Health or disease – a question of rhizomicrobial ecology? The case of Grapevine Trunk Disease

Health or disease – a question of rhizomicrobial ecology? The case of Grapevine Trunk Disease | 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 Health or disease – a question of rhizomicrobial ecology? The case of Grapevine Trunk Disease Islam M. Khattab, Tyra Magold, Florian Lenk, Gunnar Sturm, Noemi Flubacher, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8146628/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Plant and Soil → Version 1 posted 6 You are reading this latest preprint version Abstract Aims The incidence of the apoplectic breakdown associated with grapevine trunk diseases (GTDs) is promoted by climate change, which has become a challenge for viticulture worldwide. Outbreak of these conditional diseases is expected to depend on the rhizomicrobiome. However, the impact of the rhizomicrobiome on grapevine resilience has remained poorly understood, particularly regarding its ecological aspects. This study explores the link between GTDs, the rhizomicrobiome, and soil chemistry in vineyards along the Upper Rhine. Methods Using amplicon sequencing for both fungal and prokaryotic communities, we show that around half of the fungal rhizosphere community is endowed with pathotrophic potential, independently of the health status of the plant, including seventeen taxa known to be associated with GTD, predominantly Black Foot Disease. Results In contrast to fungi, bacterial diversity is shifted depending on the micronutrients Fe, Cu, Mn, and Zn. Moreover, taxa enriched in the rhizosphere of asymptomatic vines, such as Pseudophialocephala and Collarina for the mycobiome, and Caulobacter , Kitasatospora , and Entotheonellaceae for the bacteriome, showed correlations with soil properties. The most prominent feature associated with disease outbreaks was drastic changes of microbial co-occurrence networks. These were significantly increased in the fungi, especially for GTDs taxa, such as Fomitoporia, Stereum, Phaeomoniella , and Neofusicoccum . By contrast, there was a depletion of many bacteria and their microbial interactions under disease outbreak such as Isoptericola, Caulobacter, Rhodomicrobium and Thioprofundum . Conclusion Thus, likely microbial interactions and not the mere presence of GTDs taxa explains disease outbreak. This finding opens new strategies for sustainable management of GTDs. Climate Change and Pathogens Co-Occurrence Networks Grapevine Trunk Diseases Rhizomicrobiome Vitis vinifera Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Grapevine Trunk Diseases (GTDs) threaten viticulture worldwide, accelerated by the ongoing climate change. In France alone, yield losses in 2016 accumulated to 25%, corresponding to approximately 5000 million US $ ( https://www.maladie-du-bois-vigne.fr ) deficit. The outbreak of different forms of GTDs such as Botryosphaeria dieback, Esca syndrome, Eutypa dieback, Diaporthe dieback, and black foot disease is associated with a wide range of fungal endophytes of around 174 species (Li et al., 2023a). Unlike classical plant diseases, GTDs do not follow the Koch postulates, meaning that the expression of symptoms is not correlated to pathogen abundance, but rather depend on the condition of the host. For example, Neofusicoccum parvum , an aggressive fungus causing Botryosphaeria dieback, was found to switch to the necrotrophic phase when the host faces severe drought stress, provoking accumulation of the monolignol precursor ferulic acid. Increases in steady-state levels of ferulic acid might be interpreted by the fungus as a “plant surrender” signal, driving the fungus to secrete a Fusicoccin A aglycon, which afterwards triggers programmed plant cell death (Khattab et al., 2023). In the absence of ferulic acid, the fungus manipulates the homeostasis between defense and growth of the host by secreting an auxin mimic, 4-hydrophenylacetic acid, interfering with specific branches of phytoalexin synthesis (Flubacher et al., 2023). Conditional pathogenesis is not limited to GTDs, though. While plant-pathogen interactions are often conceptualized as a battle between two opponents, it is important to consider that this viewpoint represents a reduction of a far more complex reality. In fact, the outcome of this battle depends on numerous environmental factors, including the presence of other microorganisms that can have a major impact on the infection process. For example, resistance of a tomato genotype to soil-borne disease was associated with microbiota differing from those in a susceptible genotype (Kwak et al., 2018). As sessile organisms, plants have evolved to regulate the microbial communities in the rhizosphere (for review see Berendsen et al., 2012). The increasing number of examples, where beneficial soil microbes have been found to help plants to survive under environmental challenges (De Vries et al., 2020; Field et al., 2015; Ren et al., 2019) suggest that the interaction between plants and the so-called rhizomicrobiome might be subject of co-evolution. The model of a mutualistic relationship is also supported by findings, where taxonomic structure and function changes depending on plant developmental stage and stress conditions (Berendsen et al., 2012; Gu et al., 2022), supporting the concept of the rhizomicrobiome acting as a "second genome" for plants because of its pivotal role in promoting plant health and resilience (De Vries et al., 2020; Mendes et al., 2011). Thus, even for the same host genotype and the same physicochemical soil properties, the result of an encounter of a plant with a pathogen can vary between full breakdown and a mitigation even to a degree that the plant remains asymptomatic, depending on the composition of the rhizomicrobiome (Wei et al., 2019). Conversely, shifts in the rhizosphere microbiome could serve as predictive markers of plant resilience to pathogens (Gu et al., 2022; Wei et al., 2019). Furthermore, enriching the soil with synthetic communities of protective taxa might be used as strategy to suppress disease outbreak as shown for bacterial wilt in tomatoes (Lee et al., 2021). In the context of GTDs, the interactions of grapevine rhizomicrobiome and GTDs have hardly been investigated. Studying the interplay between the fungi causing GTDs, and the rhizomicrobiome could help to sort out either taxa with biocontrol potential to GTDs, or taxa triggering the GTDs outbreak. For instance, a study in young vineyards in China showed that the relative abundance of GTD fungi was irrelevant to their pathogenesis, while the symptoms of GTDs were more linked to the incidence of Fusarium spp. in the rhizosphere (Li et al., 2023b). Likewise, wood microbiome analysis for vineyards of different locations in Greece showed that symptomatic wood harboured more Acremonium alternatum and Kalmusia variispora , fungi not known as causes of GTD symptoms, while members of the bacterial family Bacillaceae were depleted in those symptomatic vines (Fotios et al., 2021). This observation corroborates findings, where a specific member of the Bacillaceae , Bacillus subtilis PTA-271, was found to exert biocontrol activity in planta against Neofusicoccum parvum , one of the most aggressive GTD fungi (Trotel-Aziz et al., 2019). This bacterial strain modulated accumulation of transcripts for defence-related genes, including those that are regulated by the major defence hormones, salicylic acid and jasmonates. In addition, a glutathione transferase was activated that was proposed to be involved in the catabolic breakdown of the fungal pathogenicity factors (-)-terremutin and (R)-mellein (Trotel-Aziz et al., 2019). Likewise, the immediate inoculation of Trichoderma species to pruning wounds in grapevine inhibited infection progress of Neofusicoccum parvum and Diplodia seriata and achieved a high degree of plant protection (Pollard-Flamand et al., 2022). The composition and the function of the soil microbiome is shaped by the physiochemical properties of the soil. Soil acidification significantly reduced the potential of microbial communities to inhibit the infections with the phytopathogenic fungus, Fusarium (Li et al., 2023). Here, inoculating healthy plants with microbiomes from acidified soils resulted in a remarkable decrease in their ability to resist infection process as well as a downregulation of sulfur metabolism (Li et al., 2023).. Also, micronutrient availability can modulate richness and diversity of the soil microbiome. A survey of 180 sites in China revealed that the structure and function of soil microbiomes was strongly linked to the metallic micronutrients iron, manganese, copper, and zinc (Dai et al., 2023). Specifically, increased Fe and Zn concentrations correlated with ecosystem productivity, which might be a direct consequence of improved plant-nutrient availability, or an indirect effect from altered microbiome composition and gene activity (Dai et al., 2023a). At least for Fe, direct modulation of microbiome-pathogen interactions was demonstrated for the colonisation of tomatoes by Ralstonia solanacearum (Gu et al., 2020). Here, under Fe-limited conditions, the rhizomicrobiome of tomato plants was able to outcompete this bacterial pathogen by secreting siderophores. As a result, its growth was suppressed. When this limitation was removed by supplementing iron, the rhizobiome failed to mitigate the infection with Ralstonia (Gu et al., 2020). In viticulture, even small variations in soil properties or water management can significantly affect yields and flavour of economically relevant varieties, such as Chardonnay, Merlot, and Pinot Noir, a phenomenon traditionally known as terroir . Those effects are proposed to be linked with shifts in their commensal microbiome (Gilbert et al., 2014). Taxonomic abundance and diversity of both, soil bacterial and fungal flora, is strongly dependent on cultivation practices (Coller et al., 2019). Additionally, the influence of soil microbiome is not confined to the soil, because bacterial taxa isolated from grapevine foliage were tightly associated with the communities in the soil, suggesting that soil may serve as a reservoir for vine-associated microbial flora (Zarraonaindia et al., 2015). The interplay between grapevine rhizomicrobiome, soil properties, and GTD outbreak has not been addressed yet. However, filling these knowledge gaps is crucial for developing a sustainable approach for grapevine resilience against GTD. Traditional plant breeding methods in viticulture are time consuming and no longer sufficient to cope with the rapidly progressing climate-borne challenges. Targeting beneficial rhizomicrobiota promoting grapevine resistance might act as a fast and sustainable approach against GTDs. This study employed a microbial-ecological strategy, probing the rhizomicrobiome from symptomatic and asymptomatic vines coming from the same vineyard, and sampling over a transsect of more than ten vineyards differing in soil composition to explore the role of the rhizomicrobiome and soil nutrient dynamics for improved grapevine resilience against trunk diseases. By uncovering key interactions between microbes, nutrients, and GTD symptoms, the study contributes to the development of novel strategies for sustainable management of GTDs. Methodology Sampling of rhizosphere soil. To identify whether the outbreak of GTDs might be associated with shifts in the composition of rhizosphere microbes, ten vineyards were sampled in August 2022 along the German side of the Upper Rhine representing Northern (Rauenberg), central (Ringsheim), and Southern (Eichstetten, Ihringen) domains within the viticulture region Baden. The majority (eight sites) comprised the commercially important variety Müller-Thurgau, and two sites the traditional variety Silvaner ( Fig. 1 ). Rhizosphere soil was collected at 20 cm below the surface from the root-hair zone of plants that either displayed GTD symptoms or were asymptomatic. The soil was immediately transferred to dry ice and remained there during transport, before long-term storage at -80°C. Each vineyard is represented by six rhizosphere samples, three samples from symptomatic, and three from asymptomatic plants. Extraction of DNA and amplicon sequencing. Soil DNA was extracted from aliquots of 400 mg soil using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden- Germany) following the instructions of the manufacturer. Phenolic compounds were removed by washing the DNA with 10% v/v of sodium acetate, then, DNA concentration was quantified using the Qubit® 3.0 fluorometer (Thermo Fisher Scientific) with the Qubit™ dsDNA HS Assay Kit, and quality assessed spectrophotometrically (NanoDrop™ 2000/2000c spectrophotometer, Thermo Fisher Scientific). To analyse the taxonomic structure of the microbial community, 10 ng of the purified DNA were used as template to either amplify 16S ribosomal RNA gene of prokaryotes, or the Internal Transcribed Spacer (ITS) of fungi. For the 16S rRNA, V4-V5 region was targeted using, 0.16 µM of the primer set 5’-GTGCCAGCMGCCGCGGTAA-3’ and 5’-CCGTCAATTCCTTTGAGTTT-3’ ligated with the Illumina adapter. For the ITS, the ITS2 region was addressed with the same concentrations of primers 5’-GCATCGATGAAGAACGCAGC-3’ and 5’-TCCTCCGCTTATTGATATGC-3’. To increase specificity, the PCR was conducted using touchdown cycling at 52–56°C. Annealing took place at 55°C. After PCR, an amplification step was included using 0.04 U/µL of Q5 High-Fidelity DNA Polymerase in presence of Q5 High GC Enhancer (Thermo Fisher). Amplicons were then cleaned up with the DNA Clean & Concentrator Kit (Zymo research, Germany) and subsequently used to prepare amplicon sequencing libraries. Here, 110 ng of cleaned amplicon were selected for a fragment size of 400–600 bp in two steps using 0.4x and 0.7x Agencourt Ampure XP beads (Beckman Coulter). Upon size selection, amplicons were ligated to dual index primers NEBNext® Multiplex Oligos for Illumina® (New England Biolabs, Frankfurt, Germany) following the protocol of the manufacturer, and cleaned afterwards using Ampure XP beads. The prepared libraries were diluted, pooled for equimolarity, and sequenced on a Illumina Novaseq platform to generate 150000 pair-end reads per sample (2 × 250 bp) (Novogene, Munich, Germany). Soil chemical analysis. For every vineyard, 6 soil cores were pooled to form a composite representative sample. Soil samples were then sent for chemical analysis using standards assays on soil type, pH, as well as content of macronutrients and micronutrients (Agricultural Analytical and Research Authority of the State of Rheinland-Pfalz, Speyer) following the rules of the German Fertiliser Regulation (DüV). Analysis of sequence reads. The obtained paired-end reads from the Illumina sequencer were subjected to quality assessment using the FASTQC tool (Andrews, 2010). Low-quality reads and Illumina adapters were trimmed, and subsequently merged using FASTP (Chen et al. , 2018). The merged fastq reads were further denoised to filter out chimeric reads as well as reads shorter than 250 bp using the DADA2 plugin in the QIIME2 pipeline based on the denoise-single method(Bolyen et al., 2019). The samples were then mapped to the corresponding sequences and their frequencies calculated, and the resulting operational taxonomic units (OTUs) were then classified either using the database Silva_99(Robeson et al., 2021) for 16S reads, or the UNITE database(Abarenkov et al., 2024) for reads of fungal ITS. The fungal OTUs were classified with respect to their trophic mode using the FUNGuild database (Nguyen et al., 2016). Since fungal taxa associated with GTDs are not specified in this or alternative databases, we classified, for the current study, wood-trophic taxa that had been previously reported as GTD causal agents(Li et al., 2023a; Martín et al., 2022) as GTDs community. To visualise the high complexity, heatmaps were constructed based on relative frequencies using the ComplexHeatmap software and the circlize tool, clustering variants of rows and columns variants based on their Euclidean distances (Gu, 2022). Statistical analysis. To identify rhizomicrobiome members correlated with the outbreak of GTDs, we probed for potential differential abundance among the Müller-Thurgau vineyards using the Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) tool implemented in R. This tool has been developed to derive statistically consistent parameters on the base of samples that differ in size (Lin & Peddada, 2020). Here, the status of the plant (asymptomatic versus asymptomatic) was set as covariate of interest, while the OTUs classified with respect to their role in GTDs were scored per rhizosphere sample. Significantly shifted OTUs were then plotted on a log-linear scale over the plant status to yield log-fold changes, test statistics, standard errors, P values, adjusted P values, and differential abundance. Diversity metrics and correlations analyses. To quantify differences in the composition of the rhizomicrobiome in relation to chemical soil profiles and GTD outbreak, we used several parameters. To address α-diversity (the diversity in a given location), we used the Shannon index as overall estimate (Shannon, 1948). To account for the fact that rare OTUs might be underrepresented due to sampling bias, we also calculated the Chao1 indices (Chao, 1987), and the Faith Phylogenetic Diversity (Faith_PD) index, a parameter that also considers phylogenetic relationships between the taxa (Faith, 1992). All these parameters were calculated using the respective QIIME2 tools. The non-parametrical Kruskal-Wallis test was used to test statistical significance for differences in the a-diversity indices over chemical profile of the soil and the GTD symptomatics. As alternative approach to assess β-diversity (i.e., differences between different locations), we conducted a Principal Coordinate Analysis to detect commonalities between the sites. For parametrisation, we used here either the Bray-Curtis distance by means of the We used the vegan package for R for ecological analyses (Oksanen et al., 2024)., or the Weighted-Unifrac distance (implemented in QIIME2). For visualisation, we employed differentially coloured polygons through the ggplot2 plugin of R, and the stat_ellipse command set at a confidence level of 95%. To classify the chemical profiles of the vineyards, a Principal Component Analysis was carried out using the FactoMinor and factoextra packages of R. In addition, Pearson correlations between rhizomicrobiome diversity metrics and soil chemical profile were calculated and plotted using Hmisc and corrplot packages of R (Harrell, 2024; Wei & Simko, 2021). Construction of Co-occurrence networks. Changes in the rhizomicrobiome dynamics and interactions under GTDs outbreak were studied by calculating co-occurrence networks either for asymptomatic or symptomatic vines with a resolution to the genus level. Correlation networks were assessed and visualized using R packages phyloseq (McMurdie & Holmes, 2013), microbiome (Lahti & Shetty, 2017), Hmisc (Harrell, 2024), igraph (Csardi & Nepusz, 2006), and ggplot2 (Wickham, 2016). Fungal and prokaryotic community networks were constructed at the genus level. After elimination of non-annotated OTUs, pairwise correlations were determined using Spearman’s correlation coefficients, filtered based on thresholds of P 0.6 for correlation strength. Results Differences in chemical profile are reflected in differences of the rhizomicrobiome. To detect potential shifts in the rhizomicrobiome depending on the chemical profiles of the soil, we probed the rhizosphere of grapevines in ten vineyards along the German side of the Upper Rhine representing the Northern, the central, and the Southern part of the viticulture region Baden ( Fig. 1 ). A Principal Component Analysis of soil chemical properties (Suppl. Figure 1a) revealed several types of chemical profile. Soils of vineyards C, D, I, and J showed similar chemical characteristics. Vineyards G