# Structural and Transcriptional Diversity in the Repertoire of Sclerotinia sclerotiorum Effector Candidates

Darcy Jones, Sylvain Raffaele

► To cite this version:

Darcy Jones, Sylvain Raffaele. Structural and Transcriptional Diversity in the Repertoire of Sclerotinia sclerotiorum Effector Candidates. Molecular Plant-Microbe Interactions, 2026, 10.1094/MPMI-08-25-0101-R. hal-05472890

HAL Id: hal-05472890

https://hal.inrae.fr/hal-05472890v1

Submitted on 23 Jan 2026

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons CC BY 4.0 - Attribution - International License
RESEARCH

# Structural and Transcriptional Diversity in the Repertoire of *Sclerotinia sclerotiorum* Effector Candidates

Darcy A. B. Jones<fnref n="1" />,<fnref n="2" />,† and Sylvain Raffaele<fnref n="1" />,†

Accepted for publication 6 November 2025.

The necrotrophic fungal pathogen *Sclerotinia sclerotiorum* is distinguished by its exceptionally broad host range, infecting numerous plant species across diverse genera. Although historically characterized by a seemingly unspecialized infection strategy, increasing evidence indicates that *S. sclerotiorum* employs a diverse suite of virulence mechanisms, including effectors for host and environment manipulation. Leveraging recent advances in structural genomics and the growing availability of genomic and transcriptomic resources, we systematically re-examined the effector repertoire of the reference strain 1980 using updated computational approaches. We identified 215 effector-like proteins, which were classified into structural families based on predicted tertiary structures. Transcriptome meta-analysis across hundreds of publicly available samples revealed diverse expression patterns among effectors, highlighting both conserved and condition-specific expression dynamics. Several effector families were found to be expanded in *S. sclerotiorum*, including groups with predicted antimicrobial functions and unique domain architectures. Within families, structural and surface property variation suggests potential functional diversification. Furthermore, coexpression network analysis uncovered putative gene clusters that may operate synergistically with selected effectors. This work refines our understanding of effector diversity and organization in *S. sclerotiorum* and provides a framework for prioritizing candidates for functional studies, ultimately contributing to the broader understanding of effector evolution in generalist fungal pathogens.

**Keywords:** effectors, fungal pathogen, gene coexpression, protein structure, *Sclerotinia sclerotiorum*

Fungal plant pathogens secrete small proteins known as effectors to promote disease by manipulating host plant cellular processes (Lovelace et al. 2023). In the conventional model of plant-

**Funding:** Support was provided by the Agence Nationale de la Recherche (ANR-10-LABX-41, ANR-11-IDEX-0002-02, ANR-19-CE20-15, ANR-21-CE20-10, and ANR-21-CE20-30) and a post-doc grant from the Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement SPE division to D. A. B. Jones.

**e-Xtra:** Supplementary material is available online.

The author(s) declare no conflict of interest.

<figure><img src="image_2.png" /></figure>

pathogen interactions, pathogens release nonspecific virulence factors and resource-scavenging proteins into their hosts, potentially triggering a pattern-triggered immunity (PTI) response (Yu et al. 2024). This response is initiated when the host recognizes these proteins, their degradation products, or molecular signatures of pathogens. To counteract PTI, fungi secrete effector proteins that suppress the resistance response or kill the host to benefit the pathogen. In turn, plants evolved resistance mechanisms relying on receptors that recognize effector proteins or their activities, leading to effector-triggered immunity (Yu et al. 2024). Pathogens may then acquire mutations in effector genes to restore virulence, resulting in cycles of effector–receptor interactions and highly specific pathogen–host interactions. This dynamic interplay embodies the molecular arms race model of plant–pathogen interactions, in which the coevolution of pathogens with their hosts usually leads to high levels of specialization toward a narrow range of potential hosts.

Effector proteins have diverse molecular functions and often lack amino acid sequence similarity to other effectors and known proteins or domains, complicating in silico identification (Jones et al. 2021; Sperschneider and Dodds 2022). Instead, effectors are typically identified by indirect but necessary protein characteristics such as the presence of secretion signals or high cysteine content (Saunders et al. 2012). However, recent advances using a combination of primary sequence pattern searches and structural modeling showed that fungal effector repertoires are dominated by a limited number of families that share common structural features despite extensive sequence variability (Derbyshire and Raffaele 2023a; Seong and Krasileva 2023). This revealed structural families of effectors conserved over long evolutionary periods and lineage-specific expansions in some effector families. For instance, the MAX effector family is present in many Ascomycete species and includes hundreds of structural homologs in the fungal genus *Magnaporthe* (de Guillen et al. 2015; Lahfa et al. 2024). The RALPH effector family is also present in the genome of several Ascomycete fungi and expanded in the powdery mildew pathogen *Blumeria graminis* (Praz et al. 2017). Although it remains challenging to discern the evolutionary processes driving the generation of these effector families, their convergence or shared lineage suggests common modes of pathogen–host interaction. This indicates that certain structural motifs may be inherently more suited for effector functions. The expansion and diversification of effector families raises questions about the functional relevance of structural redundancy and relatedness, particularly in the context of pathogens able to colonize diverse hosts.

The necrotrophic fungus *Sclerotinia sclerotiorum* stands out for its broad host range, encompassing hundreds of plant genera (Derbyshire and Denton-Giles 2016; Derbyshire and Raffaele 2023b; Mbengue et al. 2016). To facilitate infection across such

<footnote n="1">¹LIPME, INRAE - CNRS, Université de Toulouse, Castanet Tolosan, France</footnote>

<footnote n="2">²MIAT, INRAE, Université de Toulouse, Castanet Tolosan, France</footnote>

<footnote>†Corresponding authors: D. A. B. Jones; darcy.jones@inrae.fr, and S. Raffaele; sylvain.raffaele@inrae.fr</footnote>
a diverse array of plants, *S. sclerotiorum* secretes various nonspecific toxins and enzymes that kill host cells, including polygalacturonases, Necrosis- and ethylene-inducing peptide 1 (Nep1)-like proteins (NLPs), and numerous cell wall-degrading enzymes (Derbyshire et al. 2017; Guyon et al. 2014). A key molecule secreted by *S. sclerotiorum* during infection is oxalic acid, which induces host cell death and regulates disease progression through the progressive acidification of the extracellular environment (Cessna et al. 2000). Like effectors of biotrophic pathogens, toxins, enzymes, and effectors secreted by *S. sclerotiorum* can be recognized by certain host genotypes and trigger resistance responses (Yang et al. 2023; Zhang et al. 2013). Beyond its apparent simplistic brute force infection strategy, *S. sclerotiorum* also harbors virulence mechanisms dedicated to the colonization of certain hosts. Indeed, *S. sclerotiorum* has evolved mechanisms to neutralize or tolerate some host defense responses. For instance, *S. sclerotiorum* secretes an isothiocyanate hydrolase to detoxify defense isothiocyanate produced by Brassicales (Chen et al. 2020). Similarly, the gain or loss of fungal gene induction by the Brassica phytoalexin camalexin coincides with the ability of *Sclerotinia* species to infect *Arabidopsis thaliana* (Kusch et al. 2022). A very efficient secreted protein translation machinery (Badet et al. 2017) and the secretion of small RNA to silence host genes (Derbyshire et al. 2019) may also help *S. sclerotiorum* to overcome plant defenses.

Diverse transcriptomics studies have been conducted on *S. sclerotiorum* and supported time- and host-dependent gene expression profiles (Allan et al. 2019; Ibrahim et al. 2021). The list of *S. sclerotiorum* secreted proteins with a contribution to virulence has increased rapidly since the sequencing of its genome (Amselem et al. 2011; Derbyshire et al. 2017) and notably includes SsSSVP1 (Lyu et al. 2016), translocated into the host cytoplasm to interact with the cytochrome B component QCR8 and inhibit plant cell respiration; SSITL (Zhu et al. 2013), proposed to be involved in suppressing jasmonic acid/ethylene-mediated resistance; SsNE2 (Seifbarghi et al. 2020), SsSm1/SsCP1 (Pan et al. 2018; Yang et al. 2018), and SsINE1-5 (Newman et al. 2023), triggering cell death in *Nicotiana benthamiana*; SsSCP/SCFE1 (Yang et al. 2023), triggering PTI responses in a receptor-like kinase dependent manner in several plant species; SsERP1/SsCND1 (Ding et al. 2021; Fan et al. 2021), proposed to be involved in compound appresoria formation or the suppression of ethylene signaling; and SsPINE1 (Wei et al. 2022b), which inhibits plant polygalacturonase inhibitors. Together, these known effectors suggest that *S. sclerotiorum* exhibits a sophisticated plasticity of virulence toward different hosts. However, the mechanisms underlying how and when these more targeted interactions are induced and regulated are still largely not understood.

Recent improvements in structural genomics and the extensive published datasets offer novel opportunities to consolidate the current knowledge of the *S. sclerotiorum* infection strategy and to use that information to guide targeted effector discovery efforts. In this manuscript, we revisited the predicted effector repertoire of *S. sclerotiorum* with recent bioinformatics methods and updated literature to identify 215 effector-like proteins in the genome of the reference strain 1980. We applied transcriptome meta-analysis to characterize the expression *S. sclerotiorum* effectors in hundreds of samples and structure predictions to organize them into structural families. We highlight effector structural families expanded in *S. sclerotiorum* and original domain combinations. Expression patterns and surface properties appeared very variable within effector structural families, arguing in favor of functional diversification. Through a global coexpression analysis, we identified gene clusters that may function in conjunction with selected effectors. These analyses can serve as a basis to prioritize experimental characteriza-

tion of *S. sclerotiorum* effectors and improve our understanding of fungal effector evolution in the context of generalist pathogenicity.

## Results

### *S. sclerotiorum* effector candidate repertoire and structural landscape revisited

To update effector candidate predictions from the *S. sclerotiorum* proteome, we predicted protein functions using a comprehensive pipeline (Fig. 1A), including manual curation, InterProScan (Jones et al. 2014), SwissProt, dbCAN (Zhang et al. 2018), PHI-base (Urban et al. 2022), and PDB search results, as well as effector and extracellular (and subcellular) localization prediction software such as SignalP (Teufel et al. 2022), TargetP (Armenteros et al. 2019), and EffectorP (Sperschneider and Dodds 2022) using the Predector pipeline (Jones et al. 2021). This identified 819 predicted secreted proteins and 358 effector-like proteins (Supplementary Table S1). To focus on proteins that might have evolved toward host and host microbiome manipulation and diverged from ancestral housekeeping roles, we excluded members of common extracellular families (e.g., cell wall-degrading enzymes and hydrophobins) that are likely to have canonical activity (i.e., with complete domains and/or high coverage database matches in SwissProt) from our initial candidate set. Combining this annotation filter with secretion signal prediction (SignalP), subcellular localization information (TMBed, DeepLoc, TMHMM, and TargetP), and effector-like properties (fungiSMASH, EffectorP, ApoplastP, and Predector), we selected 215 high-confidence effector candidates (Supplementary Table S1). Seventy-one had some sequence similarity to known effector proteins, and 22 corresponded to previously identified *Sclerotinia* virulence proteins, including SsVP1 (Lyu et al. 2016), SsSCP/SCFE1 (Yang et al. 2023), SsPINE1 (Wei et al. 2022b), SSITL (Zhu et al. 2013), and SsINE1-6 (Newman et al. 2023).

To identify structural families of effectors within the *S. sclerotiorum* genome, we used a two-step pipeline (Fig. 1A). First, we clustered all Sclerotiniaceae proteins contained in AlphaFold DB (Varadi et al. 2022) using FoldSeek (van Kempen et al. 2024) and stringent clustering parameters. We found 13,126 clusters overall containing more than one member, of which 6,433 contained *S. sclerotiorum* members (Supplementary Table S2). Four hundred and forty-eight *Sclerotinia* clusters contained predicted secreted proteins, and 142 had members in our high-confidence effector candidate set, of which 37 clusters contained multiple *S. sclerotiorum* members. For practical reasons, we will refer to clusters and superclusters based on the name of characterized effectors or the dominant PFAM domain or structural fold they contain, although this may not reflect the structural and functional diversity that each contains. Second, to determine structural relationships between clusters, we performed pairwise similarity comparisons for effector-like proteins in Dali (Supplementary Table S3). Excluding singletons and structural similarity $Z \le 2.5$, this yielded a structural similarity network (SSN) of 214 proteins grouped in 119 clusters with 863 structural similarity links (Fig. 1B; Supplementary Table S4). The SSN included 136 high-confidence effector candidates (63% of the 215 identified), 32 other secreted proteins (CAZYMes, peptidases, and weak effector candidates), and 47 non-secreted homologs (Supplementary Table S4). There was pervasive structural similarity throughout the network, with 594 links (69%) connecting proteins from different clusters. Fourteen clusters covering 53 proteins only had internal structural similarity links, including the phytocyanin domain cluster (5 proteins), the dynein-like cluster (7 proteins), the peptidase S1/SAP (PEPS1) cluster (9 proteins), and the polygalacturonase cluster (12 proteins). Mean confidence assessed
by predicted local difference test (pLDDT) was ≥70 for 188 proteins (88%) and ≥90 for 95 proteins (44%) (Fig. 1C). Clusters with average pLDDT < 70 included the hydrophobin supercluster, the phytocyanin domain cluster, the dynein-like cluster, and 14 singletons. The median effector score from Predector over the network was 0.9885, with mean scores for clusters of 3 or more effector candidates ranging from -0.325 (Spherulin) to 3.2 (dynein-like cluster) (Fig. 1D). Predector effector scores above 1 indicate a confident prediction of effector-like properties (e.g., secreted, small, or cysteine-rich), whereas a score between 0 and 1 indicates a less confident prediction in which some (but not all) common characteristics of effectors are missing (Jones et al. 2021). Profile hidden Markov model (HMM) searches of *S. sclerotiorum* effector clusters against the entire Fungal UniProt dataset identified numerous homologs (Fig. 1E), with 92 clusters (77%) showing homologs across the fungal kingdom (Supplementary Table S1).

