related to the membrane part; however, the term ‘cell’ (2, 0.003%) was rarely observed.

In the molecular function (MF) category, the terms ‘binding’ (28,739, 50.79%) and ‘catalytic activity’ (19,867, 35.11%) were most highly enriched, followed by ‘transporter activity’ (2385, 4.22%). The subcategory ‘antioxidant activity’ (102, 0.18%) was related to few genes.

Quantitative real-time PCR for validation of differentially expressed genes (DEGs)

Fourteen transcripts (Sm72993, Sm77681, Sm70239, Sm72448, Sm76207, Sm76684, Sm78917, Sm79801, Sm80489, Sm76483, Sm79379, Sm73177, Sm79054, and Sm75119) were selected based on expression levels (Fig. 3) and divided into two groups. In one group, six transcripts (Sm72993, Sm77681, Sm70239, Sm72448, Sm76207, and Sm76684) showed upregulated expression during the interaction with F. graminearum (or mycoparasitism) at 1.5 and/or 3.5 days. The transcripts encoded a glucan 1,3-beta-glucosidase (Sm72993), Tfo1: an Ac-like transposon (Tfo1) (Sm77681), glutathione synthetase (Sm70239), endochitinase B (Sm72448), filamentous hemagglutinin adhesin (FHA) (Sm76207), and ATP-binding cassette (ABC) transporter (Sm76684). The other group contained eight transcripts (Sm78917, Sm79801, Sm80489, Sm76483, Sm79379, Sm73177, Sm79054, and Sm75119) with decreased expression levels during the interaction with F. graminearum at both incubation times. They encode an uncharacterized mitochondrial protein ymf40 (Sm78917), RNase H domain-containing protein (Sm79801), thioredoxin-2 (Sm80489), glutathione S-transferase omega-like 2 (Sm76483) related to cell wall organization and biogenesis, thioredoxin reductase (Sm79379), NADPH-cytochrome P450 reductase (Sm73177) in ergosterol biosynthesis (biologically), uncharacterized protein (Sm79054), and uncharacterized membrane protein YJL163C, which may be related to the major facilitator superfamily (MFS) domain, and general substrate transporter (Sm75119). The qPCR results summarized in Fig. 4 were consistent with the RNA-sequencing results (TPM values), supporting the reliability of the sequencing results.

Fig. 3
figure 3

Heat map of 14 transcripts of S. mycoparasitica with/without the host F. graminearum at different incubation times (1.5 and 3.5 days). Colors from blue to red indicate that the transcript levels expressed as log2 TPM values from low to high. Each column represents each sample group (A, S.m. 1.5 days; B, S.m. 1.5 days interaction with F.g. 3-ADON; C, S.m. 3.5 days; D, S.m. 3.5 days interaction with F.g. 3-ADON)

Fig. 4
figure 4

Relative expression levels of 14 transcripts of S. mycoparasitica determined by quantitative real-time PCR. with/without the host F. graminearum at different incubation times (1.5 and 3.5 days) determined by quantitative real-time PCR. A, S.m. 1.5 days; B, S.m. 1.5 days interaction with F.g. 3-ADON; C, S.m. 3.5 days; D, S.m. 3.5 days interaction with F.g. 3-ADON. A and C are compared with B and D, respectively. The y-axis represents the log2 fold change (A and C are zero indicated by the asterisk symbol). Error bars indicate the standard deviation of three replicates. The 18SrRNA was used as an internal control. Finally, the set of functional transcripts depicted Glucan 1,3-beta-glucosidase (Sm72993); Tfo1: Ac-like transposon (Sm77681); Glutathione synthetase (Sm70239); Endochitinase B (Sm72448); Filamentous hemagglutinin/adhesin (Sm76207); ATP-binding cassette (ABC) transporter (Sm76684); Uncharacterized mitochondrial protein ymf40 (Sm78917); RNase H domain-containing protein (Sm79801); Thioredoxin-2 (Sm80489); Glutathione S-transferase omega-like 2 (Sm76483); Thioredoxin reductase (Sm79379); NADPH–cytochrome P450 reductase (Sm73177); Uncharacterized protein (Sm79054); Uncharacterized membrane protein YJL163C (Sm75119)


The rational classification of transcripts that encode proteins is critical for the effective utilization of genome sequences for functional and evolutionary studies (Tatusov et al. 2000). This is of particular importance for microbial genome analyses (Galperin et al. 2019) aimed at improving fungal and bacterial biotechnology as well as applications in agricultural settings to produce food commodities (Ganeshan et al. 2021).