and H were categorized separately, mostly due to their low levels of organic Carbon (C), Nitrogen (N), and Boron (B), whereas vineyard F exhibited the opposite profile, characterized by elevated concentrations of these elements. In addition, vineyards E and B were clustered together with comparably high contents of Mn and K ( Suppl. Figure 1; Suppl. Figure 2 ). To study the rhizomicrobiome structure in the context of such diverse soil characteristics, amplicon sequencing analysis was performed, using the 16S rRNA for prokaryotes, and the Internal Transcribed Spacer (ITS) for fungi. Following the removal of low-quality and chimeric reads, a total of 8,345,161 fungal ITS reads were processed from 60 samples, identifying 8,893 featured fungal operational taxonomic units (OTUs). On the phylum level, the taxonomic structure was relatively comparable between the vineyards. The most dominant fungi were the Ascomycota (between 73% in vineyard D, up to 93% in vineyard G), followed by Basidiomycota . Furthermore, the phylum Rozellomycota was more prevalent in Vineyard F ( Suppl. Figure 1b ). Among the twenty most dominant fungal genera, seventeen belonged to the phylum Ascomycota. In addition, Fusarium was the most abundant across all vineyards, shaping 12–23% of the total fungal community. Based on the most dominant fungi, the Rauenberg vineyards clustered together using Euclidean distance ( Fig. 2a ), mirroring the pattern observed in the PCA of soil chemical properties ( Suppl. Figure 1a ). These two vineyards exhibited the lowest relative abundance of Fusarium, but higher levels of two other pathogenic genera, Penicillium and Dactylonectria , as well as an elevated presence of the beneficial fungus Solicocczyma , known to promote root growth (Albornoz et al., 2025). The Eichstetten vineyard also showed distinct profiles of other two fungi, Fusidium and Subulicystidium ( Fig. 2a ). The 16S amplicons reads exhibited higher chimeric read rates, with approximately 30% of reads filtered out. Following denoising step, 3,796,969 high-quality 16S reads remained, representing 84,221 prokaryotic OTUs. Here, the prokaryotic community in the vineyard rhizosphere was significantly enriched, with 10 times more OTUs than observed in the fungal community. At the species level, 1566 Amplicon Sequence Variants (ASVs) of the fungal community were identified, along with 2786 ASVs of prokaryotic origin. Among the prokaryotes, Actinomycetota were the dominant bacterial phylum shaping the rhizobacteriome across the vineyards, with relative abundances ranging from 36% in vineyard I up to 51% in in one vineyard in Ihringen (J), despite similarity of the two vineyards with respect to soil characteristics ( Suppl. Figure 1a ). Pseudomonadota, Chloroflexota , and Acidobacteriota were next in relative abundance, respectively ( Suppl. Figure 1c ). In terms of archaeal communities (overall constituting only a minor fraction), the Crenarchaeota were more abundant in vineyards B and F. Additionally, analysis of the twenty most dominant bacterial genera in the vineyard rhizomicrobiome showed that fourteen belonged to the phylum Actinomycetota . Despite this, the most dominant genus overall was KD4 - 96 from the phylum Chloroflexota , followed by Nocardioides ( Fig. 2b ). Notably, the two vineyards from Ihringen (I and F) exhibited distinct profiles characterized by higher abundances of Tepidiforma and Rokubacteriales , while vineyard (J) in particular showed a pronounced enrichment of Kribbella . Wood-colonising fungi dominate in the vineyard rhizomicrobiome. To evaluate the ecological impact of the fungal taxa in the rhizomicrobiome and their potential to colonize woody tissues of grapevine, the defined OTUs were annotated resolving to the genus level using the FUNGuild database, which classifies fungal taxa based on their trophic mode (saprotroph, symbiotroph, or pathotroph), their associated hosts, and, in case of pathogens, their preferred colonization target (wood, root or leaf). Around half of the fungal taxa were pathotrophs ( Fig. 3a ). Most of them were opportunistic pathotrophs, otherwise living as saprotrophs or symbiotrophs, suggesting that their function might vary depending on the condition of the host. In contrast, beneficial taxa (non-pathogenic with symbiotic potential) were found to be less prevalent compared to pathogenic taxa, ranging from only 2.9% to 7.4% ( Fig. 3a ). As alternative approach, we investigated the relative abundance of wood-trophic taxa including those classified as GTDs according to Li et al. (2023a) and Martín et al. (2022). The highest (84–94%) incidence of pathogenic wood-colonising fungi was found in the Southern part of the sampling area, in the vineyards of Ihringen, and Ringsheim,(E, I, And C) whereas the vineyards of Rauenberg (A, and B) in the Northern part harboured less wood pathotrophs, comprising 68–74% of the wood trophic community. By contrast, the Rauenberg vineyards showed the highest abundance of the non-pathogenic saprotrophs ( Fig. 3b ). Abundance of GTD-associated fungi depends on vineyards, but not on symptom expression. To test, how the abundance of GTD-associated fungi in the rhizosphere relates to the respective vineyard and to the expression of GTD symptoms, we focussed on OTUs from the ITS amplicon sequencing that have been annotated as associated with GTDs (Li et al., 2023a; Martín et al., 2022). In fact, seventeen taxa could be detected in the vineyard rhizosphere linked with GTDs of different type, including Black Foot Disease, Botryosphaeria Dieback, ESCA, Eutypa Dieback, or Diaporthe Dieback. Here, the taxa associated with Black Foot Disease, such as g_ Dactylonectria , g_Thelonectria , and g_Neonectria were generally the most prevalent GTD. They were more abundant in the vineyards of Rauenberg (A and B), in the Northern part of the transsect that shared a similar overall profile of GTD-associated fungi, reported by their clustering in terms of Euclidean distance ( Fig. 3c ). Contrasting with Black Foot Disease, fungi associated with Botryosphaeria Dieback ( g_Diplodia, g_Dothiorella , and g_Neofusicoccum ) were significantly rarer, and found mainly in vineyards G, H, and I, near Ihringen in the Southern part of the transsect, characterised by loess soils and a warm and dry climate. The third disease, Esca, was represented by six OTUs: g_Coprinellus , g_Cadophora , g_Phaeoacremonium, g_Stereum , g_Fomitiporia , and g_Phaeomaniella . Among them, g_Coprinellus was the most abundant Esca taxon in all tested vineyards, particularly in vineyards D, G, and H, followed by g_Phaeoacremonium , especially in vineyard G. Three OTUs associated with Eutypa Dieback: g_Cryptovalsa, g_Neoascochyta , as well as g_Didymella which was significantly detected in all vineyards. To a low extent, OTU g_Diaporthe , associated with Diaporthe Dieback was found, without a particular vineyard preference. To assess whether the outbreak of GTDs is linked with a higher abundance of GTD taxa, their relative abundance in the rhizosphere of symptomatic versus asymptomatic vines was calculated, pooling over all vineyards. Here, g_ Coprinellus was the only OTU that showed significant accumulation for symptomatic plants ( P < 0.01, Kruskal-Wallis test) ( Fig. 3c ). Thus, with exception of g_ Coprinellus , we do not see any link between disease outbreak and abundance of GTD-associated fungi. GTD outbreak correlates with rhizomicrobiome shifts To test whether GTD outbreak correlated with significant shifts of the rhizomicrobiome, a differential abundance analysis was carried out, using ANCOM-BC. Since the covariate of interest was the health status of the vine, all rhizosphere samples were pooled into two categories, symptomatic versus asymptomatic vines. Then, for the shifted OTUs, the Log Fold Changes (LFC) for symptomatic over asymptomatic vines, and their statistical significance were calculated, regardless of the cultivar or geographical location. This approach revealed significant shifts of both, the fungal ( Fig. 4a ), and the prokaryotic ( Fig. 4b ) rhizomicrobiome that were also dependent on the chemical properties of the soil. These shifts are described in the following: Fungal shifts : In the fungal community, seven OTUs at the genus level were depleted in symptomatic vines ( Fig. 4a ). Five of these belong to the Ascomycota : Cistella , Pseudophialocephala , Populomyces , Tetracoccosporium , and Collarina . Additionally, two genera from different phyla shifted in parallel: Mucor from Mucoromycota , and Limnoperdon from Basidiomycota . On the other hand, nine OTUs were enriched under disease outbreak; Among them were g_Coprinellus , proposed as driver of Esca, as well as the epiphytic pathogenic fungus, g_Aureobasidium . To assess how soil chemical profiles correlate with the abundance of rhizomicrobiome fungi associated to the asymptomatic phase, we calculated Pearson correlations between these taxa and individual soil nutrients, adding those fungal phyla that had been found to be generally abundant in the rhizomicrobiome ( Fig. 4a ). We saw significant associations of specific fungal taxa with specific traits of soil chemistry ( Fig. 5a ). For instance, the phylum Basidiomycota was positively correlated with the micronutrients Fe, Cu, and Zn, but negatively correlated with CaCO₃ and pH (alkalinity). Likewise, the phylum Rozellomycota showed positive correlations with Org_C and N, but also with the micronutrient Boron (B). Generally, the taxa that decreased during disease outbreaks, seemed more responsive to soil micronutrients. Here, Collarina as the taxon with the largest fluctuations with respect to soil properties exhibited positive correlations with Zn, Cu, Mn, Fe, Mg, and C.N ratio, followed by g_ Pseudophialocephala , with significant correlations with Zn, Cu, Fe, Mg, and K. Also, for Cistella a link with Fe levels was observed. It is worth noting that fungal members that displayed positive correlations with Fe, became scarce for increases in CaCO 3 and pH. Prokaryotic shifts : The abundance of many bacterial OTUs dropped significantly in symptomatic vines ( Fig. 4b ). Among those taxa that correlated with healthy vines, the two phyla Actinomycetota and Pseudomonadota exhibited five OTUs that were depleted in symptomatic plants. The most affected taxa were Isoptericola , Thioprofundum , Caulobacter , Rhodomicrobium , as well as Chryseolinea from p_Bacteroidota . Other phyla had only one depleted OTU. For instance, in p_Bacillota only one OTU ( Xylanivirga ) decreased, as well as in p_Entotheonellaeota ( Entotheonellaceae ). On the other hand, there were also several rhizobacteriome members which accumulated significantly in symptomatic vines: Puia , and Ferruginibacter , from p_Bacteroidota , were enriched most, but also four OTUs from Pseudomonadota , as well as a single OTU from each of the phyla Verrucomicrobiota, p_Patescibacteria and Myxococcota were significantly increased. We searched for chemical properties of the soil that correlated with these changes of bacterial abundance, but also with dominance of specific prokaryotic phyla independent of disease symptomatics (Fig. 5b) . The generally most dominant bacterial phylum, p_Actinomycetota , exhibited a negative correlation with only CaCO₃ while p_Pseudomonadota were negatively correlated with P, K, and Zn. Positive correlations were seen for the p_ Acidobacteriota with B and for p_Crenarchaeota with Cu. Soil properties had also a significant impact on several taxa associated with the asymptomatic phase of GTDs. Specifically, g_Caulobacter displayed a positive correlation with C.N ratio, as well as with Mg, Fe, Cu, and Zn, but a negative correlation with CaCO 3 (Fig. 5b) . Along with Caulobacter , also Entotheonellaceae became enriched depending on C.N ratio, Fe, and CaCO 3 , but were depleted in alkaline pH. By contrast, Thioprofundum , an OTU from the Pseudomonadota , was the only taxon exhibiting a positive correlation with pH. Rhizomicrobial co-occurrence networks shift depending on GTD symptoms To assess whether the interactions and relationships among different rhizomicrobiome taxa are influenced by the health status of the vine, co-occurrence networks were inferred for both, the fungal ( Fig. 6a,b ) and the prokaryotic ( Fig. 6d,e ) microbiome in symptomatic versus asymptomatic vines. Here, the shift of the co-occurrence networks responded qualitatively different in fungi versus prokaryotes. While 1680 significant correlations were detected among the fungal taxa in healthy vines, there was an increase to 1856 significant correlations under GTD outbreak ( Fig. 6g ). A salient component of this increase was the doubling for correlations of GTD-associated taxa with other fungal taxa upon host transition to the symptomatic phase ( Suppl_table1; Fig. 6c ). Here, most GTD taxa sharply changed their correlation profiles. For instance, Fomitiporia displayed thirteen significant correlations under disease outbreak, but none in asymptomatic plants. Likewise, correlations of Stereum were amplified 8-fold. Furthermore, this fungus extended its associations with other GTD taxa, such as Phaeomoniella and Fomitiporia , as well as with other six wood-saprotrophic and pathogenic taxa ( Suppl_table1) . Likewise, Phaeomoniella , in symptomatic vines, displayed 29 correlations, not only with Fomitiporia , but also with eleven other pathogenic taxa, contrasting with only 10 correlations in healthy vines. The aggressive genus, Neofusicoccum , responsible for Botryosphaeria dieback, entertained 15 different correlations during GTD outbreak, compared to only 8 in the asymptomatic phase. For Diaporthe dieback linked with g_Diaporthe , a significant correlation, with g_Paurocotylis , was only seen in symptomatic vines ( Suppl_table1 ). The inverse case, where associations between two pathogens turned loose during GTD outbreak, was far rarer – here, the significant correlation between Phaoeoacremonium and Stereum was detected only in healthy vines. A third case, where the pathogenic partner of a GTD fungus was swapped by another pathogenic partner, is represented by Coprinellus , which was more prevalent in symptomatic vines ( Fig. 4a ). This fungus correlates with g_Keissleriella in asymptomatic vines, but switches to a significant correlation with g_Tulasnella under the conditions of a GTD outbreak. Contrasting with fungi, the connectivity for the 1167 prokaryotic genera, was drastically decreased from 11856 significant correlations in the healthy vines to 6361 significant correlations under GTDs outbreak (Fig. 6g; Suppl_table2) . Here, taxa associated with the asymptomatic phase showed different interaction profiles with other soil prokaryotes. For instance, Entotheonellaceae constituted a core node with six strong pairwise correlations in the rhizosphere of healthy vines, but upon GTD outbreak turned into a peripheral node with only two correlations. Also, Isoptericola and lost their correlations and even disappeared from the co-occurrence network, while Thioprofundum, Kitasatospora, Rhodomirobium, Methylothermalis, Illumatobacter, and Caulobacter robustly lost their positive correlations with other bacteria in symptomatic plants ( Fig. 6f; Suppl_table2 ). Only few taxa showed an inverse pattern: Steroidobacter and Chryseolinea established more positive correlations only in symptomatic vines. Bacterial diversity depends mainly on soil, fungal biodiversity also on geography As markers for ecosystem robustness, we determined a panel of diversity metrics for the rhizomicrobiome over the different vineyards with their differences in soil parameters and geographical location, either in asymptomatic plants or under disease outbreak. We did not observe any salient changes in the diversity metrics, neither of prokaryotes, nor of fungi in association with GTD outbreak ( Suppl. Figure 2 ). Only for two vineyards, E and G, Shannon entropy, an alpha-diversity index, which accounts for the species richness and evenness within an ecosystem (here, environmental sample), shifted significantly in the bacterial community. Bray_Curtis distance as a quantitative index for community diversity between samples, did not reveal any significant differences in bacteria community between asymptomatic symptomatic vines, but it was significantly shifted in the rhizosphere fungi of symptomatic vines in two other vineyards, D and F. To link rhizomicrobiome richness with soil parameters, alpha diversity was characterised using Shannon, Chao1, and Faith's Phylogenetic Diversity (PD) indices over the different vineyards ( Suppl. Figure 3 ), and then correlated either with macronutrients ( Suppl. Figure 4 ) or micronutrients ( Fig. 7 ). Generally, the Shannon index remained relatively stable across vineyards, with exceptions observed in the fungal community of vineyard I, and the bacterial communities of vineyards C and E. This index showed no significant correlation, neither with macronutrient nor micronutrient levels. Similarly, Chao1, a richness index that estimates diversity based on abundant taxa, revealed no significant correlation with soil properties, although the prokaryotes in vineyards C and E were significantly different from the other vineyards. Thus, parameters estimating diversity based merely on differences in abundance of taxa, not considering their identity, remained inconspicuous. The outcome changed, when also phylogenetic relationships were included into the parametrisation. Here, Faith's PD, which considers the phylogenetic differences among taxa, was the most variable parameter among the alpha diversity metrics for both, fungal and prokaryotic, communities ( Suppl. Figure 3 ). This was especially pronounced in the prokaryotic community, where Faith's PD showed a positive correlation with the principal component of soil properties, Fe, Cu, Zn ( Fig. 7 ), and Mg ( Suppl. Figure 4 ). Beta diversity, which evaluates the dissimilarities among different ecosystems, was characterized through Principal Coordinate Analysis (PCoA). Again, this analysis was either conducted either disregarding phylogenetic relationships, based on the Bray-Curtis distance ( Fig. 7b ), or incorporating taxonomic distance, using Weighted-Unifrac distance ( Fig. 7a ). As already seen for alpha diversity, the integration of phylogenetic distance revealed differences that otherwise would have gone unnoticed. While only 4% of prokaryotic beta-diversity could be explained by the first principal PCoAs on the base of Bray-Curtis distance ( Fig. 7b ), the first two Unifrac PCoAs accounted for 30% of the observed diversity ( Fig. 7a ). This improved sensitivity allowed to detect several interesting features: Here, beta-diversity of fungi was clearly different depending on geographical location ( Fig. 7a ). For instance, the selected vineyards in Rauenberg and Ringsheim in the North of the study area were significantly different from those in Eichstetten in the South. However, this difference in fungal diversity seemed to be uncoupled from soil parameters, with exception of CaCO 3 ( Fig. 7c, Suppl.Fig. S4 ). Even disregarding the phylogenetic relationships among OTUs, using the Bray_Curtis distance, the Rauenberg vineyards were significantly divergent with a strong correlation with soil parameters including PC1, Fe, Zn, K and Cu, respectively ( Fig. 7c, Suppl.Fig. S4 ). In contrast to fungal biodiversity, the prokaryotic community seemed more dependent on soil parameters. This dependence was so strong that it was not only seen for the Unifrac, but also for the Bray_Curtis distances, where phylogenetic relationship is left aside. Relevant soil parameters were here Fe, Zn, Mg and Cu ( Fig. 7d, Suppl. Fig. S4 ). Discussion This study explored the relationship between the outbreak of grapevine trunk diseases (GTDs) and rhizomicrobiome composition and dynamics over ten vineyards, sampled along a North-South transsect in the Upper Rhine valley. The transition from the latent to the necrotrophic phase of the GTD-associated fungi was accompanied by taxonomic