## Expression diversity in *S. sclerotiorum* structurally related effector families

We hypothesized that effector genes with divergent expression patterns may play non-redundant roles during infection. If true, comparative expression profiling of effectors from structurally conserved families would provide support for functional diversification within structural families. To document the expression patterns of *S. sclerotiorum* effector genes as comprehensively as possible, we collected RNA-seq data for 425 samples from 159 distinct treatments in 30 studies (Supplementary Table S5). To obtain comparable transcript level estimates, we remapped reads to the *S. sclerotiorum* reference genome and normalized read counts across samples as gene length normalized trimmed mean of M values (GeTMM) (Smid et al. 2018) (Fig. 2A; Supplementary Table S6). GeTMM values below zero indicate extremely low expression values and can generally be considered unexpressed. The dataset included 42 treatments (105

<figure><img src="image_3.png" /><figcaption>Fig. 1. The structural landscape of the *Sclerotinia sclerotiorum* effectome. A, Overview of the bioinformatics pipeline used to identify and classify *S. sclerotiorum* effector repertoire. Methods are labeled with italic blue font. Overlapping protein (left) and cluster (right) sets are shown as concentric circles with the number of proteins (left) and clusters (right) indicated. In the first step (left), 358 effector candidates were identified from *S. sclerotiorum* predicted proteins with expert annotation tools. Among those, 215 were considered high-confidence effector candidates that distributed in 153 structural clusters established with proteomes of Sclerotiniaceae species (right). B, These 215 high-confidence effector candidates were submitted to structural similarity search with Dali to establish the structural similarity network (SSN). The SSN includes 214 *S. sclerotiorum* proteins as dots, among which 136 high-confidence effector candidates, colored according to the structural cluster they belong to, with connections corresponding to Dali Z scores ≥ 2.5. Characterized *S. sclerotiorum* virulence proteins are labelled in gray italics, and major superclusters are labeled in black (see Supplementary Table S4 for details). Distribution of C, AlphaFold2 pLDDT score; D, effector score (see methods); and E, number of HMM hits in Fungal UniProt across the *S. sclerotiorum* effector SSN.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>Fig. 2. Expression diversity among *Sclerotinia sclerotiorum* effector genes. A, Overview of the 425 RNA-seq samples analyzed in this work showing the median GeTMM (library and gene length normalized read pair counts) value of 21 published *S. sclerotiorum* effectors and virulence factors in the central heatmap, as well as the RNA-seq sample host (or in vitro), infection stage, and unusual treatments indicated with colored rings toward the outside (see Supplementary Table S5 for details). All subsequent radial heatmap plots of RNA-seq expression throughout the manuscript are normalized to the same scale. B, Distribution treatments yielding the highest GeTMM value for each gene across *S. sclerotiorum* effector structural similarity network (SSN). Dots are colored following labels from A. C, Distribution of the sum for each gene of GeTMM for all in planta samples across the *S. sclerotiorum* effector SSN. D, Structural and expression similarity for members of *S. sclerotiorum* CFEM supercluster. Protein structures are oriented according to the result of a multiple structural alignment and organized along a structural similarity neighbor-joining tree (green). Expression pattern for each gene is shown as a gray radar plot of the same color code and scale as in A (the colored ring corresponds to a GeTMM value 8.0). Dotted lines connect gene expression patterns to their position in an expression correlation neighbor-joining tree (purple). E, A tanglegram showing the best arrangement between structural similarity (right, green) and expression similarity (left, purple) trees for *S. sclerotiorum* 214 proteins featured in the SSN. F and G, Structural and expression similarity for members of the F, *S. sclerotiorum* Hrip supercluster and G, PEPS1 cluster, with data shown as in D.</figcaption></figure>
samples) collected in vitro, of which the majority were cultured on potato dextrose agar or potato dextrose broth (PDB), and 117 treatments corresponding to the following infected plants: *Arabidopsis thaliana*, *Brassica napus*, *Brassica oleracea*, *Brassica rapa*, *Brassica villosa*, *Beta vulgaris* subsp. vulgaris, *Cicer arietinum*, *Glycine max*, *Helianthus annuus*, *Lupinus angustifolius*, *Lactuca sativa*, *Pisum sativum*, *Phaseolus vulgaris*, *Ricinus communis*, and *Solanum lycopersicum*. For the 214 genes in our SSN, the median GeTMM was 3.12 across in vitro samples and 3.84 across in planta samples, supporting the preferential expression of selected genes during plant colonization. To document variability in expression patterns of genes encoding *S. sclerotiorum* effectors, we mapped host plants with the highest GeTMM value (Fig. 2B) and the sum of GeTMM for all in planta samples (Fig. 2C) on the effector SSN. Under in vitro conditions (68 genes), *L. sativa* (46 genes) and *B. napus* (43 genes) were the most frequent top inducing conditions for *S. sclerotiorum* effector candidate genes, probably reflecting in part the more abundant sampling for these conditions. All 15 host plants appeared as the top inducing condition for at least one effector gene, with *B. oleracea*, *B. rapa*, and *G. max* being frequently represented relative to the number of samples present in our dataset (Fig. 2B).

The level of in planta expression varied quite significantly within most of the effector families, with median GeTMM values ranging from -0.6 (sscle_04g035620; highest expression in vitro) to 11.3 (sscle_07g055350; highest expression in *B. napus*) in the CFEM family harboring four members in *S. sclerotiorum* (Fig. 2D). The other two CFEM-encoding genes were generally above the median expression of in planta samples and showed highest expression in *B. napus* (sscle_06g050280) and in vitro (sscle_02g017140). In this small family already, the structure similarity tree was not congruent with a gene expression similarity tree. To determine the relationship between

structure similarity and expression similarity across the *S. sclerotiorum* effector repertoire, we constructed a tanglegram with neighbor-joining structure similarity and expression correlation trees (Fig. 2E). A Mantel test indicated that structural distances and expression distances for effector candidates are unrelated (P = 0.163; 9,999 permutations). *S. sclerotiorum* Hrip effectors provide another clear case of expression diversity within a structural family, with the median in planta GeTMM ranging from -1 (sscle_11g081610; expressed in a very few samples) to 4.9 (sscle_11g084330) and the highest expression in *B. napus* (sscle_08g067710 and sscle_11g084330), *G. max* (sscle_11g081610), and in vitro (sscle_01g004200, sscle_02g017190, sscle_14g097310, and sscle_14g101990) (Fig. 2F). Interestingly, all Hrip members except sscle_08g067710 and sscle_11g081610 had at least one of the virus-infected treatments among the five most expressed samples. As another example, *S. sclerotiorum* peptidase S1 effectors had a total in planta GeTMM ranging from -0.96 (sscle_04g035550) to 11.7 (sscle_07g058540), with the highest expression in *B. napus* (sscle_04g035550, sscle_07g058540, sscle_08g065930, sscle_10g075920, and sscle_12g091310), *R. communis* (sscle_07g055630), *G. max* (sscle_09g074710), and in vitro (sscle_02g022040 and sscle_03g025560) (Fig. 2G). Such diverse expression patterns suggest that effectors from the same structural family may not play fully redundant roles during infection.

### Sclerotiniaceae-specific and expanded families of *S. sclerotiorum* effectors

HMM searches identified homologs of 92 *S. sclerotiorum* effector clusters across the fungal kingdom, leaving three clusters restricted to the Sclerotiniaceae family and an additional 18 restricted to the Pezizomycotina subphylum (Table 1). Using HMM hits across Fungi UniRef50 as a reference for the

<table><caption>Table 1. Sclerotinia sclerotiorum effector protein clusters with restricted taxonomic distribution based on hidden Markov model (HMM) searches of the aligned structures against the Fungal UniProtKB dataset<sup>a</sup></caption><thead><tr><th>Restrict.</th><th>Cluster</th><th>S. sclerotiorum 1980 genes</th><th>Number of members in cluster</th><th>Other taxa</th><th>Description</th></tr></thead><tbody><tr><td rowspan="3">Sclerotiniaceae</td><td>A0A4S8QTN4 (SSVP1-like)</td><td>sscle_01g003850 (SSVP1)</td><td>16 (11)</td><td>Botryotinia spp., Botrytis spp., Monilinia spp., Sclerotinia borealis, S. trifoliorum</td><td>SsSSVP1; CVNH domain; structural similarity with sscle_05g041050</td></tr><tr><td>A0A4S8QV41 (Alt-A1-like)</td><td>sscle_15g106070</td><td>10 (13)</td><td>Botryotinia spp., Botrytis spp., Monilinia spp., Sclerotinia trifoliorum</td><td>Structural similarity with sscle_02g015390, sscle_01g011350, sscle_01g000660, sscle_16g107570, sscle_03g024000 from Alt-A1 superfamily</td></tr><tr><td>A0A4Z1E5N2 (Alt-A1-like)</td><td>sscle_01g000660</td><td>10 (11)</td><td>Botryotinia spp., Botrytis spp., Sclerotinia borealis, S. trifoliorum</td><td>Alt-A1 superfamily; MoNIS1-like protein; structural similarity with sscle_16g107570, sscle_02g016170, sscle_06g051210, sscle_03g029740, sscle_03g024000, sscle_06g052360</td></tr><tr><td rowspan="2">Helotiales</td><td>A0A1D9PY34</td><td>sscle_03g022790</td><td>2 (9)</td><td></td><td>Hydrophobin domain</td></tr><tr><td>A0A4Z1GVU7 (Alt-A1-like)</td><td>sscle_16g107570</td><td>11 (8)</td><td>Rutstroemia spp., Botryotinia spp., Botrytis spp., Monilinia spp., Sclerotinia trifoliorum</td><td>Alt-A1 superfamily; structural similarity with sscle_02g016170, sscle_06g051210, sscle_03g029740, sscle_03g024000, sscle_06g052360</td></tr><tr><td rowspan="2">Leotio-mycetes</td><td>A0A384J6U0 (Alt-A1-like)</td><td>sscle_02g015390, sscle_01g011350</td><td>18 (31)</td><td>Leotiomycetes</td><td>Alt-A1 superfamily; CVNH domain</td></tr><tr><td>A0A4S8R6M1 (Alt-A1-like)</td><td>sscle_03g024730</td><td>16 (39)</td><td></td><td>Alt-A1 superfamily</td></tr><tr><td rowspan="4">Leotiomyceta</td><td>A0A1D9Q705</td><td>sscle_06g054810</td><td>2 (41)</td><td></td><td>CVNH domain; GnK2-KP4 superfamily</td></tr><tr><td>A0A4S8QZ19</td><td>sscle_15g105160</td><td>15 (204)</td><td></td><td></td></tr><tr><td>A0A4Z1ETC7</td><td>sscle_09g072740</td><td>15 (177)</td><td></td><td></td></tr><tr><td>A0A4Z1HLZ7 (Alt-A1-like)</td><td>sscle_03g029740</td><td>12 (605)</td><td></td><td>AA1-like domain</td></tr><tr><td>Sordario-myceta</td><td>A0A4Z1KCI8</td><td>sscle_12g087440</td><td>9 (28)</td><td></td><td></td></tr></tbody></table>

<footnote><sup>a</sup> Restrict., taxonomic restrictions for the cluster; Cluster, representative UniProt/AlphaFoldDB entry for the cluster and superfamily assignment; Number of members in cluster, number of Sclerotiniaceae protein structures were present in the cluster and number of HMM hits in the Fungal UniProtKB between brackets.</footnote>
taxonomic distribution of *S. sclerotiorum* effectors, we tested for enrichment of each effector cluster into Sclerotiniaceae genomes (Supplementary Table S7; Fig. 3A). Seventeen clusters (14%) were depleted from Sclerotiniaceae genomes (enrichment *P* < 0.5, adjusted *P* < 0.05), including CFEM, chitin-binding, carbohydrate

binding module (CBM1), and expansin domain proteins, carbohydrate esterases, peptide hydrolases, and serine proteases. Forty-three clusters (36%) and four superfamilies were enriched in Sclerotiniaceae genomes (enrichment *P* > 2, adjusted *P* < 0.05). Among the most enriched clusters were necrosis-

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Properties of effector structural families expanded in Sclerotiniaceae fungi. A, Distribution of enrichment in Sclerotiniaceae fungi (color) and enrichment *P* value (chi-squared test, bubble size) across the *Sclerotinia sclerotiorum* effectome structural similarity network. Effector families discussed in the main text and in other figure panels are labeled on the network. B, Residue conservation score, expression pattern and hidden Markov model (HMM) conservation for nine *S. sclerotiorum* effectors of the Alt-A1 superfamily. C, Predicted structure of the complex formed by the Alt-A1 effector ssclc_03g024000 and its experimentally determined targets cotton PR5 (GhPR5) and *Arabidopsis thaliana* NRP1 (AtNRP1). The surface of ssclc_03g024000 is colored according to residue conservation score as in B. D, Residue conservation score, expression pattern, and HMM conservation for the four *S. sclerotiorum* effectors of the GnK2/KP4 superfamily. E, Surface map of residue conservation for two GnK2/KP4 effectors. F, Surface map of conserved regions for the four S. sclerotiorum effectors. G, Conserved regions for the four S. sclerotiorum effectors. The color coding for the surface map and the conserved regions follows the color coding in the main text. The black and white regions in the surface map and the conserved regions indicate that no conserved regions were found. The black and white regions in the surface map and the conserved regions indicate that no conserved regions were found. The black and white regions in the surface map and the conserved regions indicate that no conserved regions were found. The black and white regions in the surface map and the conserved regions indicate that no conserved regions were found.</figcaption></figure>
and ethylene-inducing proteins (cluster Q079H6, 11.2 fold, P = 0), polygalacturonase 6 (cluster H6W9D7, 3.6 fold, P = 2.4E-132), crystallin-like clusters A7EHR5 (15.1 fold, P = 2.1E-43) and A0A4Z1F8T7 (10.3 fold, P = 10.0E-26), and Hrip2 clusters A0A395J775 (2.6 fold, P = 5.5E-11) and A0A4S8QKL2 (3.0 fold, P = 3.0E-09). The most enriched superfamilies were Alt-A1-like (clusters A0A4Z1GVU7, A0A4S8QV41, A0A384J6U0, A0A4Z1E5N2, A0A4V4HU67, A0A4Z1JXT5, M7TSP5, A0A4S8R6M1, A0A4Z1EA15, and A0A4Z1HLZ7, 6.1 fold, P = 6.5E-123; Table 1; Fig. 3B), SSVP1-like (clusters A0A4S8QTN4, A0A8A3PNU2, A0A395IJM4, A0A4Z1KCC2, A0A1D9Q985, and A0A8A3PRW1, 2.3 fold, P = 3.4E-46), and KP6-like (clusters M7UPB7 and A7EKU2, 3.3 fold, P = 1.0E-07).