Despite being widespread, mycoparasitism is probably not ancestral within the kingdom Fungi. Nevertheless, the necrotrophic generalist paradigm is based on soil-born fungi (Domsch et al. 1980; Schroers et al. 1999), such as Clonostachys and Trichoderma (Sun et al. 2015), as evolutionary distinct and more ancient fungal hyperparasites compared with biotrophic Sphaerodes/Melanospora hyperparasites and specialists. Accordingly, S. mycoparasitica is a useful reference point from which to discuss transcriptomic divergence across fungal taxa with variation in mycoparasitic biocontrol behavior, cellular organization, and ecological lifestyles.

Generally, a series of cellular activities and genetic regulatory mechanisms are involved in the mycoparasitic process. These include recognition–attachment and penetration–parasitism, and these events in S. mycoparasitica are triggered at different stages of parasitism on Fusarium hosts (Vujanovic and Goh 2010). Here, we present the first functional transcript profile of the biotrophic mycoparasitism of S.m. on F. graminearum 3-ADON host. While the functional classification of transcripts displays S. mycoparasitica polyphagous features among distinctive mycoparasitic lifestyles, the ontology and differential expression of genes remark series of processes related to biocontrol, resistance, and detoxification of mycotoxins. Fusarium production of several types of toxins dispersed throughout food chain provides to the S.m.-BCA multiple advantages in positive modulation of the health risk for humans and animals.

Functional classification (Clusters of Orthologous Groups)

In this study, in the interactive RNA-Seq S.m. profile, the largest number of transcripts were assigned to the group ‘function unknown’, suggesting that a substantial portion of the basic molecular mechanisms underlying biotrophic mycoparasitism have yet to be discovered (Naranjo-Ortiz and Gabaldón 2019). The ‘general function prediction only’ is the largest functional group reported in the genomes of various necrotrophic mycoparasites [T. atroviride (1246 of 7648, 16.29%); T. virens (1307 of 7875, 16.60%); T. reesei (1030 of 6833, 15.07%); T. brevicompactum (1937 of 12058, 16.06%); C. rosea (3489 of 8615, 40.5%)], with ‘function unknown’ accounting for fewer transcripts [T. atroviride (397 of 7648, 5.19%); T. virens (400 of 7875, 5.08%); T. reesei (383 of 6833, 5.61%); T. brevicompactum (587 of 12,058, 4.87%); C. rosea (1602 of 8615, 18.60%)] (Shentu et al. 2014; Sun et al. 2015). An improved knowledge of the relationship between mycoparasitic lifestyles and biocontrol behaviors seems vital for the selection of bioinoculants for the control of F. graminearum, a cause of Fusarium head blight (Kim and Vujanovic 2016). Further, the clusters ‘Post-translational modification, protein turnover, and chaperones’ (7.76%) and ‘Signal transduction mechanisms’ (7.66%) were the second and third largest groups, similar to results for the three Trichoderma species [T. atroviride (8.11% and 6.88%); T. virens (7.94% and 7.02%); T. reesei (8.18% and 7.55%)]. However, for C. rosea and T. brevicompactum, the second and third largest groups were the clusters for ‘transcription’ and ‘carbohydrate transport and metabolism’ [C. rosea (24.20% and 22.10%); T. brevicompactum (7.94% and 7.44%)]. Additionally, the smallest group was the cluster ‘Extracellular structures’ S.m., while the clusters ‘nuclear structure’ in C. rosea and Tbrevicompactum and “cell motility” in other three Trichoderma species were the least well-represented.

The similarities and differences in COG classification between S. mycoparasitica and other fungi seem to be coordinated with their lifestyles (i.e., mycoparasitic T. atroviride, T. virens, and C. rosea and saprophytic T. reesei and T. brevicompactum). However, the polyphagous behavior of S.m. against fungal species within the genus Fusarium contributed to additional differences in its transcriptomic profile compared with those of mycoparasitic generalists. Further comparative proteomics and secretomic analyses are needed to better explain these relationships.