shifts in both, the fungal and the prokaryotic, rhizomicrobiome. Using a panel of diversity metrics, we detected correlations between the incidence of prokaryotic taxa associated with the latent, asymptomatic phase and soil properties, whereas for the respective fungal taxa also geographic location was found to be relevant. We identified taxa associated with the latent “asymptomatic” phase and their interactions with rhizosphere flora and the soil properties. The geographical location and soil properties showed variable effects on the composition and diversity metrics of both fungal and bacterial communities. The analysis of correlation networks revealed that GTD-associated fungi increased their mutual association after outbreak of the disease. These findings stimulate several questions in the context of GTD outbreak that will be discussed in the following: Why are the prokaryotes responsive to soil chemistry, while the fungi are not? What are potential mechanisms behind the association of specific prokaryotes with grapevine health? What does the altered fungal correlation network mean for our concept of health and disease? Soil ecology: fungi respond by metabolic state, bacteria by proliferation The rhizomicrobiome plays a central role for maintaining plant health and resilience against environmental challenges (reviewed in Berendsen et al., 2012; Gu et al., 2022). Therefore, insight into the grapevine rhizomicrobiome structure, as well as environmental key factors shaping its richness, diversity, and the incidence of beneficial taxa is relevant for sustainable viticulture. Along this line, two salient features emerge from our analysis: For the fungal community, it is host status, rather than soil properties, that seems to define disease outbreak. The dominant fungal phyla in the vineyard rhizosphere were Ascomycota and Basidiomycota , respectively ( Suppl . Figure 1), similar to previous studies performed on different cultivars, different geographical and environmental conditions (Bao et al., 2022; Berlanas et al., 2019; Coller et al., 2019; Lailheugue et al., 2024). In addition, there were no significant differences in the incidence of symbiotrophic or pathotrophic taxa among the different vineyards ( Fig. 3a ). However, around 50% of the detected fungal taxa were multi-trophic with a pathotrophic potential. This is consistent with a scenario, where disease outbreak is not defined by increased abundance of GTD associated taxa, but rather by changes in their behaviour. In fact, such a conditional pathogenesis has been demonstrated to link to changes in secondary metabolites, for instance, in the fungal GTD model Neofusicoccum parvum (Khattab et al., 2022; Flubacher et al., 2023). While secondary metabolism in fungi is very rich and elaborate, most of the underlying gene clusters are silent, if not activate by specific (mostly unknown) signals (for review see Brakhage and Schroekh, 2011). It is, therefore, not surprising that, for the rhizomicrobiome fungi, differences in soil ecology will be reflected in changes of metabolic state, rather than in changes of abundance. In contrast to fungi, prokaryotic phyla seemed more influenced by geography and environmental factors. In our study, Actinomycetota emerged as the predominant bacterial phylum, followed by Pseudomonadota , Chloroflexota , and Acidobacteriota ( Suppl. Figure 1c ). With exception of the Chloroflexota , these dominating phyla have been found in other vineyard studies, albeit at variable composition. For example, similar profiles of Actinomycetota and Pseudomonadota were reported across various cultivars in the grape-growing regions of Huailai County, China by Bao et al. (2022). A strong dependence on geography can also been concluded by differences in the dominant phyla of the grapevine rhizosphere among different regions in Europe. While the Pseudomonadota was the dominant phylum, followed by Actinomycetota in vineyards located in Bordeaux, France (Lailheugue et al., 2024), Italy (Marasco et al., 2018), and Spain (Berlanas et al., 2019), a different study on ten vineyards across four sites in Italy identified Acidobacteriota as most abundant phylum (Coller et al., 2019). Such comparisons between different studies have to be taken with care, because timing of sampling might also affect the rhizomicrobiome structure (Berlanas et al., 2019). However, for the data from the current study, seasonal differences can be excluded, because the samples were collected in August, because outbreak of GTDs becomes fully manifest in late summer. Instead, our data lead to the conclusion that the substantial differences in prokaryotic abundance might derive from soil properties. This is supported, for instance, by the negative correlation between Pseudomonadota and content of Zn, K and P, or by the strong positive correlation between Acidobacteriota and B and nitrogen content ( Fig. 5b ). Globally, bacterial diversity metrics, especially those incorporating phylogenetic relationships such as Faith_PD and Unifrac, showed a significant correlation with soil properties, especially the content of micronutrients Fe, Cu, Mn, and Zn ( Fig. 7c, Suppl. Figure 4 ). Such elements are key players for enzymes regulating microbial proliferation and many biological processes, e.g. Fe for N fixation, Zn and Cu for immunocompetence, and Fe and Mn for respiration. Both elements, Fe and Mn could also serve as electron donors and acceptors, during soil redox reactions of C, N, and S (Dai et al., 2023b; Dubinsky et al., 2010; Whalen et al., 2018). In contrast to fungi that can respond to environmental conditions by releasing previously silent metabolic modules, bacteria need to respond by altering the composition of their consortia that are often coupled by concerted metabolisation of given substrates, giving rise to the concept of Metabolically Cohesive Consortia (for a conceptual review see Pascual-García et al., 2020). In shorthand, the fungal communities in the rhizomicrobiome respond by adjusting their metabolic state, while the bacteria respond by altering their proliferation. Disease outbreak is associated with shifts in the bacterial rhizomicrobiome Under disease outbreak, we observed strong shifts in the composition of the bacterial rhizomicrobiome (contrasting with the fungi, which will be discussed in the next section), reflected in the co-occurrence networks. The positive correlations, as reported by edges among the network nodes, dropped drastically almost to the half among ( Fig. 6g ). Salient features were the depletion of Isoptericola , Thioprofundum , Caulobacter , Rhodomicrobium , as well as Chryseolinea ( Fig. 4b ). These taxa might, therefore, used as indicators for grapevine health. In addition, some taxa lost their positive correlations or completely disappeared from the co-occurrence network, e.g. Isoptericola ( Fig. 6e; Suppl. Table 2 ). Possible reasons might be increased competition for nutrients, reduced functionality, but also the rise of outbreak-associated bacterial communities. Since around 50% of the fungal flora in the rhizomicrobiome have pathotrophic potential ( Fig. 3a ), the depleted or less connected bacterial networks might be a factor driving the transition to fungal pathogenicity. While the underlying mechanisms remain to be elucidated for grapevine, current findings from other crops suggest that root exudates released from healthy plants promote the establishment of a rhizomicrobiome that promotes nutrient cycling, enhances plant immunity, and maintains soil health (Chen et al., 2024; Du et al., 2024; Wilhelm et al., 2023). Under the severe stress conditions that often herald a GTD outbreak (Khattab et al., 2022), either the absence or the modification of such root exudates might contribute to the disruption of the established microbial networks, such that beneficial microbiota become depleted, while pathogens become promoted, and conditional fungal pathogens alter their lifestyle towards parasitism. Harnessing the established bacterial networks in health-associated rhizomicrobiomes, ( Fig. 6 ), might be a strategy for sustainable biocontrol of GTDs. Micronutrients supplements might help in this regard since they showed strong correlations with phylogenetic diversity metrics as well as with higher abundance of health-associated bacteria ( Fig. 5; Fig. 7 ). Whether such bacteria are actively promoting plant resilience, or whether they are just attracted to plants endowed with resilience cannot be inferred from a correlative study, of course. H owever, these bacteria are at least candidates, and it is worthwhile to probe them individually for a potential activation of plant immunity. This would also be interesting for application, because those, where activation of immunity can be confirmed could be developed into new sustainable biocontrol agents against GTDs. The potential of this approach has already been by studies, where co-inoculation with members of Actinomycetota and Bacillota mitigated GTD progression (for review see Cobos et al., 2022). This mitigation could be direct, by allelopathic control. For instance, Streptomyces either endophytic sp. VV/E1, or two rhizosphere isolates, sp. VV/R1 and sp. VV/R4, significantly inhibited the growth of Dactylonectria sp. and Ilyonectria sp., fungi involved in black foot disease, as well as of Phaeomoniella chlamydospora , and P. minimum , associated with the Esca disease (Álvarez-Pérez et al., 2017). In addition to direct growth inhibition, secreted compounds might also act indirectly, by activating host defence. Likewise, In fact, Bacillus pumilus (S32) and Paenibacillus sp. (S19) were shown to secrete antifungal volatiles, including 1-octen-3-ol and 2,5-dimethyl pyrazine that not only suppressed the Esca fungus Phaeomoniella chlamydospora but also upregulated phytoalexin biosynthesis genes of grapevine (Haidar et al., 2016). In a similar manner, Bacillus subtilis PTA-271 could simultaneously enhance defence signalling and reduce the growth rate of Neofusicoccum parvum in grapevine (Trotel-Aziz et al., 2019). GTD outbreak: a matter of fungal ecology rather than pathogen incidence? Contrasting with other grapevine diseases, such as Downy Mildew (caused by the oomycete Plasmopara viticola ), or Powdery Mildew (caused by the ascomycete Erysiphe necator ), the causal chain in GTDs is far from elucidated. Classically, pathogens are identified through demonstrating that they are necessary and sufficient for symptomatics, an approach known as Koch Postulates (Loeffler, 1884). This classical approach fails for GTDs, because symptoms can rarely be attributed to absence and presence of a given fungus. Moreover, attempts to compare symptomatic vines with asymptomatic controls did not yield significant differences in the composition of the mycobiome. In one of the (few) rigorous comparisons, the authors arrive at the provocative conclusion “What if esca disease of grapevine were not a fungal disease?” (Hofstetter et al., 2012), arguing that the presence of certain wood-decaying fungi in both asymptomatic or symptomatic trunks might be due to their saprotrophic lifestyle breaking down tissue that had died for other reasons, such as overpruning or frost damage. If it is not the mere presence of a microbe that leads to pathogenesis, it might be the conditions that render a microbe into a pathogen. The findings of the current study, mainly the shifts in the correlation networks, support such a contextual model of pathogenesis, clearly transcending the Koch Postulates. On the one hand, most prevalent GTD associated taxa, namely those reported in the context of black foot disease, showed no significant shifts compared to the asymptomatic phase ( Fig. 3c ). The only apparent candidate, Coprinellus , seems to be only a hitchhiker and not a driver. In controlled infection studies, it failed to induce foliar symptoms or trigger disease outbreak contrasting with other Esca fungi (Brown et al., 2020). Moreover, unlike many GTDs taxa which are wood-obligate saprotrophs, Coprinellus was detected also in grapevine leaves (Cui et al., 2024). Therefore, Coprinellus might rather be a saprotroph, potentially feeding on decayed plant material following the outbreak. Also for the other sixteen GTD associated taxa, abundance changes after outbreak were insignificant. However, what was significant, was the dynamics of interactions which were amplified within the fungal community, prominently in those with pathotrophic potential ( Suppl_Table 1 ). Here, some GTD taxa strongly increased their mutual correlations, especially Esca-associated fungi, such as Phaeomoniella with Fomitiporia , Stereum with Fomitiporia , and Stereum with Phaeomoniella . These patterns are consistent with a model, where these fungi initiate mutualistic interactions and benefit each other through co-colonisation or metabolic-cross feeding. Whether the opportunistic shift of these Esca taxa towards pathogenic behaviour is triggered by a breakdown in plant immunity due to loss of beneficial microbes remains to be elucidated by targeted infection experiments in the presence of tailored rhizomicrobiomes. Outlook It has to be kept in mind that the nature of a agroecological study as the current one is descriptive, and the outcome is confined to correlations. Since the sampling had to be in August, when symptoms are clearly manifest, the sampling represents a static snapshot. To infer the causal chain from such snapshots alone is principally not possible. Nevertheless, several candidates, both for pathogenesis, as well as for salutogenesis, could be identified. To integrate the temporal dynamics of pathogenesis, these candidates need to be tested functionally in controlled infection assays to assess, for instance changes in the root secretome under GTDs outbreak as well as direct promoting or inhibiting interactions between microbes, or activation of plant immunity. The ultimate goal will be to develop new biocontrol agents to prevent or even to cure grapevine trunk diseases. Declarations Funding declaration and acknowledgement This work was supported by Microbes for future project (M4F), which was funded by Strategy fund of Karlsruhe Institute of Technology. The authors have no relevant financial or non-financial interests to disclose. All authors read and approved the final manuscript. Data availability: Sequencing rawdata: NCBI bioproject ( Vineyards_rhizomicrobiome), Accession: PRJNA1328367, ID: 1328367 ( https://www.ncbi.nlm.nih.gov/bioproject/1328367) Bioinformatics : Github respiratory, “Vineyards_rhizomicrobiome_Upper_Rhine” (https://github.com/Khattab2022/Vineyards_rhizomicrobiome_Upper_Rhine) References Abarenkov, K., Nilsson, R. H., Larsson, K. H., Taylor, A. F. S., May, T. W., Frøslev, T. G., Pawlowska, J., Lindahl, B., Põldmaa, K., Truong, C., Vu, D., Hosoya, T., Niskanen, T., Piirmann, T., Ivanov, F., Zirk, A., Peterson, M., Cheeke, T. E., Ishigami, Y., … Kõljalg, U. (2024). The UNITE database for molecular identificationãnd taxonomic communication of fungiãnd other eukaryotes: sequences, taxaãnd classifications r econsider ed. Nucleic Acids Research , 52 (D1), D791–D797. https://doi.org/10.1093/nar/gkad1039 Albornoz, F., Carvajal, M., Catrileo, D., Gebauer, M., & Godoy, L. (2025). Volatile organic compounds produced after exposure of tomato roots to the soil yeast Solicoccozyma terrea modulate root nitrate transporters in tomato. Plant and Soil . https://doi.org/10.1007/s11104-025-07393-8 Álvarez-Pérez, J. M., González-García, S., Cobos, R., Olego, M. Á., Ibañez, A., Díez-Galán, A., Garzón-Jimeno, E., & Coque, J. J. R. (2017). Use of endophytic and rhizosphere actinobacteria from grapevine plants to reduce nursery fungal graft infections that lead to young grapevine decline. Applied and Environmental Microbiology , 83 (24). https://doi.org/10.1128/AEM.01564-17 Bao, L., Sun, B., Wei, Y., Xu, N., Zhang, S., Gu, L., & Bai, Z. (2022). Grape Cultivar Features Differentiate the Grape Rhizosphere Microbiota. Plants , 11 (9). https://doi.org/10.3390/plants11091111 Berendsen, R. L., Pieterse, C. M. J., & Bakker, P. A. H. M. (2012). The rhizosphere microbiome and plant health. In Trends in Plant Science (Vol. 17, Issue 8, pp. 478–486). https://doi.org/10.1016/j.tplants.2012.04.001 Berlanas, C., Berbegal, M., Elena, G., Laidani, M., Cibriain, J. F., Sagües, A., & Gramaje, D. (2019). The fungal and bacterial rhizosphere microbiome associated with grapevine rootstock genotypes in mature and young vineyards. Frontiers in Microbiology , 10 (MAY). https://doi.org/10.3389/fmicb.2019.01142 Bolyen. E; Rideout J.R; Dillon M.R; Bokulich N.A.; Abnet C.C.; Al-Ghalith G.A.; Alexander H.; Alm E.J.; Arumugam M. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology , 37 (8), 850–852. https://doi.org/10.1038/s41587-019-0190-3 Brown, A. A., Lawrence, D. P., & Baumgartner, K. (2020). Role of basidiomycete fungi in the grapevine trunk disease esca. Plant Pathology , 69 (2), 205–220. https://doi.org/10.1111/ppa.13116 Chao, A. (1987). Estimating the Population Size for Capture-Recapture Data with Unequal Catchability (Vol. 43, Issue 4). https://www.jstor.org/stable/2531532 Chen, Q., Song, Y., An, Y., Lu, Y., & Zhong, G. (2024). Soil Microorganisms: Their Role in Enhancing Crop Nutrition and Health. In Diversity (Vol. 16, Issue 12). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/d16120734 Cobos, R., Ibañez, A., Diez-Galán, A., Calvo-Peña, C., Ghoreshizadeh, S., & Coque, J. J. R. (2022). The Grapevine Microbiome to the Rescue: Implications for the Biocontrol of Trunk Diseases. In Plants (Vol. 11, Issue 7). MDPI. https://doi.org/10.3390/plants11070840 Coller, E., Cestaro, A., Zanzotti, R., Bertoldi, D., Pindo, M., Larger, S., Albanese, D., Mescalchin, E., & Donati, C. (2019). Microbiome of vineyard soils is shaped by geography and management. Microbiome , 7 (1). https://doi.org/10.1186/s40168-019-0758-7 Cui, S., Zhou, L., Fang, Q., Xiao, H., Jin, D., & Liu, Y. (2024). Growth period and variety together drive the succession of phyllosphere microbial communities of grapevine. Science of the Total Environment , 950 . https://doi.org/10.1016/j.scitotenv.2024.175334 Dai, Z., Guo, X., Lin, J., Wang, X., He, D., Zeng, R., Meng, J., Luo, J., Delgado-Baquerizo, M., Moreno-Jiménez, E., Brookes, P. C., & Xu, J. (2023a). Metallic micronutrients are associated with the structure and function of the soil microbiome. Nature Communications , 14 (1). https://doi.org/10.1038/s41467-023-44182-2 Dai, Z., Guo, X., Lin, J., Wang, X., He, D., Zeng, R., Meng, J., Luo, J., Delgado-Baquerizo, M., Moreno-Jiménez, E., Brookes, P. C., & Xu, J. (2023b). Metallic micronutrients are associated with the structure and function of the soil microbiome. Nature Communications , 14 (1). https://doi.org/10.1038/s41467-023-44182-2 De Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O., & Williams, A. (2020). Harnessing rhizosphere microbiomes for drought-resilient crop production . https://doi.org/https://doi.org/10.1126/science.aaz5192 Dubinsky, E. A., Silver, W. L., & Firestone, M. K. (2010). Tropical forest soil microbial communities couple iron and carbon biogeochemistry. Ecology , 91 (9), 2604–2612. https://doi.org/10.1890/09-1365.1 Du, Y., Han, X., & Tsuda, K. (2024). Microbiome-mediated plant disease resistance: recent advances and future directions. In Journal of General Plant Pathology . Springer. https://doi.org/10.1007/s10327-024-01204-1 Faith, D. P. (1992). Conservation evaluation and phylogenetic diversity. In Biological Conservation (Vol. 61). Field, K. J., Pressel, S., Duckett, J. G., Rimington, W. R., & Bidartondo, M. I. (2015). Symbiotic options for the conquest of land. In Trends in Ecology and Evolution (Vol. 30, Issue 8, pp. 477–486). Elsevier Ltd. https://doi.org/10.1016/j.tree.2015.05.007 Flubacher, N., Baltenweck, R., Hugueney, P., Fischer, J., Thines, E., Riemann, M., Nick, P., & Khattab, I. M. (2023). The fungal metabolite 4-hydroxyphenylacetic acid from Neofusicoccum parvum modulates defence responses in grapevine. Plant Cell and Environment , 46 (11), 3575–3591. https://doi.org/10.1111/pce.14670 Fotios, B., Sotirios, V., Elena, P., Anastasios, S., Stefanos, T., Danae, G., Georgia, T., Aliki, T., Epaminondas, P., Emmanuel, M., George, K., Kalliope, P. K., & Dimitrios, K. G. (2021). Grapevine wood microbiome analysis identifies key fungal pathogens and potential interactions with the bacterial community implicated in grapevine trunk disease appearance. Environmental Microbiomes , 16 (1). https://doi.org/10.1186/s40793-021-00390-1 Gilbert, J. A., Van Der Lelie, D., & Zarraonaindia, I. (2014). Microbial terroir for wine grapes. In Proceedings of the National Academy of Sciences of the United States of America (Vol. 111, Issue 1, pp. 5–6). https://doi.org/10.1073/pnas.1320471110 Gu, S., Wei, Z., Shao, Z., Friman, V. P., Cao, K., Yang, T., Kramer, J., Wang, X., Li, M., Mei, X., Xu, Y., Shen, Q., Kümmerli, R., & Jousset, A. (2020). Competition for iron drives phytopathogen control by natural rhizosphere microbiomes. Nature Microbiology , 5 (8), 1002–1010. https://doi.org/10.1038/s41564-020-0719-8 Gu, Y., Banerjee, S., Dini-Andreote, F., Xu, Y., Shen, Q., Jousset, A., & Wei, Z. (2022). Small changes in rhizosphere microbiome composition predict disease outcomes earlier than pathogen density variations. ISME Journal , 16 (10), 2448–2456. https://doi.org/10.1038/s41396-022-01290-z Gu, Z. (2022). Complex heatmap visualization. IMeta , 1 (3). https://doi.org/10.1002/imt2.43 Haidar, R., Roudet, J., Bonnard, O., Dufour, M. C., Corio-Costet, M. F., Fert, M., Gautier, T., Deschamps, A., & Fermaud, M. (2016). Screening and modes of action of antagonistic bacteria to control the fungal pathogen Phaeomoniella chlamydospora involved in grapevine trunk diseases. Microbiological Research , 192 , 172–184. https://doi.org/10.1016/j.micres.2016.07.003 Hofstetter, V., Buyck, B., Croll, D., Viret, O., Couloux, A., & Gindro, K. (2012). What if esca disease of grapevine were not a fungal disease? Fungal Diversity , 54 , 51–67. https://doi.org/10.1007/s13225-012-0171-z Khattab, I. M., Fischer, J., Kaźmierczak, A., Thines, E., & Nick, P. (2023). Ferulic acid is a putative surrender signal to stimulate programmed cell death in grapevines after infection with Neofusicoccum parvum. Plant Cell and Environment , 46 (1), 339–358. https://doi.org/10.1111/pce.14468 Kwak, M. J., Kong, H. G., Choi, K., Kwon, S. K., Song, J. Y., Lee, J., Lee, P. A., Choi, S. Y., Seo, M., Lee, H. J., Jung, E. J., Park, H., Roy, N., Kim, H., Lee, M. M., Rubin, E. M., Lee, S. W., & Kim, J. F. (2018). Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nature Biotechnology , 36 (11), 1100–1116. https://doi.org/10.1038/nbt.4232 Lailheugue, V., Darriaut, R., Tran, J., Morel, M., Marguerit, E., & Lauvergeat, V. (2024). Both the scion and rootstock of grafted grapevines influence the rhizosphere and root endophyte microbiomes, but rootstocks have a greater impact. Environmental Microbiome , 19 (1). https://doi.org/10.1186/s40793-024-00566-5 Lee, S. M., Kong, H. G., Song, G. C., & Ryu, C. M. (2021). Disruption of Firmicutes and Actinobacteria abundance in tomato rhizosphere causes the incidence of bacterial wilt disease. ISME Journal , 15 (1), 330–347. https://doi.org/10.1038/s41396-020-00785-x Lin, H., & Peddada, S. Das. (2020). Analysis of compositions of microbiomes with bias correction. Nature Communications , 11 (1). https://doi.org/10.1038/s41467-020-17041-7 Li, Y., Li, X., Zhang, W., Zhang, J., Wang, H., Peng, J., Wang, X., & Yan, J. (2023a). Belowground microbiota analysis indicates that Fusarium spp. exacerbate grapevine trunk disease. Environmental Microbiome , 18 (1). https://doi.org/10.1186/s40793-023-00490-0 Li, Y., Li, X., Zhang, W., Zhang, J., Wang, H., Peng, J., Wang, X., & Yan, J. (2023b). Belowground microbiota analysis indicates that Fusarium spp. exacerbate grapevine trunk disease. Environmental Microbiome , 18 (1). https://doi.org/10.1186/s40793-023-00490-0 Marasco, R., Rolli, E., Fusi, M., Michoud, G., & Daffonchio, D. (2018). Grapevine rootstocks shape underground bacterial microbiome and networking but not potential functionality. Microbiome , 6 (1). https://doi.org/10.1186/s40168-017-0391-2 Martín, L., García-García, B., & Alguacil, M. del M. (2022). Interactions of the Fungal Community in the Complex Patho-System of Esca, a Grapevine Trunk Disease. International Journal of Molecular Sciences , 23 (23). https://doi.org/10.3390/ijms232314726 Mendes, R., Kruijt, M., Bruijn, I. de, Dekkers, E., van der voort, M., & Schneider, J. H. M. (2011). Deciphering the RhizosphereMicrobiome for Disease-Suppressive Bacteria. Science , 332 (6033), 1093–1097. https://doi.org/10.1126/science.1202007 Nguyen, N. H., Song, Z., Bates, S. T., Branco, S., Tedersoo, L., Menke, J., Schilling, J. S., & Kennedy, P. G. (2016). FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology , 20 , 241–248. https://doi.org/10.1016/j.funeco.2015.06.006 Pascual-García, A., Bonhoeffer, S., & Bell, T. (2020). Metabolically cohesive microbial consortia and ecosystem functioning. In Philosophical Transactions of the Royal Society B: Biological Sciences (Vol. 375, Issue 1798). Royal Society Publishing. https://doi.org/10.1098/rstb.2019.0245 Pollard-Flamand, J., Boulé, J., Hart, M., & Úrbez-Torres, J. R. (2022). Biocontrol Activity of Trichoderma Species Isolated from Grapevines in British Columbia against Botryosphaeria Dieback Fungal Pathogens. Journal of Fungi , 8 (4). https://doi.org/10.3390/jof8040409 Ren, B., Wang, X., Duan, J., & Ma, J. (2019). Rhizobial tRNA-derived small RNAs are signal molecules regulating plant nodulation . https://www.science.org Robeson, M. S., O’Rourke, D. R., Kaehler, B. D., Ziemski, M., Dillon, M. R., Foster, J. T., & Bokulich, N. A. (2021). RESCRIPt: Reproducible sequence taxonomy reference database management. PLoS Computational Biology , 17 (11). https://doi.org/10.1371/journal.pcbi.1009581 Shannon, C. E. (1948). A Mathematical Theory of Communication. In The Bell System Technical Journal (Issue 3). Trotel-Aziz, P., Abou-Mansour, E., Courteaux, B., Rabenoelina, F., Clément, C., Fontaine, F., & Aziz, A. (2019). Bacillus subtilis PTA-271 counteracts botryosphaeria dieback in grapevine, triggering immune responses and detoxification of fungal phytotoxins. Frontiers in Plant Science , 24 . https://doi.org/10.3389/fpls.2019.00025 Wei, Z., Gu, Y., Friman, V.-P., Kowalchuk, G. A., Xu, Y., Shen, Q., & Jousset, A. (2019). Initial soil microbiome composition and functioning predetermine future plant health. In Sci. Adv (Vol. 5). https://www.science.org Whalen, E. D., Smith, R. G., Grandy, A. S., & Frey, S. D. (2018). Manganese limitation as a mechanism for reduced decomposition in soils under atmospheric nitrogen deposition. Soil Biology and Biochemistry , 127 , 252–263. https://doi.org/10.1016/j.soilbio.2018.09.025 Wilhelm, R. C., Amsili, J. P., Kurtz, K. S. M., van Es, H. M., & Buckley, D. H. (2023). Ecological insights into soil health according to the genomic traits and environment-wide associations of bacteria in agricultural soils. ISME Communications , 3 (1). https://doi.org/10.1038/s43705-022-00209-1 Zarraonaindia, I., Owens, S. M., Weisenhorn, P., West, K., Hampton-Marcell, J., Lax, S., Bokulich, N. A., Mills, D. A., Martin, G., Taghavi, S., van der Lelie, D., & Gilbert, J. A. (2015). The soil microbiome influences grapevine-associated microbiota. MBio , 6 (2). https://doi.org/10.1128/mBio.02527-14 Supplementary Files Supplementarytable1.xlsx Supplementarytable2.xlsx supplementaryfiguresIK.pptx Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2026 Read the published version in Plant and Soil → Version 1 posted Editorial decision: Major revisions 12 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 02 Dec, 2025 Editor invited by journal 01 Dec, 2025 Editor assigned by journal 01 Dec, 2025 First submitted to journal 25 Nov, 2025 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8146628","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":553976073,"identity":"cd963d29-7f9c-4ced-913f-2b8fb2e6e3da","order_by":0,"name":"Islam M. Khattab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACNgbmBiCVkADiMDNU2BiwgVg8eLUwQrWwMQO1nEkzYGMjoIUBRQtj22EDBkJa+PgPNn66wZCWxz+//+DnArbzxnzyDYwP3rbhcZhEYrN0DkNOscQxZmbpGTy3zYAOYzaci1cLYwNQS0ViwzFmBmkeids2QC1s0rz4tPAfbP4N0jIfaMtvHoNzIC3sv/FqYUhsAzksccMxZjZpnoQDIIexMePVIpHYZp1jkJa48ViymfWMA8nGbGyJzZJzzuHWIt9/+PDtnIrkxHmHDz6+XfjPznB+8+GDH96U4dYCAQYoPHBMjYJRMApGwSigBAAAs7FHZT5hkdoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2370-0766","institution":"Karlsruher Institut für Technologie Campus Süd: Karlsruher Institut fur Technologie","correspondingAuthor":true,"prefix":"","firstName":"Islam","middleName":"M.","lastName":"Khattab","suffix":""},{"id":553976074,"identity":"7c9accac-110e-41fe-9990-5470f8422a83","order_by":1,"name":"Tyra Magold","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Tyra","middleName":"","lastName":"Magold","suffix":""},{"id":553976075,"identity":"f988ce2f-5a15-4c3a-84bf-b72a7323c6ee","order_by":2,"name":"Florian Lenk","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Florian","middleName":"","lastName":"Lenk","suffix":""},{"id":553976076,"identity":"df656503-e1bf-420c-837a-695774044787","order_by":3,"name":"Gunnar Sturm","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Gunnar","middleName":"","lastName":"Sturm","suffix":""},{"id":553976077,"identity":"a09f8fb5-68ed-449b-84b0-3e5e77695ace","order_by":4,"name":"Noemi Flubacher","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Noemi","middleName":"","lastName":"Flubacher","suffix":""},{"id":553976078,"identity":"68982196-03fb-4af0-979d-5ef7e0508245","order_by":5,"name":"Anne-Kristin Kaster","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Anne-Kristin","middleName":"","lastName":"Kaster","suffix":""},{"id":553976079,"identity":"8db7a17f-f3cd-4e06-af85-0b2b3fe67436","order_by":6,"name":"Peter Nick","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Nick","suffix":""}],"badges":[],"createdAt":"2025-11-18 14:30:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8146628/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8146628/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11104-026-08541-4","type":"published","date":"2026-04-14T15:59:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":97670570,"identity":"15d48ebc-d0d9-4c1c-b7b2-58d6dcc40651","added_by":"auto","created_at":"2025-12-08 09:30:58","extension":"odt","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":8835,"visible":true,"origin":"","legend":"","description":"","filename":"coverletter.odt","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/291cb1ca706218a994528565.odt"},{"id":97488497,"identity":"a1d6aa84-ec4e-4898-85c6-fac0e3f68671","added_by":"auto","created_at":"2025-12-05 01:53:48","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10787,"visible":true,"origin":"","legend":"","description":"","filename":"plsoPLSOD2504506.xml","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/5630d6695b56ec1adb145e80.xml"},{"id":97488488,"identity":"47510719-450b-4a13-88c5-152636492a90","added_by":"auto","created_at":"2025-12-05 01:53:48","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1195,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD250450666945.go.xml","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/f285a6ae61adf738cdfbd3b8.xml"},{"id":97488493,"identity":"480caa08-f19b-4a5f-ad1e-9cd66ffac28e","added_by":"auto","created_at":"2025-12-05 01:53:48","extension":"xml","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":856,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD2504506Import.xml","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/c1499710b4fc1a5535c0bf8c.xml"},{"id":97488504,"identity":"66fcf544-dd0e-4644-8217-835b57ab6875","added_by":"auto","created_at":"2025-12-05 01:53:49","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":112847,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD25045060enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/2d0cb94c97fc62ac35c94083.xml"},{"id":97488509,"identity":"4555b444-44f5-480f-849e-7a1709e70267","added_by":"auto","created_at":"2025-12-05 01:53:50","extension":"pptx","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16103008,"visible":true,"origin":"","legend":"","description":"","filename":"FiguresVineyardsmanuscriptIK27.07.25.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/6e55baa1ccb118ffcc125b72.pptx"},{"id":97488495,"identity":"5d1a284f-52ec-4cc4-90b3-89809c73b696","added_by":"auto","created_at":"2025-12-05 01:53:48","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111154,"visible":true,"origin":"","legend":"","description":"","filename":"PLSOD25045060structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/8ecf9818d525e6303d663179.xml"},{"id":97488494,"identity":"ef5e8872-d0fd-40b1-80a2-5edabc81008d","added_by":"auto","created_at":"2025-12-05 01:53:48","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":124995,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/aa166896ef02c235c0f8bae3.html"},{"id":97488507,"identity":"5351f727-6171-49be-b655-80f4cade709a","added_by":"auto","created_at":"2025-12-05 01:53:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":467616,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/036fecb3459950b0c1d06ef1.png"},{"id":97488490,"identity":"8b6e3be7-10ec-477a-b704-4f9b002f2938","added_by":"auto","created_at":"2025-12-05 01:53:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126364,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/90ec94bfcb221b11ccc6bb06.png"},{"id":97488506,"identity":"817802d1-1b91-419a-9899-0fb5aa79edb5","added_by":"auto","created_at":"2025-12-05 01:53:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":219081,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/f98c717c5a53b886030c5998.png"},{"id":97488502,"identity":"64a91b96-e697-42d2-89d4-1e4f133a7cbc","added_by":"auto","created_at":"2025-12-05 01:53:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":120429,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/5c643a33b8bf9cc62f056c66.png"},{"id":97669793,"identity":"caa094ed-d0f4-4773-a5f5-ea5431825459","added_by":"auto","created_at":"2025-12-08 09:28:58","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":233268,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/428eefada3167ca8cf60a28c.png"},{"id":97488503,"identity":"2e28f4ce-942e-420a-9890-895a94dfab07","added_by":"auto","created_at":"2025-12-05 01:53:49","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":398309,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/ac600d01bef114f33681c9e5.png"},{"id":97488499,"identity":"84486e81-88b9-4e47-a739-1d33da108d74","added_by":"auto","created_at":"2025-12-05 01:53:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":277027,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/f8570d38f8178f84f0187d88.png"},{"id":107350902,"identity":"284b63be-ad1a-488f-a625-0e984507dded","added_by":"auto","created_at":"2026-04-20 16:06:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2246245,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/d9a486ab-7d14-4574-9056-7abf2b7f66ed.pdf"},{"id":97488505,"identity":"43647b28-f031-4f82-8742-b5e9e6931f42","added_by":"auto","created_at":"2025-12-05 01:53:49","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":17888,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/131685aeea5568518fab597a.xlsx"},{"id":97488508,"identity":"1df0c61a-19e8-4fe4-aec7-533b8f6e79aa","added_by":"auto","created_at":"2025-12-05 01:53:49","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":15539,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/59c4a87fc81d9125bcba5b83.xlsx"},{"id":97669856,"identity":"60a7e265-3481-4617-ad74-99ad4e5c993f","added_by":"auto","created_at":"2025-12-08 09:29:06","extension":"pptx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":1878037,"visible":true,"origin":"","legend":"","description":"","filename":"supplementaryfiguresIK.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8146628/v1/bdc4d9769deed894aaf755f6.pptx"}],"financialInterests":"","formattedTitle":"Health or disease – a question of rhizomicrobial ecology? The case of Grapevine Trunk Disease","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGrapevine Trunk Diseases (GTDs) threaten viticulture worldwide, accelerated by the ongoing climate change. In France alone, yield losses in 2016 accumulated to 25%, corresponding to approximately 5000\u0026nbsp;million US\u003cspan\u003e$\u003c/span\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.maladie-du-bois-vigne.fr\u003c/span\u003e\u003cspan address=\"https://www.maladie-du-bois-vigne.fr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) deficit. The outbreak of different forms of GTDs such as Botryosphaeria dieback, Esca syndrome, Eutypa dieback, Diaporthe dieback, and black foot disease is associated with a wide range of fungal endophytes of around 174 species (Li et al., 2023a). Unlike classical plant diseases, GTDs do not follow the Koch postulates, meaning that the expression of symptoms is not correlated to pathogen abundance, but rather depend on the condition of the host. For example, \u003cem\u003eNeofusicoccum parvum\u003c/em\u003e, an aggressive fungus causing Botryosphaeria dieback, was found to switch to the necrotrophic phase when the host faces severe drought stress, provoking accumulation of the monolignol precursor ferulic acid. Increases in steady-state levels of ferulic acid might be interpreted by the fungus as a \u0026ldquo;plant surrender\u0026rdquo; signal, driving the fungus to secrete a Fusicoccin A aglycon, which afterwards triggers programmed plant cell death (Khattab et al., 2023). In the absence of ferulic acid, the fungus manipulates the homeostasis between defense and growth of the host by secreting an auxin mimic, 4-hydrophenylacetic acid, interfering with specific branches of phytoalexin synthesis (Flubacher et al., 2023).\u003c/p\u003e\u003cp\u003eConditional pathogenesis is not limited to GTDs, though. While plant-pathogen interactions are often conceptualized as a battle between two opponents, it is important to consider that this viewpoint represents a reduction of a far more complex reality. In fact, the outcome of this battle depends on numerous environmental factors, including the presence of other microorganisms that can have a major impact on the infection process. For example, resistance of a tomato genotype to soil-borne disease was associated with microbiota differing from those in a susceptible genotype (Kwak et al., 2018). As sessile organisms, plants have evolved to regulate the microbial communities in the rhizosphere (for review see Berendsen et al., 2012). The increasing number of examples, where beneficial soil microbes have been found to help plants to survive under environmental challenges (De Vries et al., 2020; Field et al., 2015; Ren et al., 2019) suggest that the interaction between plants and the so-called rhizomicrobiome might be subject of co-evolution. The model of a mutualistic relationship is also supported by findings, where taxonomic structure and function changes depending on plant developmental stage and stress conditions (Berendsen et al., 2012; Gu et al., 2022), supporting the concept of the rhizomicrobiome acting as a \"second genome\" for plants because of its pivotal role in promoting plant health and resilience (De Vries et al., 2020; Mendes et al., 2011).\u003c/p\u003e\u003cp\u003eThus, even for the same host genotype and the same physicochemical soil properties, the result of an encounter of a plant with a pathogen can vary between full breakdown and a mitigation even to a degree that the plant remains asymptomatic, depending on the composition of the rhizomicrobiome (Wei et al., 2019). Conversely, shifts in the rhizosphere microbiome could serve as predictive markers of plant resilience to pathogens (Gu et al., 2022; Wei et al., 2019). Furthermore, enriching the soil with synthetic communities of protective taxa might be used as strategy to suppress disease outbreak as shown for bacterial wilt in tomatoes (Lee et al., 2021).