Other effectors with remarkable taxonomic distribution included the GnK2/KP4 superfamily, formed by one cluster enriched in Sclerotiniaceae (A0A4S8R124, 5.2 fold, P = 7.9E-07; Table 1) and three clusters not enriched (A0A1D9Q705, A0A1D9QMU9, and A7EYN8), and the dynein-like cluster (A0A384JF26), encompassing seven S. sclerotiorum proteins, making it the second most enriched cluster represented in S. sclerotiorum (2.4 fold, P = 0.03), after polygalacturonase 6 (12 S. sclerotiorum members). To get insight into how structural cluster expansion may affect S. sclerotiorum effector functions, we mapped the evolutionary conservation rate on protein surfaces using ConSurf (Yariv et al. 2023) and compared expression profiles and taxonomic distribution for S. sclerotiorum Alt-A1 (Fig. 3B) and GnK2/KP4 (Fig. 3D) clusters. Variable positions tended to reside in the terminal loops and external sheets of the Alt-A1 beta barrel, with conserved surface positions varying significantly across members of this superfamily (Fig. 3B). Sscle_06g052360 and sscle_03g024000 were the closest structural analogs of Verticillium dahliae PevD1 effector (PDB 5XMZ), which interacts with cotton osmotin-like GhPR5 (Zhang et al. 2019), and A. thaliana asparagine-rich AtNRP1 (Zhou et al. 2017). The predicted structure of the sscle_03g024000 complex with GhPR5 and AtNRP1 indicated that the interface with these two plant proteins may be highly similar, encompassing the most conserved and the most variable residues at the surface of sscle_03g024000 (Fig. 3C). A comparison across the S. sclerotiorum Alt-A1 superfamily indicated that this interface shows variable topology and conservation, suggesting that the mechanisms of interaction with plant proteins may not be conserved. Genes encoding S. sclerotiorum Alt-A1 proteins showed variable expression patterns and levels, with the four least-expressed homologs overall corresponding to taxonomically restricted effectors (Fig. 3B). In the GnK2/KP4 family, the broadly distributed homolog sscle_16g110210 showed the highest expression, whereas the expression of the other three homologs was specific to a few in planta and in vitro conditions (Fig. 3D). Across GnK2/KP4s, variable residues tended to accumulate at the extremity of alpha helices. Considering the slight differences in their topology, this created a clear conserved beta-strand side and a variable alpha-helical side in the taxonomically restricted sscle_05046070, which was less apparent in sscle_16g110210 (Fig. 3D and E). Together, these analyses suggested that structural family expansion could be accompanied by functional diversification in S. sclerotiorum effectors.

## Domain fusion contributes to S. sclerotiorum effector diversity

The analysis of the *Ustilago maydis* Tin2-like effector family revealed fusion events between the core Tin2 fold and diverse intrinsically disordered regions (Seong and Krasileva 2023). To determine the extent to which domain fusion contributes to the structural diversity of S. sclerotiorum effectors, we searched for evidence of domain association and shuffling in our curated

effectorome dataset. For this, we first mapped mature protein length on the S. sclerotiorum effector SSN to identify extreme size ratios between structurally related proteins, possibly indicative of domain extensions (Fig. 4A). The median size ratio between neighboring proteins in the network was 1.27, consistent with the structural clustering of proteins with roughly the same size. Eleven effectors (5.1%) were structurally related to effectors three times their size, and an additional 37 were structurally related to effectors twice their size.

Among them, sscle_06g055310 (IEB1, mature length 165 aa) and sscle_10g080000 (270 aa) shared structural similarity with sscle_04g034810 (AXP, 480 aa) (Fig. 4B). The three transcripts are highly expressed in planta, indicating they are not pseudogenes. Sscle_06g055310 shows similarity to the C-terminal domain (aa 319-480) of sscle_04g034810 (Dali Z score = 8.8, 25% local amino acid similarity). Both domains are homologs of a clostridial nontoxic non-hemagglutinin protein binding to botulinum neurotoxins (BoNTs) (PDB 3V0A, Z = 9.7 and 12.5) and of an arabinose-binding domain of alpha-L-arabinofuranosidase B (PDB 1WD4, Z = 8.6 and 33.5). The N-terminal domain (amino acids 1 to 317) of sscle_04g034810 shows similarity to sscle_10g080000 (Z = 10.0, 34% local amino acid similarity), to the catalytic domain of alpha-L-arabinofuranosidase B (PDB 6SXT, Z = 59.2), and to nontoxic non-hemagglutinin (PDB 3V0A, Z = 14.4). Analogy with the BoNT/A-NTNH/A complex (Košenina et al. 2024) suggests that the association of several S. sclerotiorum effector domains and proteins may serve to protect them from the acidic and protease-rich environment of the apoplast.

Another possible case of domain shuffling or convergent evolution involves analogy between the three-helix bundle domains of sscle_05g041050 (178 aa) and sscle_14g102170 (317 aa, Z = 3.5, 34% local amino acid similarity); the expansin-like domains of sscle_14g102170 and sscle_02g019600 (304 aa, Z = 3.3, 36% local amino acid similarity), and the ceratoplatanin-like domains of sscle_02g019600 and sscle_14g98920 (130 aa, Z = 15.9, 48% local amino acid similarity) (Fig. 4C). The four genes show contrasted patterns of expression, suggesting that single-domain and dual-domain effectors do not play redundant roles. Domain shuffling/convergence events and structural similarity also connect the KP6 and small cysteine-rich protein (SCP) superfamilies of effectors in S. sclerotiorum (Fig. 4D). The KP6-like effector sscle_05g046060 (INE2, 85 aa) showed structural similarity with the subtilisin/inhibitor I9 domain from sscle_01g007640 (95 aa, Z = 4.6, 42% local amino acid similarity) and sscle_06g053560 (SER1, 402 aa, Z = 7.3, 38% local amino acid similarity). SER1 also includes a peptidase S8 domain analogous to the spherulin 4-like domain found in sscle_02g015100 (276 aa, Z = 2.6, 39% local amino acid similarity) and sscle_02g011890 (398 aa, Z = 8.0). Sscle_02g011890 features a WSC domain with structural similarity to the WSC domain in sscle_01g008940 (358 aa, Z = 4.5, 32% local amino acid similarity) and that of sscle_01g008950 (237 aa, Z = 11.1, 27% local amino acid similarity). Sscle_01g008940 includes an SCP domain analogous to three S. sclerotiorum effectors consisting of an isolated 125- to 146-aa SCP domain (sscle_06g048920, sscle_07g061770, and sscle_03g024510), as well as three effectors with a surface-exposed SCP domain embedded in a 357- to 423-aa structure (sscle_05g046240, sscle_08g062810, and sscle_14g098170). In the SCP superfamily, sscle_08g062810 and sscle_14g098170 are ubiquitously expressed, whereas sscle_05g046240 and sscle_07g061770 show more specific expression patterns. Although sscle_07g061770, sscle_03g024510, and sscle_05g046240 are weakly expressed in lettuce, tomato, and B. rapa, sscle_06g048920 is strongly expressed during colonization of these hosts, suggesting some form of transcriptional complementarity among SCP
effectors. Visual inspection suggested that effector candidates resulting from domain fusions/convergence may also include ssclc_02g019600, ssclc_03g022550, ssclc_05g042460, ssclc_06g053560, ssclc_07g057820, ssclc_08g062810, ssclc_08g066880, ssclc_12g087440, and ssclc_14g102170.

## Surface variation in *S. sclerotiorum* effector-like structural families

Several effector structural families maintain a robust 3D structure over evolution by increasing energetic conflicts, a property termed frustration, at surface residues (Derbyshire and Raffaele 2023a). Increased surface frustration is generally associated with higher reactivity and the ability to establish intermolecular interactions (Ferreiro et al. 2007). With an aim to use surface frus-

tration as a proxy for functional diversification in effector structural families, we calculated frustration in the predicted structure of the 248 *S. sclerotiorum* effectors (Supplementary Table S1; Fig. 5A). The mean frustration indices ranged from -0.96 in ssclc_01g010490 (highly frustrated) to 0.98 in ssclc_08g064590 (not frustrated), with a median of -0.10. The strength of surface frustration generally varied significantly within effector structural families, with an amplitude of frustration index of 0.682 in the Hrip family, 0.35 in the Alt-A1 family, and 0.662 in the SCP family. Surface frustration was also quite variable in the ceratoplatanin family (frustration index from -0.263 to 0.146), with ssclc_10g076600 (CP1) and ssclc_16g107670 standing out for being close structural analogs (Z = 25.4), with a similar surface distribution of residue frustration indices (Supplementary

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Evidence for protein domain shuffling or convergence across *Sclerotinia sclerotiorum* effectors. **A**, Distribution of mature protein length (number of amino acids, aa) across the *S. sclerotiorum* effectome structural similarity network. Long proteins connecting distant superclusters are candidate domain fusion proteins. Effectors discussed in the main text and in other figure panels are labeled on the network. **B**, Evidence for recombination between a botulinum neurotoxin (BoNT)-like fold domain (purple) and a glycoside hydrolase 131 N-terminal domain (green) in the AXP effector. **C**, Domain shuffling/convergence events between a three-helix bundle domain (pink), an expansin-like domain (mauve), and a ceratoplatanin-like domain (cyan). **D**, Domain shuffling/convergence events and structural similarity subtilisin/inhibitor I9 domains (green), peptidase S8, and spherulin 4-like domains (yellow), WSC domain (orange), and SCP domain (red) connecting the KP6 and SCP superfamilies of effectors in *S. sclerotiorum*. The closest structural analogs from the network are connected by dotted lines labelled with Dali Z score for structural similarity and amino acid similarity percentage in local sequence alignments (light blue). Protein structures are oriented according to multiple structural alignments. The expression pattern of each gene is indicated as a radar plot organized as in Figure 2.</figcaption></figure>
Fig. S1). Whereas the expression of sscl_10g076600 is among the highest on lettuce and low on castor bean and tomato, the opposite is true for sscl_16g107670 (Supplementary Fig. S1). In most conditions, sscl_16g107670 is expressed much more strongly than sscl_10g076600. These observations suggest that some ceratoplatanins may have similar interactors but function in diverse cellular contexts or with contrasted stoichiometry.

To test for a link between surface frustration and protein-protein interactions, we used AlphaFold 2.0 multimer prediction to model the complex between sscl_10g076600 and its plant target *A. thaliana* PR1 (Yang et al. 2018) and mapped frustration onto sscl_10g076600 surface (Fig. 5B). The predicted interface between sscl_10g076600 and AtPR1 corresponded to residues with extreme frustration indices (either high or low), but other regions of sscl_10g076600 showed extreme frustration index

residues, making it difficult to assess the specificity of the link between frustration and target binding. Next, we calculated frustration variation upon binding as the difference between sscl_10g076600 frustration in its monomer model structure and sscl_10g076600 frustration in the sscl_10g076600-AtPR1 multimer structure (Fig. 5C). Residues of high frustration variation upon binding clustered at the sscl_10g076600-AtPR1 interface, consistent with release of frustration upon protein-protein interaction (Ferreiro et al. 2007).

The *S. sclerotiorum* genome harbors eight proteins with an SCP domain that show contrasted surface frustration patterns (Supplementary Fig. S2; Fig. 5D). Sscle_01g008950, sscl_06g048920, and sscl_06g048920 showed the most highly frustrated residues, suggesting that they might be the most reactive variants. Sscle_06g048920 is SCFE1, recognized by the

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Variation in protein surface frustration across *Sclerotinia sclerotiorum* effector families. A, Distribution of the mean frustration index (FI) of surface-exposed residues across the *S. sclerotiorum* effectome structural similarity network. B and C, Multimer model of *S. sclerotiorum* ceratoplatanin (CP) protein sscl_07g076600 (full surface) with its host target *Arabidopsis thaliana* PR1 (gray ribbons). The sscl_07g076600 surface is colored according to B, the FI calculated on the sscl_07g076600 monomer structure model and according to C, variation in residue FI between sscl_07g076600 monomer and multimer models. D, Projection maps and surface of three SCP domain-containing proteins colored according to residue FI. E and F, Multimer model of *S. sclerotiorum* SCFE1 (full surface) with its host receptor *A. thaliana* RLP30 (gray ribbons). SCFE1 surface is colored according to the FI calculated E, on the SCFE1 monomer structure model or F, according to variation in residue FI upon AtRLP30 binding.</figcaption></figure>
A. *thaliana* receptor-like protein RLP30 (Yang et al. 2023). To determine how surface frustration related to host recognition, we modeled the complex between SCFE1 and AtRLP30 and mapped frustration onto SCFE1 surface (Fig. 5E). Residues with extreme frustration indices (either high or low) were largely restricted to the predicted interface with AtRLP30. Frustration variation upon binding was low in some regions of the SCFE1–AtRLP30 interface, possibly because SCFE1 has not evolved to target AtRLP30 but some other targets in plant cells. The few structures of effector–host target complex documented to date are not sufficient to generalize reliable predictions on protein binding based on effector structure and frustration. Frustration patterns may also relate to other functional properties of effectors (e.g., secretion out of the fungal cell) and should therefore be interpreted with caution.

