Shifting Fungal Networks: How Dactylonectria macrodidyma Shapes Grapevine Mycobiome in Diverse Soils

Shifting Fungal Networks: How Dactylonectria macrodidyma Shapes Grapevine Mycobiome in Diverse Soils | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Shifting Fungal Networks: How Dactylonectria macrodidyma Shapes Grapevine Mycobiome in Diverse Soils Catarina Leal, Maria Julia Carbone, Ales Eichmeier, Thomas Kiss, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6156675/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the impact of Dactylonectria macrodidyma on fungal community dynamics in grapevines grown in sandy and clay soils, highlighting how soil properties influence pathogen-induced disruptions. High-throughput sequencing and microbial network analyses revealed that D. macrodidyma significantly reduces fungal diversity in root microbiomes, with the effect being more pronounced in sandy soils at later time points. The pathogen altered fungal community composition by displacing beneficial taxa such as Clonostachys and Trichoderma , while promoting pathogenic genera including Ilyonectria and Botrytis . SparCC network analysis indicated that D. macrodidyma increased competitive interactions in sandy soil, while fostering cooperative pathogenic networks in clay soil, reflecting distinct soil-dependent microbial responses. Additionally, functional guild prediction revealed a shift toward pathogenic dominance, with declines in symbiotrophic and saprotrophic fungi, suggesting potential consequences for nutrient cycling and microbial stability. These findings underscore the need for soil-specific disease management strategies in viticulture. Approaches such as organic amendments and biocontrol agents could help restore microbial diversity, promote beneficial taxa, and mitigate pathogen proliferation. This study provides critical insights into the ecological impact of D. macrodidyma on grapevine microbiomes, informing the development of targeted interventions to enhance plant health and sustainability in viticulture. Black-foot disease Microbial diversity Microbial networks pathogen-microbe interaction Soilborne fungal pathogens Vitis vinifera Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Grapevines form intricate associations with a diverse array of microorganisms, significantly enhancing plant growth, productivity, health, and the unique qualities of wine (Gilbert et al. 2014; Müller et al. 2016; Trivedi et al. 2020). The rhizosphere, the soil area near the plant roots enriched by root exudates and influenced by oxygen levels, serves as a critical habitat for a wide variety of microorganisms including bacteria, filamentous fungi, yeasts, and nematodes (Philippot et al. 2013; Reinhold-Hurek et al. 2015; Trivedi et al. 2020). These organisms confer several benefits to the plant, such as enhancing nutrient absorption through mechanisms like nitrogen fixation, mobilization of essential nutrients from minerals, and improved soil exploration, and increasing resistance to environmental and biological stresses (Trivedi et al. 2020). Microorganisms that inhabit the rhizosphere often originate from the surrounding soil, which acts as a microbial bank (Lennon and Jones 2011; Philippot et al. 2013). Plants actively select these microorganisms through the emission of rhizodeposits, a selective process influenced by the plant's own biochemical outputs (Philippot et al. 2013). Research has underscored that the plant species and genotype, as well as its age and developmental stage, significantly affect the structure and activity of rhizosphere microbiota. These influences are largely due to variations in the quantity and types of rhizodeposits produced (Philippot et al. 2013; Wagner et al. 2016; Gallart et al. 2018; Liu et al. 2022). In grapevines, it has been recently demonstrated that rootstock genotypes distinctly shape the microbial diversity within the rhizosphere (Marasco et al., 2018; D’Amico et al. 2018; Berlanas et al. 2019). Additionally, the structure of the grapevine-associated fungal microbiome in this niche undergoes complex, dynamic changes throughout the growing season (Liu and Howell 2021). Several factors influence the microorganisms living in the soil, including soil physicochemical properties, moisture content, biogeographical processes, and agricultural management practices (Fernández-Calviño et al. 2010; Burns et al. 2015; Zarraonaindia et al. 2015; Holland et al. 2016; Vink et al. 2021). These organisms fulfil diverse ecological functions. For example, soil fungi are crucial for the decomposition of organic residues, thereby enhancing soil fertility and supporting plant growth and development. Conversely, certain fungi act as soilborne pathogens, causing plant diseases that pose significant challenges for disease management due to their ability to survive in the soil for extended periods (Armengol and Gramaje 2016). Understanding the biology and epidemiology of soilborne fungal diseases is complex, given the intricacies of the soil environment (Koike et al. 2003). Grapevine is susceptible to several soilborne fungal diseases, including Armillaria root rot, Phytophthora crown and root rot, Verticillium wilt, as well as Petri and black foot (BF) diseases (Bettiga 2013). Black foot disease, in particular, is a critical component of the grapevine trunk disease complex and is a major contributor to young vine decline globally (Gramaje and Armengol 2011; Carlucci et al. 2017; Gramaje et al. 2018). This disease is caused by various species within the genera Dactylonectria , Ilyonectria , Neonectria , Thelonectria , Campylocarpon , and Cylindrocladiella , with Dactylonectria species being the most prevalent in regions such as Italy, Portugal, Spain, South Africa, Algeria, and Uruguay (Carlucci et al. 2017; Pintos et al. 2018; Berlanas et al. 2017; Langenhoven et al. 2018; Aigoun-Mouhous et al. 2019; Carbone et al. 2022). The typical internal symptoms of BF include wood necrosis starting from the base of the rootstock and sunken necrotic lesions on the roots (Halleen et al. 2006; Alaniz et al. 2007). Externally, affected vines exhibit reduced vigor, shortened internodes, delayed bud break, chlorotic foliage with necrotic margins, wilting, and often die within a short period (Agustí-Brisach and Armengol 2013). Nursery vines frequently become infected with BF pathogens during the rooting phase within nursery fields, a key stage in the propagation process (Gramaje and Armengol 2011). Research has consistently identified the nursery field as the primary source of inoculum for these pathogens (Halleen et al. 2007; Agustí-Brisach et al. 2013, 2014; Berlanas et al. 2017). BF pathogens produce conidia that disperse in soil water, while some species also generate chlamydospores, enhancing their ability to endure in soil for extended periods (Halleen et al. 2006; Petit et al. 2011). Additionally, certain soil conditions, such as poor drainage, high moisture content, and heavy texture, have been recognized as factors that promote BF incidence (Halleen et al. 2006). Substantial efforts have been made to mitigate the incidence of BF in grapevines. Although various control strategies, including chemical, physical, biological, and integrated approaches, have been employed, none have singularly achieved effective disease management (Eichmeier et al., 2018; Gramaje et al. 2018). In response, recent studies have shifted towards culture-independent methods, utilizing high-throughput amplicon sequencing (HTAS) to elucidate the complex microbial communities within the grapevine rhizosphere and root-endosphere across diverse environmental conditions (D´Amico et al. 2018; Marasco et al. 2018; Berlanas et al. 2019; Martínez-Diz et al. 2019; Liu and Howell 2021; Carbone et al. 2021). These studies also explore interactions within the grapevine-BF pathobiome (Berlanas et al. 2019; Carbone et al. 2021). However, the influence of BF pathogens, particularly fungi like D. macrodidyma , on native microorganisms remains poorly understood. In this work, we aim to examine the composition and interactions within the fungal microbiome of the rhizosphere and endosphere (root) of grapevine grafted plants inoculated with D. macrodidyma using ITS HTAS. We tested the following hypotheses: (1) The pathogen D. macrodidyma significantly alters the composition and structure of the fungal microbiome in the rhizosphere and endosphere of grafted grapevine plants, reducing microbial diversity in these niches, and (2) the presence of D. macrodidyma generates competitive and/or synergistic interactions with native microorganisms in the grapevine microbiome, affecting key ecological functions such as organic matter decomposition or resistance to environmental stress. MATERIALS AND METHODS Experimental Design and Treatments. In April 2019, one-year-old Tempranillo grapevine plants grafted onto 110 Richter rootstock were obtained from Villanueva Nursery in Navarra, Spain. These plants were then transferred into 11-liter pots containing two different soil types: clay loam (hereafter referred to as "clay") and sandy loam ("sandy") (Supplementary Table 1). The soil was collected from vineyards in Logroño, northern Spain, and classified following the U.S. Department of Agriculture's soil textural classification system. To prepare the soil, twenty bulk samples were randomly collected from inter-row areas at a depth of 40–50 cm, approximately one meter away from vine trunks—ten samples from each vineyard. These samples were homogenized in a greenhouse before use to ensure consistency. The potted cuttings were maintained in a greenhouse under natural conditions for 19 months. From April to November 2019 and March to November 2020, plants were irrigated every two days, whereas from December 2019 to February 2020, watering was reduced to once per week to simulate natural winter conditions. Greenhouse temperatures were regulated to resemble outdoor environmental conditions, allowing the plants to enter dormancy. Three months after planting (July 2019), a subset of 18 plants per soil type was inoculated with a conidial suspension (1 × 10⁵ conidia mL⁻¹) of Dactylonectria macrodidyma strain BV-0054 (Berlanas et al. 2020). Another group of 18 plants per soil type received distilled water as a control treatment. The experiment followed a completely randomized design, with irrigation adjusted according to the specific water requirements of each plant. Sampling of grapevine rhizosphere and roots. Rhizosphere and root samples from grapevine plants were collected at four different time points: before inoculation with Dactylonectria macrodidyma (T0), and at three (T1), nine (T2), and sixteen (T3) months post-inoculation. Sampling was conducted using a sterile spade, targeting areas near the stem at depths corresponding to the highest root density. Collected samples were immediately placed into sterile bags, stored on dry ice at the time of collection, and promptly transported to the laboratory for further processing. Rhizosphere soil attached to roots was separated following the methodology described by Berlanas et al. (2019), and the obtained rhizosphere fraction was stored at -80°C until DNA extraction. Roots were carefully washed with tap water, disinfected by immersion in 1% sodium hypochlorite for 30 seconds, and subsequently rinsed three times with distilled water. The cleaned roots were then stored at -80°C until DNA extraction. DNA extraction and sequencing. Rhizosphere DNA was extracted using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), while root endosphere DNA was obtained with the DNeasy