Functional (Gene Ontology) classification

Enzymatic processes using biocatalysts play an important role in the biocontrol efficiency of mycoparasites. The enrichment for ‘binding’ and ‘catalytic activity’ may be crucial for the mode of action during mycoparasitism. Enrichment for ‘binding’, ‘catalytic activity’, and ‘transporter activity’ has also been reported in Clonostachys rosea 67–1 against Sclerotinia sclerotiorum (Sun et al. 2015). The overrepresentation of ‘transporter activity’ (including ABC transporters) has also been found in zearalenone-induced Clonostachys rosea (Kosawang et al. 2014a), which might be connected to our research on the biodegradation of zearalenone by S. mycoparasitica (Kim and Vujanovic 2017).

Quantitative real-time PCR and differentially expressed genes

Several mycoparasitism-related differentially expressed genes (DEGs) have been reported. Signal transduction or signaling-related proteins, including G-proteins and mitogen-activated protein kinases (MAPKs), are involved in a series of processes, e.g., recognition or sensing the host or prey, (interspecific) communication, and attachment of parasitic appressorium-like structure(s) to the host (Omero et al. 1999; Reithner et al. 2007; Sun et al. 2020; Yang 2017; Zeilinger et al. 2005). In the S. mycoparasitica transcriptome, the expression levels of transcripts encoding G-proteins and MAPKs were higher during the interaction with the host than in controls (without host interactions) (data are not shown).

During the interaction between S. mycoparasitica and Fusarium host, the expression of the transcript (Sm76207) annotated as a filamentous hemagglutinin adhesin increased. We assumed that the adhesin may facilitate the attachment process by self-cell–cell adhesion and/or host cell-surface adherence and contribute to the virulence and infectivity (Bernardi et al. 2018).

Cell wall-degrading enzymes (e.g., endochitinases, endoglucanases, and serine proteases) are required not only for penetration for successful mycoparasitism but also for cell wall remodeling during active growth, recycling during aging, and autolysis of cell wall components (Carsolio et al. 1994; Geremia et al. 1993; Gruber and Seidl-Seiboth 2012; Reithner et al. 2011; Thrane et al. 1997). S. mycoparasitica showed the upregulation of Sm72448 and Sm72993 encoding endochitinase B and glucan 1,3-beta-glucosidase, respectively. In the S.m. transcriptome, 31 transcripts were annotated to 19 chitinases (glycoside hydrolase family 18) or chitinase-related proteins (ChiA, ChiA1, ChiB, ChiB1, etc.). However, the number of genes is not likely to be correlated with the catalytic efficiency of enzymes on certain substrates. In a broad comparison, the number of chitinase-encoding genes of S.m. is intermediate between those of the mycoparasitic C. rosea (14) and Trichoderma species (29 in mycoparasitic T. atroviride, 36 in mycoparasitic T. virens, and 20 in saprophytic T. reesei), reviewed by Gruber and Seidl-Seiboth (2012). In particular, chiB1 of C. rosea is induced during fungal–fungal interactions and is highly induced by colloidal chitin (Tzelepis et al. 2015). The presence of chitinases in S.m. further implies that hydrophobic cell wall proteins (e.g., hydrophobins) and carbohydrate-binding proteins are involved in the protection of its own cell wall to manage the accessibility of chitins during hyphal development.

Toxin-related transcripts were also upregulated during the mycoparasitism, including toxin-encoding genes (e.g., killer toxin subunits alpha/beta) and toxin transport-encoding genes (e.g., putative HC-toxin efflux carrier TOXA), which may facilitate the mycoparasitism. These killer toxin-like chitinases have been reported in Trichoderma and Clonostachys.

Mycoparasites might express genes to effectively regulate biological functions related to the host interaction, such as genes related to the response to stimulus (stress response), secondary metabolite metabolisms, and detoxification (defense mechanism). In this regard, the ABC transporter is an important membrane protein for the uptake and efflux of various molecules. In the case of another mycoparasite, C. rosea IK726, ABC transporter G5 is involved in the detoxification of Fusarium mycotoxins (e.g., zearalenone) and xenobiotics (e.g., fungicides), resulting in overall cell protection (Dubey et al. 2014; Kosawang et al. 2014b; Víglaš and Olejníková 2021). Regarding the S. mycoparasitica interactive transcriptome, the transcripts for different types of ABC transporters, including the ABC G superfamily, were expressed and might be involved in the detoxification or transformation of Fusarium mycotoxins. Recently, it was reported that ABC transporters along with MFS may play important roles during the mycoparasitism of Coniothyrium minitans on Sclerotinia sclerotiorum (Zhao et al. 2020). According to Nygren et al. (2018), during interactions between C. rosea and both Botrytis cinerea and F. graminearum, the gene encoding hydantoinase/oxoprolinase, which is involved in glutathione biosynthesis, was upregulated. The expression of abcG18 in C. rosea was significantly increased during the interaction with F. graminearum, implying a specific response to F. graminearum. Many of the genes encoding MFS transporters were also highly expressed during the interaction with F. graminearum; however, this was not a focus of the study.