\u003c/p\u003e\u003cp\u003eIn the context of GTDs, the interactions of grapevine rhizomicrobiome and GTDs have hardly been investigated. Studying the interplay between the fungi causing GTDs, and the rhizomicrobiome could help to sort out either taxa with biocontrol potential to GTDs, or taxa triggering the GTDs outbreak. For instance, a study in young vineyards in China showed that the relative abundance of GTD fungi was irrelevant to their pathogenesis, while the symptoms of GTDs were more linked to the incidence of \u003cem\u003eFusarium spp.\u003c/em\u003e in the rhizosphere (Li et al., 2023b). Likewise, wood microbiome analysis for vineyards of different locations in Greece showed that symptomatic wood harboured more \u003cem\u003eAcremonium alternatum\u003c/em\u003e and \u003cem\u003eKalmusia variispora\u003c/em\u003e, fungi not known as causes of GTD symptoms, while members of the bacterial family \u003cem\u003eBacillaceae\u003c/em\u003e were depleted in those symptomatic vines (Fotios et al., 2021). This observation corroborates findings, where a specific member of the \u003cem\u003eBacillaceae\u003c/em\u003e, \u003cem\u003eBacillus subtilis\u003c/em\u003e PTA-271, was found to exert biocontrol activity \u003cem\u003ein planta\u003c/em\u003e against \u003cem\u003eNeofusicoccum parvum\u003c/em\u003e, one of the most aggressive GTD fungi (Trotel-Aziz et al., 2019). This bacterial strain modulated accumulation of transcripts for defence-related genes, including those that are regulated by the major defence hormones, salicylic acid and jasmonates. In addition, a glutathione transferase was activated that was proposed to be involved in the catabolic breakdown of the fungal pathogenicity factors (-)-terremutin and (R)-mellein (Trotel-Aziz et al., 2019). Likewise, the immediate inoculation of \u003cem\u003eTrichoderma\u003c/em\u003e species to pruning wounds in grapevine inhibited infection progress of \u003cem\u003eNeofusicoccum parvum\u003c/em\u003e and \u003cem\u003eDiplodia seriata\u003c/em\u003e and achieved a high degree of plant protection (Pollard-Flamand et al., 2022).\u003c/p\u003e\u003cp\u003eThe composition and the function of the soil microbiome is shaped by the physiochemical properties of the soil. Soil acidification significantly reduced the potential of microbial communities to inhibit the infections with the phytopathogenic fungus, \u003cem\u003eFusarium\u003c/em\u003e (Li et al., 2023). Here, inoculating healthy plants with microbiomes from acidified soils resulted in a remarkable decrease in their ability to resist infection process as well as a downregulation of sulfur metabolism (Li et al., 2023).. Also, micronutrient availability can modulate richness and diversity of the soil microbiome. A survey of 180 sites in China revealed that the structure and function of soil microbiomes was strongly linked to the metallic micronutrients iron, manganese, copper, and zinc (Dai et al., 2023). Specifically, increased Fe and Zn concentrations correlated with ecosystem productivity, which might be a direct consequence of improved plant-nutrient availability, or an indirect effect from altered microbiome composition and gene activity (Dai et al., 2023a). At least for Fe, direct modulation of microbiome-pathogen interactions was demonstrated for the colonisation of tomatoes by \u003cem\u003eRalstonia solanacearum\u003c/em\u003e (Gu et al., 2020). Here, under Fe-limited conditions, the rhizomicrobiome of tomato plants was able to outcompete this bacterial pathogen by secreting siderophores. As a result, its growth was suppressed. When this limitation was removed by supplementing iron, the rhizobiome failed to mitigate the infection with \u003cem\u003eRalstonia\u003c/em\u003e (Gu et al., 2020).\u003c/p\u003e\u003cp\u003eIn viticulture, even small variations in soil properties or water management can significantly affect yields and flavour of economically relevant varieties, such as Chardonnay, Merlot, and Pinot Noir, a phenomenon traditionally known as \u003cem\u003eterroir\u003c/em\u003e. Those effects are proposed to be linked with shifts in their commensal microbiome (Gilbert et al., 2014). Taxonomic abundance and diversity of both, soil bacterial and fungal flora, is strongly dependent on cultivation practices (Coller et al., 2019). Additionally, the influence of soil microbiome is not confined to the soil, because bacterial taxa isolated from grapevine foliage were tightly associated with the communities in the soil, suggesting that soil may serve as a reservoir for vine-associated microbial flora (Zarraonaindia et al., 2015).\u003c/p\u003e\u003cp\u003eThe interplay between grapevine rhizomicrobiome, soil properties, and GTD outbreak has not been addressed yet. However, filling these knowledge gaps is crucial for developing a sustainable approach for grapevine resilience against GTD. Traditional plant breeding methods in viticulture are time consuming and no longer sufficient to cope with the rapidly progressing climate-borne challenges. Targeting beneficial rhizomicrobiota promoting grapevine resistance might act as a fast and sustainable approach against GTDs. This study employed a microbial-ecological strategy, probing the rhizomicrobiome from symptomatic and asymptomatic vines coming from the same vineyard, and sampling over a transsect of more than ten vineyards differing in soil composition to explore the role of the rhizomicrobiome and soil nutrient dynamics for improved grapevine resilience against trunk diseases. By uncovering key interactions between microbes, nutrients, and GTD symptoms, the study contributes to the development of novel strategies for sustainable management of GTDs.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cb\u003eSampling of rhizosphere soil.\u003c/b\u003e To identify whether the outbreak of GTDs might be associated with shifts in the composition of rhizosphere microbes, ten vineyards were sampled in August 2022 along the German side of the Upper Rhine representing Northern (Rauenberg), central (Ringsheim), and Southern (Eichstetten, Ihringen) domains within the viticulture region Baden. The majority (eight sites) comprised the commercially important variety M\u0026uuml;ller-Thurgau, and two sites the traditional variety Silvaner (\u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e). Rhizosphere soil was collected at 20 cm below the surface from the root-hair zone of plants that either displayed GTD symptoms or were asymptomatic. The soil was immediately transferred to dry ice and remained there during transport, before long-term storage at -80\u0026deg;C. Each vineyard is represented by six rhizosphere samples, three samples from symptomatic, and three from asymptomatic plants.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExtraction of DNA and amplicon sequencing.\u003c/b\u003e Soil DNA was extracted from aliquots of 400 mg soil using the DNeasy PowerSoil Pro Kit (Qiagen, Hilden- Germany) following the instructions of the manufacturer. Phenolic compounds were removed by washing the DNA with 10% v/v of sodium acetate, then, DNA concentration was quantified using the Qubit\u0026reg; 3.0 fluorometer (Thermo Fisher Scientific) with the Qubit\u0026trade; dsDNA HS Assay Kit, and quality assessed spectrophotometrically (NanoDrop\u0026trade; 2000/2000c spectrophotometer, Thermo Fisher Scientific). To analyse the taxonomic structure of the microbial community, 10 ng of the purified DNA were used as template to either amplify 16S ribosomal RNA gene of prokaryotes, or the Internal Transcribed Spacer (ITS) of fungi. For the 16S rRNA, V4-V5 region was targeted using, 0.16 \u0026micro;M of the primer set 5\u0026rsquo;-GTGCCAGCMGCCGCGGTAA-3\u0026rsquo; and 5\u0026rsquo;-CCGTCAATTCCTTTGAGTTT-3\u0026rsquo; ligated with the Illumina adapter. For the ITS, the ITS2 region was addressed with the same concentrations of primers 5\u0026rsquo;-GCATCGATGAAGAACGCAGC-3\u0026rsquo; and 5\u0026rsquo;-TCCTCCGCTTATTGATATGC-3\u0026rsquo;. To increase specificity, the PCR was conducted using touchdown cycling at 52\u0026ndash;56\u0026deg;C. Annealing took place at 55\u0026deg;C. After PCR, an amplification step was included using 0.04 U/\u0026micro;L of Q5 High-Fidelity DNA Polymerase in presence of Q5 High GC Enhancer (Thermo Fisher). Amplicons were then cleaned up with the DNA Clean \u0026amp; Concentrator Kit (Zymo research, Germany) and subsequently used to prepare amplicon sequencing libraries. Here, 110 ng of cleaned amplicon were selected for a fragment size of 400\u0026ndash;600 bp in two steps using 0.4x and 0.7x Agencourt Ampure XP beads (Beckman Coulter). Upon size selection, amplicons were ligated to dual index primers NEBNext\u0026reg; Multiplex Oligos for Illumina\u0026reg; (New England Biolabs, Frankfurt, Germany) following the protocol of the manufacturer, and cleaned afterwards using Ampure XP beads. The prepared libraries were diluted, pooled for equimolarity, and sequenced on a Illumina Novaseq platform to generate 150000 pair-end reads per sample (2 \u0026times; 250 bp) (Novogene, Munich, Germany).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSoil chemical analysis.\u003c/b\u003e For every vineyard, 6 soil cores were pooled to form a composite representative sample. Soil samples were then sent for chemical analysis using standards assays on soil type, pH, as well as content of macronutrients and micronutrients (Agricultural Analytical and Research Authority of the State of Rheinland-Pfalz, Speyer) following the rules of the German Fertiliser Regulation (D\u0026uuml;V).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalysis of sequence reads.\u003c/b\u003e The obtained paired-end reads from the Illumina sequencer were subjected to quality assessment using the FASTQC tool (Andrews, 2010). Low-quality reads and Illumina adapters were trimmed, and subsequently merged using FASTP (Chen \u003cem\u003eet al.\u003c/em\u003e, 2018). The merged fastq reads were further denoised to filter out chimeric reads as well as reads shorter than 250 bp using the DADA2 plugin in the QIIME2 pipeline based on the denoise-single method(Bolyen et al., 2019). The samples were then mapped to the corresponding sequences and their frequencies calculated, and the resulting operational taxonomic units (OTUs) were then classified either using the database Silva_99(Robeson et al., 2021) for 16S reads, or the UNITE database(Abarenkov et al., 2024) for reads of fungal ITS. The fungal OTUs were classified with respect to their trophic mode using the FUNGuild database (Nguyen et al., 2016). Since fungal taxa associated with GTDs are not specified in this or alternative databases, we classified, for the current study, wood-trophic taxa that had been previously reported as GTD causal agents(Li et al., 2023a; Mart\u0026iacute;n et al., 2022) as GTDs community. To visualise the high complexity, heatmaps were constructed based on relative frequencies using the ComplexHeatmap software and the circlize tool, clustering variants of rows and columns variants based on their Euclidean distances (Gu, 2022).\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical analysis.\u003c/b\u003e To identify rhizomicrobiome members correlated with the outbreak of GTDs, we probed for potential differential abundance among the M\u0026uuml;ller-Thurgau vineyards using the Analysis of Compositions of Microbiomes with Bias Correction (ANCOM-BC) tool implemented in R. This tool has been developed to derive statistically consistent parameters on the base of samples that differ in size (Lin \u0026amp; Peddada, 2020). Here, the status of the plant (asymptomatic versus asymptomatic) was set as covariate of interest, while the OTUs classified with respect to their role in GTDs were scored per rhizosphere sample. Significantly shifted OTUs were then plotted on a log-linear scale over the plant status to yield log-fold changes, test statistics, standard errors, \u003cem\u003eP\u003c/em\u003e values, adjusted \u003cem\u003eP\u003c/em\u003e values, and differential abundance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eDiversity metrics and correlations analyses.\u003c/b\u003e To quantify differences in the composition of the rhizomicrobiome in relation to chemical soil profiles and GTD outbreak, we used several parameters. To address α-diversity (the diversity in a given location), we used the Shannon index as overall estimate (Shannon, 1948). To account for the fact that rare OTUs might be underrepresented due to sampling bias, we also calculated the Chao1 indices (Chao, 1987), and the Faith Phylogenetic Diversity (Faith_PD) index, a parameter that also considers phylogenetic relationships between the taxa (Faith, 1992). All these parameters were calculated using the respective QIIME2 tools. The non-parametrical Kruskal-Wallis test was used to test statistical significance for differences in the a-diversity indices over chemical profile of the soil and the GTD symptomatics.\u003c/p\u003e\u003cp\u003eAs alternative approach to assess β-diversity (i.e., differences between different locations), we conducted a Principal Coordinate Analysis to detect commonalities between the sites. For parametrisation, we used here either the Bray-Curtis distance by means of the We used the \u003cem\u003evegan\u003c/em\u003e package for R for ecological analyses (Oksanen et al., 2024)., or the Weighted-Unifrac distance (implemented in QIIME2). For visualisation, we employed differentially coloured polygons through the ggplot2 plugin of R, and the stat_ellipse command set at a confidence level of 95%. To classify the chemical profiles of the vineyards, a Principal Component Analysis was carried out using the FactoMinor and factoextra packages of R. In addition, Pearson correlations between rhizomicrobiome diversity metrics and soil chemical profile were calculated and plotted using Hmisc and corrplot packages of R (Harrell, 2024; Wei \u0026amp; Simko, 2021).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConstruction of Co-occurrence networks.\u003c/b\u003e Changes in the rhizomicrobiome dynamics and interactions under GTDs outbreak were studied by calculating co-occurrence networks either for asymptomatic or symptomatic vines with a resolution to the genus level. Correlation networks were assessed and visualized using R packages phyloseq (McMurdie \u0026amp; Holmes, 2013), microbiome (Lahti \u0026amp; Shetty, 2017), Hmisc (Harrell, 2024), igraph (Csardi \u0026amp; Nepusz, 2006), and ggplot2 (Wickham, 2016). Fungal and prokaryotic community networks were constructed at the genus level. After elimination of non-annotated OTUs, pairwise correlations were determined using Spearman\u0026rsquo;s correlation coefficients, filtered based on thresholds of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for statistical significance and |r| \u0026gt;0.6 for correlation strength.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDifferences in chemical profile are reflected in differences of the rhizomicrobiome.\u003c/b\u003e To detect potential shifts in the rhizomicrobiome depending on the chemical profiles of the soil, we probed the rhizosphere of grapevines in ten vineyards along the German side of the Upper Rhine representing the Northern, the central, and the Southern part of the viticulture region Baden (\u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e). A Principal Component Analysis of soil chemical properties (Suppl. Figure\u0026nbsp;1a) revealed several types of chemical profile. Soils of vineyards C, D, I, and J showed similar chemical characteristics. Vineyards G and H were categorized separately, mostly due to their low levels of organic Carbon (C), Nitrogen (N), and Boron (B), whereas vineyard F exhibited the opposite profile, characterized by elevated concentrations of these elements. In addition, vineyards E and B were clustered together with comparably high contents of Mn and K (\u003cb\u003eSuppl. Figure\u0026nbsp;1; Suppl. Figure\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eTo study the rhizomicrobiome structure in the context of such diverse soil characteristics, amplicon sequencing analysis was performed, using the 16S rRNA for prokaryotes, and the Internal Transcribed Spacer (ITS) for fungi. Following the removal of low-quality and chimeric reads, a total of 8,345,161 fungal ITS reads were processed from 60 samples, identifying 8,893 featured fungal operational taxonomic units (OTUs). On the phylum level, the taxonomic structure was relatively comparable between the vineyards. The most dominant fungi were the \u003cem\u003eAscomycota\u003c/em\u003e (between 73% in vineyard D, up to 93% in vineyard G), followed by \u003cem\u003eBasidiomycota\u003c/em\u003e. Furthermore, the phylum \u003cem\u003eRozellomycota\u003c/em\u003e was more prevalent in Vineyard F (\u003cb\u003eSuppl. Figure\u0026nbsp;1b\u003c/b\u003e). Among the twenty most dominant fungal genera, seventeen belonged to the phylum Ascomycota. In addition, \u003cem\u003eFusarium\u003c/em\u003e was the most abundant across all vineyards, shaping 12\u0026ndash;23% of the total fungal community. Based on the most dominant fungi, the Rauenberg vineyards clustered together using Euclidean distance (\u003cb\u003eFig.\u0026nbsp;2a\u003c/b\u003e), mirroring the pattern observed in the PCA of soil chemical properties (\u003cb\u003eSuppl. Figure\u0026nbsp;1a\u003c/b\u003e). These two vineyards exhibited the lowest relative abundance of Fusarium, but higher levels of two other pathogenic genera, \u003cem\u003ePenicillium\u003c/em\u003e and \u003cem\u003eDactylonectria\u003c/em\u003e, as well as an elevated presence of the beneficial fungus \u003cem\u003eSolicocczyma\u003c/em\u003e, known to promote root growth (Albornoz et al., 2025). The Eichstetten vineyard also showed distinct profiles of other two fungi, \u003cem\u003eFusidium\u003c/em\u003e and \u003cem\u003eSubulicystidium\u003c/em\u003e (\u003cb\u003eFig.\u0026nbsp;2a\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eThe 16S amplicons reads exhibited higher chimeric read rates, with approximately 30% of reads filtered out. Following denoising step, 3,796,969 high-quality 16S reads remained, representing 84,221 prokaryotic OTUs. Here, the prokaryotic community in the vineyard rhizosphere was significantly enriched, with 10 times more OTUs than observed in the fungal community. At the species level, 1566 Amplicon Sequence Variants (ASVs) of the fungal community were identified, along with 2786 ASVs of prokaryotic origin. Among the prokaryotes, Actinomycetota were the dominant bacterial phylum shaping the rhizobacteriome across the vineyards, with relative abundances ranging from 36% in vineyard I up to 51% in in one vineyard in Ihringen (J), despite similarity of the two vineyards with respect to soil characteristics (\u003cb\u003eSuppl. Figure\u0026nbsp;1a\u003c/b\u003e). \u003cem\u003ePseudomonadota, Chloroflexota\u003c/em\u003e, and \u003cem\u003eAcidobacteriota\u003c/em\u003e were next in relative abundance, respectively (\u003cb\u003eSuppl. Figure\u0026nbsp;1c\u003c/b\u003e). In terms of archaeal communities (overall constituting only a minor fraction), the \u003cem\u003eCrenarchaeota\u003c/em\u003e were more abundant in vineyards B and F. Additionally, analysis of the twenty most dominant bacterial genera in the vineyard rhizomicrobiome showed that fourteen belonged to the phylum \u003cem\u003eActinomycetota\u003c/em\u003e. Despite this, the most dominant genus overall was \u003cem\u003eKD4\u003c/em\u003e-\u003cem\u003e96\u003c/em\u003e from the phylum \u003cem\u003eChloroflexota\u003c/em\u003e, followed by \u003cem\u003eNocardioides\u003c/em\u003e (\u003cb\u003eFig.\u0026nbsp;2b\u003c/b\u003e). Notably, the two vineyards from Ihringen (I and F) exhibited distinct profiles characterized by higher abundances of \u003cem\u003eTepidiforma\u003c/em\u003e and \u003cem\u003eRokubacteriales\u003c/em\u003e, while vineyard (J) in particular showed a pronounced enrichment of \u003cem\u003eKribbella\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003cb\u003eWood-colonising fungi dominate in the vineyard rhizomicrobiome.