Modular promoter evolution shapes infection-specific transcription in oomycete effectors

doi:10.21203/rs.3.rs-7847175/v1

Download PDF

Research Article

Modular promoter evolution shapes infection-specific transcription in oomycete effectors

https://doi.org/10.21203/rs.3.rs-7847175/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Effector proteins are critical determinants of pathogenicity in oomycetes, mediating complex strategies to reprogram host cellular processes and avoid immune detection. Although secreted effectors are known to be induced during infection stages, the regulatory mechanisms driving their expression remain underexplored, particularly when compared to core metabolic genes involved in biological processes. In the current study, promoter regions of secreted effector-encoding and putative intracellular secreted proteins were systematically investigated across five oomycetes (Phytophthora infestans, Phytophthora sojae, Plasmopara halstedii, Pythium ultimum (Globisporangium ultimum) and Hyaloperonospora arabidopsidis). Using a combination of known transcription factor motif scanning (JASPAR) with de novo motif discovery, cis-regulatory elements were identified. These included motifs bound by stress-responsive transcription factors such as C2H2 zinc finger, bZIP, AP2/ERF, MYB and homeodomain proteins. Effector-associated promoters displayed signatures in line with rapid transcriptional activation, including TATA-box, Initiator (Inr), FPR elements and novel diversified motifs, consistent with dynamic expression patterns observed during host-pathogen interactions. In contrast, structural genes showed promoter motifs linked to constitutive expression, such as CCAAT-box and repetitive motifs. Complementary amino acid composition analyses further revealed that several non-secreted proteins share residue-level profiles with experimentally validated effectors, suggesting potential effector-like functions that may have been overlooked due to unconventional secretion or annotation gaps. The identification of conserved regulatory signatures provides a foundation for uncovering core regulators of virulence and developing targeted pathogen control strategies.

Oomycete pathogens

cis-regulatory motifs

comparative promoter analysis

unconventional secretion

secreted genes

virulence gene expression

Secreted effectors, whether apoplastic or cytoplasmic, engage directly with host targets to suppress immunity or manipulate host functions (Kamoun 2006). In contrast, putative intracellular infection-enhancing candidates, if present, are likely to function via the regulation of expression and secretion of apoplastic and cytoplasmic effectors, thereby supporting infection indirectly. Other proteins not predicted to be secreted may also utilize unconventional protein secretion (UPS) pathways to enter the host cell, bypassing the classical secretion route such as ER-Golgi route (Liu et al. 2014) and still have a function in the interaction with targets in the plant. Examples of such UPS-associated components include autophagy-related proteins (Atgs), Rab GTPases, CARTS (carriers from the trans-Golgi network to the cell surface) and Golgi-associated proteins like GrpA (Malhotra 2013).

Oomycetes are filamentous eukaryotic pathogens, belonging to the kingdom Straminipila, a diverse clade that also includes brown algae and diatoms (Burki et al. 2007). Despite morphological resemblance to fungi, oomycetes evolved independently and have developed highly specialized mechanisms for host colonization (Beakes et al. 2012; Thines and Kamoun 2010; Thines 2014). Studies suggests that oomycetes have originated from a photosynthetic ancestor but underwent at least three independent events and lost plastid over the past 400 to 800 million years, ultimately adapting to a heterotrophic lifestyle (Matari and Blair 2014; Burki et al. 2020). Their lifestyles range from obligate biotrophy, as seen in Plasmopara halstedii and Hyaloperonospora arabidopsidis, to facultative necrotrophy in Pythium ultimum (syn. Globisporangium ultimum and hemibiotrophy (shift from biotrophic to necrotrophic stage) in Phytophthora infestans and Phytophthora sojae (Fawke et al. 2015). What unites these pathogens is their ability to secrete effector proteins, molecular tools that are delivered into host tissues to suppress immune responses and reprogram host cells, giving the pathogen a significant advantage during plant colonisation (Hein et al. 2009).