In addition to ABC transporters, our results revealed various functional groups of genes, such as glutathione S-transferases (along with glutathione synthetase and glutathione reductase) as well as cytochrome P450 enzymes (with NADPH-cytochrome P450 reductase), with potential roles in detoxification and stress response in the active defense system (Gullner et al 2018). In the transcriptome of S. mycoparasitica, the expression levels of these transcripts were altered during the interaction with the Fusarium host. Further, the diverse thioredoxin (proteins)-encoding genes showed variation in gene expression levels. According to Fernandez and Wilson (2014), the glutathione and thioredoxin antioxidation systems in Magnaporthe oryzae could facilitate biotrophic colonization and are potential determinants of rice blast disease. Glutathione reductase is required for neutralizing plant-generated reactive oxygen species (ROS) and thioredoxin proteins contribute to cell-wall integrity (Fernandez and Wilson 2014). In S. mycoparasitica, the thioredoxin and thioredoxin reductase may differ from those of M. oryzae or from those affected by glucose availability, since they were downregulated in the S.m. transcriptome.

In the future, detailed comparative analyses could be applied to various sets of specific genes related to mycoparasitism to elucidate the distinct molecular mechanisms underlying biotrophic mycoparasitism in S. mycoparasitica for its polyphagous activity against Fusarium species related to Fusarium head blight (FHB). In order to understand this specific mycoparasitism–FHB system, mobile elements or transposons such as the activator (Ac) Tfo1 discovered in S. mycoparasitica that also occurs in its fungal (Fusarium) and plant (Gramineae) hosts (Okuda 1998) deserve further study through the food chain.


In this study, we conducted transcriptomic analyses of the early hyphal–hyphal interaction of Sphaerodes mycoparasitica to get to know the biotrophic mycoparasitism against Fusarium graminearum. De novo assembly from RNA-Seq data and functional annotation of transcriptome were performed, while qPCR was used to confirm the reliability of the sequencing results. The RNA-Seq profile demonstrates that the specific-biotrophic mycoparasitism is a complex process. Indeed, a broad range of transcripts with known (e.g., endochitinases, glucanases, serine proteases, adhesins) and unknown functions are displayed by the mycoparasite to successfully invade F. graminearum. We discovered a plethora of ABC transporters which suggests that S. mycoparasitica has particular mycoparasitism-associated ability to sustain toxic environment or detoxify mycotoxins made by F. graminearum. Interestingly, S. mycoparasitica downregulated expression of thioredoxin reductase gene required for antioxidant stress resistance which is an opposite finding about this gene compared to mycoparasitic generalists. These findings indicate that S. mycoparasitica has remarkable resistance in combatting Fusarium, which was not previously demonstrated or reported elsewhere. Taken together, our study results provide novel insights into possible mechanisms underlying antioxidant roles of ABC, glutathione synthetase and cytochrome P450 genes combined with pathogenicity related degradative enzymes in S. mycoparasitica for controlling F. graminearum. This RNA-Seq study results enhance opportunity to future discoveries in harnessing the full potential of this specific-mycoparasitism, its evolutionary features, and biocontrol advantages.

Availability of data and materials

All relevant data and material are presented in this paper. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.



Next-generation sequencing


Quantitative real-time PCR


RNA sequencing


Differentially expressed genes


Clusters of Orthologous Groups


Gene Ontology


Reactive oxygen species


  1. Alfiky A, Weisskopf L (2021) Deciphering Trichoderma–plant–pathogen interactions for better development of biocontrol applications. J Fungi 7:61.

    CAS  Article  Google Scholar 

  2. Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, Martin MJ, Natale DA, O’Donovan C, Redaschi N, Yeh LSL (2004) UniProt: the universal protein knowledgebase. Nucleic Acids Res 32(1):115–119.