## Systematic gene coexpression analysis

Considering the relative heterogeneity of expression patterns within effector structural families, we conducted a systematic coexpression analysis of all *S. sclerotiorum* genes to identify groups of genes with similar expression patterns. Coexpressed genes were identified using Leiden clustering on a graph constructed from Pearson correlation of the principal components, followed by clustering on a partial correlation PC algorithm (Kalisch et al. 2012) (PC clusters) to identify statistically independent genes. Looking at the expression patterns, two of the main differential groups were in vitro and *L. sativa* samples. We found that lettuce expression patterns (all from one experiment) were overall very different from other samples, possibly indicating a major difference in the *Sclerotinia* isolate or environmental conditions. Lettuce samples were excluded from the coexpression analyses, as they tended to mask signals in other samples.

There were 971 clusters identified by Pearson correlation clustering, and 4,602 by the PC clustering, many of which corresponded to singletons or gene pairs (Supplementary Table S8). We obtained 244 coexpression clusters with more than 10 members and 7 with more than 50 members. Of these, 106 and 39 clusters contained at least one secreted or effector candidate protein, respectively, with 25 and 4 clusters significantly enriched in these terms (Fisher's exact test, *P* < 0.01). To get insights into the putative biological role of coexpression clusters, we analyzed GO, InterPro, and reactome term enrichments in each cluster (Supplementary Table S9). Overall, clusters often grouped seemingly disparate processes; however, some clusters harbored overrepresented virulence-related functions and processes. We identified six coexpression clusters of more than 25 genes enriched in secreted proteins, representing a total of 302 genes, among which 23 encoded effector candidates (Table 2).

Coexpression clusters generally did not contain multiple members of the same structural effector family. A notable exception was cluster E, which included a scytalone dehydratase protein SCD1 (sscle_03g031470) and a scytalone dehydratase-like effector candidate sscle_05g044730 (SCD2) (Fig. 6B). This cluster includes 30 genes, among which 7 are classified as effector candidates herein that present a peak of expression early during infection of *C. arietinum*, *G. max*, and *P. sativum*. In *S. sclerotiorum*, SCD1, THR1, and PKS13 (Li et al. 2018; Liang et al. 2018) are involved in the biosynthesis of dihydroxy-naphthalene melanin, notably during compound appressoria and sclerotia development, but do not contribute to virulence on soybean, *Brassica carinata*, tomato, faba bean, pea, and *Arabidopsis*. In *Magnaporthe oryzae* and *Colletotrichum*, but not in *B. cinerea*, the accumulation of dihydroxy-naphthalene melanin is required for host colonization (Schumacher 2016). Scytalone dehydratases typically belong to intracellular, enzymatically active members of the NTF2-like protein families, whereas the secreted NTF2-

like proteins tend to bind small molecules without enzymatic activity (Eberhardt et al. 2013).

To evaluate the likelihood that several secreted members of cluster E would bind the same small molecule, we calculated the affinity to (S)-DNPA, a tricyclic intermediate of the biosynthesis of polyketide natural products bound by a *Catenulispora acidiphila* NTF2-like protein (6VW4) using molecular docking. 6VW4 and 3B6C (*Streptomyces coelicolor* ActR) are currently the only two experimentally determined structures featuring bound S-DNPA. Docking with MolModa (Kochnev et al. 2024) estimated an affinity to S-DNPA ranging from –6.677 to –9.379 kcal/mol in these proteins (Supplementary Table S10). Five members of cluster E had a predicted affinity to S-DNPA within this range: SCD1 and SCD2 (–8.131 and –7.622 kcal/mol respectively), sscle_14g098170 (–7.063 kcal/mol), sscle_07g060180 (ERP1/CND1, –6.893 kcal/mol), and sscle_01g004400 (–7.474 kcal/mol) (Fig. 6C). Sscle_14g098170 includes an atypical SCP domain (A158-K268) predicted to form a 246A³ pocket to which S-DNPA may bind. SsERP1/CND1 is required for full virulence and compound appressoria formation (Ding et al. 2021; Fan et al. 2021) and was predicted to display a 431A³ pocket to which S-DNPA may bind. Sscle_01g004400, with structural similarity to the lysozyme degrading bacterial cell wall, exhibited a 418A³ pocket to which S-DNPA docked, corresponding to the 2-acetamido-2-deoxy-alpha-D-glucopyranose binding site in *Clostridium perfringens* phage endolysin (4KRU). These analyses suggest that some *S. sclerotiorum* coexpressed genes may encode proteins sharing small molecule ligands.

Cluster B, represented by sscle_01g008850, is highly expressed in *G. max*, *P. vulgaris*, and *R. communis* samples and contains three candidate effectors, including a KP4-like effector (sscle_16g110210; Fig. 3D), and two candidate UstYa-like cyclic peptide mycotoxin biosynthesis genes. We also observed coexpressed clusters enriched in core infection protein functions, such as cell wall and apoplastic protein degradation. For example, clusters A and D (best represented by sscle_04g032050 and sscle_01g005680) are both enriched in secreted CAZymes and are generally upregulated in in planta samples at later infection stages (Table 2; Supplementary Fig. S1). Cluster D contains a candidate CFEM effector (Fig. 2D) homologous to *Blumeria BEC2* (sscle_07g055350).

We identified 1305 *S. sclerotiorum*-expressed genes encoding for proteins mostly intrinsically disordered (>50% disordered, at least one disordered segment >30 amino acids), among which 19 were effector candidates. Out of 66 coexpression clusters of five or more members including at least one effector candidate, 49 (74%) also included at least one disordered protein (Supplementary Fig. S2). Sixteen clusters even included over 20% of disordered proteins, such as cluster B (Table 2), which harbored 14 intrinsically disordered proteins, none of which was predicted secreted. Coexpression with effector candidates might be a strategy to identify intrinsically disordered proteins with a role in the infection process.

Overall, many effector candidates and members of structural clusters appear to either have consistently high or low expressed levels. Most known effectors showed some expression variability but were highly expressed in at least one group of samples (Supplementary Table S11). SsNE1, SsNE3, and SL2, for example, were barely expressed in *Arabidopsis*, *Brassica oleracea*, *B. rapa*, and *Beta vulgaris* but were highly expressed in a subset of *Brassica napus* (Seifbarghi et al. 2017) and in vitro samples. Nep1 was unusual in that it was only highly expressed in *L. sativa*. Numerous candidates had very low expression in almost all samples, including the published effectors SsINE3, SsNE5, and SsPINE, suggesting that they may function in a specific spatial or temporal context, or at low concentration.
Discussion

Species within the Sclerotiniaceae are relatively unusual among fungal pathogens due to their exceptionally broad host ranges (Navaud et al. 2018). Even those considered host-restricted tend to be limited only at higher taxonomic levels, rather than to individual plant species. This broad host adaptability has historically supported the notion that Sclerotiniaceae rely primarily on non-specific toxins and general virulence factors to establish infection. In *S. sclerotiorum*, for example, oxalic acid is a well-characterized virulence factor essential for pathogenesis (Cessna et al. 2000). However, growing evidence indicates that *S. sclerotiorum* also engages in more intricate interactions with its hosts, including endophytic interactions and possible transient biotrophy (Peyraud et al. 2019; Q. Shang et al. 2024).

This elaborate lifestyle involves the manipulation of plant defense mechanisms in ways that can be context-dependent and, in some cases, host-specific (Allan et al. 2019; Kusch et al. 2022; Newman et al. 2023; Wei et al. 2022b; Zhu et al. 2013). In this study, we investigated both previously characterized and novel candidate effector genes in *S. sclerotiorum* by employing tertiary structural clustering approaches alongside a comprehensive re-analysis of publicly available transcriptomic datasets.

An updated overview of the *S. sclerotiorum* effector repertoire

The pathogenicity of *S. sclerotiorum* not only relies on toxins and cell wall-degrading enzymes but also on a diverse array of proteinaceous effectors that manipulate host immunity and metabolism. Earlier studies had predicted about a hundred can-

<table><caption>Table 2. Selected *Sclerotinia sclerotiorum* coexpressed gene clusters containing effector candidates</caption><thead><tr><th>Cluster</th><th>Number of members</th><th>Notable members<sup>a</sup></th><th>Expression trends</th><th>Functions</th></tr></thead><tbody><tr><td>A; 04g032050</td><td>85; 6 effectors; 0 disordered</td><td>sscle_04g034810 (SsAXP),<br>sscle_10g075470,<br>sscle_15g102320,<br>sscle_10g078830,<br>sscle_07g055890,<br>sscle_14g101360,<br>sscle_10g076980 (putative TF).</td><td>Low expression in vitro and during early-stage infection of several plants.</td><td>Numerous predicted secreted CAZymes, cell-wall proteins, and sugar transmembrane-transporters. Also enriched in terms related to glycogen metabolism and AA plasma membrane transport. The six effector-like proteins noted in "notable members" are probably normal CAZymes, not "true" effectors. Published effector SsAXP is an arabinofuranosidase.</td></tr><tr><td>B; 01g008850</td><td>65; 3 effectors; 14 disordered</td><td>sscle_16g110210 (Gnk2/KP4),<br>sscle_08g065980,<br>sscle_06g050410,<br>sscle_08g065970 (UstYa-like mycotoxin biosynthesis protein),<br>sscle_08g065960 (UstYa-like mycotoxin biosynthesis protein)</td><td>Up-regulated during late-stage infection of Soybean, and down in Sunflower. Variable within in vitro treatments, so may be related to experimental batches.</td><td>A mixture of core processes and potential virulence functions. Enriched in proteins related to transmembrane transporter, lipid biosynthesis, sugar catabolism, and the urea cycle. Includes two colocated mycotoxin biosynthesis genes and 3 effector candidates.</td></tr><tr><td>C; 01g011120</td><td>55; 2 effectors; 0 disordered</td><td>sscle_08g062920 (SsERP1 homolog),<br>sscle_07g062060,<br>sscle_03g031670 (PTH2),<br>sscle_02g014310 (TF)</td><td>Generally low expression in vitro, and generally high in planta.</td><td>Mixture of core and virulence functions. Several hydrolases and oxidoreductases, particularly related to fatty acyl-CoA biosynthesis, gluconeogenesis, and ethanol oxidation. Four proteins involved in regulation of peroxisome assembly and protein import.</td></tr><tr><td>D; 01g005680</td><td>39; 2 effectors; 0 disordered</td><td>sscle_07g055350 (BghBEC2 like),<br>sscle_04g033820 (effector-like CAZyme)</td><td>Low expression in several in vitro samples, and generally high expression during late stage in planta infection. May have reduced expression during early stages of chickpea, soybean, and Arabidopsis infection.</td><td>Seven secreted CAZymes and other non-secreted hydrolases and CAZymes. Several cell membrane and Golgi/ER transporters including mostly putative sugar and amino acid transporters. Also contains a catechol metabolism protein.</td></tr><tr><td>E; 03g031470</td><td>30; 7 effectors; 3 disordered</td><td>sscle_05g046060 (SsINE2),<br>sscle_07g060180 (SsERP1/SsCND1),<br>sscle_01g004400 (lysozyme),<br>sscle_14g098170,<br>sscle_02g021580,<br>sscle_05g044730 (secreted NTF2-like domain superfamily protein/SCD2),<br>sscle_15g106410 (Cerato-ulmin hydrophobin family protein),<br>sscle_14g098760 (TF),<br>sscle_03g031470 (THR1*),<br>sscle_03g031480 (SCD1*),<br>sscle_03g031520 (PKS13*)</td><td>Upregulated during early infection of chickpea, soybean, and field pea. Moderate expression in most other samples.</td><td>Several known and candidate effector proteins. Includes a cupredoxin/laccase, secreted LysM domain-containing protein, and cellobiose dehydrogenase. Non-secreted proteins include transporters, lyases, and hydrolases. The three members indicated with asterisks (THR1, SCD1 and PKS13) are all published and proposed to be involved in melanin biosynthesis. Enriched GO terms include melanin biosynthesis, lysozyme activity, peptidoglycan catabolism, cell wall macromolecule catabolism, and oxido-reductase activity.</td></tr><tr><td>F; 02g021280</td><td>28; 3 effectors; 2 disordered</td><td>sscle_11g084720 (ZtNIP2 homolog),<br>sscle_01g007640 (possible peptidase/proteinase inhibitor),<br>sscle_10g078670 (possible metallopeptidase)</td><td>Expression typically lower in in vitro samples compared to in planta.</td><td>Contains five predicted secreted proteases (including effector-like). Intracellular functions include two RNases, proton ATPase transmembrane transporters, and Transcription initiation factor IID subunit.</td></tr></tbody></table>

<footnote>a Effector candidates and effector-like proteins are highlighted in bold.</footnote>
didate effectors from the *S. sclerotiorum* genome (Derbyshire et al. 2017; Guyon et al. 2014; Heard et al. 2015; Mousavi-Derazmahalleh et al. 2019). Our work identified 358 effector-like proteins, with 215 classified as high-confidence candidates in the reference strain 1980. Out of 215 high-confidence effector candidates, 136 clustered into 92 sequence-unrelated but structurally conserved (SUSS) families, together with 78 other proteins. The finding that fungal effector repertoires are dominated by a limited number of families that share common structural

features despite extensive sequence variability (Derbyshire and Raffaele 2023a; Rocafort et al. 2022; Seong and Krasileva 2021, 2023) is therefore also apparent at the scale of the *S. sclerotiorum* genome. Structural similarity among effectors could arise from either convergent evolution or conservation of ancestral structures despite sequence divergence. Compensatory mutations and energetic frustration repatterning enable uncoupling structure and sequence conservation (Derbyshire and Raffaele 2023a). Variation in surface-exposed residues within SUSS families may

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** *Sclerotinia sclerotiorum* coexpression clusters enriched in effector candidates. A, Size distribution of the 214 clusters with more than 10 members from a partial correlation coexpression network and their secreted/effector gene content. Clusters marked with asterisks are enriched in secreted genes (Fisher's exact test, *P* < 0.01). B, Median expression of six selected clusters with infection relevant functional enrichment and/or coexpression profiles, which are detailed further in Table 2. C, Expression of members of the sscl_03g031470 (SCD1) coexpression cluster, which includes melanin biosynthesis genes (PKS13, THR1, SCD1) and several effector candidates (gene names indicated in bold). To highlight trends in expression values, gene length normalized trimmed mean of M values (GeTMM) normalized counts are asinh transformed and Z-scaled independently for each gene. This cluster is upregulated during early infection stages of chickpea (Mwape et al. 2021), soybean (Ranjan et al. 2019; Wei et al. 2022a), and field pea (Sang et al. 2019). D, Docking of S-DNPA (ball and sticks) onto its known interacting NTF2-like protein (PDB: 6VW4) and *S. sclerotiorum* effector candidates. The best poses with docking score are shown. HPI, hours postinoculation.; S-DNPA, 2-[(3S)-9-hydroxy-1-methyl-10-oxo-3,4-dihydrobenzo[g]isochromen-3-yl]acetic acid.</figcaption></figure>
alter target binding specificity while possibly maintaining some core targets across family members.