Plant Pro Kit (Qiagen, Hilden, Germany), following the manufacturer's protocols. Prior to DNA extraction, the outer bark was carefully removed from the roots, and the peeled root tissues were ground to a fine powder in liquid nitrogen. DNA concentration from each sample was measured using the Invitrogen Qubit4 Fluorometer and the Qubit dsDNA HS Assay (Thermo Fisher Scientific, Waltham, USA), and extracts were standardized to a concentration of 10 ng/µL. After quantification, DNA samples were pooled in pairs, resulting in nine replicates per plant compartment, soil type, treatment (inoculated vs. non-inoculated), and sampling time, with the exception of Sampling T0. At T0, only four samples were collected per plant compartment and soil type, leading to two DNA replicates per compartment and soil type. In total, 224 DNA samples were analyzed. The fungal ITS2 region was amplified using the primers ITS86F (Turenne et al., 1999) and ITS4 (White et al., 1990), modified to include Illumina adapters. PCR reactions were carried out in a final volume of 25 μL, containing 2.5 μL of template DNA, 0.5 μM of each primer, 12.5 μL of Supreme NZYTaq 2x Green Master Mix (NZYTech, Lisboa, Portugal), and ultrapure water to reach the final volume. The thermal cycling conditions included an initial denaturation at 95°C for 5 minutes, followed by 35 cycles of 95°C for 30 seconds, 49°C for 30 seconds, and 72°C for 30 seconds, with a final elongation at 72°C for 10 minutes. A second PCR step was performed to attach oligonucleotide indices, maintaining the same conditions but with only five cycles and an annealing temperature of 60°C. Negative controls, containing no DNA, were included in all PCR rounds to monitor potential contamination during library preparation. PCR products were visualized on 2% agarose gels stained with GreenSafe (NZYTech, Lisboa, Portugal) and examined under UV light to confirm the expected library size. Libraries were purified using Mag-Bind RXNPure Plus magnetic beads (Omega Biotek, Norcross, GA, USA) according to the manufacturer's instructions. They were then pooled in equimolar amounts based on DNA concentration data obtained with the Qubit dsDNA HS Assay (Thermo Fisher Scientific, Waltham, USA). The pooled library was sequenced using an Illumina MiSeq PE300 run (Illumina, San Diego, USA). A positive control, consisting of DNA from a previously analyzed grapevine rhizosphere sample using ITS2 high-throughput amplicon sequencing (HTAS), was included (Martínez-Diz et al., 2019). All control samples underwent sequencing to assess potential contamination throughout the entire process. Data analysis followed the methodology described by Carbone et al. (2021). Data analysis of high-throughput amplification assay, fungal diversity, taxonomy distribution and statistical analysis. The sequencing data underwent quality assessment using FastQC 0.10.1. Further data processing was carried out with SEED 2.0 (Vetrovský et al., 2018). Forward and reverse raw reads for each sample were merged into paired-end sequences using fastq-join 1.1.2, a tool from the ea-tools suite (Aronesty, 2011). Quality filtering was then applied with a Q = 30 threshold, trimming sequences to a minimum length of 250 bases while discarding any ambiguous bases. Sequences were subsequently sorted according to barcode motifs and assigned to their respective sample names. To extract fungal ITS sequences, ITSx 1.0.11 (Bengtsson-Palme et al., 2013) was used. Next, sequences were clustered into operational taxonomic units (OTUs), and chimeric sequences were eliminated using Usearch-UPARSE 8.1.1861 (Edgar, 2013), applying a 97% pairwise identity threshold against the UNITE fungal dynamic database (Abarenkov et al., 2010). Consensus sequences for each OTU were generated using MAFFT 7.222 (Katoh et al., 2009). OTU identification was performed utilizing BLAST tools (blastn, tblastx, and makeblastdb 2.5.0+) through the NCBI BLAST platform (https://blast.ncbi.nlm.nih.gov/Blast.cgi). To facilitate sample comparability, the dataset was normalized using the total sum scaling (TSS) method. Alpha diversity was assessed using the Shannon and Chao1 indices, calculated through the Phyloseq package within the MicrobiomeAnalyst 2.0 platform (Lu et al., 2023). To evaluate sequencing depth and generate rarefaction curves, the rarefaction analysis tool in MicrobiomeAnalyst was utilized. To determine taxa with significant differences in relative abundance (at the genus level or higher) across treatments and sampling times, the Linear Discriminant Analysis Effect Size (LEfSe) method (Segata et al., 2011) was applied via MicrobiomeAnalyst. The analysis was conducted using a logarithmic Linear Discriminant Analysis (LDA) score threshold of 2.0, with a False Discovery Rate (FDR)-adjusted significance cutoff of P ≤ 0.1. Co-occurrence network analysis was performed to investigate potential interactions among genera, using the integrated Network Analysis Pipeline (iNAP) (Feng et al., 2022). Significant associations were identified based on a P value threshold of 0.05, with 120 permutations and a correlation cutoff of 0.3. The resulting network was visualized using Cytoscape version 3.10.0 (Shannon et al., 2003). Functional Prediction of Fungal Communities .The functional roles of fungal communities in root and rhizosphere samples from sandy and clay soils were analyzed at three time points (T1, T2, and T3) using FUNGuild v1.0, following the methodology outlined by Nguyen et al. (2016). Fungal OTUs were grouped into three primary trophic modes: pathotrophs, saprotrophs, and symbiotrophs. More specific functional guilds included plant pathogens, fungal parasites, lichen parasites, undefined saprotrophs, soil saprotrophs, wood saprotrophs, dung saprotrophs, plant saprotrophs, endophytes, and arbuscular mycorrhizal fungi. OTUs that could not be assigned to known taxa within the database were categorized as "unknown". To enhance classification reliability, only guild assignments with probable or highly probable confidence levels were included in the analysis. The relative abundance of OTUs within each functional guild was quantified across compartments (roots and rhizosphere), soil types (sandy and clay), and sampling times (T1–T3). To ensure comparability, all data were normalized to 100% within each sample. The potential influence of D. macrodidyma inoculation on fungal functional composition was examined using Welch’s t-test, comparing inoculated and non-inoculated treatments at each time point. Statistical significance was set at P < 0.05, with significant results highlighted in graphical representations. Quantification of Dactylonectria macrodidyma Inoculum Levels. Droplet digital PCR (ddPCR) was utilized to quantify D. macrodidyma inoculum levels, analyzing DNA extracted from both the rhizosphere and root endosphere. Specific primers and a probe targeting the histone 3 region were employed, following the protocol described by Hrycan et al. (2023). Each ddPCR reaction mixture contained 750 nM of each primer, 1× Supermix for Probes (Bio-Rad, Hercules, CA, USA), 250 nM of the probe, and 2 µL of DNA, bringing the final reaction volume to 20 µL. Droplet generation was performed using the QX200™ droplet generator (Bio-Rad). PCR amplification was carried out in a Bio-Rad C1000 Touch thermal cycler, starting with a 10-minute activation step at 95°C, followed by 40 cycles of 94°C for 30 seconds and 55°C for 60 seconds, concluding with a final extension at 98°C for 10 minutes. After amplification, the droplets were analyzed using the QX200™ droplet reader (Bio-Rad), and data interpretation was performed using QuantaSoft™ software (Bio-Rad). For calibration, DNA from the D. macrodidyma isolate BV-0054 was used as a standard, establishing a detection threshold of 3,000 based on prior evaluations with two positive controls—one containing DNA from a pure culture of BV-0054 and another from a grapevine root confirmed to harbor D. macrodidyma . A non-template control (water) was included to monitor potential contamination. Each sample was processed in triplicate to ensure result reliability and reproducibility. Following Bio-Rad's guidelines (https://www.bio-rad.com/webroot/web/pdf/lsr/literature/Bulletin_6407.pdf), wells with fewer than three positive droplets were classified as negative. RESULTS Sequencing Data and Diversity Estimates. Following paired-end alignment, quality filtering, and the removal of chimeric sequences and singletons, a total of 1,853,314 fungal ITS2 sequences were obtained from 215 samples (nine samples were removed from the analysis due to the low number of sequences read), clustering into 708 fungal OTUs. Based on Good’s coverage values, 99.943% of the total fungal species richness was captured, indicating comprehensive sequencing depth (Supplementary Table S2). The Chao1 richness estimator varied across different compartments and soil types, ranging from 30.11 to 59.27 in roots grown in sandy soil, 30.46 to 46.75 in roots from clay soil, 121 to 150 in the rhizosphere of sandy soil, and 48.7 to 164 in the rhizosphere of clay soil. Shannon diversity estimates followed a similar pattern, with values ranging from 1.0 to 2.3 in sandy-soil roots, 1.1 to 2.1 in clay-soil roots, 2.02 to 3.09 in the rhizosphere of sandy soil, and 2.21 to 3.41 in the rhizosphere of clay soil (Supplementary Table S3). All sequencing data have been deposited under BioProject accession number PRJNA1218043, with the Sequence Read Archive (SRA) experiment available under accession number SRR32220025. Impact of Dactylonectria macrodidyma on Fungal Alpha Diversity. The inoculation with Dactylonectria macrodidyma did not significantly altered the alpha diversity of fungal communities in both the root and rhizosphere microbiomes, (Figs. 1 and 2). In the root microbiome of sandy soil, inoculation with D. macrodidyma led to a significant decrease in fungal diversity at T3 (16 months) ( P < 0.05) (Fig. 1a). The non-inoculated control maintained a stable diversity throughout the experiment. In clay soil, the effect of D. macrodidyma inoculation on root fungal diversity was more pronounced at T2, where inoculated samples showed a significant reduction in alpha diversity ( P < 0.05) compared to controls (Fig. 1b). This difference was not significant at T3 (16 months). In the rhizosphere microbiome, the inoculation of D. macrodidyma did not affect microbiome fungal diversity at any time for any type of soil (fig. 2). In fact, the alpha-diversity in rhizosphere of inoculated and not inoculated plants was maintained stable for the entire duration of the experiment in both soil types. Changes in Fungal Community Composition Following Dactylonectria macrodidyma Inoculation. The introduction of D. macrodidyma resulted in notable shifts in the composition of the fungal community in both the root and rhizosphere microbiomes, particularly in relation to pathogenic and beneficial fungi (Fig. 3 and 4). High-throughput sequencing and linear discriminant analysis (LEfSe) revealed that D. macrodidyma not only dominated the fungal community in inoculated samples but also led to the suppression of key beneficial taxa. In the rhizosphere microbiome of sandy soil, D. macrodidyma slightly decreased in abundance at T2 and T3. This coincided with a decline in beneficial fungi such as Clonostachys . Cadophora , another genus involved in grapevine trunk diseases, also decrease its abundance over time (Fig. 3a). In clay soil, the establishment of D. macrodidyma was slower, with significant increases in its abundance observed only at T3 (Fig. 3). Despite this delayed