Promoter regions serve as critical regulatory hubs, containing cis-elements that respond to environmental and developmental signals to orchestrate precise, stage-specific gene expression. Although several transcription factor binding motifs (TFBS) have been reported in oomycetes, most existing studies have focused on individual genomes or specific gene families (Roy et al. 2013; Bharti et al. 2023). Prior analyses indicate that the initiator (Inr) element, typically located 100 to 400 base pairs upstream of the start codon, is commonly enriched in promoters of genes encoding secreted effectors and often substitutes the canonical TATA box (Seidl et al. 2012; Bharti and Thines 2023). Additionally, these studies have identified both Inr and the fungal promoter recognition (FPR) element, though the presence and role of the TATA box remain uncertain. Although the Inr element often serves as the primary regulatory signal replacing the TATA box in many oomycete genes, TATA motifs are likely still present in some gene subsets requiring efficient expression. So far, a systematic comparison of promoter architecture across oomycete species and secretion categories, particularly between classical and unconventional secretion, is still lacking. It was the aim of this study to contribute to filling this knowledge gap by the examination of complete genomes and one-to-one orthologous genes conserved across five oomycetes using a combination of de novo motif discovery and known motif annotation using JASPAR and ELM (Eukaryotic Linear Motif) databases.

The methods and computational framework of this study was designed to identify promoter architecture in secreted effector-encoding and structural genes across oomycete species (Fig. 1). The proteomes of five oomycete species, Plasmopara halstedii (GCA_900000015.1), Phytophthora sojae (GCA_000149755.1), Phytophthora infestans (GCA_000142945.1), Hyaloperonospora arabidopsidis (GCA_000173235.2) and Pythium ultimum (GCA_000143045.1), were obtained from the Ensembl Protists release 59 (Yates et al. 2022). To identify secretion signal in proteins, SignalP 6.0 was employed. This tool uses transformer protein language model to detect N-terminal signal peptides, specifically distinguishing three functional segments: the positively charged N-region, the hydrophobic H-region and the C-region defined by the cleavage site, which is recognized by signal peptidases (Teufel et al. 2022). Subsequently, transmembrane domains were predicted using DeepTM, classifying residues into topological states (Li et al. 2023). Based on the outputs from these predictions, proteins were categorized into structural types, including SP + Glob (signal peptide with globular domain), SP + TM (signal peptide with transmembrane domain), TM only and Glob only.

Prediction and classification of effectors-encoding genes

Effector candidates were identified using the Prediction of Oomycete Effectors (POOE) tool, which employs an SVM classifier trained on protein language model embeddings optimized for oomycete sequences (Zhao et al. 2024). Proteins with a specificity score > 0.9 were retained as high-confidence effectors. Complementary detection was performed with WideEffHunter v1.0, a hybrid pipeline integrating HMM-based motif/domain scanning, PHI-base homology using BLASTp and cysteine content profiling (Carreón-Anguiano et al. 2022). Proteins under 300 amino acids with > 3% cysteine were considered cysteine-rich effectors. Candidates were further classified into motif-only, domain-only, PHI-only, or unknown categories based on the presence of canonical features matched with known and validated effector genes (e.g., RXLR, CRN, TPR, HAT domains).

Amino acid composition and positional analysis

To compare the physiochemical features among the groups of secreted proteins, non-secreted proteins and validated effectors from literature (Sperschneider et al. 2016; Carreón-Anguiano et al. 2022), the amino acid frequencies were calculated. For the positional comparisons across groups, two regions obtained from SignalP 6.0, the N-terminal signal peptide (approximately the first 25–30 residues) and the conserved effector region (up to 150 residues) were considered. Frequencies of individual amino acids as well as grouped biochemical classes (polar, hydrophobic, negatively charged, positively charged, and aromatic) were determined using custom Python and R scripts. Positional heatmaps were produced in R with ggplot2, and amino acid group percentages were summarized for each protein category (secreted, non-secreted, validated effectors).

Identification of orthologous genes

Strict one-to-one orthologs across the five oomycete species were identified using automated shell scripts and Ensembl REST APIs, with paralogs excluded from analysis (Yates et al. 2015). Protein domains were annotated using Ensembl’s InterProScan pipeline, integrating PFAM, SMART, PROSITE and SUPERFAMILY databases (Finn et al. 2017). Additional structural features were predicted via Ensembl-integrated tools: coiled-coil domains (ncoils), signal peptides (SignalP v3.0) and transmembrane regions (TMHMM). Transcriptome data (PRJEB49134) from Bharti and Thines (2023) was incorporated to identify orthologs with conserved pathogenicity-associated expression (Krogh et al. 2001; Dyrløv Bendtsen et al. 2004; Bharti et al. 2023; Dyer et al. 2025). Principal component analysis (PCA) and differential gene expression (DGE) were performed using DESeq2 on 16 time points to capture temporal expression dynamics (Love et al. 2014).