A remarkable property of the *S. sclerotiorum* effector repertoire was the presence of diverse families with putative antimicrobial activity. These included yeast killer proteins KP4 and KP6, crystallin-like toxin, and BoNTs. KP4 and KP6 proteins are encoded by persistent double-stranded RNA viruses in strains of the smut fungus *Ustilago maydis* and primarily function in intermicrobial competition (Brown 2011). These fungi secrete the KP4 and KP6 toxins to kill competing *Ustilago* strains that lack the corresponding toxin resistance genes. The primary mechanism of KP4's antifungal activity involves blocking calcium uptake and interfering with downstream calcium-regulated signal pathways in sensitive fungal strains. Yeasts also produce killer toxins with a βγ-crystallin fold that kill sensitive strains by inhibiting β-glucan synthesis (Antuch et al. 1996). The analysis of structural families in effectors and the prediction of antimicrobial activity suggest a broad occurrence and ancestral role of fungal effectors in competition with other microbes (Derbyshire and Raffaele 2023a; Mesny and Thomma 2024; Snelders et al. 2020, 2021). Some of these secreted proteins also harbor plant immunity modulation activity (Thynne et al. 2024) and could possibly have phytotoxic activity that has not been detected to date.

We observed structural homologs of known effectors and effector-like families in *S. sclerotiorum*, including CFEM proteins, Alt-A1/PevD1-like proteins (AA1), ceratoplatanin, polygalacturonases, and Hrips. Members of the AA1 family from *A. tenuissima*, *V. dahlia*, *M. oryzae*, and *S. sclerotiorum* were shown to induce hypersensitive cell death in plants (Kulye et al. 2012; Newman et al. 2023; Zhang et al. 2017, 2019). In addition, AA1 members can promote systemic acquired resistance against tobacco mosaic virus, inhibit the antifungal activity of host pathogenesis-related protein 5 (PR5)-like proteins, and induce reactive oxygen species accumulation and defense gene expression. The AA1 family includes the necrosis-inducing proteins SsNE1 and SsNE2 (Newman et al. 2023) and a putative virulence factor SsV263 (sscle_03g028510) (Liang et al. 2013). CFEM proteins suppress plant immunity by interfering with host immune modules such as the NPR1-NIMIN2 pathway or dampening plant wall-associated kinase-triggered cell death (S. Shang et al. 2024). Furthermore, CFEM proteins diversify functionally into roles such as heme-iron acquisition and delivery (Asp-type) and direct immunity suppression (Asn-type), both vital for colonization in iron-deficient environments (Wang et al. 2022; Zuo et al. 2022). They also contribute to appressorium development and cell wall integrity. In *Magnaporthe* and *Ustilago*, Hrip2 suppresses host resistance responses and can act as an elicitor (Chen et al. 2014). In the *Magnaporthe*-rice pathosystem, Hrip2 was associated with decreased phytoalexin production by the host (Nie et al. 2019). The crystal structure of MoHrip2 resembles a thaumatin-like fold, and the authors describe a specific 14-aa region necessary to act as an elicitor in *N. benthamiana* (Liu et al. 2016), which is not present in *Sclerotinia* homologs. *Ustilago* Hrip localized to the plant nucleus and cytoplasm and suppressed expression of several known resistance responses, including BAX-induced cell death and PR proteins (Wang et al. 2020).

We also observed novel candidate effector families that may be broadly conserved, including the SSITL-like and ERP1-like families, as well as one effector family (SSVP1-like) that appears to be restricted to Sclerotiniaceae. Each of these is associated with manipulating host defenses. SSITL and ERP both interfere with normal hormone signaling (jasmonic acid/ethylene and ethylene, respectively), which is crucial for *Sclerotinia* infection (Fan et al. 2021; Tang et al. 2020). SSITL is known to act at early stages of infection, and ERP is expressed during early infection

as well, suggesting that it also acts in concert with SSITL to manipulate similar processes. SSVP1, on the other hand, interacts with a subunit of cytochrome C, which is an early protein activating apoptosis in response to extracellular stimuli (Lyu et al. 2016). We also identified a family of seven effector candidates with weak structural similarity to the rat DYNLT4 dynein light chain. *M. oryzae* dynein-related proteins could have dual roles in the fungus and to manipulate host nuclear activity (Egan et al. 2015; Lin et al. 2023). *S. sclerotiorum* dynein-like secreted proteins also show structural homology to *Colletotrichum* stress-response protein vygo (UniProt L2GGY5) and *Bacillus* antifungal protein AFN1 (UniProt E6Y8N2).

### *S. sclerotiorum* effector candidates lacking structural analogs

Out of 215 *S. sclerotiorum* high-confidence effector candidates, 47 did not group with other Sclerotiniaceae proteins based on structural similarity (Supplementary Table S1). These included the SsNE1 and SsNE3 cell death-inducing effectors (Newman et al. 2023), sscle_08g068200 featuring homology to carbohydrate-binding lectins, and sscle_12g090620 orthogonal to *B. cinerea* bhp1 hydrophobin (Terhem and van Kan 2014). Nineteen effector candidates were predicted to contain intrinsically disordered regions corresponding to >30% of their sequence, a property that has been reported in some bacterial, fungal, and oomycete effectors (Jaswal et al. 2024; Jeon et al. 2025; O'Brien et al. 2018; Schneider et al. 2010; Yang et al. 2020). Due to their conformational plasticity, intrinsically disordered region-containing effectors could potentially interact with diverse host proteins. This could support generalist pathogenicity through promiscuous binding to host targets across multiple species.

### Diverse patterns of effector gene expression

Fungal pathogens deploy their effector arsenals in a highly coordinated and temporally regulated fashion during infection. For instance, *B. cinerea* shows temporal coregulation patterns of its candidate effector proteins (Wei et al. 2024), and *V. inaequalis* demonstrates five temporal waves of effector gene expression (Rocafort et al. 2022). *M. oryzae* exhibits major temporal shifts in gene expression, grouping effectors into distinct coexpression modules that align with different infection phases (e.g., appressorium formation, biotrophic colonization, and necrotrophic growth). Notably, structurally conserved effector families, such as MAX effectors and ADP-ribose transferases, are also temporally coexpressed (Yan et al. 2023). The coordinated expression of effectors can also be host-dependent, allowing pathogens such as *S. sclerotiorum* to tailor their effector deployment to specific host species (Kusch et al. 2022; Mousavi-Derazmahalleh et al. 2019; Newman et al. 2023).

Here, we observed several examples of published effectors that are consistently highly expressed in vitro (INE4, SsITL, SsNE1, SsNE6, and SM1/Sscp1), indicating that effector mining based on increased expression in planta in necrotrophic fungi may erroneously filter out candidates. The current study did not reveal consistent expression patterns for effectors with a similar structure, and effectors from the same structural family sometimes exhibited very contrasted expression levels. Some effector candidates had expression above the median in most samples, others exhibited expression only in a restricted number of samples, and others were barely expressed at all. This is perhaps best exemplified by the Hrip group (Fig. 2F), in which sscle_11g084330 had a relatively high expression in almost all samples, whereas others showed a more restricted expression pattern: sscle_11g081610 (*Lactuca sativa* and early *G. max*), sscle_02g017190 (in vitro, *B. napus*, *P. sativum*, *P. vulgaris*, and some *C. arietinum*), and sscle_01g004200 (most except
*L. sativa*). This suggests that these many effector candidates may have distinct (possibly host specific) functions and suggests that SUSS effector families are diversified rather than redundant. Functional diversification within the SUSS family is illustrated by *V. dahliae* CFEM proteins, among which the Asp type is thought to sequester iron from the host xylem, whereas the Asn type primarily functions in suppressing host immunity (Wang et al. 2022).

Coexpression may be crucial when virulence requires the concerted action of multiple effectors, such as those involved in protein-protein interactions. The tandem alcohol oxidase-peroxidase enzymes from *Colletotrichum orbiculare* are cosecreted early during plant penetration and are essential for pathogenicity. Their genes are found in a head-to-head arrangement, leading to tight coexpression, and biochemical studies reveal a fine-tuned interplay as they oxidize plant cuticular alcohols. This colocalization and coexpression are consistent with their cooperative activity in vivo (Bissaro et al. 2022). In addition to coexpression, genomic organization may play a role in the emergence of complex virulence functions involving multiple effectors. The emergence of multi-domain effectors may be related to complementary activities required for virulence. The *V. dahliae* tetrapeptide repeat protein contains a non-canonical CFEM domain mediating membrane targeting and a tandem repeat region with a toxic activity on cell membranes. Both domains are required for its full virulence (Liu et al. 2024).

The heterogeneous expression patterns observed within *S. sclerotiorum* SUSS families suggest functional diversification, implying a more intricate and context-dependent host manipulation than previously thought. To understand how structural variations translate into functional differences, future research should aim at characterizing the molecular function of high-confidence effector candidates in diverse host and environment contexts. This line of research will be instrumental in deciphering the contribution of effectors to *S. sclerotiorum*'s ability to infect a broad range of hosts.

## Materials and Methods

All code and details of software used can be found online at https://github.com/darcyabjones/20230424-sclerotinia_transcriptomics_metaanalysis.

### Protein functional annotation

We predicted the functions of proteins in the reference *S. sclerotiorum* 1980 genome (NCBI RefSeq GCF_000146945.2) using numerous tools, including domain and family prediction with InterProScan v.5.62-94.0 (Jones et al. 2021), identifying homologs with known function with MM-Seqs2 v.13.45111 (Steinegger and Söding 2017) (-e 0.01 -s 7 --num-iterations 3 --realign -a) searches against the SwissProt v2023_02 and PDB (downloaded 2023-06-13) databases, secondary metabolite cluster prediction with fungiMASH v.7.0.0 (Blin et al. 2023), transmembrane domain prediction with TMBed v.1.0.0 (Bernhofer and Rost 2022), and subcellular localization prediction with DeepLoc2 v.2.0 (Thumuluri et al. 2022) (fast mode). Additional analyses were performed using the Predector pipeline v.1.2.7 (Jones et al. 2021), which includes annotations from SignalP (Teufel et al. 2022), TMHMM (Krogh et al. 2001), TargetP (Armenteros et al. 2019), EffectorP (Sperschneider and Dodds 2022; Sperschneider et al. 2016, 2018a), and ApoplastP (Sperschneider et al. 2018b).

### Protein structure prediction and analysis

All predicted protein structures for the sequences from the Sclerotiniaceae family were downloaded from the AlphaFold database (Varadi et al. 2022) (downloaded 16 June 2023).

To focus on confident structural predictions of mature proteins, we removed signal peptides predicted using TargetP v.2 (Armenteros et al. 2019) and low-quality regions on the end of clusters with a 3-aa sliding window with an average LDDT < 70 (https://github.com/darcyabjones/bioscripts/blob/main/bin/trim_alphafold_cifs.py). Clusters of similar protein structures were identified using FoldSeek commit: 75d3069 (van Kempen et al. 2024) and a pairwise "easy-search" pipeline (-s 9.5 -c 0.7 --cov-mode 2 -a). The search results were used to create a directed graph in igraph (Csárdi et al. 2025). Clusters of proteins were then identified using the Leiden algorithm (Traag et al. 2019) implemented in the "leidenalg" python package v.0.10.0 (https://github.com/vtraag/leidenalg). Representative members of each cluster was selected as the members with the highest in-degree. To predict the evolutionary conservation of clusters, we constructed HMMs from structure alignments using Caretta v.0.1.1 (Akdel et al. 2020). We then used JackHMMER to identify matches to these HMMs in the Fungal UniProt database subset, using three iterations and a final e-value threshold of 1e-5. We then found the lowest common ancestor of each cluster's HMM matches using TaxonKit v.0.14.2 (Shen and Ren 2021). To identify structural clusters enriched in Sclerotiniaceae genomes, we used chi-squared tests with the chisq.test function in R, using the total number of HMM hits for all other structural clusters over the total number of HMM hits outside Sclerotiniaceae for these clusters to estimate enrichment fold and expected frequencies. *P* values were corrected for multiple testing using Bonferroni correction. Intrinsically disordered regions were analyzed using the EspritZ server version 1.3 with X-ray prediction type and 5% false positive rate threshold (Walsh et al. 2012). *S. sclerotiorum* proteins were considered disordered when >50% of amino acids were disordered and at least one disordered segment of >30 amino acids was detected. For protein domain analyses, the sequence of superimposed in 3D structures was aligned with the EMBOSS Needle algorithm, and the percentage of amino acid similarity along the longest aligned region is reported.