effect, the pathogen still managed to outcompete other fungal species, particularly beneficial taxa such as Trichoderma , which were less abundant in inoculated treatments at T3. Temporal Changes in Fungal Abundance and Pathogen Dominance. The temporal dynamics of fungal abundance revealed that D. macrodidyma not only persisted in the fungal community over time but also progressively dominated the rhizosphere and root microbiomes of inoculated plants. In sandy soil, D. macrodidyma 's abundance increased sharply at T2 and remained high at T3, displacing other fungal taxa, including Clonostachys and Trichoderma (Fig. 4). In clay soil, the pathogen’s growth trajectory was slower but equally impactful by T3, with D. macrodidyma becoming one of the most abundant taxa in the rhizosphere microbiome (Fig. 4). Interestingly, the presence of D. macrodidyma also correlated with a reduction in beneficial fungal taxa, which declined significantly in inoculated samples compared to controls. LEfSe analysis confirmed that D. macrodidyma was a key determinant of fungal community structure in both soils, particularly at T3. In sandy soil, inoculated treatments were characterized by a higher relative abundance of pathogenic genera such as Diaporthe and Botrytis , while beneficial taxa were significantly underrepresented (Fig. 4). In clay soil, D. macrodidyma co-occurred with other pathogens such as Ilyonectria , contributing to the overall reduction in fungal diversity and the establishment of a pathogen-dominated community (Fig. 4). Fungal Interaction Networks: The Role of Dactylonectria macrodidyma in Microbial Dynamics. SparCC network analysis further highlighted the impact of D. macrodidyma on fungal community interactions, revealing that inoculation led to significant changes in the co-occurrence patterns of fungal taxa (Fig. 5). In both soil types, D. macrodidyma altered the structure and complexity of fungal networks, with a shift toward more competitive interactions as the pathogen established dominance. In sandy soil, inoculated treatments displayed an increase in negative correlations between fungal taxa, with D. macrodidyma driving a more competitive and less cooperative microbial network. By T3, the number of negative correlations in the rhizosphere microbiome of inoculated treatments had increased significantly compared to the control (237 negative correlations vs. 160 in the control) (Fig. 5). This suggests that the presence of D. macrodidyma led to heightened competition for resources, suppressing the growth of other fungi. In clay soil, the network dynamics were slightly different, with a greater number of positive correlations observed in inoculated samples at T3 (260 positive correlations vs. 138 in the control) (Fig. 5). This shift suggests that, in clay soil, D. macrodidyma promoted a more cooperative fungal community, potentially facilitating the survival of other pathogenic species. The network in clay soil was dominated by pathogenic genera such as Ilyonectria and Botrytis , which established stable associations with D. macrodidyma . Impact of Dactylonectria macrodidyma Inoculation on the Grapevine Root and Rhizosphere Microbiomes in Sandy and Clay Soils. To assess the effect of D. macrodidyma inoculation on the microbiomes of grapevine root and rhizosphere in both sandy and clay soils, the microbial loads (in copies/µl) were measured across inoculated and non-inoculated plants at different time points (T1, T2, and T3). The results were compared between root and rhizosphere samples in both soil types. Error bars represent standard deviations across biological replicates. Sandy Soil Root Microbiome. In sandy soil, the microbial load of D. macrodidyma exhibited variability depending on the time point and inoculation status (Fig. 6). At T1, the microbial load in inoculated plants was higher (approximately 17 copies/µl) compared to non-inoculated plants (~12 copies/µl), though the difference was not statistically significant based on error margins. By T2, both inoculated and non-inoculated plants showed a decline in microbial load, with inoculated plants showing a slight reduction to ~5 copies/µl, and non-inoculated plants at nearly 2 copies/µl. This trend continued at T3, with inoculated and non-inoculated plants maintaining low microbial loads of around 5 and 4 copies/µl, respectively. Rhizosphere Microbiome. In the sandy soil rhizosphere, a more pronounced effect of D. macrodidyma inoculation was observed (Fig. 6). At T1, inoculated plants exhibited a significantly higher microbial load (~38 copies/µl) compared to non-inoculated plants (~5 copies/µl). A similar trend was observed at T2, with inoculated plants maintaining a higher microbial load (~6 copies/µl) compared to non-inoculated plants (~2 copies/µl). By T3, the microbial loads in both inoculated (~7 copies/µl) and non-inoculated plants (~2 copies/µl) remained relatively stable, showing no major changes. Clay Soil Root Microbiome. In the clay soil root microbiome, D. macrodidyma inoculation showed a substantial impact, particularly at later time points (Fig. 6). At T1, the microbial load in inoculated plants was approximately 30 copies/µl, compared to a negligible load in non-inoculated plants. This difference was further amplified at T2, where inoculated plants exhibited a marked increase in microbial load (~40 copies/µl), while non-inoculated plants showed no significant change (~0 copies/µl). At T3, inoculated plants continued to show an elevated microbial load (~47 copies/µl), while the non-inoculated plants remained consistently low (~0 copies/µl). Rhizosphere Microbiome. In the clay soil rhizosphere, the impact of inoculation was even more evident (Fig. 6). At T1, the microbial load in inoculated plants reached approximately 60 copies/µl, while non-inoculated plants exhibited negligible microbial loads (~0 copies/µl). This trend persisted at T2, with inoculated plants showing the highest microbial load recorded (~72 copies/µl), and non-inoculated plants remaining low (~2 copies/µl). By T3, the microbial load in inoculated plants decreased to ~14 copies/µl, but remained substantially higher than non-inoculated plants (~5 copies/µl). Functional Prediction of Fungal Communities. The functional potential of fungal communities in the root and rhizosphere microbiomes of grapevine plants under D. macrodidyma inoculation was assessed by categorizing fungal taxa into functional guilds, including pathogens, saprotrophs, symbiotrophs, and unknown guilds (Figs. 7 and 8). The relative abundances of these guilds were analyzed across soil types (sandy vs. clay), compartments (root vs. rhizosphere), and time points (T1, T2, and T3), providing insight into the ecological shifts driven by D. macrodidyma inoculation. Root Microbiome Functional Composition. In the root microbiome of sandy soil, the relative abundance of pathogens increased over time in inoculated samples, reaching a significant peak at T3 (P < 0.05) compared to non-inoculated controls (Fig. 7). This was accompanied by a reduction in symbiotrophic fungi at T2 and T3, suggesting a potential disruption of beneficial fungal associations. The proportion of saprotrophs remained relatively stable across time points in both treatments. In clay soil roots, a similar trend was observed, with a significant increase in pathogenic fungi in inoculated samples at T2 (P < 0.05), which remained elevated at T3. Unlike in sandy soil, symbiotrophic fungi in clay soil did not exhibit a significant decline, suggesting that the effect of D. macrodidyma on beneficial fungi may be soil-dependent. The unknown guild was consistently the largest category across all conditions, with a slight increase in inoculated samples at T3. Rhizosphere Microbiome Functional Composition. The rhizosphere microbiome exhibited distinct functional responses compared to the root microbiome. In sandy soil, pathogen abundance significantly increased in inoculated treatments at T2 and T3 (P < 0.05), correlating with the dominance of D. macrodidyma (Fig. 8). Unlike in roots, saprotrophs showed a marked decline over time in inoculated samples, suggesting a shift in fungal community dynamics favoring pathogens over decomposers. The symbiotrophic guild was relatively stable across time points. In the clay soil rhizosphere, pathogen abundance was significantly elevated in inoculated samples at all time points, with the largest difference observed at T2 (P < 0.05). Interestingly, the symbiotrophic guild exhibited an increase at T3 in inoculated treatments, unlike the pattern observed in sandy soil. This suggests that in clay soil, certain symbiotic fungi may persist despite the dominance of D. macrodidyma. The saprotrophic guild remained low throughout the experiment, showing no significant differences between treatments. DISCUSSION Understanding the interactions between plant pathogens and microbial communities is essential to advance sustainable agriculture (Vishwakarma et al. 2020). In viticulture, microbial communities in the root and rhizosphere microbiomes play pivotal roles in plant health by mediating nutrient acquisition, promoting growth, and suppressing pathogens (Visconti et al. 2024). However, the introduction of aggressive pathogens like D. macrodidyma disrupts these microbial dynamics, reducing diversity and altering community composition (Fournier et al. 2022). This study aimed to elucidate the effects of D. macrodidyma on fungal community dynamics in grapevines grown in sandy and clay soils, providing critical insights into how soil properties influence pathogen impacts and microbial resilience. Our findings reveal that D. macrodidyma significantly alters fungal diversity, community composition, and microbial interaction networks over time. These changes are not uniform across soil types, highlighting the complexity of plant-microbe-pathogen interactions. Such disruptions in microbial communities have far-reaching implications for grapevine health and productivity, emphasizing the urgent need for targeted management strategies tailored to soil type and pathogen dynamics. Notably, D. macrodidyma inoculation reduced fungal alpha diversity in root microbiomes, with significant declines observed in sandy soil at T3 and in clay soil at T2. This sustained reduction in diversity underscores the pathogen’s competitive ability to displace other fungal taxa, particularly in environments with distinct soil properties. Sandy soils, characterized by lower organic matter content and reduced water retention, provide less buffering capacity against pathogen proliferation (Leal et al. 2024a). This finding aligns with prior research demonstrating that environmental stressors, such as drought, exacerbate pathogen dominance and disrupt microbial diversity (Carbone et al. 2021; Leal et al. 2024a). In contrast, the rhizosphere microbiome exhibited stable fungal diversity across soil types and time points, irrespective of inoculation. This stability reflects the inherent resilience of the rhizosphere microbiome, which benefits from the buffering capacity of the surrounding soil (Feng et al. 2024; Kaźmierczak et al. 2024). While root microbiomes rapidly respond to pathogen invasion through shifts in microbial community structure, the rhizosphere often shows delayed or less pronounced changes, as previously noted in studies on plant-microbe interactions (Kaźmierczak et al. 2024; Feng et al. 2024). The introduction of D. macrodidyma led to pronounced changes in fungal community composition, favoring pathogenic taxa while suppressing beneficial fungi. In sandy soil, D. macrodidyma proliferation coincided with