Motif discovery and promoter architecture analysis

Promoter regions extending 1,000 bp upstream of the transcription start site were extracted for all retained orthologs (Yates et al. 2015). De novo motif discovery was conducted using MEME Suite under ZOOPS (Zero or One Occurrence Per Sequence) and OOPS (One Occurrence Per Sequence) models, identifying up to 10 motifs (6–18 bp) per group, with significance defined by q-values < 0.05 (Bailey et al. 2009). A first-order Markov model from shuffled sequences served as background. Identified motifs were scanned across sequences using FIMO and MAST (Bailey and Gribskov 1998; Grant et al. 2011) and functionally annotated using GOMO (Gene Ontology for Motifs) and AMA (Average Motif Affinity) for GO term association (Buske et al. 2010). Further comparisons against JASPAR 2022 and ELM 2024 databases (Dinkel et al. 2012; Kumar et al. 2024) were performed using Tomtom to assign potential TF binding identities and short linear motifs responsible for post-translational modifications (Gupta et al. 2007; Rauluseviciute et al. 2024). Only motifs passing both E-value and q-value thresholds (< 0.05) were retained. Motif clustering and phylogenetic analysis were visualized via motifStack and KEGG Mapper (Ou et al. 2018; Kanehisa and Sato 2020). GO enrichment was performed on conserved, differentially expressed Pl. halstedii genes with associated transcription factors.

Across all five species, the majority of genes were classified as non-effectors with signal peptide and globular domains (SP + Glob) via SignalP6.0, comprising ~ 59–72% of the proteomes (Fig. 2a). Effectors accounted for a smaller, yet biologically significant fraction (~ 22–32%). Within the non-secreted group, the subset of effector-encoding genes was consistently limited (~ 4.6–7.3% across species).

Orthologous protein group analysis

Orthologous protein analysis revealed varying conservation across the oomycete species. The largest conserved group was shared by all five species (Pl. halstedii ∩ Ph. sojae ∩ Ph. infestans ∩ Py. ultimum ∩ Hy. arabidopsidis; Fig. 2c), comprising 598 orthologs. Orthologous groups containing only two species ranged from 44 (Phal ∩ Harab) to 435 (Phal ∩ Pinf). Among these, 151 were classified as putative effectors, while the remainder represented non-effector genes (Fig. 2d). Effector prediction using the POOE tool showed a clear separation between effectors and non-effectors, with higher prediction and confidence scores for the effector class (Fig. 2e–g). These classifications provided the foundation for downstream analyses of transcription factor binding sites (TFBSs) and promoter architecture, which combined de novo motif discovery with known motif matches from the JASPAR database.

Identification of effector proteins and secretome analysis

To explore whether effector proteins display conserved sequence signatures across oomycetes, amino acid composition was analyzed in both orthologous gene groups shared among the five species (Fig. 3) and in their complete proteomes (Fig. S1). In the orthologous gene set (Fig. 3a), signal peptide (SP) regions exhibited a strong enrichment of hydrophobic (L, I, V, M), polar uncharged residue (S) and small residues (A). Quantitative summaries (Fig. 3b) confirmed that secreted effectors consistently carried higher proportions of hydrophobic (L) and small residues (A) in the SP, whereas validated effectors showed marked enrichment for polar uncharged (S) and small residues (A) in their conserved domains. When extended to the full proteomes of each species (Fig. S1a), similar patterns were observed. Signal peptides across all five oomycetes retained the canonical hydrophobic and positively charged composition, underscoring the universality of secretion signals (Fig. S1b).

Stage-specific transcriptional reprogramming and regulatory motif associations

Principal component analysis (PCA) of Pl. halstedii transcriptome profiles revealed a clear separation of samples into distinct developmental stages, explaining a substantial proportion of variance (PC1 = 49%, PC2 = 29%) (Fig. 2b). Zoospore samples (T5min, T15min) clustered tightly apart from later infection phases, reflecting their unique transcriptional state. Early infection stages (T4h–T12h) grouped together, followed by colonization samples (T24h–T120h) and finally sporulation stages (T288h–T296h), which formed a separate cluster. This progression illustrates dynamic transcriptional reprogramming aligned with pathogen development and host colonization. Integration of conserved promoter motifs with expression profiles revealed distinct regulatory modules associated with infection phases (Fig. 4b). Expression analysis across time points showed that effector-associated orthologs were most strongly induced during infection (IF) and colonization (CO) stages, with a gradual decline during sporulation (SP). While effector orthologs tended to peak during these infection-associated stages, non-effector orthologs exhibited a broader mix of expression patterns without clear enrichment for any single developmental stage, and many were linked to core metabolic or housekeeping functions such as ribosome biogenesis, mitochondrial carriers, redox enzymes. No pronounced enrichment of cysteine-rich proteins was detected in either group; across orthologs (Fig. 4b) and in the whole-proteome analyses except G. ultimum (Fig. S1), cysteine content distributions were broadly similar between secreted and non-secreted proteins.