    CAS  Article  Google Scholar 

  3. Bernardi B, Kayacan Y, Wendland J (2018) Expansion of a telomeric FLO/ALS-like sequence gene family in Saccharomycopsis fermentans. Front Genet 9:536.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Birr T, Jensen T, Preußke N, Sönnichsen FD, De Boevre M, De Saeger S, Hasler M, Verreet J-A, Klink H (2021) Occurrence of Fusarium mycotoxins and their modified forms in forage maize cultivars. Toxins 13:110.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30(15):2114–2120.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Carsolio C, Gutierrez A, Jimenez B, Van Montagu M, Herrera-Estrella A (1994) Characterization of ech-42, a Trichoderma harzianum endochitinase gene expressed during mycoparasitism. Proc Natl Acad Sci USA 91(23):10903–10907

    CAS  Article  Google Scholar 

  7. Domsch KH, Gams W, Anderson T-H (1980) Compendium of soil fungi, vol 1. Academic Press, London, P 859 (Reprint Eching: IHW-Verlag. 1993)

  8. Dubey MK, Jensen DF, Karlsson M (2014) An ATP-binding cassette pleiotropic drug transporter protein is required for xenobiotic tolerance and antagonism in the fungal biocontrol agent Clonostachys rosea. Mol Plant Microbe Interact 27(7):725–732.

    CAS  Article  PubMed  Google Scholar 

  9. Fernandez J, Wilson RA (2014) Characterizing roles for the glutathione reductase, thioredoxin reductase and thioredoxin peroxidase-encoding genes of Magnaporthe oryzae during rice blast disease. PLoS ONE 9(1):e87300–e87300.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, Potter SC, Punta M, Qureshi M, Sangrador-Vegas A, Salazar GA, Tate J, Bateman A (2015) The Pfam protein families database: towards a more sustainable future. Nucleic Acids Res 44(D1):D279–D285.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  11. Galperin MY, Kristensen DM, Makarova KS, Wolf YI, Koonin EV (2019) Microbial genome analysis: the COG approach. Brief Bioinform 20(4):1063–1070.

    CAS  Article  PubMed  Google Scholar 

  12. Ganeshan S, Kim SH, Vujanovic V (2021) Scaling-up production of plant endophytes in bioreactors: concepts, challenges and perspectives. Bioresour Bioprocess 8(1):63.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Geremia RA, Goldman GH, Jacobs D, Ardiles W, Vila SB, Van Montagu M, Herrera-Estrella A (1993) Molecular characterization of the proteinase-encoding gene, prb1, related to mycoparasitism by Trichoderma harzianum. Mol Microbiol 8(3):603–613

    CAS  Article  Google Scholar 

  14. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29(7):644–652.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Gruber S, Seidl-Seiboth V (2012) Self versus non-self: fungal cell wall degradation in Trichoderma. Microbiology (reading, England) 158(1):26–34.

    CAS  Article  Google Scholar 

  16. Gullner G, Komives T, Király L, Schröder P (2018) Glutathione S-transferase enzymes in plant-pathogen interactions. Front Plant Sci 9:1836.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Gupta KJ, Mur LAJ, Brotman Y (2014) Trichoderma asperelloides suppresses nitric oxide generation elicited by Fusarium oxysporum in Arabidopsis roots. Mol Plant-Microbe Interact 27:307–314.

    CAS  Article  PubMed  Google Scholar 

  18. Haas BJ, Papanicolaou A, Yassour M, Grabherr M, Blood PD, Bowden J, Couger MB, Eccles D, Li B, Lieber M, MacManes MD, Ott M, Orvis J, Pochet N, Strozzi F, Weeks N, Westerman R, William T, Dewey CN, Henschel R, LeDuc RD, Friedman N, Regev A (2013) De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis. Nat Protoc 8(8):1494–1512.

    CAS  Article  Google Scholar 

  19. Karlsson I, Persson P, Friberg H (2021) Fusarium head blight from a microbiome perspective. Front Microbiol 12:371.

    Article  Google Scholar 

  20. Kim SH, Vujanovic V (2016) Relationship between mycoparasites lifestyles and biocontrol behaviors against Fusarium spp. and mycotoxins production. Appl Microbiol Biotechnol 100(12):5257–5272.