reductions in beneficial taxa such as Clonostachys . Similarly, in clay soil, D. macrodidyma outcompeted Trichoderma . These findings support the hypothesis that pathogens exploit competitive advantages in specific soil environments to establish dominance, as noted by Bettenfeld et al. (2022). Beneficial fungi such as Clonostachys, and Trichoderma play vital roles in plant health by promoting growth, enhancing nutrient uptake, stimulating plant defenses, and suppressing pathogens through biocontrol mechanisms (Bahadoor et al. 2023; Woo et al. 2023). Their suppression has significant consequences for plant health and ecosystem functionality (Woźniak et al. 2019; Leal et al., 2024b). Further, LEfSe analysis confirmed that D. macrodidyma significantly influenced fungal community structure by selectively promoting pathogenic genera, including Ilyonectria . This genus often co-occurs with D. macrodidyma , creating pathogenic clusters that exacerbate grapevine trunk diseases (Probst et al. 2019). SparCC network analysis revealed substantial alterations in fungal co-occurrence patterns following D. macrodidyma inoculation. In sandy soils, the pathogen’s dominance led to an increased number of negative correlations among fungal taxa, indicative of heightened competition. This disruption of cooperative microbial networks intensifies resource competition and suppresses less competitive species (Trivedi et al. 2020), supporting our findings on alpha diversity. Conversely, in clay soils, an increase in positive correlations among fungal taxa suggests a shift toward cooperative interactions, which may facilitate the survival of other pathogens. This finding is consistent with Leal et al. (2024b), who demonstrated that clay soils foster stable associations among microbial taxa. The ability of clay soils to retain moisture and nutrients further supports microbial diversity and stability (Adeniji et al. 2024). Temporal abundance data highlight the persistence and dominance of D. macrodidyma in the grapevine microbiome. In sandy soils, the pathogen’s abundance increased sharply, displacing beneficial taxa such as Clonostachys and Trichoderma , corroborating findings by Carbone et al. (2021). In clay soils , D. macrodidyma established more slowly but ultimately led to similar reductions in fungal diversity and the proliferation of pathogenic genera like Ilyonectria and Botrytis . The slower shifts in clay soils reflect their higher cation exchange capacity and moisture retention, which provide a more stable environment for microbial communities (Riaz and Marschner, 2020). The functional prediction analysis further supports the role of D. macrodidyma in shifting microbial functions toward pathogenic dominance. Across both soil types, pathogens became significantly more abundant in inoculated samples, particularly at later time points, while symbiotrophic and saprotrophic fungi exhibited contrasting responses. In sandy soil, symbiotrophic fungi declined over time, suggesting a disruption of beneficial plant-microbe interactions, whereas in clay soil, they remained relatively stable, likely benefiting from the increased nutrient retention of the soil matrix (Leal et al. 2024a). In both rhizosphere and root compartments, the suppression of saprotrophs in inoculated treatments suggests a potential reduction in decomposition processes and nutrient cycling, which could have long-term effects on soil health and plant resilience (Finlay and Thorn 2019). Therefore, D. macrodidyma not only alters community composition but also shifts fungal functional roles, reinforcing its capacity to disrupt ecosystem stability. These findings underscore the need for targeted management strategies in viticulture. Soil amendments, such as organic matter addition, can enhance microbial diversity and resilience, potentially mitigating the impacts of pathogens like D. macrodidyma (Labarga et al. 2025). Biocontrol agents offer a promising approach to counteract pathogen dominance by promoting beneficial microbial taxa and restoring ecological balance (Leal et al. 2024b). Understanding the soil-dependent nature of pathogen dynamics is crucial for developing tailored interventions. For instance, clay soils may require strategies targeting cooperative pathogen interactions, while sandy soils could benefit from practices enhancing microbial competition to suppress pathogens. CONCLUSIONS This study highlights the profound impact of Dactylonectria macrodidyma on fungal community dynamics in grapevines and underscores the critical role of soil properties in shaping these interactions. Our findings demonstrate that D. macrodidyma significantly reduces fungal diversity, alters community composition, and disrupts microbial interaction networks, with its effects varying across sandy and clay soils. The pathogen’s dominance in sandy soils, characterized by rapid proliferation and displacement of beneficial fungi, reflects the vulnerability of these low-buffering soils to pathogen-induced disruptions. Conversely, while clay soils provided a more stable environment, D. macrodidyma ultimately led to cooperative pathogen interactions, further highlighting the complexity of plant-microbe-pathogen relationships in distinct soil environments. Additionally, D. macrodidyma not only reshapes fungal communities but also shifts their functional roles, increasing pathogen prevalence while suppressing symbiotrophic and saprotrophic fungi. This suggests potential consequences for nutrient cycling, decomposition, and plant resilience, reinforcing the need to consider both taxonomic and functional aspects of microbial communities in pathogen management. These results emphasize the need for soil-specific management strategies in viticulture to mitigate the impacts of aggressive pathogens. Interventions such as organic matter amendments and the application of biocontrol agents could enhance microbial diversity and resilience, counteracting pathogen dominance and restoring ecological balance. Additionally, understanding the temporal and spatial dynamics of microbial communities across soil types is essential for developing targeted and sustainable agricultural practices. Ultimately, this study provides valuable insights into the interplay between soil properties, microbial communities, and plant pathogens, offering a foundation for future research aimed at optimizing vineyard management practices and improving grapevine health in the face of pathogen challenges. Declarations Author contributions: CL and DG conceived and designed the study. CL, MJC and DG wrote the manuscript. CL, RB, LR and MJC run the greenhouse experiment and carried out the sampling. AE, TK, DT, CL and DG performed the bioinformatics and statistical analyses. All authors read and approved the final manuscript. Availability of data and material: The datasets generated and analysed during the current study are available in the NCBI Sequence Read Archive (SRA) under the BioProject number PRJNA1218043 (Acc. No. SRR32220025). The author(s) declare no conflict of interest. References Abarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. 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The rhizosphere microbiome and its beneficial effects on plants–current knowledge and perspectives. J. Adv. Microbiol. 58(1):59-69. Zarraonaindia, I., Owens, S. M., Weisenhorn, P., West, K., Hampton-Marcell, J., Lax, S., Bokulich, N., Mills, D., Martin, G., Taghavi, S., van der Lelie, D., and Gilbert, J. A. 2015. The soil microbiome influences grapevine-associated microbiota. MBio. 6(2):10-1128. Additional Declarations The authors declare no competing interests. Supplementary Files SuppTable1.jpg SuppTable2.jpg SuppTable3.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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09:06:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3313704,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6156675/v1/a2bfaf8d-fcde-42a2-a7e2-5972e4900357.pdf"},{"id":78130431,"identity":"2ed97e1f-ca81-45b9-a0e0-d3dc61744115","added_by":"auto","created_at":"2025-03-10 08:42:09","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":206540,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6156675/v1/cef6f1a39fc9ed26f9c46d79.jpg"},{"id":78130429,"identity":"32482539-391f-4344-8506-4b4c519f157e","added_by":"auto","created_at":"2025-03-10 08:42:09","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":684958,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6156675/v1/4eeba02cc8b90f30503843ba.jpg"},{"id":78130721,"identity":"fdf4a870-af58-4b35-9500-2a3858ed6b35","added_by":"auto","created_at":"2025-03-10 08:50:10","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":367318,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTable3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6156675/v1/8ca7ea88d338538428aa1bc2.jpg"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eShifting Fungal Networks: How \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eDactylonectria macrodidyma\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Shapes Grapevine Mycobiome in Diverse Soils\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION ","content":"\u003cp\u003eGrapevines form intricate associations with a diverse array of microorganisms, significantly enhancing plant growth, productivity, health, and the unique qualities of wine (Gilbert et al. 2014; Müller et al. 2016; Trivedi et al. 2020). The rhizosphere, the soil area near the plant roots enriched by root exudates and influenced by oxygen levels, serves as a critical habitat for a wide variety of microorganisms including bacteria, filamentous fungi, yeasts, and nematodes (Philippot et al. 2013; Reinhold-Hurek et al. 2015; Trivedi et al. 2020). These organisms confer several benefits to the plant, such as enhancing nutrient absorption through mechanisms like nitrogen fixation, mobilization of essential nutrients from minerals, and improved soil exploration, and increasing resistance to environmental and biological stresses (Trivedi et al. 2020).\u003c/p\u003e\n\u003cp\u003eMicroorganisms that inhabit the rhizosphere often originate from the surrounding soil, which acts as a microbial bank (Lennon and Jones 2011; Philippot et al. 2013). Plants actively select these microorganisms through the emission of rhizodeposits, a selective process influenced by the plant's own biochemical outputs (Philippot et al. 2013). Research has underscored that the plant species and genotype, as well as its age and developmental stage, significantly affect the structure and activity of rhizosphere microbiota. These influences are largely due to variations in the quantity and types of rhizodeposits produced (Philippot et al. 2013; Wagner et al. 2016; Gallart et al. 2018; Liu et al. 2022). In grapevines, it has been recently demonstrated that rootstock genotypes distinctly shape the microbial diversity within the rhizosphere (Marasco et al., 2018; D’Amico et al. 2018; Berlanas et al. 2019). Additionally, the structure of the grapevine-associated fungal microbiome in this niche undergoes complex, dynamic changes throughout the growing season (Liu and Howell 2021).