Across orthologous groups, promoters of secreted genes did not yield a consistent, cross-species motif signature using either de novo discovery or JASPAR/Tomtom mapping (i.e., “unknown” motif identity). By contrast, non-secreted orthologs showed recurrent matches to varying effector categories (EC) and were associated with stress- and regulation-linked transcription factors families (bHLH/bZIP/AP2-like/homeobox/zinc finger; Fig. 4b), and their promoter motif profiles overlapped with those observed in validated effectors (Fig. 3a). This concordance was supported at the protein level: non-secreted orthologs exhibited amino-acid composition patterns closer to validated effectors than to secreted ortholog. In addition, the motifs showed strong enrichment of helix-loop-helix (bHLH), zinc finger (C2H2-type), AP2/EREBP and bZIP regulators, with additional contributions from leucine zipper and homeobox factors (Fig. 4a, Fig. S2). Effector orthologs were enriched for short linear motifs or ELMs linked to host interaction, stress resilience and intracellular trafficking, whereas non-effector orthologs predominantly contained motifs associated with cytoskeletal maintenance, nuclear transport and general cellular homeostasis (File S1).

Genome-wide promoter motif analysis and positional conservation

The genome-wide promoter scan across all five oomycete species (Fig. 5a) revealed a broad distribution of motifs across promoter bins (along the − 1,000 bp upstream regions), with motifs enrichment of clear positional preferences. Importantly, the analysis included both secreted and non-secreted genes, enabling identification of general cis-regulatory patterns that are conserved across species. Beyond enrichment, motifs exhibited clear positional preferences (Fig. 5a). Effector-like signatures were supported at both the protein and promoter levels, as genome-wide analyses revealed that non-secreted genes shared promoter motif patterns more closely aligned with validated effectors than with secreted genes. By contrast, the analysis restricted to validated effector genes (Fig. 5b) showed sharper positional conservation de novo motifs at defined upstream intervals. Several effector-associated motifs, including RXLR- and Crinkler-linked elements, were enriched within narrow upstream windows (250–400 bp from the ATG). This positional bias indicates that effectors are under tighter transcriptional control, likely reflecting their need for rapid and stage-specific activation during infection.

Effector proteins remain a central focus in understanding oomycete virulence. While the classical view emphasizes secreted effectors such as RxLRs, CRNs, elicitins and NLP toxins. In the present study, the framework was expanded by examining transcriptional regulation of both secreted and non-secreted proteins with potential roles in virulence (Kamoun 2006; Liu et al. 2014; Dulal and Wilson 2024). Among genes carrying signal peptides with globular domains (SP + Glob), Phytophthora infestans (3.4%) and Phytophthora sojae (1.7%) exhibited the highest proportions of putative secreted effectors, while Pythium ultimum (syn. Globisporangium ultimum) displayed the lowest (0.3%). This subset likely represents the most probable “true effectors” that directly engage host targets.

Importantly, across all five species, a consistently small yet notable fraction of non-secreted effector-encoding genes (~ 4.6–7.3%) was also identified (Fig. 2a). These non-secreted candidates are particularly intriguing, as their promoter architectures and amino acid compositions resembled those of validated effectors more than those of secreted orthologs. This convergence suggests that non-secreted proteins may represent an underexplored component of oomycete pathogenicity, potentially acting via unconventional secretion pathways or conserved effector-like regulatory programs.