    CAS  Article  PubMed  Google Scholar 

  21. Kim SH, Vujanovic V (2017) Biodegradation and biodetoxification of Fusarium mycotoxins by Sphaerodes mycoparasitica. AMB Express 7(1):145.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. Kim SH, Vujanovic V (2018) Changes in mycoparasite-Fusarium hosts interfaces in response to hostile environment as revealed by water contact angle and atomic force microscopy. Biol Control 121:247–255.

    Article  Google Scholar 

  23. Kim SH, Lahlali R, Karunakaran C, Vujanovic V (2021) Specific mycoparasite-Fusarium graminearum molecular signatures in germinating seeds disabled fusarium head blight pathogen’s infection. Int J Mol Sci 22:5.

    CAS  Article  Google Scholar 

  24. Koressaar T, Remm M (2007) Enhancements and modifications of primer design program Primer3. Bioinformatics 23(10):1289–1291.

    CAS  Article  PubMed  Google Scholar 

  25. Kosawang C, Karlsson M, Jensen DF, Dilokpimol A, Collinge DB (2014a) Transcriptomic profiling to identify genes involved in Fusarium mycotoxin deoxynivalenol and zearalenone tolerance in the mycoparasitic fungus Clonostachys rosea. BMC Genomics 15:55.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Kosawang C, Karlsson M, Vélëz H, Rasmussen PH, Collinge DB, Jensen B, Jensen DF (2014b) Zearalenone detoxification by zearalenone hydrolase is important for the antagonistic ability of Clonostachys rosea against mycotoxigenic Fusarium graminearum. Fungal Biol 118(4):364–373.

    CAS  Article  PubMed  Google Scholar 

  27. Legrand F, Picot A, Cobo-Díaz JF, Chen W, Le Floch G (2017) Challenges facing the biological control strategies for the management of fusarium head blight of cereals caused by F. graminearum. Biol Control 113:26–38.

    Article  Google Scholar 

  28. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25(4):402–408.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  29. Moreno-Ruiz D, Salzmann L, Fricker MD, Zeilinger S, Lichius A (2021) Stress-activated protein kinase signalling regulates mycoparasitic hyphal-hyphal interactions in Trichoderma atroviride. J Fungi 7:365.

    CAS  Article  Google Scholar 

  30. Naranjo-Ortiz MA, Gabaldón T (2019) Fungal evolution: major ecological adaptations and evolutionary transitions. Biol Rev Biol Proc Camb Philos Soc 94(4):1443–1476.

    Article  Google Scholar 

  31. Nygren K, Dubey M, Zapparata A, Iqbal M, Tzelepis GD, Durling MB, Jensen DF, Karlsson M (2018) The mycoparasitic fungus Clonostachys rosea responds with both common and specific gene expression during interspecific interactions with fungal prey. Evol Appl 11(6):931–949.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. Okuda M, Ikeda K, Namiki F, Nishi K, Tsuge T (1998) Tfo1: an Ac -like transposon from the plant pathogenic fungus Fusarium oxysporum. Mol Gen Genet 258:599–607.

    CAS  Article  PubMed  Google Scholar 

  33. Omero C, Inbar J, Rocha-Ramirez V, Herrera-Estrella A, Chet I, Horwitz BA (1999) G protein activators and cAMP promote mycoparasitic behaviour in Trichoderma harzianum. Mycol Res 103(12):1637–1642.

    CAS  Article  Google Scholar 

  34. Petersen TN, Brunak S, von Heijne G, Nielsen H (2011) SignalP 4.0: discriminating signal peptides from transmembrane regions. Nat Methods 8(10):785–786.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Powell AJ, Vujanovic V (2021) Evolution of fusarium head blight management in wheat: scientific perspectives on biological control agents and crop genotypes protocooperation. Appl Sci 11:8960.

    CAS  Article  Google Scholar 

  36. Powell S, Szklarczyk D, Trachana K, Roth A, Kuhn M, Muller J, Arnold R, Rattei T, Letunic I, Doerks T, Jensen LJ, von Mering C, Bork P (2011) eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res 40(1):D284–D289.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. Reithner B, Schuhmacher R, Stoppacher N, Pucher M, Brunner K, Zeilinger S (2007) Signaling via the Trichoderma atroviride mitogen-activated protein kinase Tmk 1 differentially affects mycoparasitism and plant protection. Fungal Genet Biol 44(11):1123–1133.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Reithner B, Ibarra-Laclette E, Mach RL, Herrera-Estrella A (2011) Identification of mycoparasitism-related genes in Trichoderma atroviride. Appl Environ Microbiol 77(13):4361–4370.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. Schroers H-J, Samuels G, Seifert KA, Gams W (1999) Classification of the mycoparasite Gliocladium roseum in Clonostachys as C. rosea, its relationship to Bionectria ochroleuca, and notes on other Gliocladium-like fungi. Mycologia 91(2):365–385.