\u003c/p\u003e\n\u003cp\u003eSeveral factors influence the microorganisms living in the soil, including soil physicochemical properties, moisture content, biogeographical processes, and agricultural management practices (Fernández-Calviño et al. 2010; Burns et al. 2015; Zarraonaindia et al. 2015; Holland et al. 2016; Vink et al. 2021). These organisms fulfil diverse ecological functions. For example, soil fungi are crucial for the decomposition of organic residues, thereby enhancing soil fertility and supporting plant growth and development. Conversely, certain fungi act as soilborne pathogens, causing plant diseases that pose significant challenges for disease management due to their ability to survive in the soil for extended periods (Armengol and Gramaje 2016). Understanding the biology and epidemiology of soilborne fungal diseases is complex, given the intricacies of the soil environment (Koike et al. 2003).\u003c/p\u003e\n\u003cp\u003eGrapevine is susceptible to several soilborne fungal diseases, including Armillaria root rot, Phytophthora crown and root rot, Verticillium wilt, as well as Petri and black foot (BF) diseases (Bettiga 2013). Black foot disease, in particular, is a critical component of the grapevine trunk disease complex and is a major contributor to young vine decline globally (Gramaje and Armengol 2011; Carlucci et al. 2017; Gramaje et al. 2018). This disease is caused by various species within the genera \u003cem\u003eDactylonectria\u003c/em\u003e, \u003cem\u003eIlyonectria\u003c/em\u003e, \u003cem\u003eNeonectria\u003c/em\u003e, \u003cem\u003eThelonectria\u003c/em\u003e, \u003cem\u003eCampylocarpon\u003c/em\u003e, and \u003cem\u003eCylindrocladiella\u003c/em\u003e, with \u003cem\u003eDactylonectria\u003c/em\u003e species being the most prevalent in regions such as Italy, Portugal, Spain, South Africa, Algeria, and Uruguay (Carlucci et al. 2017; Pintos et al. 2018; Berlanas et al. 2017; Langenhoven et al. 2018; Aigoun-Mouhous et al. 2019; Carbone et al. 2022). The typical internal symptoms of BF include wood necrosis starting from the base of the rootstock and sunken necrotic lesions on the roots (Halleen et al. 2006; Alaniz et al. 2007). Externally, affected vines exhibit reduced vigor, shortened internodes, delayed bud break, chlorotic foliage with necrotic margins, wilting, and often die within a short period (Agustí-Brisach and Armengol 2013).\u003c/p\u003e\n\u003cp\u003eNursery vines frequently become infected with BF pathogens during the rooting phase within nursery fields, a key stage in the propagation process (Gramaje and Armengol 2011). Research has consistently identified the nursery field as the primary source of inoculum for these pathogens (Halleen et al. 2007; Agustí-Brisach et al. 2013, 2014; Berlanas et al. 2017). BF pathogens produce conidia that disperse in soil water, while some species also generate chlamydospores, enhancing their ability to endure in soil for extended periods (Halleen et al. 2006; Petit et al. 2011). Additionally, certain soil conditions, such as poor drainage, high moisture content, and heavy texture, have been recognized as factors that promote BF incidence (Halleen et al. 2006).\u003c/p\u003e\n\u003cp\u003eSubstantial efforts have been made to mitigate the incidence of BF in grapevines. Although various control strategies, including chemical, physical, biological, and integrated approaches, have been employed, none have singularly achieved effective disease management (Eichmeier et al., 2018; Gramaje et al. 2018). In response, recent studies have shifted towards culture-independent methods, utilizing high-throughput amplicon sequencing (HTAS) to elucidate the complex microbial communities within the grapevine rhizosphere and root-endosphere across diverse environmental conditions (D´Amico et al. 2018; Marasco et al. 2018; Berlanas et al. 2019; Martínez-Diz et al. 2019; Liu and Howell 2021; Carbone et al. 2021). These studies also explore interactions within the grapevine-BF pathobiome (Berlanas et al. 2019; Carbone et al. 2021). However, the influence of BF pathogens, particularly fungi like \u003cem\u003eD. macrodidyma\u003c/em\u003e, on native microorganisms remains poorly understood. In this work, we aim to examine the composition and interactions within the fungal microbiome of the rhizosphere and endosphere (root) of grapevine grafted plants inoculated with \u003cem\u003eD. macrodidyma\u003c/em\u003e using ITS HTAS. We tested the following hypotheses: (1) The pathogen \u003cem\u003eD. macrodidyma\u003c/em\u003e significantly alters the composition and structure of the fungal microbiome in the rhizosphere and endosphere of grafted grapevine plants, reducing microbial diversity in these niches, and (2) the presence of \u003cem\u003eD. macrodidyma\u003c/em\u003e generates competitive and/or synergistic interactions with native microorganisms in the grapevine microbiome, affecting key ecological functions such as organic matter decomposition or resistance to environmental stress.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS ","content":"\u003cp\u003e\u003cstrong\u003eExperimental Design and Treatments.\u003c/strong\u003eIn April 2019, one-year-old Tempranillo grapevine plants grafted onto 110 Richter rootstock were obtained from Villanueva Nursery in Navarra, Spain. These plants were then transferred into 11-liter pots containing two different soil types: clay loam (hereafter referred to as \u0026quot;clay\u0026quot;) and sandy loam (\u0026quot;sandy\u0026quot;) (Supplementary Table 1). The soil was collected from vineyards in Logro\u0026ntilde;o, northern Spain, and classified following the U.S. Department of Agriculture\u0026apos;s soil textural classification system. To prepare the soil, twenty bulk samples were randomly collected from inter-row areas at a depth of 40\u0026ndash;50 cm, approximately one meter away from vine trunks\u0026mdash;ten samples from each vineyard. These samples were homogenized in a greenhouse before use to ensure consistency. The potted cuttings were maintained in a greenhouse under natural conditions for 19 months. From April to November 2019 and March to November 2020, plants were irrigated every two days, whereas from December 2019 to February 2020, watering was reduced to once per week to simulate natural winter conditions. Greenhouse temperatures were regulated to resemble outdoor environmental conditions, allowing the plants to enter dormancy. Three months after planting (July 2019), a subset of 18 plants per soil type was inoculated with a conidial suspension (1 \u0026times; 10⁵ conidia mL⁻\u0026sup1;) of \u003cem\u003eDactylonectria macrodidyma\u0026nbsp;\u003c/em\u003estrain BV-0054 (Berlanas et al. 2020). Another group of 18 plants per soil type received distilled water as a control treatment. The experiment followed a completely randomized design, with irrigation adjusted according to the specific water requirements of each plant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSampling of grapevine rhizosphere and roots.\u0026nbsp;\u003c/strong\u003eRhizosphere and root samples from grapevine plants were collected at four different time points: before inoculation with \u003cem\u003eDactylonectria macrodidyma\u0026nbsp;\u003c/em\u003e(T0), and at three (T1), nine (T2), and sixteen (T3) months post-inoculation. Sampling was conducted using a sterile spade, targeting areas near the stem at depths corresponding to the highest root density. Collected samples were immediately placed into sterile bags, stored on dry ice at the time of collection, and promptly transported to the laboratory for further processing. Rhizosphere soil attached to roots was separated following the methodology described by Berlanas et al. (2019), and the obtained rhizosphere fraction was stored at -80\u0026deg;C until DNA extraction. Roots were carefully washed with tap water, disinfected by immersion in 1% sodium hypochlorite for 30 seconds, and subsequently rinsed three times with distilled water. The cleaned roots were then stored at -80\u0026deg;C until DNA extraction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and sequencing.\u0026nbsp;\u003c/strong\u003eRhizosphere DNA was extracted using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), while root endosphere DNA was obtained with the DNeasy Plant Pro Kit (Qiagen, Hilden, Germany), following the manufacturer\u0026apos;s protocols. Prior to DNA extraction, the outer bark was carefully removed from the roots, and the peeled root tissues were ground to a fine powder in liquid nitrogen. DNA concentration from each sample was measured using the Invitrogen Qubit4 Fluorometer and the Qubit dsDNA HS Assay (Thermo Fisher Scientific, Waltham, USA), and extracts were standardized to a concentration of 10 ng/\u0026micro;L. After quantification, DNA samples were pooled in pairs, resulting in nine replicates per plant compartment, soil type, treatment (inoculated vs. non-inoculated), and sampling time, with the exception of Sampling T0. At T0, only four samples were collected per plant compartment and soil type, leading to two DNA replicates per compartment and soil type. In total, 224 DNA samples were analyzed. The fungal ITS2 region was amplified using the primers ITS86F (Turenne et al., 1999) and ITS4 (White et al., 1990), modified to include Illumina adapters. PCR reactions were carried out in a final volume of 25 \u0026mu;L, containing 2.5 \u0026mu;L of template DNA, 0.5 \u0026mu;M of each primer, 12.5 \u0026mu;L of Supreme NZYTaq 2x Green Master Mix (NZYTech, Lisboa, Portugal), and ultrapure water to reach the final volume. The thermal cycling conditions included an initial denaturation at 95\u0026deg;C for 5 minutes, followed by 35 cycles of 95\u0026deg;C for 30 seconds, 49\u0026deg;C for 30 seconds, and 72\u0026deg;C for 30 seconds, with a final elongation at 72\u0026deg;C for 10 minutes. A second PCR step was performed to attach oligonucleotide indices, maintaining the same conditions but with only five cycles and an annealing temperature of 60\u0026deg;C. Negative controls, containing no DNA, were included in all PCR rounds to monitor potential contamination during library preparation. PCR products were visualized on 2% agarose gels stained with GreenSafe (NZYTech, Lisboa, Portugal) and examined under UV light to confirm the expected library size. Libraries were purified using Mag-Bind RXNPure Plus magnetic beads (Omega Biotek, Norcross, GA, USA) according to the manufacturer\u0026apos;s instructions. They were then pooled in equimolar amounts based on DNA concentration data obtained with the Qubit dsDNA HS Assay (Thermo Fisher Scientific, Waltham, USA). The pooled library was sequenced using an Illumina MiSeq PE300 run (Illumina, San Diego, USA). A positive control, consisting of DNA from a previously analyzed grapevine rhizosphere sample using ITS2 high-throughput amplicon sequencing (HTAS), was included (Mart\u0026iacute;nez-Diz et al., 2019). All control samples underwent sequencing to assess potential contamination throughout the entire process. Data analysis followed the methodology described by Carbone et al. (2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis of high-throughput amplification assay,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efungal diversity, taxonomy distribution and statistical analysis.\u0026nbsp;\u003c/strong\u003eThe sequencing data underwent quality assessment using FastQC 0.10.1. Further data processing was carried out with SEED 2.0 (Vetrovsk\u0026yacute; et al., 2018). Forward and reverse raw reads for each sample were merged into paired-end sequences using fastq-join 1.1.2, a tool from the ea-tools suite (Aronesty, 2011). Quality filtering was then applied with a Q = 30 threshold, trimming sequences to a minimum length of 250 bases while discarding any ambiguous bases. Sequences were subsequently sorted according to barcode motifs and assigned to their respective sample names. To extract fungal ITS sequences, ITSx 1.0.11 (Bengtsson-Palme et al., 2013) was used. Next, sequences were clustered into operational taxonomic units (OTUs), and chimeric sequences were eliminated using Usearch-UPARSE 8.1.1861 (Edgar, 2013), applying a 97% pairwise identity threshold against the UNITE fungal dynamic database (Abarenkov et al., 2010). Consensus sequences for each OTU were generated using MAFFT 7.222 (Katoh et al., 2009). OTU identification was performed utilizing BLAST tools (blastn, tblastx, and makeblastdb 2.5.0+) through the NCBI BLAST platform (https://blast.ncbi.nlm.nih.gov/Blast.cgi). To facilitate sample comparability, the dataset was normalized using the total sum scaling (TSS) method. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlpha diversity was assessed using the Shannon and Chao1 indices, calculated through the Phyloseq package within the MicrobiomeAnalyst 2.0 platform (Lu et al., 2023). To evaluate sequencing depth and generate rarefaction curves, the rarefaction analysis tool in MicrobiomeAnalyst was utilized. To determine taxa with significant differences in relative abundance (at the genus level or higher) across treatments and sampling times, the Linear Discriminant Analysis Effect Size (LEfSe) method (Segata et al., 2011) was applied via MicrobiomeAnalyst. The analysis was conducted using a logarithmic Linear Discriminant Analysis (LDA) score threshold of 2.0, with a False Discovery Rate (FDR)-adjusted significance cutoff of \u003cem\u003eP\u003c/em\u003e \u0026le; 0.1.\u003c/p\u003e\n\u003cp\u003eCo-occurrence network analysis was performed to investigate potential interactions among genera, using the integrated Network Analysis Pipeline (iNAP) (Feng et al., 2022). Significant associations were identified based on a P value threshold of 0.05, with 120 permutations and a correlation cutoff of 0.3. The resulting network was visualized using Cytoscape version 3.10.0 (Shannon et al., 2003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Prediction of Fungal Communities\u003c/strong\u003e.The functional roles of fungal communities in root and rhizosphere samples from sandy and clay soils were analyzed at three time points (T1, T2, and T3) using FUNGuild v1.0, following the methodology outlined by Nguyen et al. (2016). Fungal OTUs were grouped into three primary trophic modes: pathotrophs, saprotrophs, and symbiotrophs. More specific functional guilds included plant pathogens, fungal parasites, lichen parasites, undefined saprotrophs, soil saprotrophs, wood saprotrophs, dung saprotrophs, plant saprotrophs, endophytes, and arbuscular mycorrhizal fungi. OTUs that could not be assigned to known taxa within the database were categorized as \u0026quot;unknown\u0026quot;. To enhance classification reliability, only guild assignments with probable or highly probable confidence levels were included in the analysis.\u003c/p\u003e\n\u003cp\u003eThe relative abundance of OTUs within each functional guild was quantified across compartments (roots and rhizosphere), soil types (sandy and clay), and sampling times (T1\u0026ndash;T3). To ensure comparability, all data were normalized to 100% within each sample. The potential influence of \u003cem\u003eD. macrodidyma\u0026nbsp;\u003c/em\u003einoculation on fungal functional composition was examined using Welch\u0026rsquo;s t-test, comparing inoculated and non-inoculated treatments at each time point. Statistical significance was set at P \u0026lt; 0.05, with significant results highlighted in graphical representations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of \u003cem\u003eDactylonectria macrodidyma\u003c/em\u003e Inoculum Levels.\u0026nbsp;\u003c/strong\u003eDroplet digital PCR (ddPCR) was utilized to quantify \u003cem\u003eD. macrodidyma\u003c/em\u003e inoculum levels, analyzing DNA extracted from both the rhizosphere and root endosphere. Specific primers and a probe targeting the histone 3 region were employed, following the protocol described by Hrycan et al. (2023). Each ddPCR reaction mixture contained 750 nM of each primer, 1\u0026times; Supermix for Probes (Bio-Rad, Hercules, CA, USA), 250 nM of the probe, and 2 \u0026micro;L of DNA, bringing the final reaction volume to 20 \u0026micro;L. Droplet generation was performed using the QX200\u0026trade; droplet generator (Bio-Rad). PCR amplification was carried out in a Bio-Rad C1000 Touch thermal cycler, starting with a 10-minute activation step at 95\u0026deg;C, followed by 40 cycles of 94\u0026deg;C for 30 seconds and 55\u0026deg;C for 60 seconds, concluding with a final extension at 98\u0026deg;C for 10 minutes. After amplification, the droplets were analyzed using the QX200\u0026trade; droplet reader (Bio-Rad), and data interpretation was performed using QuantaSoft\u0026trade; software (Bio-Rad). For calibration, DNA from the \u003cem\u003eD. macrodidyma\u003c/em\u003e isolate BV-0054 was used as a standard, establishing a detection threshold of 3,000 based on prior evaluations with two positive controls\u0026mdash;one containing DNA from a pure culture of BV-0054 and another from a grapevine root confirmed to harbor \u003cem\u003eD. macrodidyma\u003c/em\u003e. A non-template control (water) was included to monitor potential contamination. Each sample was processed in triplicate to ensure result reliability and reproducibility. Following Bio-Rad\u0026apos;s guidelines (https://www.bio-rad.com/webroot/web/pdf/lsr/literature/Bulletin_6407.pdf), wells with fewer than three positive droplets were classified as negative.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eSequencing Data and Diversity Estimates.\u003c/strong\u003eFollowing paired-end alignment, quality filtering, and the removal of chimeric sequences and singletons, a total of 1,853,314 fungal ITS2 sequences were obtained from 215 samples (nine samples were removed from the analysis due to the low number of sequences read), clustering into 708 fungal OTUs. Based on Good\u0026rsquo;s coverage values, 99.943% of the total fungal species richness was captured, indicating comprehensive sequencing depth (Supplementary Table S2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Chao1 richness estimator varied across different compartments and soil types, ranging from 30.11 to 59.27 in roots grown in sandy soil, 30.46 to 46.75 in roots from clay soil, 121 to 150 in the rhizosphere of sandy soil, and 48.7 to 164 in the rhizosphere of clay soil. Shannon diversity estimates followed a similar pattern, with values ranging from 1.0 to 2.3 in sandy-soil roots, 1.1 to 2.1 in clay-soil roots, 2.02 to 3.09 in the rhizosphere of sandy soil, and 2.21 to 3.41 in the rhizosphere of clay soil (Supplementary Table S3). All sequencing data have been deposited under BioProject accession number PRJNA1218043, with the Sequence Read Archive (SRA) experiment available under accession number SRR32220025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of \u003cem\u003eDactylonectria macrodidyma\u003c/em\u003e on Fungal Alpha Diversity.\u003c/strong\u003e The inoculation with \u003cem\u003eDactylonectria macrodidyma\u003c/em\u003e did not significantly altered the alpha diversity of fungal communities in both the root and rhizosphere microbiomes, (Figs. 1 and 2). In the root microbiome of sandy soil, inoculation with \u003cem\u003eD. macrodidyma\u003c/em\u003e led to a significant decrease in fungal diversity at T3 (16 months) (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Fig. 1a). The non-inoculated control maintained a stable diversity throughout the experiment. In clay soil, the effect of \u003cem\u003eD. macrodidyma\u003c/em\u003e inoculation on root fungal diversity was more pronounced at T2, where inoculated samples showed a significant reduction in alpha diversity (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) compared to controls (Fig. 1b). This difference was not significant at T3 (16 months). In the rhizosphere microbiome, the inoculation of \u003cem\u003eD. macrodidyma\u003c/em\u003e did not affect microbiome fungal diversity at any time for any type of soil (fig. 2). In fact, the alpha-diversity in rhizosphere of inoculated and not inoculated plants was maintained stable for the entire duration of the experiment in both soil types.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChanges in Fungal Community Composition Following \u003cem\u003eDactylonectria macrodidyma\u003c/em\u003e Inoculation.\u003c/strong\u003e \u0026nbsp;The introduction of \u003cem\u003eD. macrodidyma\u003c/em\u003e resulted in notable shifts in the composition of the fungal community in both the root and rhizosphere microbiomes, particularly in relation to pathogenic and beneficial fungi (Fig. 3 and 4). High-throughput sequencing and linear discriminant analysis (LEfSe) revealed that \u003cem\u003eD. macrodidyma\u003c/em\u003e not only dominated the fungal community in inoculated samples but also led to the suppression of key beneficial taxa. In the\u0026nbsp;rhizosphere microbiome of\u0026nbsp;sandy soil, \u003cem\u003eD. macrodidyma\u003c/em\u003e slightly decreased in abundance at T2 and T3. This coincided with a decline in beneficial fungi such as\u0026nbsp;\u003cem\u003eClonostachys\u003c/em\u003e. \u003cem\u003eCadophora\u003c/em\u003e, another genus involved in grapevine trunk diseases, also decrease its abundance over time (Fig. 3a). In\u0026nbsp;clay soil, the establishment of \u003cem\u003eD. macrodidyma\u003c/em\u003e was slower, with significant increases in its abundance observed only at T3 (Fig. 3). Despite this delayed effect, the pathogen still managed to outcompete other fungal species, particularly beneficial taxa such as\u0026nbsp;\u003cem\u003eTrichoderma\u003c/em\u003e, which were less abundant in inoculated treatments at T3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal Changes in Fungal Abundance and Pathogen Dominance.\u003c/strong\u003e\u0026nbsp;The temporal dynamics of fungal abundance revealed that \u003cem\u003eD. macrodidyma\u003c/em\u003e not only persisted in the fungal community over time but also progressively dominated the rhizosphere and root microbiomes of inoculated plants. In\u0026nbsp;sandy soil, \u003cem\u003eD. macrodidyma\u003c/em\u003e\u0026apos;s abundance increased sharply at T2 and remained high at T3, displacing other fungal taxa, including\u0026nbsp;\u003cem\u003eClonostachys\u003c/em\u003e and\u0026nbsp;\u003cem\u003eTrichoderma\u003c/em\u003e (Fig. 4). In\u0026nbsp;clay soil, the pathogen\u0026rsquo;s growth trajectory was slower but equally impactful by T3, with \u003cem\u003eD. macrodidyma\u003c/em\u003e becoming one of the most abundant taxa in the rhizosphere microbiome (Fig. 4). Interestingly, the presence of \u003cem\u003eD. macrodidyma\u003c/em\u003e also correlated with a reduction in beneficial fungal taxa, which declined significantly in inoculated samples compared to controls. LEfSe analysis confirmed that \u003cem\u003eD. macrodidyma\u003c/em\u003e was a key determinant of fungal community structure in both soils, particularly at T3. In\u0026nbsp;sandy soil, inoculated treatments were characterized by a higher relative abundance of pathogenic genera such as\u0026nbsp;\u003cem\u003eDiaporthe\u003c/em\u003e and\u0026nbsp;\u003cem\u003eBotrytis\u003c/em\u003e, while beneficial taxa were significantly underrepresented (Fig. 4). In\u0026nbsp;clay soil, \u003cem\u003eD. macrodidyma\u003c/em\u003e co-occurred with other pathogens such as\u0026nbsp;\u003cem\u003eIlyonectria\u003c/em\u003e, contributing to the overall reduction in fungal diversity and the establishment of a pathogen-dominated community (Fig. 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFungal Interaction Networks: The Role of \u003cem\u003eDactylonectria macrodidyma\u003c/em\u003e in Microbial Dynamics.