In a study, G. ultimum showed minimal classical RXLR and Crinkler content, reflecting distinct evolutionary pressures in this necrotrophic oomycete (Lévesque et al. 2010). Downstream analyses revealed lineage-specific conservation of RXLR domains, which were absent in G. ultimum, suggesting that this typical effector group was either lost during evolution or had not yet evolved in this species (File S1). Using high-confidence effector predictions across five species, this study uncovered regulatory motifs that are significantly enriched in the promoter regions of secreted effectors. These included canonical elements such as TATA-box and Inr + FPR, as well as stress-responsive motifs bound by C2H2 zinc finger, bZIP, MYB, AP2/ERF and homeodomain TFs. In addition, motifs such as CREB-like, G-box and W-box were frequently identified across orthologous effector clusters, suggesting conserved regulatory circuits (File S1). Strikingly, the promoter motif architecture of Py. ultimum non-secreted genes showed positional overlaps with validated effectors: motifs at ~ 100–150 bp upstream resembled the canonical effector-associated signatures (Fig. 5a–b), and additional enrichments were observed at ~ 350 bp and 400–450 bp, aligning with validated effector motifs. This positional concordance suggests that despite lacking classical RXLR/CRN repertoires, Py. ultimum retains effector-like regulatory programs that may contribute to its pathogenic strategy.

Promoter architectures of structural genes, those not directly classified as effectors, exhibited features consistent with steady-state expression, including enrichment of CCAAT- and other repetitive motifs commonly linked to metabolic regulation. Several structural proteins involved in essential cellular processes (such as RNA polymerase subunits, proteasome components, ribosomal proteins) carried highly conserved, motif-rich promoter regions across all species. While these genes are unlikely to function as effectors, their conserved promoter complexity suggests that distinct cis-elements govern the regulation of different protein classes, reflecting the need for tight control of both pathogenicity-related and housekeeping functions (Fig. 4b).

In the current study, secreted proteins retained canonical signal peptide features, non-secreted orthologs displayed amino acid composition patterns and promoter motif architectures that were more similar to validated effectors than to secreted genes. This convergence at both the protein and regulatory levels suggests that non-secreted orthologs may share effector-like properties, potentially reflecting unconventional secretion or conserved regulatory programs for host interaction. This further emphasizes the need to re-evaluate effector prediction frameworks, incorporating regulatory architecture alongside protein features.

Cysteine-rich profiles are often linked to effector functionality, especially in fungal pathogens, where cysteine content enhances stability, structural specificity and resistance to plant defenses. Fungal effectors typically exceed four cysteine residues, while oomycete effectors have lower counts, with nearly 36% being devoid of cysteines altogether (Stergiopoulos and de Wit 2009; Sperschneider et al. 2016). Notably, no major differences in cysteine richness were observed between oomycete proteomes and validated effectors, with the exception of G. ultimum. This indicates that cysteine content is not a reliable discriminator of pathogenic function across oomycetes with diverse lifestyles.

Overall, this study advances the understanding of effector regulation by providing a systematic, motif-level view of oomycete promoter architectures. The identification of conserved regulatory elements provides valuable leads for functional characterization of master regulators and offers potential targets for broad-spectrum pathogen control. These findings highlight positional conservation as a regulatory layer for effector gene expression.

This study uncovers distinct promoter architectures that differentiate effector and non-effector genes in pathogenic oomycetes. Secreted effectors are marked by dynamic, stress-responsive motifs like Inr + FPR, TATA box and bZIP/AP2-like (GCC-box) elements. By contrast, a small notable set of structural and putative non-secreted genes are enriched in constitutive expression, including motifs (CCAAT- and repetitive motifs) involved in metabolism, translation and protein homeostasis. The amino acid compositions and promoter architectures more closely aligned with validated effectors than with secreted orthologs, suggesting that unconventional secretion or effector-like regulation may play underappreciated roles in pathogenicity. The discovery of novel, degenerate motifs and conserved regulatory signatures across orthologous effector groups points to a modular promoter logic. Importantly, cysteine richness was not a consistent discriminator across species, with only limited divergence observed in Pythium ultimum. Instead, motif positional conservation within 100–500 bp upstream of the transcription start site emerged as a key regulatory layer, underscoring the precision of effector gene activation during infection.

Ethics approval

Not applicable

Consent for publication

The publisher has the authors’ permission to publish their research findings.

Funding

SB and MT were supported by LOEWE in the framework of The Centre for Translational Biodiversity Genomics (TBG), funded by the government of Hessen. The funder had no role in the study design, data collection and interpretation, nor in the decision to submit the work for publication or preparation of the manuscript.

Data availability

All scripts and datasets generated and analyzed in this study are publicly available through a GitHub repository at https://github.com/sakshianil/POG. The repository contains all computational analysis codes and associated data files relevant to the research.