### RNA-seq data treatment

Publicly available sequencing data were gathered from NCBI SRA repositories and literature (detailed in Supplementary Table S1). Adapter sequences were trimmed, and low-quality reads were filtered out using fastp v.0.23.2 (Chen et al. 2018) (--qualified质量_phred 5 --length Required 20 --detect Burden_for_pe). Basic quality control was performed using Falco (de Sena Brandine and Smith 2021) and MultiQC (Ewels et al. 2016). Sequencing strandedness details were often missing from publications and so was predicted using how_are_we_stranded_here (Signal and Kahlke 2022). Trimmed reads were aligned to the *S. sclerotiorum* 1980 genome (NCBI RefSeq GCF_000146945.2) using HISAT2 v.2.2.1 (Kim et al. 2019), with parameters --downstream-transcriptome-assembly --max-intronlen 20000 --no-unal, and filtered for a minimum mapping quality of 5. Transcripts were assembled from individual stranded RNA-seq samples using StringTie v.2.2.1 (Kovaka et al. 2019), then merged, and transcript abundance was quantified with all RNA-seq samples. Because fungal genes are unusually close together, transcript assemblers such as StringTie often merge adjacent loci on the same strand. To avoid this issue, we semi-manually curated the StringTie loci using coverage and jaccard similarity (https://github.com/darcyabjones/20230424-sclerotinia_transcriptomics_metaanalysis/blob/main/03-RNAseq/03c-stringtie_curate.ipynb). Counts of aligned reads to reference genes were quantified using featureCounts from the subRead suite of program v.2.0.3 (Liao et al. 2014) again using the sample strandedness predicted previously where
applicable. Counts from featureCounts were processed individ-
ually for each NCBI BioProject using DESeq2 v.1.48.1 (Love
et al. 2014), filtering low read counts using the R HTSFilter
package v.1.0.1 (Rau et al. 2013).

Gene coexpression

To construct gene coexpression clustering and network es-
timation, counts were normalized using the GeTMM method
(Smid et al. 2018), which accounts for gene length and within-
and between-sample variation. These normalized counts were
then scaled using the hyperbolic arcsine transformation (asinh)
(Johnson and Krishnan 2022). All samples from the RNA-seq
were used, and the mean of replicate treatments was taken.
Gene coexpression networks were constructed from pairwise
Pearson correlations, where an edge was defined if the corre-
lation was significantly different from 0 (Bonferroni–Hochberg
adjusted *P* value on the lower triangle <0.05). To remove arv
 artifacts caused by overrepresentation of some treatments, prin-
cipal component analysis was performed, and we used the
first 25 principal components for correlation analysis (standard-
normal scaled after removal of extreme outliers). Clusters from
this initial set were identified using Leiden clustering (Traag
et al. 2019) in the “leidenalg” python package v.0.10.0 (https://
github.com/vtraag/leidenalg), using the CPM partitioner and a
resolution score of 0.1. This relatively low-resolution parameter
was selected to remove spurious edges connecting otherwise dis-
tinct connected components. These clusters were then merged
based again using correlation *P* value thresholds (*P* < 1e-5),
where the harmonic mean of the proportion of inter-cluster edges
that link the two clusters and the proportion of nodes with edges
linking the two clusters exceeds 0.5. To identify higher confi-
dence clusters with some directional information, we used the
PC algorithm implemented in the “pcalg” R package (Kalisch
et al. 2012), using Pearson partial correlation to identify depen-
dency relationships. We again used Leiden clustering (resolution
0.01) to select subsets of these components and selected a sin-
gle representative for each cluster by selecting the node with
the highest weighted (by absolute correlation value) out-degree,
breaking ties randomly.

Analysis of protein surface properties

Residue surface exposure was determined using GETAREA
with 1.4A water probe radius and gradient calculation dis-
abled (Fraczkiewicz and Braun 1998). Residues with a solvent-
accessible surface greater than 50% were considered surface ex-
posed. Residue frustration was calculated using FrustratometeR
(Rausch et al. 2021) with electrostatics k = 4.15 enabled. Single-
residue frustration indices are reported. For molecular docking,
molecules were loaded in Molmoda (Kochnev et al. 2024) with
desalt activated and the method ‘medium’ for 3D coordinate gen-
eration; molecules were protonated and pockets detected with
default parameters. S-DNPA docking was performed using the
S-DNPA model from 6VW4_A, using the centroid of the largest
pockets, extended to cubes of 10A side, using exhaustiveness
8 and maximum rotatable bonds 15, keeping only the highest
scoring pose. Surface maps were generated using Surfmap 2.2.0
(Schweke et al. 2022) following structural alignment using the
Matchmaker tool in UCSF Chimera version 1.17.3 (Pettersen
et al. 2004). Residue conservation was determined using the
ConSurf webserver with default parameters (Yariv et al. 2023).
The structures of effectors in complex with their host target and
host receptor proteins were determined using AlphaFold2 multi-
mer (num_relax = 0, template_mode = none, msa_mode = mm-
seqs2_uniref_env, pair_mode = unpaired_pair, num_recycles =
3). To calculate frustration variation upon binding, frustration in-
dices were calculated on the AlphaFold predicted structure for
the effector alone, then on the AlphaFold multimer structure for

the effector and its known interacting protein. Frustration vari-
ation corresponded to the distance between the maximal and
minimal frustration index between single and multimer struc-
tures for each residue.

Acknowledgments

The authors are grateful to Laurence Godiard, Shantala Mounichetty,
Joris Macquet, and Florent Delplace for fruitful discussions. NotebookLM
v13_Dec._2024 was used to polish some sections of the introduction and
discussion of this manuscript.

**Literature Cited**

Akdel, M., Durairaj, J., de Ridder, D., and van Dijk, A. D. J. 2020. Caretta – A multiple protein structure alignment and feature extraction suite. Comput. Struct. Biotechnol. J. 18:981-992.

Allan, J., Regmi, R., Denton-Giles, M., Kamphuis, L. G., and Derbyshire, M. C. 2019. The host generalist phytopathogenic fungus *Sclerotinia sclerotiorum* differentially expresses multiple metabolic enzymes on two different plant hosts. Sci. Rep. 9:19966.

Amselem, J., Cuomo, C. A., van Kan, J. A. L., Viaud, M., Benito, E. P., Couloux, A., Coutinho, P. M., de Vries, R. P., Dyer, P. S., Fillinger, S., Fournier, E., Gout, L., Hahn, M., Kohn, L., Lapalu, N., Plummer, K. M., Pradier, J.-M., Quévillon, E., Sharon, A., Simon, A., ten Have, A., Tudzynski, B., Tudzynski, P., Wincker, P., Andrew, M., Anthouard, V., Beever, R. E., Beffa, R., Benoit, I., Bouzid, O., Brault, B., Chen, Z., Choquer, M., Collémare, J., Cotton, P., Danchin, E. G., Da Silva, C., Gautier, A., Giraud, C., Giraud, T., Gonzalez, C., Grossetete S., Güldener, U., Henrissat, B., Howlett, B. J., Kodira, C., Kretschmer, M., Lappartient, A., Leroch, M., Levis, C., Mauceli, E., Neuvéglise, C., Oeser, B., Pearson, M., Poulain, J., Poussereau, N., Quesneville, H., Rascle, C., Schumacher, J., Ségurens, B., Sexton, A., Silva, E., Sirven, C., Soanes, D. M., Talbot, N. J., Templeton, M., Yandava, C., Yarden, O., Zeng, Q., Rollins, J. A., Lebrun, M.-H., and Dickman, M. 2011. Genomic analysis of the necrotrophic fungal pathogens *Sclerotinia sclerotiorum* and *Botrytis cinerea*. PLoS Genet. 7:e1002230.

Antuch, W., Güntert, P., and Wüthrich, K. 1996. Ancestral βγ-crystallin precursor structure in a yeast killer toxin. Nat. Struct. Biol. 3:662-665.

Armenteros, J. J. A., Salvatore, M., Emanuelsson, O., Winther, O., von Heijne, G., Elofsson, A., and Nielsen, H. 2019. Detecting sequence signals in targeting peptides using deep learning. Life Sci. Alliance 2:e201900429.

Badet, T., Peyraud, R., Mbengue, M., Navaud, O., Derbyshire, M., Oliver, R. P., Barbacci, A., and Raffaele, S. 2017. Codon optimization underpins generalist parasitism in fungi. eLife 6:e22472.

Bernhofer, M., and Rost, B. 2022. TMbed: Transmembrane proteins predicted through language model embeddings. BMC Bioinform. 23: 326.

Bissaro, B., Kodama, S., Nishiuchi, T., Díaz-Rovira, A. M., Hage, H., Ribeaucourt, D., Haon, M., Grisel, S., Simaan, A. J., Beisson, F., Forget, S. M., Brumer, H., Rosso, M.-N., Guallar, V., O'Connell, R., Lafond, M., Kubo, Y., and Berrin, J.-G. 2022. Tandem metalloenzymes gate plant cell entry by pathogenic fungi. Sci. Adv. 8:eade9982.

Blin, K., Shaw, S., Augustijn, H. E., Reitz, Z. L., Biermann, F., Alanjary, M., Fetter, A., Terlouw, B. R., Metcalf, W. W., Helfrich, E. J. N., van Wezel, G. P., Medema, M. H., and Weber, T. 2023. antiSMASH 7.0: New and improved predictions for detection, regulation, chemical structures and visualisation. Nucleic Acids Res. 51:W46-W50.

Brown, D. W. 2011. The KP4 killer protein gene family. Curr. Genet. 57: 51-62.

Cessna, S. G., Sears, V. E., Dickman, M. B., and Low, P. S. 2000. Oxalic acid, a pathogenicity factor for *Sclerotinia sclerotiorum*, suppresses the oxidative burst of the host plant. Plant Cell 12:2191-2199.

Chen, J., Ullah, C., Reichelt, M., Beran, F., Yang, Z.-L., Gershenzon, J., Hammerbacher, A., and Vassão, D. G. 2020. The phytopathogenic fungus *Sclerotinia sclerotiorum* detoxifies plant glucosinolate hydrolysis products via an isothiocyanate hydrolase. Nat. Commun. 11:3090.

Chen, M., Zhang, C., Zi, Q., Qiu, D., Liu, W., and Zeng, H. 2014. A novel elicitor identified from Magnaporthe oryzae triggers defense responses in tobacco and rice. Plant Cell Rep. 33:1865-1879.

Chen, S., Zhou, Y., Chen, Y., and Gu, J. 2018. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34:i884-i890.

Csárdi, G., Nepusz, T., Müller, K., Horvát, S., Traag, V., Zanini, F., and Noom, D. 2025. iggraph for R: R interface of the iggraph library for graph theory and network analysis. (v2.1.4). Zenodo. https://doi.org/10.5281/zenodo.14736815
de Guillen, K., Ortiz-Vallejo, D., Gracy, J., Fournier, E., Kroj, T., and Padilla, A. 2015. Structure analysis uncovers a highly diverse but structurally conserved effector family in phytopathogenic fungi. PLoS Pathog. 11:e1005228.

Derbyshire, M., Denton-Giles, M., Hegedus, D., Seifbarghy, S., Rollins, J., van Kan, J., Seidl, M. F., Faino, L., Mbengue, M., Navaud, O., Raffaele, S., Hammond-Kosack, K., Heard, S., and Oliver, R. 2017. The complete genome sequence of the phytopathogenic fungus *Sclerotinia sclerotiorum* reveals insights into the genome architecture of broad host range pathogens. Genome Biol. Evol. 9:593-618.

Derbyshire, M., Mbengue, M., Barascud, M., Navaud, O., and Raffaele, S. 2019. Small RNAs from the plant pathogenic fungus *Sclerotinia sclerotiorum* highlight host candidate genes associated with quantitative disease resistance. Mol. Plant Pathol. 20:1279-1297.

Derbyshire, M. C., and Denton-Giles, M. 2016. The control of Sclerotinia stem rot on oilseed rape (*Brassica napus*): Current practices and future opportunities. Plant Pathol. 65:859-877.

Derbyshire, M. C., and Raffaele, S. 2023a. Surface frustration re-patterning underlies the structural landscape and evolvability of fungal orphan candidate effectors. Nat. Commun. 14:5244.

Derbyshire, M. C., and Raffaele, S. 2023b. Till death do us pair: Co-evolution of plant-necrotroph interactions. Curr. Opin. Plant Biol. 76:102457.

de Sena Brandine, G., and Smith, A. D. 2021. Falco: High-speed FastQC emulation for quality control of sequencing data. F1000Research 8: 1874.

Ding, Y., Chen, Y., Yan, B., Liao, H., Dong, M., Meng, X., Wan, H., and Qian, W. 2021. Host-induced gene silencing of a multifunction gene *Ss-cnd1* enhances plant resistance against *Sclerotinia sclerotiorum*. Front. Microbiol. 12:693334.

Eberhardt, R. Y., Chang, Y., Bateman, A., Murzin, A. G., Axelrod, H. L., Hwang, W. C., and Aravind, L. 2013. Filling out the structural map of the NTF2-like superfamily. BMC Bioinform. 14:327.

Egan, M. J., McClintock, M. A., Hollyer, I. H. L., Elliott, H. L., and Reck-Peterson, S. L. 2015. Cytoplasmic dynein is required for the spatial organization of protein aggregates in filamentous fungi. Cell Rep. 11:201-209.