\u003c/strong\u003e\u0026nbsp;SparCC network analysis further highlighted the impact of \u003cem\u003eD. macrodidyma\u003c/em\u003e on fungal community interactions, revealing that inoculation led to significant changes in the co-occurrence patterns of fungal taxa (Fig. 5). In both soil types, \u003cem\u003eD. macrodidyma\u003c/em\u003e altered the structure and complexity of fungal networks, with a shift toward more competitive interactions as the pathogen established dominance. In\u0026nbsp;sandy soil, inoculated treatments displayed an increase in negative correlations between fungal taxa, with \u003cem\u003eD. macrodidyma\u003c/em\u003e driving a more competitive and less cooperative microbial network. By T3, the number of negative correlations in the rhizosphere microbiome of inoculated treatments had increased significantly compared to the control (237 negative correlations vs. 160 in the control) (Fig. 5). This suggests that the presence of \u003cem\u003eD. macrodidyma\u003c/em\u003e led to heightened competition for resources, suppressing the growth of other fungi. In\u0026nbsp;clay soil, the network dynamics were slightly different, with a greater number of positive correlations observed in inoculated samples at T3 (260 positive correlations vs. 138 in the control) (Fig. 5). This shift suggests that, in clay soil, \u003cem\u003eD. macrodidyma\u003c/em\u003e promoted a more cooperative fungal community, potentially facilitating the survival of other pathogenic species. The network in clay soil was dominated by pathogenic genera such as\u0026nbsp;\u003cem\u003eIlyonectria\u003c/em\u003e and\u0026nbsp;\u003cem\u003eBotrytis\u003c/em\u003e, which established stable associations with \u003cem\u003eD. macrodidyma\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of\u0026nbsp;\u003cem\u003eDactylonectria macrodidyma\u003c/em\u003e Inoculation on the Grapevine Root and Rhizosphere Microbiomes in Sandy and Clay Soils. \u003c/strong\u003eTo assess the effect of \u003cem\u003eD. macrodidyma\u003c/em\u003e inoculation on the microbiomes of grapevine root and rhizosphere in both sandy and clay soils, the microbial loads (in copies/\u0026micro;l) were measured across inoculated and non-inoculated plants at different time points (T1, T2, and T3). The results were compared between root and rhizosphere samples in both soil types. Error bars represent standard deviations across biological replicates.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSandy Soil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRoot Microbiome.\u003c/strong\u003e\u0026nbsp;In sandy soil, the microbial load of \u003cem\u003eD. macrodidyma\u003c/em\u003e exhibited variability depending on the time point and inoculation status (Fig. 6). At T1, the microbial load in inoculated plants was higher (approximately 17 copies/\u0026micro;l) compared to non-inoculated plants (~12 copies/\u0026micro;l), though the difference was not statistically significant based on error margins. By T2, both inoculated and non-inoculated plants showed a decline in microbial load, with inoculated plants showing a slight reduction to ~5 copies/\u0026micro;l, and non-inoculated plants at nearly 2 copies/\u0026micro;l. This trend continued at T3, with inoculated and non-inoculated plants maintaining low microbial loads of around 5 and 4 copies/\u0026micro;l, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRhizosphere Microbiome.\u003c/strong\u003e\u0026nbsp;In the sandy soil rhizosphere, a more pronounced effect of \u003cem\u003eD. macrodidyma\u003c/em\u003e inoculation was observed (Fig. 6). At T1, inoculated plants exhibited a significantly higher microbial load (~38 copies/\u0026micro;l) compared to non-inoculated plants (~5 copies/\u0026micro;l). A similar trend was observed at T2, with inoculated plants maintaining a higher microbial load (~6 copies/\u0026micro;l) compared to non-inoculated plants (~2 copies/\u0026micro;l). By T3, the microbial loads in both inoculated (~7 copies/\u0026micro;l) and non-inoculated plants (~2 copies/\u0026micro;l) remained relatively stable, showing no major changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClay Soil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRoot Microbiome.\u003c/strong\u003e In the clay soil root microbiome, \u003cem\u003eD. macrodidyma\u003c/em\u003e inoculation showed a substantial impact, particularly at later time points (Fig. 6). At T1, the microbial load in inoculated plants was approximately 30 copies/\u0026micro;l, compared to a negligible load in non-inoculated plants. This difference was further amplified at T2, where inoculated plants exhibited a marked increase in microbial load (~40 copies/\u0026micro;l), while non-inoculated plants showed no significant change (~0 copies/\u0026micro;l). At T3, inoculated plants continued to show an elevated microbial load (~47 copies/\u0026micro;l), while the non-inoculated plants remained consistently low (~0 copies/\u0026micro;l).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRhizosphere Microbiome.\u003c/strong\u003e\u0026nbsp;In the clay soil rhizosphere, the impact of inoculation was even more evident (Fig. 6). At T1, the microbial load in inoculated plants reached approximately 60 copies/\u0026micro;l, while non-inoculated plants exhibited negligible microbial loads (~0 copies/\u0026micro;l). This trend persisted at T2, with inoculated plants showing the highest microbial load recorded (~72 copies/\u0026micro;l), and non-inoculated plants remaining low (~2 copies/\u0026micro;l). By T3, the microbial load in inoculated plants decreased to ~14 copies/\u0026micro;l, but remained substantially higher than non-inoculated plants (~5 copies/\u0026micro;l).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional Prediction of Fungal Communities.\u003c/strong\u003e\u0026nbsp;The functional potential of fungal communities in the root and rhizosphere microbiomes of grapevine plants under \u003cem\u003eD. macrodidyma\u003c/em\u003e inoculation was assessed by categorizing fungal taxa into functional guilds, including\u0026nbsp;pathogens, saprotrophs, symbiotrophs, and unknown guilds (Figs. 7 and 8). The relative abundances of these guilds were analyzed across\u0026nbsp;soil types (sandy vs. clay), compartments (root vs. rhizosphere), and time points (T1, T2, and T3), providing insight into the ecological shifts driven by \u003cem\u003eD. macrodidyma\u003c/em\u003e inoculation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRoot Microbiome Functional Composition. \u003c/strong\u003eIn the root microbiome of sandy soil, the relative abundance of pathogens increased over time in inoculated samples, reaching a significant peak at T3 (P\u0026nbsp;\u0026lt; 0.05) compared to non-inoculated controls (Fig. 7). This was accompanied by a reduction in symbiotrophic fungi at T2 and T3, suggesting a potential disruption of beneficial fungal associations. The proportion of saprotrophs remained relatively stable across time points in both treatments. In clay soil roots, a similar trend was observed, with a significant increase in pathogenic fungi in inoculated samples at T2 (P\u0026nbsp;\u0026lt; 0.05), which remained elevated at T3. Unlike in sandy soil, symbiotrophic fungi in clay soil did not exhibit a significant decline, suggesting that the effect of\u0026nbsp;D. macrodidyma\u0026nbsp;on beneficial fungi may be soil-dependent. The unknown guild was consistently the largest category across all conditions, with a slight increase in inoculated samples at T3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRhizosphere Microbiome Functional Composition. \u003c/strong\u003eThe rhizosphere microbiome exhibited distinct functional responses compared to the root microbiome. In sandy soil, pathogen abundance significantly increased in inoculated treatments at T2 and T3 (P \u0026lt; 0.05), correlating with the dominance of D. macrodidyma (Fig. 8). Unlike in roots, saprotrophs showed a marked decline over time in inoculated samples, suggesting a shift in fungal community dynamics favoring pathogens over decomposers. The symbiotrophic guild was relatively stable across time points. In the clay soil rhizosphere, pathogen abundance was significantly elevated in inoculated samples at all time points, with the largest difference observed at T2 (P \u0026lt; 0.05). Interestingly, the symbiotrophic guild exhibited an increase at T3 in inoculated treatments, unlike the pattern observed in sandy soil. This suggests that in clay soil, certain symbiotic fungi may persist despite the dominance of D. macrodidyma. The saprotrophic guild remained low throughout the experiment, showing no significant differences between treatments.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUnderstanding the interactions between plant pathogens and microbial communities is essential to advance sustainable agriculture (Vishwakarma et al. 2020). In viticulture, microbial communities in the root and rhizosphere microbiomes play pivotal roles in plant health by mediating nutrient acquisition, promoting growth, and suppressing pathogens (Visconti et al. 2024). However, the introduction of aggressive pathogens like \u003cem\u003eD. macrodidyma\u003c/em\u003e disrupts these microbial dynamics, reducing diversity and altering community composition (Fournier et al. 2022). This study aimed to elucidate the effects of \u003cem\u003eD. macrodidyma\u003c/em\u003e on fungal community dynamics in grapevines grown in sandy and clay soils, providing critical insights into how soil properties influence pathogen impacts and microbial resilience.\u003c/p\u003e\n\u003cp\u003eOur findings reveal that \u003cem\u003eD. macrodidyma\u003c/em\u003e significantly alters fungal diversity, community composition, and microbial interaction networks over time. These changes are not uniform across soil types, highlighting the complexity of plant-microbe-pathogen interactions. Such disruptions in microbial communities have far-reaching implications for grapevine health and productivity, emphasizing the urgent need for targeted management strategies tailored to soil type and pathogen dynamics.\u003c/p\u003e\n\u003cp\u003eNotably, \u003cem\u003eD. macrodidyma\u003c/em\u003e inoculation reduced fungal alpha diversity in root microbiomes, with significant declines observed in sandy soil at T3 and in clay soil at T2. This sustained reduction in diversity underscores the pathogen’s competitive ability to displace other fungal taxa, particularly in environments with distinct soil properties. Sandy soils, characterized by lower organic matter content and reduced water retention, provide less buffering capacity against pathogen proliferation (Leal et al. 2024a). This finding aligns with prior research demonstrating that environmental stressors, such as drought, exacerbate pathogen dominance and disrupt microbial diversity (Carbone et al. 2021; Leal et al. 2024a). In contrast, the rhizosphere microbiome exhibited stable fungal diversity across soil types and time points, irrespective of inoculation. This stability reflects the inherent resilience of the rhizosphere microbiome, which benefits from the buffering capacity of the surrounding soil (Feng et al. 2024; Kaźmierczak et al. 2024). While root microbiomes rapidly respond to pathogen invasion through shifts in microbial community structure, the rhizosphere often shows delayed or less pronounced changes, as previously noted in studies on plant-microbe interactions (Kaźmierczak et al. 2024; Feng et al. 2024).