  40. Schwartz S, Motorin Y (2017) Next-generation sequencing technologies for detection of modified nucleotides in RNAs. RNA Biol 14(9):1124–1137.

    Article  PubMed  Google Scholar 

  41. Shentu X-P, Liu W-P, Zhan X-H, Xu Y-P, Xu J-F, Yu X-P, Zhang C-X (2014) Transcriptome sequencing and gene expression analysis of Trichoderma brevicompactum under different culture conditions. PLoS ONE 9(4):e94203.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. Spanic V, Katanic Z, Sulyok M, Krska R, Puskas K, Vida G, Drezner G, Šarkanj B (2020) Multiple fungal metabolites including mycotoxins in naturally infected and Fusarium-inoculated wheat samples. Microorganisms 8(4):578.

    CAS  Article  PubMed Central  Google Scholar 

  43. Sun ZB, Sun MH, Li SD (2015) Identification of mycoparasitism-related genes in Clonostachys rosea 67–1 active against Sclerotinia sclerotiorum. Sci Rep 5:18169.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Sun ZB, Wang Q, Sun MH, Li SD (2020) The mitogen-activated protein kinase gene Crmapk is involved in Clonostachys chloroleuca mycoparasitism. Mol Plant Microbe Interact 33(7):902–910.

    CAS  Article  PubMed  Google Scholar 

  45. Tatusov RL, Galperin MY, Natale DA, Koonin EV (2000) The COG database: a tool for genome-scale analysis of protein functions and evolution. Nucleic Acids Res 28(1):33–36.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  46. Thrane C, Tronsmo A, Jensen DF (1997) Endo-1,3-β-glucanase and cellulase from Trichoderma harzianum: purification and partial characterization, induction of and biological activity against plant pathogenic Pythium spp. Eur J Plant Pathol 103(4):331–344.

    CAS  Article  Google Scholar 

  47. Trivedi P, Schenk PM, Wallenstein MD, Singh BK (2017) Tiny microbes, big yields: enhancing food crop production with biological solutions. Microb Biotechnol 10(5):999–1003.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Tsang C-C, Teng JLL, Lau SKP, Woo PCY (2021) Rapid genomic diagnosis of fungal infections in the age of next-generation sequencing. J Fungi 7:636.

    CAS  Article  Google Scholar 

  49. Tzelepis G, Dubey M, Jensen DF, Karlsson M (2015) Identifying glycoside hydrolase family 18 genes in the mycoparasitic fungal species Clonostachys rosea. Microbiology 161(7):1407–1419.

    CAS  Article  PubMed  Google Scholar 

  50. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, Rozen SG (2012) Primer3–new capabilities and interfaces. Nucleic Acids Res 40(15):e115.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. Víglaš J, Olejníková P (2021) An update on ABC transporters of filamentous fungi—from physiological substrates to xenobiotics. Microbiol Res 246:126684.

    CAS  Article  PubMed  Google Scholar 

  52. Vujanovic V (2021) Tremellomycetes yeasts in kernel ecological niche: early indicators of enhanced competitiveness of endophytic and mycoparasitic symbionts against wheat pathobiota. Plants 10(5):905.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  53. Vujanovic V, Chau HW (2012) Monitoring Fusarium complex mycelia replacement by mycopathogenic Sphaerodes using alcohol percentage test, qRT-PCR and HPLC. Physiol Mol Plant Pathol 80:28–34.

    CAS  Article  Google Scholar 

  54. Vujanovic V, Goh YK (2009) Sphaerodes mycoparasitica sp. nov., a new biotrophic mycoparasite on Fusarium avenaceum, F. graminearum and F. oxysporum. Mycol Res 113:1172–1180.