Ewels, P., Magnusson, M., Lundin, S., and Käller, M. 2016. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32:3047-3048.

Fan, H., Yang, W., Nie, J., Zhang, W., Wu, J., Wu, D., and Wang, Y. 2021. A novel effector protein SsERP1 inhibits plant ethylene signaling to promote *Sclerotinia sclerotiorum* infection. J. Fungi 7:825.

Ferreiro, D. U., Hegler, J. A., Komives, E. A., and Wolynes, P. G. 2007. Localizing frustration in native proteins and protein assemblies. Proc. Natl. Acad. Sci. U.S.A. 104:19819-19824.

Fraczkiewicz, R., and Braun, W. 1998. Exact and efficient analytical calculation of the accessible surface areas and their gradients for macro-molecules. J. Comput. Chem. 19:319-333.

Guyon, K., Balagué, C., Roby, D., and Raffaele, S. 2014. Secretome analysis reveals effector candidates associated with broad host range necrotrophy in the fungal plant pathogen *Sclerotinia sclerotiorum*. BMC Genomics 15:336.

Heard, S., Brown, N. A., and Hammond-Kosack, K. 2015. An interspecies comparative analysis of the predicted secretomes of the necrotrophic plant pathogens *Sclerotinia sclerotiorum* and *Botrytis cinerea*. PLoS One 10:e0130534.

Ibrahim, H. M. M., Kusch, S., Didelon, M., and Raffaele, S. 2021. Genome-wide alternative splicing profiling in the fungal plant pathogen *Sclerotinia sclerotiorum* during the colonization of diverse host families. Mol. Plant Pathol. 22:31-47.

Jaswal, R., Rajarammohan, S., Dubey, H., Kiran, K., Rawal, H., Sonah, H., Deshmukh, R., and Sharma, T. R. 2024. Intrinsically disordered Kiwellin protein-like effectors target plant chloroplasts and are extensively present in rust fungi. Mol. Biotechnol. 66:845-864.

Jeon, H., Kim, W., and Segonzac, C. 2025. The disordered effector RipAO of *Ralstonia solanacearum* destabilizes microtubule networks in *Nicotiana benthamiana* cells. Mol. Cells 48:100167.

Johnson, K. A., and Krishnan, A. 2022. Robust normalization and transformation techniques for constructing gene coexpression networks from RNA-seq data. Genome Biol. 23:1.

Jones, D. A. B., Rozano, L., Debler, J. W., Mancera, R. L., Moolhuijzen, P. M., and Hane, J. K. 2021. An automated and combinatorial method for the predictive ranking of candidate effector proteins of fungal plant pathogens. Sci. Rep. 11:19731.

Jones, P., Binns, D., Chang, H.-Y., Fraser, M., Li, W., McAnulla, C., McWilliam, H., Maslen, J., Mitchell, A., Nuka, G., Pesseat, S., Quinn, A. F., Sangrador-Vegas, A., Scheremetjew, M., Yong, S.-Y., Lopez, R., and Hunter, S. 2014. InterProScan 5: Genome-scale protein function classification. Bioinformatics 30:1236-1240.

Kalisch, M., Mächler, M., Colombo, D., Maathuis, M. H., and Bühlmann, P. 2012. Causal inference using graphical models with the R package pcalg. J. Stat. Softw. 47:1-26.

Kim, D., Paggi, J. M., Park, C., Bennett, C., and Salzberg, S. L. 2019. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37:907-915.

Kochnev, Y., Ahmed, M., Maldonado, A. M., and Durrant, J. D. 2024. MolModa: Accessible and secure molecular docking in a web browser. Nucleic Acids Res. 52:W498-W506.

Košenina, S., Škerlová, J., Zhang, S., Dong, M., and Stenmark, P. 2024. The cryo-EM structure of the BoNT/Wo-NTNH complex reveals two immunoglobulin-like domains. FEBS J. 291:676-689.

Kovaka, S., Zimin, A. V., Pertea, G. M., Razaghi, R., Salzberg, S. L., and Pertea, M. 2019. Transcriptome assembly from long-read RNA-seq alignments with StringTie2. Genome Biol. 20:278.

Krogh, A., Larsson, B., von Heijne, G., and Sonnhammer, E. L. L. 2001. Predicting transmembrane protein topology with a hidden Markov model: Application to complete genomes. J. Mol. Biol. 305:567-580.

Kulye, M., Liu, H., Zhang, Y., Zeng, H., Yang, X., and Qiu, D. 2012. Hrp1, a novel protein elicitor from necrotrophic fungus, *Alternaria tenuissima*, elicits cell death, expression of defence-related genes and systemic acquired resistance in tobacco. Plant Cell Environ. 35: 2104-2120.

Kusch, S., Larrouy, J., Ibrahim, H. M. M., Mounichetty, S., Gasset, N., Navaud, O., Mbengue, M., Zanchetta, C., Lopez-Roques, C., Donnadieu, C., Godiard, L., and Raffaele, S. 2022. Transcriptional response to host chemical cues underpins the expansion of host range in a fungal plant pathogen lineage. ISME J. 16:138-148.

Lahfa, M., Barthe, P., de Guillen, K., Cesari, S., Raji, M., Kroj, T., Le Naour—Vernet, M., Hoh, F., Gladieux, P., Roumestand, C., Gracy, J., Declerck, N., and Padilla, A. 2024. The structural landscape and diversity of *Pyricularia oryzae* MAX effectors revisited. PLoS Pathog. 20:e1012176.

Li, J., Zhang, Y., Zhang, Y., Yu, P.-L., Pan, H., and Rollins, J. A. 2018. Introduction of large sequence inserts by CRISPR-Cas9 to create pathogenicity mutants in the multinucleate filamentous pathogen *Sclerotinia sclerotiorum*. mBio 9:e00567-18.

Liang, Y., Xiong, W., Steinkellner, S., and Feng, J. 2018. Deficiency of the melanin biosynthesis genes *SCD1* and *THR1* affects sclerotial development and vegetative growth, but not pathogenicity, in *Sclerotinia sclerotiorum*. Mol. Plant Pathol. 19:1444-1453.

Liang, Y., Yajima, W., Davis, M. R., Kav, N. N. V., and Strelkov, S. E. 2013. Disruption of a gene encoding a hypothetical secreted protein from *Sclerotinia sclerotiorum* reduces its virulence on canola (*Brassica napus*). Can. J. Plant Pathol. 35:46-55.

Liao, Y., Smyth, G. K., and Shi, W. 2014. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30:923-930.

Lin, L., Tijjani, I., Guo, H., An, Q., Cao, J., Chen, X., Liu, W., Wang, Z., and Norvienyeku, J. 2023. Cytoplasmic dynein1 intermediate-chain2 regulates cellular trafficking and physiopathological development in *Magnaporthe oryzae*. iScience 26:106050.

Liu, H., Zhang, W., He, Q., Aikemu, R., Xu, H., Guo, Z., Wang, L., Li, W., Wang, G., Wang, X., and Guo, W. 2024. Re-localization of a repeat-containing fungal effector by apoplastic protein chitinase-like 1 blocks its toxicity. Nat. Commun. 15:10122.

Liu, M., Duan, L., Wang, M., Zeng, H., Liu, X., and Qiu, D. 2016. Crystal structure analysis and the identification of distinctive functional regions of the protein elicitor Mohrip2. Front. Plant Sci. 7:1103.

Love, M. I., Huber, W., and Anders, S. 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15:550.

Lovelace, A. H., Dorhmi, S., Hulin, M. T., Li, Y., Mansfield, J. W., and Ma, W. 2023. Effector identification in plant pathogens. Phytopathology 113:637-650.

Lyu, X., Shen, C., Fu, Y., Xie, J., Jiang, D., Li, G., and Cheng, J. 2016. A small secreted virulence-related protein is essential for the necrotrophic interactions of *Sclerotinia sclerotiorum* with its host plants. PLoS Pathog. 12:e1005435.

Mbengue, M., Navaud, O., Peyraud, R., Barascud, M., Badet, T., Vincent, R., Barbacci, A., and Raffaele, S. 2016. Emerging trends in molecular interactions between plants and the broad host range fungal pathogens *Botrytis cinerea* and *Sclerotinia sclerotiorum*. Front. Plant Sci. 7:422.

Mesny, F., and Thomma, B. P. H. J. 2024. AMAPEC: Accurate antimicrobial activity prediction for fungal effector proteins. bioRxiv 574150.

Mousavi-Derazmahalleh, M., Chang, S., Thomas, G., Derbyshire, M., Bayer, P. E., Edwards, D., Nelson, M. N., Erskine, W., Lopez-Ruiz, F. J., Clements, J., and Hane, J. K. 2019. Prediction of pathogenicity genes
involved in adaptation to a lupin host in the fungal pathogens *Botrytis cinerea* and *Sclerotinia sclerotiorum* via comparative genomics. BMC Genomics 20:385.

Mwape, V. W., Mobegi, F. M., Regmi, R., Newman, T. E., Kamphuis, L. G., and Derbyshire, M. C. 2021. Analysis of differentially expressed *Sclerotinia sclerotiorum* genes during the interaction with moderately resistant and highly susceptible chickpea lines. BMC Genomics 22:333.

Navaud, O., Barbacci, A., Taylor, A., Clarkson, J. P., and Raffaele, S. 2018. Shifts in diversification rates and host jump frequencies shaped the diversity of host range among *Sclerotiniaceae* fungal plant pathogens. Mol. Ecol. 27:1309-1323.

Newman, T. E., Kim, H., Khentry, Y., Sohn, K. H., Derbyshire, M. C., and Kamphuis, L. G. 2023. The broad host range pathogen *Sclerotinia sclerotiorum* produces multiple effector proteins that induce host cell death intracellularly. Mol. Plant Pathol. 24:866-881.

Nie, H., Zhang, L., Zhuang, H., Yang, X., Qiu, D., and Zeng, H. 2019. Secreted protein MoHrip2 is required for full virulence of *Magnaporthe oryzae* and modulation of rice immunity. Appl. Microbiol. Biotechnol. 103:6153-6167.

O'Brien, D. P., Perez, A. C. S., Karst, J., Cannella, S. E., Enguéné, V. Y. N., Hessel, A., Raoux-Barbot, D., Voegele, A., Subrini, O., Davi, M., Guijarro, J. I., Raynal, B., Baron, B., England, P., Hernandez, B., Ghomi, M., Hourdel, V., Malosse, C., Chamot-Rooke, J., Vachette, P., Durand, D., Brier, S., Ladant, D., and Chenal, A. 2018. Calcium-dependent disorder-to-order transitions are central to the secretion and folding of the CyaA toxin of *Bordetella pertussis*, the causative agent of whooping cough. Toxicon 149:37-44.

Pan, Y., Wei, J., Yao, C., Reng, H., and Gao, Z. 2018. SsSm1, a cerato-platanin family protein, is involved in the hyphal development and pathogenic process of *Sclerotinia sclerotiorum*. Plant Sci. 270:37-46.

Pettersen, E. F., Goddard, T. D., Huang, C. C., Couch, G. S., Greenblatt, D. M., Meng, E. C., and Ferrin, T. E. 2004. UCSF Chimera – A visualization system for exploratory research and analysis. J. Comput. Chem. 25:1605-1612.

Peyraud, R., Mbengue, M., Barbacci, A., and Raffaele, S. 2019. Intercellular cooperation in a fungal plant pathogen facilitates host colonization. Proc. Natl. Acad. Sci. U.S.A. 116:3193-3201.

Praz, C. R., Bourras, S., Zeng, F., Sánchez-Martín, J., Menardo, F., Xue, M., Yang, L., Roffler, S., Böni, R., Herren, G., McNally, K. E., Ben-David, R., Parlange, F., Oberhaensli, S., Flückiger, S., Schäfer, L. K., Wicker, T., Yu, D., and Keller, B. 2017. AvrPm2 encodes an RNase-like avirulence effector which is conserved in the two different specialized forms of wheat and rye powdery mildew fungus. New Phytol. 213:1301-1314.

Ranjan, A., Westrick, N. M., Jain, S., Piotrowski, J. S., Ranjan, M., Kessens, R., Stiegman, L., Grau, C. R., Conley, S. P., Smith, D. L., and Kabbage, M. 2019. Resistance against *Sclerotinia sclerotiorum* in soybean involves a reprogramming of the phenylpropanoid pathway and up-regulation of antifungal activity targeting ergosterol biosynthesis. Plant Biotechnol. J. 17:1567-1581.

Rau, A., Gallopin, M., Celeux, G., and Jaffrézic, F. 2013. Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics 29:2146-2152.

Rausch, A. O., Freiberger, M. I., Leonetti, C. O., Luna, D. M., Radusky, L. G., Wolynes, P. G., Ferreiro, D. U., and Parra, R. G. 2021. FrustratometeR: An R-package to compute local frustration in protein structures, point mutants and MD simulations. Bioinformatics 37:3038-3040.

Rocafort, M., Bowen, J. K., Hassing, B., Cox, M. P., McGreal, B., de la Rosa, S., Plummer, K. M., Bradshaw, R. E., and Mesarich, C. H. 2022. The *Venturia inaequalis* effector repertoire is dominated by expanded families with predicted structural similarity, but unrelated sequence, to avirulence proteins from other plant-pathogenic fungi. BMC Biol. 20:246.

Sang, H., Chang, H.-X., and Chilvers, M. I. 2019. A *Sclerotinia sclerotiorum* transcription factor involved in sclerotial development and virulence on pea. mSphere 4:e00615-18.

Saunders, D. G. O., Win, J., Cano, L. M., Szabo, L. J., Kamoun, S., and Raffaele, S. 2012. Using hierarchical clustering of secreted protein families to classify and rank candidate effectors of rust fungi. PLoS One 7:e29847.