\u003c/p\u003e\n\u003cp\u003eThe introduction of \u003cem\u003eD. macrodidyma\u003c/em\u003e led to pronounced changes in fungal community composition, favoring pathogenic taxa while suppressing beneficial fungi. In sandy soil, \u003cem\u003eD. macrodidyma\u003c/em\u003e proliferation coincided with reductions in beneficial taxa such as \u003cem\u003eClonostachys\u003c/em\u003e. Similarly, in clay soil, \u003cem\u003eD. macrodidyma\u003c/em\u003e outcompeted \u003cem\u003eTrichoderma\u003c/em\u003e. These findings support the hypothesis that pathogens exploit competitive advantages in specific soil environments to establish dominance, as noted by Bettenfeld et al. (2022). Beneficial fungi such as \u003cem\u003eClonostachys,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eTrichoderma\u0026nbsp;\u003c/em\u003eplay vital roles in plant health by promoting growth, enhancing nutrient uptake, stimulating plant defenses, and suppressing pathogens through biocontrol mechanisms (Bahadoor et al. 2023; Woo et al. 2023). Their suppression has significant consequences for plant health and ecosystem functionality (Woźniak et al. 2019; Leal et al., 2024b). Further, LEfSe analysis confirmed that \u003cem\u003eD. macrodidyma\u003c/em\u003e significantly influenced fungal community structure by selectively promoting pathogenic genera, including \u003cem\u003eIlyonectria\u003c/em\u003e. This genus often co-occurs with \u003cem\u003eD. macrodidyma\u003c/em\u003e, creating pathogenic clusters that exacerbate grapevine trunk diseases (Probst et al. 2019).\u003c/p\u003e\n\u003cp\u003eSparCC network analysis revealed substantial alterations in fungal co-occurrence patterns following \u003cem\u003eD. macrodidyma\u003c/em\u003e inoculation. In sandy soils, the pathogen’s dominance led to an increased number of negative correlations among fungal taxa, indicative of heightened competition. This disruption of cooperative microbial networks intensifies resource competition and suppresses less competitive species (Trivedi et al. 2020), supporting our findings on alpha diversity. Conversely, in clay soils, an increase in positive correlations among fungal taxa suggests a shift toward cooperative interactions, which may facilitate the survival of other pathogens. This finding is consistent with Leal et al. (2024b), who demonstrated that clay soils foster stable associations among microbial taxa. The ability of clay soils to retain moisture and nutrients further supports microbial diversity and stability (Adeniji et al. 2024).\u003c/p\u003e\n\u003cp\u003eTemporal abundance data highlight the persistence and dominance of \u003cem\u003eD. macrodidyma\u003c/em\u003e in the grapevine microbiome. In sandy soils, the pathogen’s abundance increased sharply, displacing beneficial taxa such as \u003cem\u003eClonostachys\u003c/em\u003e and \u003cem\u003eTrichoderma\u003c/em\u003e, corroborating findings by Carbone et al. (2021). In clay soils\u003cem\u003e, D. macrodidyma\u003c/em\u003e established more slowly but ultimately led to similar reductions in fungal diversity and the proliferation of pathogenic genera like \u003cem\u003eIlyonectria\u003c/em\u003e and \u003cem\u003eBotrytis\u003c/em\u003e. The slower shifts in clay soils reflect their higher cation exchange capacity and moisture retention, which provide a more stable environment for microbial communities (Riaz and Marschner, 2020).\u003c/p\u003e\n\u003cp\u003eThe functional prediction analysis further supports the role of \u003cem\u003eD. macrodidyma\u003c/em\u003e in shifting microbial functions toward pathogenic dominance. Across both soil types, pathogens became significantly more abundant in inoculated samples, particularly at later time points, while symbiotrophic and saprotrophic fungi exhibited contrasting responses. In sandy soil, symbiotrophic fungi declined over time, suggesting a disruption of beneficial plant-microbe interactions, whereas in clay soil, they remained relatively stable, likely benefiting from the increased nutrient retention of the soil matrix (Leal et al. 2024a). In both rhizosphere and root compartments, the suppression of saprotrophs in inoculated treatments suggests a potential reduction in decomposition processes and nutrient cycling, which could have long-term effects on soil health and plant resilience (Finlay and Thorn 2019). Therefore, \u003cem\u003eD. macrodidyma\u003c/em\u003e not only alters community composition but also shifts fungal functional roles, reinforcing its capacity to disrupt ecosystem stability.\u003c/p\u003e\n\u003cp\u003eThese findings underscore the need for targeted management strategies in viticulture. Soil amendments, such as organic matter addition, can enhance microbial diversity and resilience, potentially mitigating the impacts of pathogens like \u003cem\u003eD. macrodidyma\u0026nbsp;\u003c/em\u003e(Labarga et al. 2025). Biocontrol agents offer a promising approach to counteract pathogen dominance by promoting beneficial microbial taxa and restoring ecological balance (Leal et al. 2024b). Understanding the soil-dependent nature of pathogen dynamics is crucial for developing tailored interventions. For instance, clay soils may require strategies targeting cooperative pathogen interactions, while sandy soils could benefit from practices enhancing microbial competition to suppress pathogens.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eThis study highlights the profound impact of \u003cem\u003eDactylonectria macrodidyma\u003c/em\u003e on fungal community dynamics in grapevines and underscores the critical role of soil properties in shaping these interactions. Our findings demonstrate that \u003cem\u003eD. macrodidyma\u003c/em\u003e significantly reduces fungal diversity, alters community composition, and disrupts microbial interaction networks, with its effects varying across sandy and clay soils. The pathogen’s dominance in sandy soils, characterized by rapid proliferation and displacement of beneficial fungi, reflects the vulnerability of these low-buffering soils to pathogen-induced disruptions. Conversely, while clay soils provided a more stable environment, \u003cem\u003eD. macrodidyma\u003c/em\u003e ultimately led to cooperative pathogen interactions, further highlighting the complexity of plant-microbe-pathogen relationships in distinct soil environments. Additionally, \u003cem\u003eD. macrodidyma\u003c/em\u003e not only reshapes fungal communities but also shifts their functional roles, increasing pathogen prevalence while suppressing symbiotrophic and saprotrophic fungi. This suggests potential consequences for nutrient cycling, decomposition, and plant resilience, reinforcing the need to consider both taxonomic and functional aspects of microbial communities in pathogen management.\u003c/p\u003e\n\u003cp\u003eThese results emphasize the need for soil-specific management strategies in viticulture to mitigate the impacts of aggressive pathogens. Interventions such as organic matter amendments and the application of biocontrol agents could enhance microbial diversity and resilience, counteracting pathogen dominance and restoring ecological balance. Additionally, understanding the temporal and spatial dynamics of microbial communities across soil types is essential for developing targeted and sustainable agricultural practices.\u003c/p\u003e\n\u003cp\u003eUltimately, this study provides valuable insights into the interplay between soil properties, microbial communities, and plant pathogens, offering a foundation for future research aimed at optimizing vineyard management practices and improving grapevine health in the face of pathogen challenges.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e CL and DG conceived and designed the study. CL, MJC and DG wrote the manuscript. CL, RB, LR and MJC run the greenhouse experiment and carried out the sampling. AE, TK, DT, CL and DG performed the bioinformatics and statistical analyses. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eThe datasets generated and analysed during the current study are available in the NCBI Sequence Read Archive (SRA) under the BioProject number PRJNA1218043 (Acc. No. SRR32220025).\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no conflict of interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbarenkov, K., Henrik Nilsson, R., Larsson, K.-H., Alexander, I. J., Eberhardt, U., Erland, S., Hoiland, K., Kjoller, R., Larsson, E., Pennanen, T., Sen, R., Taylor, A. F. S., Tedersoo, L., Ursing, B. M., Vr\u0026aring;lstad, T., Liimatainen, K., Peintner, U., and K\u0026otilde;ljalg, U. 2010. The UNITE database for molecular identification of fungi\u0026mdash;recent updates and future perspectives. 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MBio. 6(2):10-1128.\u003c/li\u003e \n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Instituto de Ciencias de la Vid y del Vino","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Black-foot disease, Microbial diversity, Microbial networks, pathogen-microbe interaction, Soilborne fungal pathogens, Vitis vinifera","lastPublishedDoi":"10.21203/rs.3.rs-6156675/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6156675/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the impact of \u003cem\u003eDactylonectria macrodidyma\u003c/em\u003e on fungal community dynamics in grapevines grown in sandy and clay soils, highlighting how soil properties influence pathogen-induced disruptions. High-throughput sequencing and microbial network analyses revealed that \u003cem\u003eD. macrodidyma\u003c/em\u003e significantly reduces fungal diversity in root microbiomes, with the effect being more pronounced in sandy soils at later time points. The pathogen altered fungal community composition by displacing beneficial taxa such as \u003cem\u003eClonostachys\u003c/em\u003e and \u003cem\u003eTrichoderma\u003c/em\u003e, while promoting pathogenic genera including \u003cem\u003eIlyonectria\u003c/em\u003e and \u003cem\u003eBotrytis\u003c/em\u003e. SparCC network analysis indicated that \u003cem\u003eD. macrodidyma\u003c/em\u003e increased competitive interactions in sandy soil, while fostering cooperative pathogenic networks in clay soil, reflecting distinct soil-dependent microbial responses. Additionally, functional guild prediction revealed a shift toward pathogenic dominance, with declines in symbiotrophic and saprotrophic fungi, suggesting potential consequences for nutrient cycling and microbial stability. These findings underscore the need for soil-specific disease management strategies in viticulture. Approaches such as organic amendments and biocontrol agents could help restore microbial diversity, promote beneficial taxa, and mitigate pathogen proliferation. This study provides critical insights into the ecological impact of \u003cem\u003eD. macrodidyma\u003c/em\u003eon grapevine microbiomes, informing the development of targeted interventions to enhance plant health and sustainability in viticulture.\u003c/p\u003e","manuscriptTitle":"Shifting Fungal Networks: How Dactylonectria macrodidyma Shapes Grapevine Mycobiome in Diverse Soils","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-10 08:42:04","doi":"10.21203/rs.3.rs-6156675/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"277296c3-caee-46e7-8b0b-384b596af1ba","owner":[],"postedDate":"March 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T08:42:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-10 08:42:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6156675","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6156675","identity":"rs-6156675","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}