\u003c/b\u003e To evaluate the ecological impact of the fungal taxa in the rhizomicrobiome and their potential to colonize woody tissues of grapevine, the defined OTUs were annotated resolving to the genus level using the FUNGuild database, which classifies fungal taxa based on their trophic mode (saprotroph, symbiotroph, or pathotroph), their associated hosts, and, in case of pathogens, their preferred colonization target (wood, root or leaf). Around half of the fungal taxa were pathotrophs (\u003cb\u003eFig.\u0026nbsp;3a\u003c/b\u003e). Most of them were opportunistic pathotrophs, otherwise living as saprotrophs or symbiotrophs, suggesting that their function might vary depending on the condition of the host. In contrast, beneficial taxa (non-pathogenic with symbiotic potential) were found to be less prevalent compared to pathogenic taxa, ranging from only 2.9% to 7.4% (\u003cb\u003eFig.\u0026nbsp;3a\u003c/b\u003e). As alternative approach, we investigated the relative abundance of wood-trophic taxa including those classified as GTDs according to Li et al. (2023a) and Mart\u0026iacute;n et al. (2022). The highest (84\u0026ndash;94%) incidence of pathogenic wood-colonising fungi was found in the Southern part of the sampling area, in the vineyards of Ihringen, and Ringsheim,(E, I, And C) whereas the vineyards of Rauenberg (A, and B) in the Northern part harboured less wood pathotrophs, comprising 68\u0026ndash;74% of the wood trophic community. By contrast, the Rauenberg vineyards showed the highest abundance of the non-pathogenic saprotrophs (\u003cb\u003eFig.\u0026nbsp;3b\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAbundance of GTD-associated fungi depends on vineyards, but not on symptom expression.\u003c/b\u003e To test, how the abundance of GTD-associated fungi in the rhizosphere relates to the respective vineyard and to the expression of GTD symptoms, we focussed on OTUs from the ITS amplicon sequencing that have been annotated as associated with GTDs (Li et al., 2023a; Mart\u0026iacute;n et al., 2022). In fact, seventeen taxa could be detected in the vineyard rhizosphere linked with GTDs of different type, including Black Foot Disease, Botryosphaeria Dieback, ESCA, Eutypa Dieback, or Diaporthe Dieback. Here, the taxa associated with Black Foot Disease, such as g_\u003cem\u003eDactylonectria\u003c/em\u003e, \u003cem\u003eg_Thelonectria\u003c/em\u003e, and \u003cem\u003eg_Neonectria\u003c/em\u003e were generally the most prevalent GTD. They were more abundant in the vineyards of Rauenberg (A and B), in the Northern part of the transsect that shared a similar overall profile of GTD-associated fungi, reported by their clustering in terms of Euclidean distance (\u003cb\u003eFig.\u0026nbsp;3c\u003c/b\u003e). Contrasting with Black Foot Disease, fungi associated with Botryosphaeria Dieback (\u003cem\u003eg_Diplodia, g_Dothiorella\u003c/em\u003e, and \u003cem\u003eg_Neofusicoccum\u003c/em\u003e) were significantly rarer, and found mainly in vineyards G, H, and I, near Ihringen in the Southern part of the transsect, characterised by \u003cem\u003eloess\u003c/em\u003e soils and a warm and dry climate. The third disease, Esca, was represented by six OTUs: \u003cem\u003eg_Coprinellus\u003c/em\u003e, \u003cem\u003eg_Cadophora\u003c/em\u003e, \u003cem\u003eg_Phaeoacremonium, g_Stereum\u003c/em\u003e, \u003cem\u003eg_Fomitiporia\u003c/em\u003e, and \u003cem\u003eg_Phaeomaniella\u003c/em\u003e. Among them, \u003cem\u003eg_Coprinellus\u003c/em\u003e was the most abundant Esca taxon in all tested vineyards, particularly in vineyards D, G, and H, followed by \u003cem\u003eg_Phaeoacremonium\u003c/em\u003e, especially in vineyard G. Three OTUs associated with Eutypa Dieback: \u003cem\u003eg_Cryptovalsa, g_Neoascochyta\u003c/em\u003e, as well as \u003cem\u003eg_Didymella\u003c/em\u003e which was significantly detected in all vineyards. To a low extent, OTU \u003cem\u003eg_Diaporthe\u003c/em\u003e, associated with Diaporthe Dieback was found, without a particular vineyard preference. To assess whether the outbreak of GTDs is linked with a higher abundance of GTD taxa, their relative abundance in the rhizosphere of symptomatic versus asymptomatic vines was calculated, pooling over all vineyards. Here, g_\u003cem\u003eCoprinellus\u003c/em\u003e was the only OTU that showed significant accumulation for symptomatic plants (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Kruskal-Wallis test) (\u003cb\u003eFig.\u0026nbsp;3c\u003c/b\u003e). Thus, with exception of g_\u003cem\u003eCoprinellus\u003c/em\u003e, we do not see any link between disease outbreak and abundance of GTD-associated fungi.\u003c/p\u003e\n\u003ch3\u003eGTD outbreak correlates with rhizomicrobiome shifts\u003c/h3\u003e\n\u003cp\u003eTo test whether GTD outbreak correlated with significant shifts of the rhizomicrobiome, a differential abundance analysis was carried out, using ANCOM-BC. Since the covariate of interest was the health status of the vine, all rhizosphere samples were pooled into two categories, symptomatic versus asymptomatic vines. Then, for the shifted OTUs, the Log Fold Changes (LFC) for symptomatic over asymptomatic vines, and their statistical significance were calculated, regardless of the cultivar or geographical location. This approach revealed significant shifts of both, the fungal (\u003cb\u003eFig.\u0026nbsp;4a\u003c/b\u003e), and the prokaryotic (\u003cb\u003eFig.\u0026nbsp;4b\u003c/b\u003e) rhizomicrobiome that were also dependent on the chemical properties of the soil. These shifts are described in the following:\u003c/p\u003e\u003cp\u003e\u003cb\u003eFungal shifts\u003c/b\u003e: In the fungal community, seven OTUs at the genus level were depleted in symptomatic vines (\u003cb\u003eFig.\u0026nbsp;4a\u003c/b\u003e). Five of these belong to the \u003cem\u003eAscomycota\u003c/em\u003e: \u003cem\u003eCistella\u003c/em\u003e, \u003cem\u003ePseudophialocephala\u003c/em\u003e, \u003cem\u003ePopulomyces\u003c/em\u003e, \u003cem\u003eTetracoccosporium\u003c/em\u003e, and \u003cem\u003eCollarina\u003c/em\u003e. Additionally, two genera from different phyla shifted in parallel: \u003cem\u003eMucor\u003c/em\u003e from \u003cem\u003eMucoromycota\u003c/em\u003e, and \u003cem\u003eLimnoperdon\u003c/em\u003e from \u003cem\u003eBasidiomycota\u003c/em\u003e. On the other hand, nine OTUs were enriched under disease outbreak; Among them were \u003cem\u003eg_Coprinellus\u003c/em\u003e, proposed as driver of Esca, as well as the epiphytic pathogenic fungus, \u003cem\u003eg_Aureobasidium\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eTo assess how soil chemical profiles correlate with the abundance of rhizomicrobiome fungi associated to the asymptomatic phase, we calculated Pearson correlations between these taxa and individual soil nutrients, adding those fungal phyla that had been found to be generally abundant in the rhizomicrobiome (\u003cb\u003eFig.\u0026nbsp;4a\u003c/b\u003e). We saw significant associations of specific fungal taxa with specific traits of soil chemistry (\u003cb\u003eFig.\u0026nbsp;5a\u003c/b\u003e). For instance, the phylum \u003cem\u003eBasidiomycota\u003c/em\u003e was positively correlated with the micronutrients Fe, Cu, and Zn, but negatively correlated with CaCO₃ and pH (alkalinity). Likewise, the phylum \u003cem\u003eRozellomycota\u003c/em\u003e showed positive correlations with Org_C and N, but also with the micronutrient Boron (B). Generally, the taxa that decreased during disease outbreaks, seemed more responsive to soil micronutrients. Here, \u003cem\u003eCollarina\u003c/em\u003e as the taxon with the largest fluctuations with respect to soil properties exhibited positive correlations with Zn, Cu, Mn, Fe, Mg, and C.N ratio, followed by g_\u003cem\u003ePseudophialocephala\u003c/em\u003e, with significant correlations with Zn, Cu, Fe, Mg, and K. Also, for \u003cem\u003eCistella\u003c/em\u003e a link with Fe levels was observed. It is worth noting that fungal members that displayed positive correlations with Fe, became scarce for increases in CaCO\u003csub\u003e3\u003c/sub\u003e and pH.\u003c/p\u003e\u003cp\u003e\u003cb\u003eProkaryotic shifts\u003c/b\u003e: The abundance of many bacterial OTUs dropped significantly in symptomatic vines (\u003cb\u003eFig.\u0026nbsp;4b\u003c/b\u003e). Among those taxa that correlated with healthy vines, the two phyla \u003cem\u003eActinomycetota\u003c/em\u003e and \u003cem\u003ePseudomonadota\u003c/em\u003e exhibited five OTUs that were depleted in symptomatic plants. The most affected taxa were \u003cem\u003eIsoptericola\u003c/em\u003e, \u003cem\u003eThioprofundum\u003c/em\u003e, \u003cem\u003eCaulobacter\u003c/em\u003e, \u003cem\u003eRhodomicrobium\u003c/em\u003e, as well as \u003cem\u003eChryseolinea\u003c/em\u003e from \u003cem\u003ep_Bacteroidota\u003c/em\u003e. Other phyla had only one depleted OTU. For instance, in \u003cem\u003ep_Bacillota\u003c/em\u003e only one OTU (\u003cem\u003eXylanivirga\u003c/em\u003e) decreased, as well as in \u003cem\u003ep_Entotheonellaeota\u003c/em\u003e (\u003cem\u003eEntotheonellaceae\u003c/em\u003e). On the other hand, there were also several rhizobacteriome members which accumulated significantly in symptomatic vines: \u003cem\u003ePuia\u003c/em\u003e, and \u003cem\u003eFerruginibacter\u003c/em\u003e, from \u003cem\u003ep_Bacteroidota\u003c/em\u003e, were enriched most, but also four OTUs from \u003cem\u003ePseudomonadota\u003c/em\u003e, as well as a single OTU from each of the phyla \u003cem\u003eVerrucomicrobiota, p_Patescibacteria\u003c/em\u003e and \u003cem\u003eMyxococcota\u003c/em\u003e were significantly increased.\u003c/p\u003e\u003cp\u003eWe searched for chemical properties of the soil that correlated with these changes of bacterial abundance, but also with dominance of specific prokaryotic phyla independent of disease symptomatics \u003cb\u003e(Fig.\u0026nbsp;5b)\u003c/b\u003e. The generally most dominant bacterial phylum, \u003cem\u003ep_Actinomycetota\u003c/em\u003e, exhibited a negative correlation with only CaCO₃ while \u003cem\u003ep_Pseudomonadota\u003c/em\u003e were negatively correlated with P, K, and Zn. Positive correlations were seen for the p_\u003cem\u003eAcidobacteriota\u003c/em\u003e with B and for \u003cem\u003ep_Crenarchaeota\u003c/em\u003e with Cu. Soil properties had also a significant impact on several taxa associated with the asymptomatic phase of GTDs. Specifically, \u003cem\u003eg_Caulobacter\u003c/em\u003e displayed a positive correlation with C.N ratio, as well as with Mg, Fe, Cu, and Zn, but a negative correlation with CaCO\u003csub\u003e3\u003c/sub\u003e \u003cb\u003e(Fig.\u0026nbsp;5b)\u003c/b\u003e. Along with \u003cem\u003eCaulobacter\u003c/em\u003e, also \u003cem\u003eEntotheonellaceae\u003c/em\u003e became enriched depending on C.N ratio, Fe, and CaCO\u003csub\u003e3\u003c/sub\u003e, but were depleted in alkaline pH. By contrast, \u003cem\u003eThioprofundum\u003c/em\u003e, an OTU from the \u003cem\u003ePseudomonadota\u003c/em\u003e, was the only taxon exhibiting a positive correlation with pH.\u003c/p\u003e\n\u003ch3\u003eRhizomicrobial co-occurrence networks shift depending on GTD symptoms\u003c/h3\u003e\n\u003cp\u003eTo assess whether the interactions and relationships among different rhizomicrobiome taxa are influenced by the health status of the vine, co-occurrence networks were inferred for both, the fungal (\u003cb\u003eFig.\u0026nbsp;6a,b\u003c/b\u003e) and the prokaryotic (\u003cb\u003eFig.\u0026nbsp;6d,e\u003c/b\u003e) microbiome in symptomatic versus asymptomatic vines. Here, the shift of the co-occurrence networks responded qualitatively different in fungi versus prokaryotes. While 1680 significant correlations were detected among the fungal taxa in healthy vines, there was an increase to 1856 significant correlations under GTD outbreak (\u003cb\u003eFig.\u0026nbsp;6g\u003c/b\u003e). A salient component of this increase was the doubling for correlations of GTD-associated taxa with other fungal taxa upon host transition to the symptomatic phase (\u003cb\u003eSuppl_table1; Fig.\u0026nbsp;6c\u003c/b\u003e). Here, most GTD taxa sharply changed their correlation profiles. For instance, \u003cem\u003eFomitiporia\u003c/em\u003e displayed thirteen significant correlations under disease outbreak, but none in asymptomatic plants. Likewise, correlations of \u003cem\u003eStereum\u003c/em\u003e were amplified 8-fold. Furthermore, this fungus extended its associations with other GTD taxa, such as \u003cem\u003ePhaeomoniella\u003c/em\u003e and \u003cem\u003eFomitiporia\u003c/em\u003e, as well as with other six wood-saprotrophic and pathogenic taxa \u003cb\u003e( Suppl_table1)\u003c/b\u003e. Likewise, \u003cem\u003ePhaeomoniella\u003c/em\u003e, in symptomatic vines, displayed 29 correlations, not only with \u003cem\u003eFomitiporia\u003c/em\u003e, but also with eleven other pathogenic taxa, contrasting with only 10 correlations in healthy vines. The aggressive genus, \u003cem\u003eNeofusicoccum\u003c/em\u003e, responsible for Botryosphaeria dieback, entertained 15 different correlations during GTD outbreak, compared to only 8 in the asymptomatic phase. For Diaporthe dieback linked with \u003cem\u003eg_Diaporthe\u003c/em\u003e, a significant correlation, with \u003cem\u003eg_Paurocotylis\u003c/em\u003e, was only seen in symptomatic vines (\u003cb\u003eSuppl_table1\u003c/b\u003e). The inverse case, where associations between two pathogens turned loose during GTD outbreak, was far rarer \u0026ndash; here, the significant correlation between \u003cem\u003ePhaoeoacremonium\u003c/em\u003e and \u003cem\u003eStereum\u003c/em\u003e was detected only in healthy vines. A third case, where the pathogenic partner of a GTD fungus was swapped by another pathogenic partner, is represented by \u003cem\u003eCoprinellus\u003c/em\u003e, which was more prevalent in symptomatic vines (\u003cb\u003eFig.\u0026nbsp;4a\u003c/b\u003e). This fungus correlates with \u003cem\u003eg_Keissleriella\u003c/em\u003e in asymptomatic vines, but switches to a significant correlation with \u003cem\u003eg_Tulasnella\u003c/em\u003e under the conditions of a GTD outbreak.\u003c/p\u003e\u003cp\u003eContrasting with fungi, the connectivity for the 1167 prokaryotic genera, was drastically decreased from 11856 significant correlations in the healthy vines to 6361 significant correlations under GTDs outbreak \u003cb\u003e(Fig.\u0026nbsp;6g; Suppl_table2)\u003c/b\u003e. Here, taxa associated with the asymptomatic phase showed different interaction profiles with other soil prokaryotes. For instance, \u003cem\u003eEntotheonellaceae\u003c/em\u003e constituted a core node with six strong pairwise correlations in the rhizosphere of healthy vines, but upon GTD outbreak turned into a peripheral node with only two correlations. Also, \u003cem\u003eIsoptericola\u003c/em\u003e and lost their correlations and even disappeared from the co-occurrence network, while \u003cem\u003eThioprofundum, Kitasatospora, Rhodomirobium, Methylothermalis, Illumatobacter, and Caulobacter\u003c/em\u003e robustly lost their positive correlations with other bacteria in symptomatic plants (\u003cb\u003eFig.\u0026nbsp;6f; Suppl_table2\u003c/b\u003e). Only few taxa showed an inverse pattern: \u003cem\u003eSteroidobacter and Chryseolinea\u003c/em\u003e established more positive correlations only in symptomatic vines.\u003c/p\u003e\n\u003ch3\u003eBacterial diversity depends mainly on soil, fungal biodiversity also on geography\u003c/h3\u003e\n\u003cp\u003eAs markers for ecosystem robustness, we determined a panel of diversity metrics for the rhizomicrobiome over the different vineyards with their differences in soil parameters and geographical location, either in asymptomatic plants or under disease outbreak. We did not observe any salient changes in the diversity metrics, neither of prokaryotes, nor of fungi in association with GTD outbreak (\u003cb\u003eSuppl. Figure\u0026nbsp;2\u003c/b\u003e). Only for two vineyards, E and G, Shannon entropy, an alpha-diversity index, which accounts for the species richness and evenness within an ecosystem (here, environmental sample), shifted significantly in the bacterial community. Bray_Curtis distance as a quantitative index for community diversity between samples, did not reveal any significant differences in bacteria community between asymptomatic symptomatic vines, but it was significantly shifted in the rhizosphere fungi of symptomatic vines in two other vineyards, D and F.\u003c/p\u003e\u003cp\u003eTo link rhizomicrobiome richness with soil parameters, alpha diversity was characterised using Shannon, Chao1, and Faith's Phylogenetic Diversity (PD) indices over the different vineyards (\u003cb\u003eSuppl. Figure\u0026nbsp;3\u003c/b\u003e), and then correlated either with macronutrients (\u003cb\u003eSuppl. Figure\u0026nbsp;4\u003c/b\u003e) or micronutrients (\u003cb\u003eFig.\u0026nbsp;7\u003c/b\u003e). Generally, the Shannon index remained relatively stable across vineyards, with exceptions observed in the fungal community of vineyard I, and the bacterial communities of vineyards C and E. This index showed no significant correlation, neither with macronutrient nor micronutrient levels. Similarly, Chao1, a richness index that estimates diversity based on abundant taxa, revealed no significant correlation with soil properties, although the prokaryotes in vineyards C and E were significantly different from the other vineyards. Thus, parameters estimating diversity based merely on differences in abundance of taxa, not considering their identity, remained inconspicuous. The outcome changed, when also phylogenetic relationships were included into the parametrisation. Here, Faith's PD, which considers the phylogenetic differences among taxa, was the most variable parameter among the alpha diversity metrics for both, fungal and prokaryotic, communities (\u003cb\u003eSuppl. Figure\u0026nbsp;3\u003c/b\u003e). This was especially pronounced in the prokaryotic community, where Faith's PD showed a positive correlation with the principal component of soil properties, Fe, Cu, Zn (\u003cb\u003eFig.\u0026nbsp;7\u003c/b\u003e), and Mg (\u003cb\u003eSuppl. Figure\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eBeta diversity, which evaluates the dissimilarities among different ecosystems, was characterized through Principal Coordinate Analysis (PCoA). Again, this analysis was either conducted either disregarding phylogenetic relationships, based on the Bray-Curtis distance (\u003cb\u003eFig.\u0026nbsp;7b\u003c/b\u003e), or incorporating taxonomic distance, using Weighted-Unifrac distance (\u003cb\u003eFig.\u0026nbsp;7a\u003c/b\u003e). As already seen for alpha diversity, the integration of phylogenetic distance revealed differences that otherwise would have gone unnoticed. While only 4% of prokaryotic beta-diversity could be explained by the first principal PCoAs on the base of Bray-Curtis distance (\u003cb\u003eFig.\u0026nbsp;7b\u003c/b\u003e), the first two Unifrac PCoAs accounted for 30% of the observed diversity (\u003cb\u003eFig.\u0026nbsp;7a\u003c/b\u003e). This improved sensitivity allowed to detect several interesting features: Here, beta-diversity of fungi was clearly different depending on geographical location (\u003cb\u003eFig.\u0026nbsp;7a\u003c/b\u003e). For instance, the selected vineyards in Rauenberg and Ringsheim in the North of the study area were significantly different from those in Eichstetten in the South. However, this difference in fungal diversity seemed to be uncoupled from soil parameters, with exception of CaCO\u003csub\u003e3\u003c/sub\u003e (\u003cb\u003eFig.\u0026nbsp;7c, Suppl.Fig. S4\u003c/b\u003e). Even disregarding the phylogenetic relationships among OTUs, using the Bray_Curtis distance, the Rauenberg vineyards were significantly divergent with a strong correlation with soil parameters including PC1, Fe, Zn, K and Cu, respectively (\u003cb\u003eFig.