Schneider, D. R. S., Saraiva, A. M., Azzoni, A. R., Miranda, H. R. C. A. N., de Toledo, M. A. S., Pelloso, A. C., and Souza, A. P. 2010. Overexpression and purification of PWL2D, a mutant of the effector protein PWL2 from *Magnaporthe grisea*. Protein Expression Purif. 74:24-31.

Schumacher, J. 2016. DHN melanin biosynthesis in the plant pathogenic fungus *Botrytis cinerea* is based on two developmentally regulated key enzyme (PKS)-encoding genes. Mol. Microbiol. 99:729-748.

Schweke, H., Mucchielli, M.-H., Chevrollier, N., Gosset, S., and Lopes, A. 2022. SURFMAP: A software for mapping in two dimensions protein surface features. J. Chem. Inf. Model. 62:1595-1601.

Seifbarghi, S., Borhan, M. H., Wei, Y., Coutu, C., Robinson, S. J., and Hagedus, D. D. 2017. Changes in the *Sclerotinia sclerotiorum* transcriptome during infection of *Brassica napus*. BMC Genomics 18:266.

Seifbarghi, S., Borhan, M. H., Wei, Y., Ma, L., Coutu, C., Bekkaoui, D., and Hagedus, D. D. 2020. Receptor-like kinases BAK1 and SOBIR1 are required for necrotizing activity of a novel group of *Sclerotinia sclerotiorum* necrosis-inducing effectors. Front. Plant Sci. 11:1021.

Seong, K., and Krasileva, K. V. 2021. Computational structural genomics unravels common folds and novel families in the secretome of fungal phytopathogen *Magnaporthe oryzae*. Mol. Plant-Microbe Interact. 34:1267-1280.

Seong, K., and Krasileva, K. V. 2023. Prediction of effector protein structures from fungal phytopathogens enables evolutionary analyses. Nat. Microbiol. 8:174-187.

Shang, Q., Jiang, D., Xie, J., Cheng, J., and Xiao, X. 2024. The schizotrophic lifestyle of *Sclerotinia sclerotiorum*. Mol. Plant Pathol. 25: e13423.

Shang, S., Liu, G., Zhang, S., Liang, X., Zhang, R., and Sun, G. 2024. A fungal CFEM-containing effector targets NPR1 regulator NIMIN2 to suppress plant immunity. Plant Biotechnol. J. 22:82-97.

Shen, W., and Ren, H. 2021. TaxonKit: A practical and efficient NCBI taxonomy toolkit. J. Genetics Genomics 48:844-850.

Signal, B., and Kahlke, T. 2022. howAreWeStrandedHere: Quick determination of RNA-Seq strandedness. BMC Bioinform. 23:49.

Smid, M., Coebergh van den Braak, R. R. J., van de Werken, H. J. G., van Riet, J., van Galen, A., de Weerd, V., van der Vlugt-Daane, M., Bril, S. I., Lalmahomed, Z. S., Kloosterman, W. P., Wilting, S. M., Foekens, J. A., IJzermans, J. N. M., on behalf of the MATCH study group, Martens, J. W. M., and Sieuwerts, A. M. 2018. Gene length corrected trimmed mean of M-values (GeTMM) processing of RNA-seq data performs similarly in intersample analyses while improving intrasample comparisons. BMC Bioinform. 19:236.

Snelders, N. C., Petti, G. C., van den Berg, G. C. M., Seidl, M. F., and Thomma, B. P. H. J. 2021. An ancient antimicrobial protein co-opted by a fungal plant pathogen for in planta mycobiome manipulation. Proc. Natl. Acad. Sci. U.S.A. 118:e2110968118.

Snelders, N. C., Rovenich, H., Petti, G. C., Rocafort, M., van den Berg, G. C. M., Vorholt, J. A., Mesters, J. R., Seidl, M. F., Nijland, R., and Thomma, B. P. H. J. 2020. Microbiome manipulation by a soil-borne fungal plant pathogen using effector proteins. Nat. Plants 6:1365-1374.

Sperschneider, J., and Dodds, P. N. 2022. EffectorP 3.0: Prediction of apoplastic and cytoplasmic effectors in fungi and oomycetes. Mol. Plant-Microbe Interact. 35:146-156.

Sperschneider, J., Dodds, P. N., Gardiner, D. M., Singh, K. B., and Taylor, J. M. 2018a. Improved prediction of fungal effector proteins from secretomes with EffectorP 2.0. Mol. Plant Pathol. 19:2094-2110.

Sperschneider, J., Dodds, P. N., Singh, K. B., and Taylor, J. M. 2018b. ApoplastP: Prediction of effectors and plant proteins in the apoplast using machine learning. New Phytol. 217:1764-1778.

Sperschneider, J., Gardiner, D. M., Dodds, P. N., Tini, F., Covarelli, L., Singh, K. B., Manners, J. M., and Taylor, J. M. 2016. EffectorP: Predicting fungal effector proteins from secretomes using machine learning. New Phytol. 210:743-761.

Steinegger, M., and Söding, J. 2017. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 35:1026-1028.

Tang, L., Yang, G., Ma, M., Liu, X., Li, B., Xie, J., Fu, Y., Chen, T., Yu, Y., Chen, W., Jiang, D., and Cheng, J. 2020. An effector of a necrotrophic fungal pathogen targets the calcium-sensing receptor in chloroplasts to inhibit host resistance. Mol. Plant Pathol. 21:686-701.

Terhem, R. B., and van Kan, J. A. L. 2014. Functional analysis of hydrophobin genes in sexual development of *Botrytis cinerea*. Fungal Genet. Biol. 71:42-51.

Teufel, F., Almagro Armenteros, J. J., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, K. D., Winther, O., Brunak, S., von Heijne, G., and Nielsen, H. 2022. SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat. Biotechnol. 40:1023-1025.

Thumuluri, V., Almagro Armenteros, J. J., Johansen, A. R., Nielsen, H., and Winther, O. 2022. DeepLoc 2.0: Multi-label subcellular localization prediction using protein language models. Nucleic Acids Res. 50:W228-W234.

Thynne, E., Ali, H., Seong, K., Abukhalaf, M., Guerreiro, M. A., Flores-Nunez, V. M., Hansen, R., Bergues, A., Salman, M. J., Rudd, J. J., Kanyuka, K., Tholey, A., Krasileva, K. V., Kettles, G. J., and Stukenbrock, E. H. 2024. An array of *Zymoseptoria tritici* effectors suppress plant immune responses. Mol. Plant Pathol. 25:e13500.

Traag, V. A., Waltman, L., and van Eck, N. J. 2019. From Louvain to Leiden: Guaranteeing well-connected communities. Sci. Rep. 9:5233.
Urban, M., Cuzick, A., Seager, J., Wood, V., Rutherford, K., Venkatesh, S. Y., Sahu, J., Iyer, S. V., Khamari, L., De Silva, N., Martinez, M. C., Pedro, H., Yates, A. D., and Hammond-Kosack, K. E. 2022. PHI-base in 2022: A multi-species phenotype database for pathogen-host interactions. Nucleic Acids Res. 50:D837-D847.

van Kempen, M., Kim, S. S., Tumescheit, C., Mirdita, M., Lee, J., Gilchrist, C. L. M., Söding, J., and Steinegger, M. 2024. Fast and accurate protein structure search with Foldseek. Nat. Biotechnol. 42:243-246.

Varadi, M., Anyango, S., Deshpande, M., Nair, S., Natassia, C., Yordanova, G., Yuan, D., Stroe, O., Wood, G., Laydon, A., Žídek, A., Green, T., Tunyasuvunakool, K., Petersen, S., Jumper, J., Clancy, E., Green, R., Vora, A., Lutfi, M., Figurnov, M., Cowie, A., Hobbs, N., Kohli, P., Kleywegt, G., Birney, E., Hassabis, D., and Velankar, S. 2022. AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50:D439-D444.

Walsh, I., Martin, A. J. M., Di Domenico, T., and Tosatto, S. C. E. 2012. ESpritz: Accurate and fast prediction of protein disorder. Bioinformatics 28:503-509.

Wang, D., Zhang, D.-D., Song, J., Li, J.-J., Wang, J., Li, R., Klosterman, S. J., Kong, Z.-Q., Lin, F.-Z., Dai, X.-F., Subbarao, K. V., and Chen, J.-Y. 2022. *Verticillium dahliae* CFEM proteins manipulate host immunity and differentially contribute to virulence. BMC Biol. 20:55.

Wang, Y., Li, J., Xiang, S., Zhou, J., Peng, X., Hai, Y., Wang, Y., Li, S., and Wei, S. 2020. A putative effector UvHrip1 inhibits BAX-triggered cell death in *Nicotiana benthamiana*, and infection of *Ustilaginoidea virens* suppresses defense-related genes expression. PeerJ 8:e9354.

Wei, J., Zhou, Q., Zhang, J., Wu, M., Li, G., and Yang, L. 2024. Dual RNA-seq reveals distinct families of co-regulated and structurally conserved effectors in *Botrytis cinerea* infection of *Arabidopsis thaliana*. BMC Biol. 22:239.

Wei, W., Wu, X., Blahut-Beatty, L., Simmonds, D. H., and Clough, S. J. 2022a. Transcriptome profiling reveals molecular players in early soybean-*Sclerotinia sclerotiorum* interaction. Phytopathology 112:1739-1752.

Wei, W., Xu, L., Peng, H., Zhu, W., Tanaka, K., Cheng, J., Sanguinet, K. A., Vandemark, G., and Chen, W. 2022b. A fungal extracellular effector inactivates plant polygalacturonase-inhibiting protein. Nat. Commun. 13:2213.

Yan, X., Tang, B., Ryder, L. S., MacLean, D., Were, V. M., Eseola, A. B., Cruz-Mireles, N., Ma, W., Foster, A. J., Osés-Ruiz, M., and Talbot, N. J. 2023. The transcriptional landscape of plant infection by the rice blast fungus *Magnaporthe oryzae* reveals distinct families of temporally co-regulated and structurally conserved effectors. Plant Cell 35:1360-1385.

Yang, G., Tang, L., Gong, Y., Xie, J., Fu, Y., Jiang, D., Li, G., Collinge, D. B., Chen, W., and Cheng, J. 2018. A cerato-platanin protein SsCP1 targets

plant PR1 and contributes to virulence of *Sclerotinia sclerotiorum*. New Phytol. 217:739-755.

Yang, L.-N., Liu, H., Duan, G.-H., Huang, Y.-M., Liu, S., Fang, Z.-G., Wu, E.-J., Shang, L., and Zhan, J. 2020. The *Phytophthora infestans* AVR2 effector escapes R2 recognition through effector disordering. Mol. Plant-Microbe Interact. 33:921-931.

Yang, Y., Steidele, C. E., Rössner, C., Löffelhardt, B., Kolb, D., Leisen, T., Zhang, W., Ludwig, C., Felix, G., Seidl, M. F., Becker, A., Nürnberger, T., Hahn, M., Gust, B., Gross, H., Hückelhoven, R., and Gust, A. A. 2023. Convergent evolution of plant pattern recognition receptors sensing cysteine-rich patterns from three microbial kingdoms. Nat. Commun. 14:3621.

Yariv, B., Yariv, E., Kessel, A., Masrati, G., Chorin, A. B. Martz, E., Mayrose, I., Pupko, T., and Ben-Tal, N. 2023. Using evolutionary data to make sense of macromolecules with a "face-lifted" ConSurf. Protein Sci. 32:e4582.

Yu, X.-Q., Niu, H.-Q., Liu, C., Wang, H.-L., Yin, W., and Xia, X. 2024. PTI-ETI synergistic signal mechanisms in plant immunity. Plant Biotechnol. J. 22:2113-2128.

Zhang, H., Yohe, T., Huang, L., Entwistle, S., Wu, P., Yang, Z., Busk, P. K., Xu, Y., and Yin, Y. 2018. dbCAN2: A meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 46:W95-W101.

Zhang, W., Fraiture, M., Kolb, D., Löffelhardt, B., Desaki, Y., Boutrot, F. F. G., Tör, M., Zipfel, C., Gust, A. A., and Brunner, F. 2013. *Arabidopsis* RECEPTOR-LIKE PROTEIN30 and receptor-like kinase SUPPRESSOR OF BIR1-1/EVERSHED mediate innate immunity to necrotrophic fungi. Plant Cell 25:4227-4241.

Zhang, Y., Gao, Y., Liang, Y., Dong, Y., Yang, X., and Qiu, D. 2019. *Verticillium dahliae* PevD1, an Alt a 1-like protein, targets cotton PR5-like protein and promotes fungal infection. J. Exp. Bot. 70:613-626.

Zhang, Y., Liang, Y., Dong, Y., Gao, Y., Yang, X., Yuan, J., and Qiu, D. 2017. The *Magnaporthe oryzae* Alt A 1-like protein MoHrip1 binds to the plant plasma membrane. Biochem. Biophys. Res. Commun. 492: 55-60.

Zhou, R., Zhu, T., Han, L., Liu, M., Xu, M., Liu, Y., Han, D., Qiu, D., Gong, Q., and Liu, X. 2017. The asparagine-rich protein NRP interacts with the *Verticillium effector* PevD1 and regulates the subcellular localization of cryptochrome 2. J. Exp. Bot. 68:3427-3440.

Zhu, W., Wei, W., Fu, Y., Cheng, J., Xie, J., Li, G., Yi, X., Kang, Z., Dickman, M. B., and Jiang, D. 2013. A secretory protein of necrotrophic fungus *Sclerotinia sclerotiorum* that suppresses host resistance. PLoS One 8:e53901.

Zuo, N., Bai, W.-Z., Wei, W.-Q., Yuan, T.-L., Zhang, D., Wang, Y.-Z., and Tang, W.-H. 2022. Fungal CFEM effectors negatively regulate a maize wall-associated kinase by interacting with its alternatively spliced variant to dampen resistance. Cell Rep. 41:111877.