    Article  PubMed  Google Scholar 

  55. Vujanovic V, Goh YK (2010) Sphaerodes mycoparasites and new Fusarium hosts for S. mycoparasitica. Mycotaxon 114(1):179–191

    Article  Google Scholar 

  56. Vujanovic V, Goh YK (2011) Sphaerodes mycoparasitica biotrophic mycoparasite of 3-acetyldeoxynivalenol- and 15-acetyldeoxynivalenol-producing toxigenic Fusarium graminearum chemotypes. FEMS Microbiol Lett 316(2):136–143.

    CAS  Article  PubMed  Google Scholar 

  57. Vujanovic V, Kim SH (2018) Adaptability of mitosporic stage in Sphaerodes mycoparasitica towards its mycoparasitic-polyphagous lifestyle. Mycologia 109(5):701–709.

    Article  Google Scholar 

  58. Vujanovic V, Goh YK, Daida P (2012) Heat- and cold-shock responses in Fusarium graminearum 3 acetyl- and 15 acetyl-deoxynivalenol chemotypes. J Microbiol 50(1):97–102.

    CAS  Article  PubMed  Google Scholar 

  59. Vujanovic V, Daida MA, Daida P (2017) qPCR assessment of aurofusarin gene expression in mycotoxigenic Fusarium species challenged with mycoparasitic and chemical control agents. Biol Control 109:51–57.

    CAS  Article  Google Scholar 

  60. Xiao M, Zhang Y, Chen X, Lee E-J, Barber CJS, Chakrabarty R, Desgagné-Penix I, Haslam TM, Kim Y-B, Liu E, MacNevin G, Masada-Atsumi S, Reed DW, Stout JM, Zerbe P, Zhang Y, Bohlmann J, Covello PS, De Luca V, Page JE, Ro D-K, Martin VJJ, Facchini PJ, Sensen CW (2013) Transcriptome analysis based on next-generation sequencing of non-model plants producing specialized metabolites of biotechnological interest. J Biotechnol 166(3):122–134.

    CAS  Article  PubMed  Google Scholar 

  61. Xu Y-H, Jia K-Z, Tang Y-J (2018) Regulatory networks governing methionine catabolism into volatile organic sulfur-containing compounds in Clonostachys rosea. Appl Environ Microbiol 84(22):e01840-e1918.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. Yang P (2017) The gene task1 is involved in morphological development, mycoparasitism and antibiosis of Trichoderma asperellum. Biocontrol Sci Technol 27(5):620–635.

    Article  Google Scholar 

  63. Zeilinger S, Reithner B, Scala V, Peissl I, Lorito M, Mach RL (2005) Signal transduction by Tga3, a novel G protein alpha subunit of Trichoderma atroviride. Appl Environ Microbiol 71(3):1591–1597.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. Zhao H, Zhou T, Xie J, Cheng J, Chen T, Jiang D, Fu Y (2020) Mycoparasitism illuminated by genome and transcriptome sequencing of Coniothyrium minitans, an important biocontrol fungus of the plant pathogen Sclerotinia sclerotiorum. Microb Genom 6(3):e000345.

    CAS  Article  PubMed Central  Google Scholar 

  65. Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A, Smets M, Leonhardt H, Heyn H, Hellmann I, Enard W (2017) Comparative analysis of single-cell RNA sequencing methods. Mol Cell 65(4):631-643.e4.

    CAS  Article  PubMed  Google Scholar 

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The authors would like to thank the anonymous reviewers of this paper.


This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grant (RGPIN-2017-05286) and Government of Saskatchewan-Agriculture Development Fund (ID: 20160226) and Saskatchewan Wheat Development Commission (ID: 171025-65) external funding to Dr. V. Vujanovic.

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SHK and VV equally contributed to conceptualization, methodology, investigation, data analysis and interpretation, writing original draft, writing review, and editing. SHK conducted experiments. VV conceived idea, acquired funding and supervised. All authors read and approved the manuscript.

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Correspondence to Vladimir Vujanovic.

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Kim, S.H., Vujanovic, V. Early transcriptomic response of the mycoparasite Sphaerodes mycoparasitica to the mycotoxigenic Fusarium graminearum 3-ADON, the cause of Fusarium head blight. Bioresour. Bioprocess. 8, 127 (2021).

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  • Functional transcriptomics
  • Biotrophic mycoparasitism
  • Fusarium biocontrol
  • Hyphal cell–cell interaction
  • RNA-Seq
  • qPCR
  • Gene expression