\u0026nbsp;7c, Suppl.Fig. S4\u003c/b\u003e). In contrast to fungal biodiversity, the prokaryotic community seemed more dependent on soil parameters. This dependence was so strong that it was not only seen for the Unifrac, but also for the Bray_Curtis distances, where phylogenetic relationship is left aside. Relevant soil parameters were here Fe, Zn, Mg and Cu (\u003cb\u003eFig.\u0026nbsp;7d, Suppl. Fig. S4\u003c/b\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study explored the relationship between the outbreak of grapevine trunk diseases (GTDs) and rhizomicrobiome composition and dynamics over ten vineyards, sampled along a North-South transsect in the Upper Rhine valley. The transition from the latent to the necrotrophic phase of the GTD-associated fungi was accompanied by taxonomic shifts in both, the fungal and the prokaryotic, rhizomicrobiome. Using a panel of diversity metrics, we detected correlations between the incidence of prokaryotic taxa associated with the latent, asymptomatic phase and soil properties, whereas for the respective fungal taxa also geographic location was found to be relevant. We identified taxa associated with the latent \u0026ldquo;asymptomatic\u0026rdquo; phase and their interactions with rhizosphere flora and the soil properties. The geographical location and soil properties showed variable effects on the composition and diversity metrics of both fungal and bacterial communities. The analysis of correlation networks revealed that GTD-associated fungi increased their mutual association after outbreak of the disease. These findings stimulate several questions in the context of GTD outbreak that will be discussed in the following: Why are the prokaryotes responsive to soil chemistry, while the fungi are not? What are potential mechanisms behind the association of specific prokaryotes with grapevine health? What does the altered fungal correlation network mean for our concept of health and disease?\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eSoil ecology: fungi respond by metabolic state, bacteria by proliferation\u003c/h2\u003e\n \u003cp\u003eThe rhizomicrobiome plays a central role for maintaining plant health and resilience against environmental challenges (reviewed in Berendsen et al., 2012; Gu et al., 2022). Therefore, insight into the grapevine rhizomicrobiome structure, as well as environmental key factors shaping its richness, diversity, and the incidence of beneficial taxa is relevant for sustainable viticulture. Along this line, two salient features emerge from our analysis:\u003c/p\u003e\n \u003cp\u003eFor the fungal community, it is host status, rather than soil properties, that seems to define disease outbreak. The dominant fungal phyla in the vineyard rhizosphere were \u003cem\u003eAscomycota\u003c/em\u003e and \u003cem\u003eBasidiomycota\u003c/em\u003e, respectively (\u003cstrong\u003eSuppl\u003c/strong\u003e. Figure\u0026nbsp;1), similar to previous studies performed on different cultivars, different geographical and environmental conditions (Bao et al., 2022; Berlanas et al., 2019; Coller et al., 2019; Lailheugue et al., 2024). In addition, there were no significant differences in the incidence of symbiotrophic or pathotrophic taxa among the different vineyards (\u003cstrong\u003eFig.\u0026nbsp;3a\u003c/strong\u003e). However, around 50% of the detected fungal taxa were multi-trophic with a pathotrophic potential. This is consistent with a scenario, where disease outbreak is not defined by increased abundance of GTD associated taxa, but rather by changes in their behaviour. In fact, such a conditional pathogenesis has been demonstrated to link to changes in secondary metabolites, for instance, in the fungal GTD model \u003cem\u003eNeofusicoccum parvum\u003c/em\u003e (Khattab et al., 2022; Flubacher et al., 2023). While secondary metabolism in fungi is very rich and elaborate, most of the underlying gene clusters are silent, if not activate by specific (mostly unknown) signals (for review see Brakhage and Schroekh, 2011). It is, therefore, not surprising that, for the rhizomicrobiome fungi, differences in soil ecology will be reflected in changes of metabolic state, rather than in changes of abundance.\u003c/p\u003e\n \u003cp\u003eIn contrast to fungi, prokaryotic phyla seemed more influenced by geography and environmental factors. In our study, \u003cem\u003eActinomycetota\u003c/em\u003e emerged as the predominant bacterial phylum, followed by \u003cem\u003ePseudomonadota\u003c/em\u003e, \u003cem\u003eChloroflexota\u003c/em\u003e, and \u003cem\u003eAcidobacteriota\u003c/em\u003e (\u003cstrong\u003eSuppl. Figure\u0026nbsp;1c\u003c/strong\u003e). With exception of the \u003cem\u003eChloroflexota\u003c/em\u003e, these dominating phyla have been found in other vineyard studies, albeit at variable composition. For example, similar profiles of \u003cem\u003eActinomycetota\u003c/em\u003e and \u003cem\u003ePseudomonadota\u003c/em\u003e were reported across various cultivars in the grape-growing regions of Huailai County, China by Bao et al. (2022). A strong dependence on geography can also been concluded by differences in the dominant phyla of the grapevine rhizosphere among different regions in Europe. While the \u003cem\u003ePseudomonadota\u003c/em\u003e was the dominant phylum, followed by \u003cem\u003eActinomycetota\u003c/em\u003e in vineyards located in Bordeaux, France (Lailheugue et al., 2024), Italy (Marasco et al., 2018), and Spain (Berlanas et al., 2019), a different study on ten vineyards across four sites in Italy identified \u003cem\u003eAcidobacteriota\u003c/em\u003e as most abundant phylum (Coller et al., 2019). Such comparisons between different studies have to be taken with care, because timing of sampling might also affect the rhizomicrobiome structure (Berlanas et al., 2019). However, for the data from the current study, seasonal differences can be excluded, because the samples were collected in August, because outbreak of GTDs becomes fully manifest in late summer. Instead, our data lead to the conclusion that the substantial differences in prokaryotic abundance might derive from soil properties. This is supported, for instance, by the negative correlation between \u003cem\u003ePseudomonadota\u003c/em\u003e and content of Zn, K and P, or by the strong positive correlation between \u003cem\u003eAcidobacteriota\u003c/em\u003e and B and nitrogen content (\u003cstrong\u003eFig.\u0026nbsp;5b\u003c/strong\u003e). Globally, bacterial diversity metrics, especially those incorporating phylogenetic relationships such as Faith_PD and Unifrac, showed a significant correlation with soil properties, especially the content of micronutrients Fe, Cu, Mn, and Zn (\u003cstrong\u003eFig.\u0026nbsp;7c, Suppl. Figure\u0026nbsp;4\u003c/strong\u003e). Such elements are key players for enzymes regulating microbial proliferation and many biological processes, e.g. Fe for N fixation, Zn and Cu for immunocompetence, and Fe and Mn for respiration. Both elements, Fe and Mn could also serve as electron donors and acceptors, during soil redox reactions of C, N, and S (Dai et al., 2023b; Dubinsky et al., 2010; Whalen et al., 2018). In contrast to fungi that can respond to environmental conditions by releasing previously silent metabolic modules, bacteria need to respond by altering the composition of their consortia that are often coupled by concerted metabolisation of given substrates, giving rise to the concept of Metabolically Cohesive Consortia (for a conceptual review see Pascual-Garc\u0026iacute;a et al., 2020).\u003c/p\u003e\n \u003cp\u003eIn shorthand, the fungal communities in the rhizomicrobiome respond by adjusting their metabolic state, while the bacteria respond by altering their proliferation.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eDisease outbreak is associated with shifts in the bacterial rhizomicrobiome\u003c/h3\u003e\n\u003cp\u003eUnder disease outbreak, we observed strong shifts in the composition of the bacterial rhizomicrobiome (contrasting with the fungi, which will be discussed in the next section), reflected in the co-occurrence networks. The positive correlations, as reported by edges among the network nodes, dropped drastically almost to the half among (\u003cstrong\u003eFig.\u0026nbsp;6g\u003c/strong\u003e). Salient features were the depletion of \u003cem\u003eIsoptericola\u003c/em\u003e, \u003cem\u003eThioprofundum\u003c/em\u003e, \u003cem\u003eCaulobacter\u003c/em\u003e, \u003cem\u003eRhodomicrobium\u003c/em\u003e, as well as \u003cem\u003eChryseolinea\u003c/em\u003e (\u003cstrong\u003eFig.\u0026nbsp;4b\u003c/strong\u003e). These taxa might, therefore, used as indicators for grapevine health. In addition, some taxa lost their positive correlations or completely disappeared from the co-occurrence network, e.g. \u003cem\u003eIsoptericola\u003c/em\u003e (\u003cstrong\u003eFig.\u0026nbsp;6e; Suppl. Table\u0026nbsp;2\u003c/strong\u003e). Possible reasons might be increased competition for nutrients, reduced functionality, but also the rise of outbreak-associated bacterial communities. Since around 50% of the fungal flora in the rhizomicrobiome have pathotrophic potential (\u003cstrong\u003eFig.\u0026nbsp;3a\u003c/strong\u003e), the depleted or less connected bacterial networks might be a factor driving the transition to fungal pathogenicity.\u003c/p\u003e\n\u003cp\u003eWhile the underlying mechanisms remain to be elucidated for grapevine, current findings from other crops suggest that root exudates released from healthy plants promote the establishment of a rhizomicrobiome that promotes nutrient cycling, enhances plant immunity, and maintains soil health (Chen et al., 2024; Du et al., 2024; Wilhelm et al., 2023). Under the severe stress conditions that often herald a GTD outbreak (Khattab et al., 2022), either the absence or the modification of such root exudates might contribute to the disruption of the established microbial networks, such that beneficial microbiota become depleted, while pathogens become promoted, and conditional fungal pathogens alter their lifestyle towards parasitism. Harnessing the established bacterial networks in health-associated rhizomicrobiomes, (\u003cstrong\u003eFig.\u0026nbsp;6\u003c/strong\u003e), might be a strategy for sustainable biocontrol of GTDs. Micronutrients supplements might help in this regard since they showed strong correlations with phylogenetic diversity metrics as well as with higher abundance of health-associated bacteria (\u003cstrong\u003eFig.\u0026nbsp;5; Fig.\u0026nbsp;7\u003c/strong\u003e). Whether such bacteria are actively promoting plant resilience, or whether they are just attracted to plants endowed with resilience cannot be inferred from a correlative study, of course. H owever, these bacteria are at least candidates, and it is worthwhile to probe them individually for a potential activation of plant immunity. This would also be interesting for application, because those, where activation of immunity can be confirmed could be developed into new sustainable biocontrol agents against GTDs. The potential of this approach has already been by studies, where co-inoculation with members of \u003cem\u003eActinomycetota\u003c/em\u003e and \u003cem\u003eBacillota\u003c/em\u003e mitigated GTD progression (for review see Cobos et al., 2022). This mitigation could be direct, by allelopathic control. For instance, \u003cem\u003eStreptomyces\u003c/em\u003e either endophytic sp. VV/E1, or two rhizosphere isolates, sp. VV/R1 and sp. VV/R4, significantly inhibited the growth of \u003cem\u003eDactylonectria\u003c/em\u003e sp. and \u003cem\u003eIlyonectria\u003c/em\u003e sp., fungi involved in black foot disease, as well as of \u003cem\u003ePhaeomoniella chlamydospora\u003c/em\u003e, and \u003cem\u003eP. minimum\u003c/em\u003e, associated with the Esca disease (\u0026Aacute;lvarez-P\u0026eacute;rez et al., 2017). In addition to direct growth inhibition, secreted compounds might also act indirectly, by activating host defence. Likewise, In fact, \u003cem\u003eBacillus pumilus\u003c/em\u003e (S32) and \u003cem\u003ePaenibacillus\u003c/em\u003e sp. (S19) were shown to secrete antifungal volatiles, including 1-octen-3-ol and 2,5-dimethyl pyrazine that not only suppressed the Esca fungus \u003cem\u003ePhaeomoniella chlamydospora\u003c/em\u003e but also upregulated phytoalexin biosynthesis genes of grapevine (Haidar et al., 2016). In a similar manner, \u003cem\u003eBacillus subtilis\u003c/em\u003e PTA-271 could simultaneously enhance defence signalling and reduce the growth rate of \u003cem\u003eNeofusicoccum parvum\u003c/em\u003e in grapevine (Trotel-Aziz et al., 2019).\u003c/p\u003e\n\u003ch3\u003eGTD outbreak: a matter of fungal ecology rather than pathogen incidence?\u003c/h3\u003e\n\u003cp\u003eContrasting with other grapevine diseases, such as Downy Mildew (caused by the oomycete \u003cem\u003ePlasmopara viticola\u003c/em\u003e), or Powdery Mildew (caused by the ascomycete \u003cem\u003eErysiphe necator\u003c/em\u003e), the causal chain in GTDs is far from elucidated. Classically, pathogens are identified through demonstrating that they are necessary and sufficient for symptomatics, an approach known as Koch Postulates (Loeffler, 1884). This classical approach fails for GTDs, because symptoms can rarely be attributed to absence and presence of a given fungus. Moreover, attempts to compare symptomatic vines with asymptomatic controls did not yield significant differences in the composition of the mycobiome. In one of the (few) rigorous comparisons, the authors arrive at the provocative conclusion \u0026ldquo;What if esca disease of grapevine were not a fungal disease?\u0026rdquo; (Hofstetter et al., 2012), arguing that the presence of certain wood-decaying fungi in both asymptomatic or symptomatic trunks might be due to their saprotrophic lifestyle breaking down tissue that had died for other reasons, such as overpruning or frost damage. If it is not the mere presence of a microbe that leads to pathogenesis, it might be the conditions that render a microbe into a pathogen. The findings of the current study, mainly the shifts in the correlation networks, support such a contextual model of pathogenesis, clearly transcending the Koch Postulates.\u003c/p\u003e\n\u003cp\u003eOn the one hand, most prevalent GTD associated taxa, namely those reported in the context of black foot disease, showed no significant shifts compared to the asymptomatic phase (\u003cstrong\u003eFig.\u0026nbsp;3c\u003c/strong\u003e). The only apparent candidate, \u003cem\u003eCoprinellus\u003c/em\u003e, seems to be only a hitchhiker and not a driver. In controlled infection studies, it failed to induce foliar symptoms or trigger disease outbreak contrasting with other Esca fungi (Brown et al., 2020). Moreover, unlike many GTDs taxa which are wood-obligate saprotrophs, \u003cem\u003eCoprinellus\u003c/em\u003e was detected also in grapevine leaves (Cui et al., 2024). Therefore, \u003cem\u003eCoprinellus\u003c/em\u003e might rather be a saprotroph, potentially feeding on decayed plant material following the outbreak.\u003c/p\u003e\n\u003cp\u003eAlso for the other sixteen GTD associated taxa, abundance changes after outbreak were insignificant. However, what was significant, was the dynamics of interactions which were amplified within the fungal community, prominently in those with pathotrophic potential (\u003cstrong\u003eSuppl_Table 1\u003c/strong\u003e). Here, some GTD taxa strongly increased their mutual correlations, especially Esca-associated fungi, such as \u003cem\u003ePhaeomoniella\u003c/em\u003e with \u003cem\u003eFomitiporia\u003c/em\u003e, \u003cem\u003eStereum\u003c/em\u003e with \u003cem\u003eFomitiporia\u003c/em\u003e, and \u003cem\u003eStereum\u003c/em\u003e with \u003cem\u003ePhaeomoniella\u003c/em\u003e. These patterns are consistent with a model, where these fungi initiate mutualistic interactions and benefit each other through co-colonisation or metabolic-cross feeding. Whether the opportunistic shift of these Esca taxa towards pathogenic behaviour is triggered by a breakdown in plant immunity due to loss of beneficial microbes remains to be elucidated by targeted infection experiments in the presence of tailored rhizomicrobiomes.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eOutlook\u003c/h2\u003e\n \u003cp\u003eIt has to be kept in mind that the nature of a agroecological study as the current one is descriptive, and the outcome is confined to correlations. Since the sampling had to be in August, when symptoms are clearly manifest, the sampling represents a static snapshot. To infer the causal chain from such snapshots alone is principally not possible. Nevertheless, several candidates, both for pathogenesis, as well as for salutogenesis, could be identified. To integrate the temporal dynamics of pathogenesis, these candidates need to be tested functionally in controlled infection assays to assess, for instance changes in the root secretome under GTDs outbreak as well as direct promoting or inhibiting interactions between microbes, or activation of plant immunity. The ultimate goal will be to develop new biocontrol agents to prevent or even to cure grapevine trunk diseases.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding declaration and acknowledgement\u003cbr\u003e\u003c/strong\u003eThis work was supported by Microbes for future project (M4F), which was funded by Strategy fund of Karlsruhe Institute of Technology.\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing rawdata:\u0026nbsp;\u003c/strong\u003eNCBI bioproject\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003eVineyards_rhizomicrobiome), Accession: PRJNA1328367, ID: 1328367\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003ehttps://www.ncbi.nlm.nih.gov/bioproject/1328367) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatics\u003c/strong\u003e: Github respiratory, \u0026ldquo;Vineyards_rhizomicrobiome_Upper_Rhine\u0026rdquo; (https://github.com/Khattab2022/Vineyards_rhizomicrobiome_Upper_Rhine)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbarenkov, K., Nilsson, R. H., Larsson, K. H., Taylor, A. F. S., May, T. W., Fr\u0026oslash;slev, T. G., Pawlowska, J., Lindahl, B., P\u0026otilde;ldmaa, K., Truong, C., Vu, D., Hosoya, T., Niskanen, T., Piirmann, T., Ivanov, F., Zirk, A., Peterson, M., Cheeke, T. E., Ishigami, Y., \u0026hellip; K\u0026otilde;ljalg, U. (2024). The UNITE database for molecular identification\u0026atilde;nd taxonomic communication of fungi\u0026atilde;nd other eukaryotes: sequences, taxa\u0026atilde;nd classifications r econsider ed. \u003cem\u003eNucleic Acids Research\u003c/em\u003e, \u003cem\u003e52\u003c/em\u003e(D1), D791\u0026ndash;D797. https://doi.org/10.1093/nar/gkad1039\u003c/li\u003e\n\u003cli\u003eAlbornoz, F., Carvajal, M., Catrileo, D., Gebauer, M., \u0026amp; Godoy, L. (2025). Volatile organic compounds produced after exposure of tomato roots to the soil yeast Solicoccozyma terrea modulate root nitrate transporters in tomato. \u003cem\u003ePlant and Soil\u003c/em\u003e. https://doi.org/10.1007/s11104-025-07393-8\u003c/li\u003e\n\u003cli\u003e\u0026Aacute;lvarez-P\u0026eacute;rez, J. M., Gonz\u0026aacute;lez-Garc\u0026iacute;a, S., Cobos, R., Olego, M. \u0026Aacute;., Iba\u0026ntilde;ez, A., D\u0026iacute;ez-Gal\u0026aacute;n, A., Garz\u0026oacute;n-Jimeno, E., \u0026amp; Coque, J. J. R. (2017). Use of endophytic and rhizosphere actinobacteria from grapevine plants to reduce nursery fungal graft infections that lead to young grapevine decline. \u003cem\u003eApplied and Environmental Microbiology\u003c/em\u003e, \u003cem\u003e83\u003c/em\u003e(24). https://doi.org/10.1128/AEM.01564-17\u003c/li\u003e\n\u003cli\u003eBao, L., Sun, B., Wei, Y., Xu, N., Zhang, S., Gu, L., \u0026amp; Bai, Z. (2022). Grape Cultivar Features Differentiate the Grape Rhizosphere Microbiota. \u003cem\u003ePlants\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(9). https://doi.org/10.3390/plants11091111\u003c/li\u003e\n\u003cli\u003eBerendsen, R. L., Pieterse, C. M. J., \u0026amp; Bakker, P. A. H. M. (2012). The rhizosphere microbiome and plant health. In \u003cem\u003eTrends in Plant Science\u003c/em\u003e (Vol. 17, Issue 8, pp. 478\u0026ndash;486). https://doi.org/10.1016/j.tplants.2012.04.001\u003c/li\u003e\n\u003cli\u003eBerlanas, C., Berbegal, M., Elena, G., Laidani, M., Cibriain, J. F., Sag\u0026uuml;es, A., \u0026amp; Gramaje, D. (2019). The fungal and bacterial rhizosphere microbiome associated with grapevine rootstock genotypes in mature and young vineyards. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(MAY). https://doi.org/10.3389/fmicb.2019.01142\u003c/li\u003e\n\u003cli\u003eBolyen. E; Rideout J.R; Dillon M.R; Bokulich N.A.; Abnet C.C.; Al-Ghalith G.A.; Alexander H.; Alm E.J.; Arumugam M. (2019). Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. \u003cem\u003eNature Biotechnology\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(8), 850\u0026ndash;852. https://doi.org/10.1038/s41587-019-0190-3\u003c/li\u003e\n\u003cli\u003eBrown, A. A., Lawrence, D. P., \u0026amp; Baumgartner, K. (2020). Role of basidiomycete fungi in the grapevine trunk disease esca. \u003cem\u003ePlant Pathology\u003c/em\u003e, \u003cem\u003e69\u003c/em\u003e(2), 205\u0026ndash;220. https://doi.org/10.1111/ppa.13116\u003c/li\u003e\n\u003cli\u003eChao, A. (1987). \u003cem\u003eEstimating the Population Size for Capture-Recapture Data with Unequal Catchability\u003c/em\u003e (Vol. 43, Issue 4). https://www.jstor.org/stable/2531532\u003c/li\u003e\n\u003cli\u003eChen, Q., Song, Y., An, Y., Lu, Y., \u0026amp; Zhong, G. (2024). Soil Microorganisms: Their Role in Enhancing Crop Nutrition and Health. In \u003cem\u003eDiversity\u003c/em\u003e (Vol. 16, Issue 12). Multidisciplinary Digital Publishing Institute (MDPI). https://doi.org/10.3390/d16120734\u003c/li\u003e\n\u003cli\u003eCobos, R., Iba\u0026ntilde;ez, A., Diez-Gal\u0026aacute;n, A., Calvo-Pe\u0026ntilde;a, C., Ghoreshizadeh, S., \u0026amp; Coque, J. J. R. (2022). The Grapevine Microbiome to the Rescue: Implications for the Biocontrol of Trunk Diseases. In \u003cem\u003ePlants\u003c/em\u003e (Vol. 11, Issue 7). MDPI. https://doi.org/10.3390/plants11070840\u003c/li\u003e\n\u003cli\u003eColler, E., Cestaro, A., Zanzotti, R., Bertoldi, D., Pindo, M., Larger, S., Albanese, D., Mescalchin, E., \u0026amp; Donati, C. (2019). Microbiome of vineyard soils is shaped by geography and management. \u003cem\u003eMicrobiome\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(1). https://doi.org/10.1186/s40168-019-0758-7\u003c/li\u003e\n\u003cli\u003eCui, S., Zhou, L., Fang, Q., Xiao, H., Jin, D., \u0026amp; Liu, Y. (2024). Growth period and variety together drive the succession of phyllosphere microbial communities of grapevine. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e950\u003c/em\u003e. https://doi.org/10.1016/j.scitotenv.2024.175334\u003c/li\u003e\n\u003cli\u003eDai, Z., Guo, X., Lin, J., Wang, X., He, D., Zeng, R., Meng, J., Luo, J., Delgado-Baquerizo, M., Moreno-Jim\u0026eacute;nez, E., Brookes, P. C., \u0026amp; Xu, J. (2023a). Metallic micronutrients are associated with the structure and function of the soil microbiome. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1). https://doi.org/10.1038/s41467-023-44182-2\u003c/li\u003e\n\u003cli\u003eDai, Z., Guo, X., Lin, J., Wang, X., He, D., Zeng, R., Meng, J., Luo, J., Delgado-Baquerizo, M., Moreno-Jim\u0026eacute;nez, E., Brookes, P. C., \u0026amp; Xu, J. (2023b). Metallic micronutrients are associated with the structure and function of the soil microbiome. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1). https://doi.org/10.1038/s41467-023-44182-2\u003c/li\u003e\n\u003cli\u003eDe Vries, F. T., Griffiths, R. I., Knight, C. G., Nicolitch, O., \u0026amp; Williams, A. (2020). \u003cem\u003eHarnessing rhizosphere microbiomes for drought-resilient crop production\u003c/em\u003e. https://doi.org/https://doi.org/10.1126/science.aaz5192\u003c/li\u003e\n\u003cli\u003eDubinsky, E. A., Silver, W. L., \u0026amp; Firestone, M. K. (2010). Tropical forest soil microbial communities couple iron and carbon biogeochemistry. \u003cem\u003eEcology\u003c/em\u003e, \u003cem\u003e91\u003c/em\u003e(9), 2604\u0026ndash;2612. https://doi.org/10.1890/09-1365.1\u003c/li\u003e\n\u003cli\u003eDu, Y., Han, X., \u0026amp; Tsuda, K. (2024). Microbiome-mediated plant disease resistance: recent advances and future directions. In \u003cem\u003eJournal of General Plant Pathology\u003c/em\u003e. Springer. https://doi.org/10.1007/s10327-024-01204-1\u003c/li\u003e\n\u003cli\u003eFaith, D. P. (1992). Conservation evaluation and phylogenetic diversity. In \u003cem\u003eBiological Conservation\u003c/em\u003e (Vol. 61).\u003c/li\u003e\n\u003cli\u003eField, K. J., Pressel, S., Duckett, J. G., Rimington, W. R., \u0026amp; Bidartondo, M. I. (2015). Symbiotic options for the conquest of land. In \u003cem\u003eTrends in Ecology and Evolution\u003c/em\u003e (Vol. 30, Issue 8, pp. 477\u0026ndash;486). Elsevier Ltd. https://doi.org/10.1016/j.tree.2015.05.007\u003c/li\u003e\n\u003cli\u003eFlubacher, N., Baltenweck, R., Hugueney, P., Fischer, J., Thines, E., Riemann, M., Nick, P., \u0026amp; Khattab, I. M. (2023). The fungal metabolite 4-hydroxyphenylacetic acid from Neofusicoccum parvum modulates defence responses in grapevine. \u003cem\u003ePlant Cell and Environment\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(11), 3575\u0026ndash;3591. https://doi.org/10.1111/pce.14670\u003c/li\u003e\n\u003cli\u003eFotios, B., Sotirios, V., Elena, P., Anastasios, S., Stefanos, T., Danae, G., Georgia, T., Aliki, T., Epaminondas, P., Emmanuel, M., George, K., Kalliope, P. K., \u0026amp; Dimitrios, K. G. (2021). Grapevine wood microbiome analysis identifies key fungal pathogens and potential interactions with the bacterial community implicated in grapevine trunk disease appearance. \u003cem\u003eEnvironmental Microbiomes\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1). https://doi.org/10.1186/s40793-021-00390-1\u003c/li\u003e\n\u003cli\u003eGilbert, J. A., Van Der Lelie, D., \u0026amp; Zarraonaindia, I. (2014). Microbial terroir for wine grapes. In \u003cem\u003eProceedings of the National Academy of Sciences of the United States of America\u003c/em\u003e (Vol. 111, Issue 1, pp. 5\u0026ndash;6). https://doi.org/10.1073/pnas.1320471110\u003c/li\u003e\n\u003cli\u003eGu, S., Wei, Z., Shao, Z., Friman, V. P., Cao, K., Yang, T., Kramer, J., Wang, X., Li, M., Mei, X., Xu, Y., Shen, Q., K\u0026uuml;mmerli, R., \u0026amp; Jousset, A. (2020). Competition for iron drives phytopathogen control by natural rhizosphere microbiomes. \u003cem\u003eNature Microbiology\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e(8), 1002\u0026ndash;1010. https://doi.org/10.1038/s41564-020-0719-8\u003c/li\u003e\n\u003cli\u003eGu, Y., Banerjee, S., Dini-Andreote, F., Xu, Y., Shen, Q., Jousset, A., \u0026amp; Wei, Z. (2022). Small changes in rhizosphere microbiome composition predict disease outcomes earlier than pathogen density variations. \u003cem\u003eISME Journal\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(10), 2448\u0026ndash;2456. https://doi.org/10.1038/s41396-022-01290-z\u003c/li\u003e\n\u003cli\u003eGu, Z. (2022). Complex heatmap visualization. \u003cem\u003eIMeta\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(3). https://doi.org/10.1002/imt2.43\u003c/li\u003e\n\u003cli\u003eHaidar, R., Roudet, J., Bonnard, O., Dufour, M. C., Corio-Costet, M. F., Fert, M., Gautier, T., Deschamps, A., \u0026amp; Fermaud, M. (2016). Screening and modes of action of antagonistic bacteria to control the fungal pathogen Phaeomoniella chlamydospora involved in grapevine trunk diseases. \u003cem\u003eMicrobiological Research\u003c/em\u003e, \u003cem\u003e192\u003c/em\u003e, 172\u0026ndash;184. https://doi.org/10.1016/j.micres.2016.07.003\u003c/li\u003e\n\u003cli\u003eHofstetter, V., Buyck, B., Croll, D., Viret, O., Couloux, A., \u0026amp; Gindro, K. (2012). What if esca disease of grapevine were not a fungal disease? \u003cem\u003eFungal Diversity\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e, 51\u0026ndash;67. https://doi.org/10.1007/s13225-012-0171-z\u003c/li\u003e\n\u003cli\u003eKhattab, I. M., Fischer, J., Kaźmierczak, A., Thines, E., \u0026amp; Nick, P. (2023). Ferulic acid is a putative surrender signal to stimulate programmed cell death in grapevines after infection with Neofusicoccum parvum. \u003cem\u003ePlant Cell and Environment\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(1), 339\u0026ndash;358. https://doi.org/10.1111/pce.14468\u003c/li\u003e\n\u003cli\u003eKwak, M. J., Kong, H. G., Choi, K., Kwon, S. K., Song, J. Y., Lee, J., Lee, P. A., Choi, S. Y., Seo, M., Lee, H. J., Jung, E. J., Park, H., Roy, N., Kim, H., Lee, M. M., Rubin, E. M., Lee, S. W., \u0026amp; Kim, J. F. (2018). Rhizosphere microbiome structure alters to enable wilt resistance in tomato. \u003cem\u003eNature Biotechnology\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e(11), 1100\u0026ndash;1116. https://doi.org/10.1038/nbt.4232\u003c/li\u003e\n\u003cli\u003eLailheugue, V., Darriaut, R., Tran, J., Morel, M., Marguerit, E., \u0026amp; Lauvergeat, V. (2024). Both the scion and rootstock of grafted grapevines influence the rhizosphere and root endophyte microbiomes, but rootstocks have a greater impact. \u003cem\u003eEnvironmental Microbiome\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(1). https://doi.org/10.1186/s40793-024-00566-5\u003c/li\u003e\n\u003cli\u003eLee, S. M., Kong, H. G., Song, G. C., \u0026amp; Ryu, C. M. (2021). Disruption of Firmicutes and Actinobacteria abundance in tomato rhizosphere causes the incidence of bacterial wilt disease. \u003cem\u003eISME Journal\u003c/em\u003e, \u003cem\u003e15\u003c/em\u003e(1), 330\u0026ndash;347. https://doi.org/10.1038/s41396-020-00785-x\u003c/li\u003e\n\u003cli\u003eLin, H., \u0026amp; Peddada, S. Das. (2020). Analysis of compositions of microbiomes with bias correction. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(1). https://doi.org/10.1038/s41467-020-17041-7\u003c/li\u003e\n\u003cli\u003eLi, Y., Li, X., Zhang, W., Zhang, J., Wang, H., Peng, J., Wang, X., \u0026amp; Yan, J. (2023a). Belowground microbiota analysis indicates that Fusarium spp. exacerbate grapevine trunk disease. \u003cem\u003eEnvironmental Microbiome\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1). https://doi.org/10.1186/s40793-023-00490-0\u003c/li\u003e\n\u003cli\u003eLi, Y., Li, X., Zhang, W., Zhang, J., Wang, H., Peng, J., Wang, X., \u0026amp; Yan, J. (2023b). Belowground microbiota analysis indicates that Fusarium spp. exacerbate grapevine trunk disease. \u003cem\u003eEnvironmental Microbiome\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(1). https://doi.org/10.1186/s40793-023-00490-0\u003c/li\u003e\n\u003cli\u003eMarasco, R., Rolli, E., Fusi, M., Michoud, G., \u0026amp; Daffonchio, D. (2018). Grapevine rootstocks shape underground bacterial microbiome and networking but not potential functionality. \u003cem\u003eMicrobiome\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1). https://doi.org/10.1186/s40168-017-0391-2\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;n, L., Garc\u0026iacute;a-Garc\u0026iacute;a, B., \u0026amp; Alguacil, M. del M. (2022). Interactions of the Fungal Community in the Complex Patho-System of Esca, a Grapevine Trunk Disease. \u003cem\u003eInternational Journal of Molecular Sciences\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(23). https://doi.org/10.3390/ijms232314726\u003c/li\u003e\n\u003cli\u003eMendes, R., Kruijt, M., Bruijn, I. de, Dekkers, E., van der voort, M., \u0026amp; Schneider, J. H. M. (2011). Deciphering the RhizosphereMicrobiome for Disease-Suppressive Bacteria. \u003cem\u003eScience\u003c/em\u003e, \u003cem\u003e332\u003c/em\u003e(6033), 1093\u0026ndash;1097. https://doi.org/10.1126/science.1202007\u003c/li\u003e\n\u003cli\u003eNguyen, N. H., Song, Z., Bates, S. T., Branco, S., Tedersoo, L., Menke, J., Schilling, J. S., \u0026amp; Kennedy, P. G. (2016). FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. \u003cem\u003eFungal Ecology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e, 241\u0026ndash;248. https://doi.org/10.1016/j.funeco.2015.06.006\u003c/li\u003e\n\u003cli\u003ePascual-Garc\u0026iacute;a, A., Bonhoeffer, S., \u0026amp; Bell, T. (2020). Metabolically cohesive microbial consortia and ecosystem functioning. In \u003cem\u003ePhilosophical Transactions of the Royal Society B: Biological Sciences\u003c/em\u003e (Vol. 375, Issue 1798). Royal Society Publishing. https://doi.org/10.1098/rstb.2019.0245\u003c/li\u003e\n\u003cli\u003ePollard-Flamand, J., Boul\u0026eacute;, J., Hart, M., \u0026amp; \u0026Uacute;rbez-Torres, J. R. (2022). Biocontrol Activity of Trichoderma Species Isolated from Grapevines in British Columbia against Botryosphaeria Dieback Fungal Pathogens. \u003cem\u003eJournal of Fungi\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(4). https://doi.org/10.3390/jof8040409\u003c/li\u003e\n\u003cli\u003eRen, B., Wang, X., Duan, J., \u0026amp; Ma, J. (2019). \u003cem\u003eRhizobial tRNA-derived small RNAs are signal molecules regulating plant nodulation\u003c/em\u003e. https://www.science.org\u003c/li\u003e\n\u003cli\u003eRobeson, M. S., O\u0026rsquo;Rourke, D. R., Kaehler, B. D., Ziemski, M., Dillon, M. R., Foster, J. T., \u0026amp; Bokulich, N. A. (2021). RESCRIPt: Reproducible sequence taxonomy reference database management. \u003cem\u003ePLoS Computational Biology\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(11). https://doi.org/10.1371/journal.pcbi.1009581\u003c/li\u003e\n\u003cli\u003eShannon, C. E. (1948). A Mathematical Theory of Communication. In \u003cem\u003eThe Bell System Technical Journal\u003c/em\u003e (Issue 3).\u003c/li\u003e\n\u003cli\u003eTrotel-Aziz, P., Abou-Mansour, E., Courteaux, B., Rabenoelina, F., Cl\u0026eacute;ment, C., Fontaine, F., \u0026amp; Aziz, A. (2019). Bacillus subtilis PTA-271 counteracts botryosphaeria dieback in grapevine, triggering immune responses and detoxification of fungal phytotoxins. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e. https://doi.org/10.3389/fpls.2019.00025\u003c/li\u003e\n\u003cli\u003eWei, Z., Gu, Y., Friman, V.-P., Kowalchuk, G. A., Xu, Y., Shen, Q., \u0026amp; Jousset, A. (2019). Initial soil microbiome composition and functioning predetermine future plant health. In \u003cem\u003eSci. Adv\u003c/em\u003e (Vol. 5). https://www.science.org\u003c/li\u003e\n\u003cli\u003eWhalen, E. D., Smith, R. G., Grandy, A. S., \u0026amp; Frey, S. D. (2018). Manganese limitation as a mechanism for reduced decomposition in soils under atmospheric nitrogen deposition. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e, \u003cem\u003e127\u003c/em\u003e, 252\u0026ndash;263. https://doi.org/10.1016/j.soilbio.2018.09.025\u003c/li\u003e\n\u003cli\u003eWilhelm, R. C., Amsili, J. P., Kurtz, K. S. M., van Es, H. M., \u0026amp; Buckley, D. H. (2023). Ecological insights into soil health according to the genomic traits and environment-wide associations of bacteria in agricultural soils. \u003cem\u003eISME Communications\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(1). https://doi.org/10.1038/s43705-022-00209-1\u003c/li\u003e\n\u003cli\u003eZarraonaindia, I., Owens, S. M., Weisenhorn, P., West, K., Hampton-Marcell, J., Lax, S., Bokulich, N. A., Mills, D. A., Martin, G., Taghavi, S., van der Lelie, D., \u0026amp; Gilbert, J. A. (2015). The soil microbiome influences grapevine-associated microbiota. \u003cem\u003eMBio\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2). https://doi.org/10.1128/mBio.02527-14\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate Change and Pathogens, Co-Occurrence Networks, Grapevine Trunk Diseases, Rhizomicrobiome, Vitis vinifera","lastPublishedDoi":"10.21203/rs.3.rs-8146628/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8146628/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eAims\u003c/h2\u003e\u003cp\u003eThe incidence of the apoplectic breakdown associated with grapevine trunk diseases (GTDs) is promoted by climate change, which has become a challenge for viticulture worldwide. Outbreak of these conditional diseases is expected to depend on the rhizomicrobiome. However, the impact of the rhizomicrobiome on grapevine resilience has remained poorly understood, particularly regarding its ecological aspects. This study explores the link between GTDs, the rhizomicrobiome, and soil chemistry in vineyards along the Upper Rhine.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eUsing amplicon sequencing for both fungal and prokaryotic communities, we show that around half of the fungal rhizosphere community is endowed with pathotrophic potential, independently of the health status of the plant, including seventeen taxa known to be associated with GTD, predominantly Black Foot Disease.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIn contrast to fungi, bacterial diversity is shifted depending on the micronutrients Fe, Cu, Mn, and Zn. Moreover, taxa enriched in the rhizosphere of asymptomatic vines, such as \u003cem\u003ePseudophialocephala\u003c/em\u003e and \u003cem\u003eCollarina\u003c/em\u003e for the mycobiome, and \u003cem\u003eCaulobacter\u003c/em\u003e, \u003cem\u003eKitasatospora\u003c/em\u003e, and \u003cem\u003eEntotheonellaceae\u003c/em\u003e for the bacteriome, showed correlations with soil properties. The most prominent feature associated with disease outbreaks was drastic changes of microbial co-occurrence networks. These were significantly increased in the fungi, especially for GTDs taxa, such as \u003cem\u003eFomitoporia, Stereum, Phaeomoniella\u003c/em\u003e, and \u003cem\u003eNeofusicoccum\u003c/em\u003e. By contrast, there was a depletion of many bacteria and their microbial interactions under disease outbreak such as \u003cem\u003eIsoptericola, Caulobacter, Rhodomicrobium\u003c/em\u003e and \u003cem\u003eThioprofundum\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThus, likely microbial interactions and not the mere presence of GTDs taxa explains disease outbreak. This finding opens new strategies for sustainable management of GTDs.\u003c/p\u003e","manuscriptTitle":"Health or disease – a question of rhizomicrobial ecology? The case of Grapevine Trunk Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-05 01:53:41","doi":"10.21203/rs.3.rs-8146628/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-12-12T05:43:21+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-12-03T00:57:01+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-02T10:14:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Plant and Soil","date":"2025-12-01T07:34:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-01T05:20:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Plant and Soil","date":"2025-11-25T07:31:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"plant-and-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"plso","sideBox":"Learn more about [Plant and Soil](https://www.springer.com/journal/11104)","snPcode":"11104","submissionUrl":"https://submission.nature.com/new-submission/11104/3","title":"Plant and Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"bd12790b-8b06-43cf-88c9-9477b760f0a4","owner":[],"postedDate":"December 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:03:59+00:00","versionOfRecord":{"articleIdentity":"rs-8146628","link":"https://doi.org/10.1007/s11104-026-08541-4","journal":{"identity":"plant-and-soil","isVorOnly":false,"title":"Plant and Soil"},"publishedOn":"2026-04-14 15:59:23","publishedOnDateReadable":"April 14th, 2026"},"versionCreatedAt":"2025-12-05 01:53:41","video":"","vorDoi":"10.1007/s11104-026-08541-4","vorDoiUrl":"https://doi.org/10.1007/s11104-026-08541-4","workflowStages":[]},"version":"v1","identity":"rs-8146628","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8146628","identity":"rs-8146628","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}