Competing interests

The authors declare no competing interests.

Author Contributions

SB conceived, designed and carried out the computational analysis, interpreted the results and wrote the manuscript. MT edited and approved the final version of the manuscript.

Acknowledgements

SB received support for publishing through Goethe University. The funder did not participate in the study design, data collection, data interpretation, decision to submit the work for publication or preparation of the manuscript. We express our gratitude to the Thines laboratory for helpful suggestions. We thank LOEWE for providing the computational resources. We gratefully acknowledge Nikolaus Jeremic for technical assistance with high-performance computing resources.

Auron PE, Webb AC, Rosenwasser LJ et al (1984) Nucleotide sequence of human monocyte interleukin 1 precursor cDNA. Proc Natl Acad Sci USA 81:7907–7911. https://doi.org/10.1073/pnas.81.24.7907
Bailey TL, Boden M, Buske FA et al (2009) MEME Suite: Tools for motif discovery and searching. Nucleic Acids Res 37. https://doi.org/10.1093/nar/gkp335
Bailey TL, Gribskov M (1998) Combining evidence using p-values: application to sequence homology searches. Bioinformatics 14:48–54. https://doi.org/10.1093/bioinformatics/14.1.48
Beakes GW, Glockling SL, Sekimoto S (2012) The evolutionary phylogeny of the oomycete ‘fungi’. Protoplasma 249:3–19. https://doi.org/10.1007/s00709-011-0269-2
Bharti S, Ploch S, Thines M (2023) High-throughput time series expression profiling of Plasmopara halstedii infecting Helianthus annuus reveals conserved sequence motifs upstream of co-expressed genes. BMC Genomics 24:140. https://doi.org/10.1186/s12864-023-09214-7
Bharti S, Thines M (2023) Conservation of putative transcription factor binding sites of co-expressed Plasmopara halstedii genes in two Phytophthora species. Mycological Progress 22:63. https://doi.org/10.1007/s11557-023-01911-7
Bruns C, McCaffery JM, Curwin AJ et al (2011) Biogenesis of a novel compartment for autophagosome-mediated unconventional protein secretion. J Cell Biol 195:979–992. https://doi.org/10.1083/jcb.201106098
Burki F, Roger AJ, Brown MW, Simpson AGB (2020) The New Tree of Eukaryotes. Trends Ecol Evol 35:43–55. https://doi.org/10.1016/j.tree.2019.08.008
Burki F, Shalchian-Tabrizi K, Minge M et al (2007) Phylogenomics reshuffles the eukaryotic supergroups. PLoS ONE 2:e790. https://doi.org/10.1371/journal.pone.0000790
Buske FA, Bodén M, Bauer DC, Bailey TL (2010) Assigning roles to DNA regulatory motifs using comparative genomics. Bioinformatics 26:860–866. https://doi.org/10.1093/bioinformatics/btq049
Carreón-Anguiano KG, Todd JNA, Chi-Manzanero BH et al (2022) WideEffHunter: An Algorithm to Predict Canonical and Non-Canonical Effectors in Fungi and Oomycetes. Int J Mol Sci 23:13567. https://doi.org/10.3390/ijms232113567
Dinkel H, Michael S, Weatheritt RJ et al (2012) ELM–the database of eukaryotic linear motifs. Nucleic Acids Res 40:D242–D251. https://doi.org/10.1093/nar/gkr1064
Dulal N, Wilson RA (2024) Paths of Least Resistance: Unconventional Effector Secretion by Fungal and Oomycete Plant Pathogens. Mol Plant-Microbe Interactions® 37:653–661. https://doi.org/10.1094/MPMI-12-23-0212-CR
Dyer SC, Austine-Orimoloye O, Azov AG et al (2025) Ensembl 2025. Nucleic Acids Res 53:D948–D957. https://doi.org/10.1093/nar/gkae1071
Dyrløv Bendtsen J, Nielsen H, von Heijne G, Brunak S (2004) Improved Prediction of Signal Peptides: SignalP 3.0. J Mol Biol 340:783–795. https://doi.org/10.1016/j.jmb.2004.05.028
Fawke S, Doumane M, Schornack S (2015) Oomycete Interactions with Plants: Infection Strategies and Resistance Principles. Microbiol Mol Biol Rev 79:263–280. https://doi.org/10.1128/mmbr.00010-15
Finn RD, Attwood TK, Babbitt PC et al (2017) InterPro in 2017—beyond protein family and domain annotations. Nucleic Acids Res 45:D190–D199. https://doi.org/10.1093/nar/gkw1107
Grant CE, Bailey TL, Noble WS (2011) FIMO: Scanning for occurrences of a given motif. Bioinformatics 27:1017–1018. https://doi.org/10.1093/bioinformatics/btr064
Gupta S, Stamatoyannopoulos JA, Bailey TL, Noble WS (2007) Quantifying similarity between motifs. Genome Biol 8:R24. https://doi.org/10.1186/gb-2007-8-2-r24
Hein I, Gilroy EM, Armstrong MR, Birch PRJ (2009) The zig-zag-zig in oomycete-plant interactions. Mol Plant Pathol 10:547–562. https://doi.org/10.1111/j.1364-3703.2009.00547.x
Kamoun S (2006) A catalogue of the effector secretome of plant pathogenic oomycetes. Annu Rev Phytopathol 44:41–60. https://doi.org/10.1146/annurev.phyto.44.070505.143436
Kanehisa M, Sato Y (2020) KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci 29:28–35. https://doi.org/10.1002/pro.3711
Krogh A, Larsson B, von Heijne G, Sonnhammer EL (2001) Predicting transmembrane protein topology with a hidden markov model: application to complete genomes11Edited by F. Cohen. J Mol Biol 305:567–580. https://doi.org/10.1006/jmbi.2000.4315
Kumar M, Michael S, Alvarado-Valverde J et al (2024) ELM—the Eukaryotic Linear Motif resource—2024 update. Nucleic Acids Res 52:D442–D455. https://doi.org/10.1093/nar/gkad1058
Lévesque CA, Brouwer H, Cano L et al (2010) Genome sequence of the necrotrophic plant pathogen Pythium ultimum reveals original pathogenicity mechanisms and effector repertoire. Genome Biol 11:R73. https://doi.org/10.1186/gb-2010-11-7-r73
Li M, Wang H, Yang Z et al (2023) DeepTM: A deep learning algorithm for prediction of melting temperature of thermophilic proteins directly from sequences. Comput Struct Biotechnol J 21:5544–5560. https://doi.org/10.1016/j.csbj.2023.11.006
Liu T, Song T, Zhang X et al (2014) Unconventionally secreted effectors of two filamentous pathogens target plant salicylate biosynthesis. Nat Commun 5:4686. https://doi.org/10.1038/ncomms5686
Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. https://doi.org/10.1186/s13059-014-0550-8. Genome Biology 15:
Malhotra V (2013) Unconventional protein secretion: an evolving mechanism. EMBO J 32:1660–1664. https://doi.org/10.1038/emboj.2013.104
Matari NH, Blair JE (2014) A multilocus timescale for oomycete evolution estimated under three distinct molecular clock models. BMC Evol Biol 14:101. https://doi.org/10.1186/1471-2148-14-101
Ou J, Wolfe SA, Brodsky MH, Zhu LJ (2018) motifStack for the analysis of transcription factor binding site evolution. Nat Methods 15:8–9. https://doi.org/10.1038/nmeth.4555
Rauluseviciute I, Riudavets-Puig R, Blanc-Mathieu R et al (2024) JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles. Nucleic Acids Res 52:D174–D182. https://doi.org/10.1093/nar/gkad1059
Roy S, Poidevin L, Jiang T, Judelson HS (2013) Novel core promoter elements in the oomycete pathogen Phytophthora infestans and their influence on expression detected by genome-wide analysis. BMC Genomics 14. https://doi.org/10.1186/1471-2164-14-106
Seidl MF, Wang RP, van den Ackerveken G et al (2012) Bioinformatic Inference of Specific and General Transcription Factor Binding Sites in the Plant Pathogen Phytophthora infestans. PLoS ONE 7. https://doi.org/10.1371/journal.pone.0051295
Sperschneider J, Gardiner DM, Dodds PN et al (2016) Effector P: predicting fungal effector proteins from secretomes using machine learning. New Phytol 210:743–761. https://doi.org/10.1111/nph.13794
Stergiopoulos I, de Wit PJGM (2009) Fungal Effector Proteins. Annu Rev Phytopathol 47:233–263. https://doi.org/10.1146/annurev.phyto.112408.132637
Teufel F, Almagro Armenteros JJ, Johansen AR et al (2022) SignalP 6.0 predicts all five types of signal peptides using protein language models. Nat Biotechnol 40:1023–1025. https://doi.org/10.1038/s41587-021-01156-3
Thines M, Kamoun S (2010) Oomycete-plant coevolution: Recent advances and future prospects. Curr Opin Plant Biol 13:427–433. https://doi.org/10.1016/j.pbi.2010.04.001
Thines M (2014) Phylogeny and evolution of plant pathogenic oomycetes-a global overview. Eur J Plant Pathol 138:431–447. https://doi.org/10.1007/s10658-013-0366-5
Yates A, Beal K, Keenan S et al (2015) The Ensembl REST API: Ensembl Data for Any Language. Bioinformatics 31:143–145. https://doi.org/10.1093/bioinformatics/btu613
Yates AD, Allen J, Amode RM et al (2022) Ensembl Genomes 2022: an expanding genome resource for non-vertebrates. Nucleic Acids Res 50:D996–D1003. https://doi.org/10.1093/nar/gkab1007
Zhao M, Lei C, Zhou K et al (2024) POOE: predicting oomycete effectors based on a pre-trained large protein language model. https://doi.org/10.1128/msystems.01004-23. mSystems 9:

No competing interests reported.

SupplementaryS1.xlsx
File S1 A detailed compilation of datasets generated and analyzed during the study. The file includes proteome summaries, ortholog group summaries, upstream motif discovery, protein domain motif discovery, motif genomic coordinates, conserved functional details, temporal expression profiles. This file integrates multi-layered analyses linking sequence conservation, motif discovery, regulatory function and gene expression dynamics, serving as a resource for further exploration of transcriptional regulation in oomycetes.
Supfigures.pptx
Fig. S1 Conserved amino acid positional biases across signal peptide and effector regions among secreted and non-secreted proteins in oomycetes (a) Heatmaps showing amino acid frequency distributions across the first 150 amino acid positions in secreted and validated effector proteins, across five oomycete genomes. Rows represent amino acid types (one-letter codes), and columns correspond to position along the protein sequence. Frequencies are square root-scaled and normalized row-wise to highlight conserved N-terminal features. Columns represent amino acid position in signal peptide (SP; 1 to 30 amino acids) and conserved region (31 to 150 amino acids). Species-wise breakdown (Plasmopara halstedii, Phytophthora sojae, Phytophthora infestans, Hyaloperonospora arabidopsidis, and Pythium ultimum). Secreted proteins in Ph. sojae, Ph. infestans and validated effectors, exhibit strong positional bias in the SP region, with residues such as alanine (A), leucine (L), and serine (S) being highly enriched. (b) Bar plots quantify the enriched amino acid groups (aromatic, hydrophobic, negatively charged, positively charged and small/other), at each of two regions: the signal peptide and conserved core effector domain. Comparisons are made between signal peptide (red) and conserved domain (cyan) of secreted encoding protein classes. shows both shared and species-specific amino acid biases. Fig. S2 Classification and frequency of transcription factor (TF) motif classes detected in oomycete effector gene promoters. Bar plot showing the distribution of transcription factor motif classes identified in the upstream regions of secreted effector-encoding genes across five oomycete species. Each bar represents a unique TF class-subclass combination, including domain types such as Helix-Loop-Helix (bHLH, HDF), Zinc Finger (C2H2-ZF, C4-ZF, Copper-fist), Leucine Zipper (bZIP), and Homeobox (CCAAT-binding, AP2/EREBP). The Helix-Loop-Helix and C2H2-ZF families were the most frequently detected, suggesting a dominant role in effector gene regulation. The category "Unknown" includes motifs with ambiguous or uncharacterized TF associations. Bar colors denote distinct TF families or subclasses for visual clarity.

Download PDF

Reviewers invited by journal
04 Nov, 2025
Editor assigned by journal
14 Oct, 2025
Submission checks completed at journal
14 Oct, 2025
First submitted to journal
13 Oct, 2025

You are reading this latest preprint version

Modular promoter evolution shapes infection-specific transcription in oomycete effectors

Status:

Version 1

Abstract

Figures

Introduction

Methods

Prediction and classification of effectors-encoding genes

Amino acid composition and positional analysis

Identification of orthologous genes

Motif discovery and promoter architecture analysis

Results

Orthologous protein group analysis

Identification of effector proteins and secretome analysis

Stage-specific transcriptional reprogramming and regulatory motif associations

Genome-wide promoter motif analysis and positional conservation

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1