Rhizobacteria from vineyard and commercial mycorrhizal fungi induce synergistic microbiome shifts within grapevine root systems

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In viticulture, the plant growth promotion (PGP) potential of bacteria endemic to vineyard soil has been underexplored. Furthermore, most research about microbial biostimulants focuses on the effect on the plant, but little is known on how their application modify the soil and root microbial ecosystem, which may have an impact on plant growth and resistance. The objectives of this work were 1) to identify bacteria present in vineyard soils with functional PGP traits, 2) to test their PGP activity on young grapevines, in combination with AMF, 3) to assess the impact on the microbial communities and their inferred functions in the rhizosphere and plant roots. Results Two hundred bacteria were isolated from vineyards and characterized for their biochemical PGP activities. The most efficient were tested in vitro , both singly and in combination, on Lepidium sativum and grapevine plantlets. Two Pseudomonas species particularly increased in vitro growth and were selected for further testing, with and without two Glomus species, on grapevines planted in soil experiencing microbial dysbiosis in a greenhouse setting. After five months of growth, the co-application of PGP rhizobacteria and AMF significantly enhanced root biomass and increased the abundance of potentially beneficial bacterial genera in the roots, compared to untreated conditions and single inoculum treatments. Additionally, the prevalence of Botr ytis cinerea , associated with grapevine diseases, decreased in the root endosphere. The combined inoculation of bacteria and AMF resulted in a more complex bacterial network with higher metabolic functionality than single inoculation treatments. Conclusions To our knowledge, this is the first study to examine and apply bacterial strains derived from soils of the same vineyard plot in co-application with commercialized fungi. The results show a remodeling of microbial communities and their functions associated with a beneficial effect on the plant in terms of growth and presence of pathogens. The observed synergistic effect of bacteria and arbuscular mycorrhizal fungi indicates that it is important to consider the combined effects of individuals from synthetic communities applied in the field. PGPR screening metabarcoding inference microbiome engineering grapevine dysbiosis microbial network metabolic activities Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The cultivated grapevine, Vitis vinifera L., is a perennial plant of significant global economic importance, typically propagated through grafting on Vitis rootstocks. However, this species encounters various challenges from both abiotic and biotic stressors, ultimately compromising crop yield and grape berry quality. Among the abiotic factors, drought and salinity have become increasingly prominent, exerting intensified pressure in the ongoing context of climate change 1 . In terms of biotic stresses, grapevine trunk diseases (GTDs), along with pests and viruses, represent major threats to viticulture due to the limited effectiveness of available countermeasures 2 . These factors collectively exert a detrimental impact on grapevine growth and productivity. In response, a common practice involves replacing dead or unproductive vines with new, young ones, which requires at least 3 to 10 years post-establishment to become profitable 3 . During this period, young plants are exposed to environmental constraints that can significantly influence their health and development. Grapevines, like other plants, acquire most of their associated microbiota from the soil through chemoattractants exuded by the roots 4 . Soilborne pathogenic microorganisms, such as species from the Botryosphaeriaceae family 5 or the Phaeoacremonium genus 6 , are members of the microbial community which are attracted to and infect the root systems of both young and mature grapevines. It has also been observed that grapevine plants obtained from nurseries may harbor fungal pathogens, contributing to the decline of young vines. The infection process often occurs during the cutting and grafting preparation stages, which create numerous wounds that facilitate fungal colonization 7 . Infected plant material is sometimes provided by nurseries due to inadequate quality control and assessment criteria for grapevine propagation 8 . Besides these well-known incidences of GTDs originating from nurseries, some defaults affecting the vigor and longevity of young grapevines have also been reported 9 . Collectively, these low-quality grapevines do not last long, and need to be replaced shortly after planting. No comprehensive solutions currently exist to fully control soilborne pathogens that infect grapevine roots. However, several biological control strategies have been developed. The rhizosphere, defined as the narrow region of soil closely surrounding the roots, is a critical hotspot for microbe-plant interactions. Plant growth-promoting rhizobacteria (PGPR) can enhance plant development through direct nutrient transfer, hormonal regulation, or by controlling phytopathogens 10 . In grapevines, rhizobacteria have primarily been isolated and tested for their capacity to reduce the incidence of GTDs 11 . The plant growth-promoting (PGP) activities of rhizobacteria in grapevines have been assessed in vitro 12 , in greenhouse conditions 13 , and in field conditions 14 . Interestingly, the ability of grapevine rootstocks to attract rhizobacteria with PGP traits appears to be a fundamental function, independent of vineyard location 15 and rootstock genotype 16 . Another noteworthy group of microorganisms with the potential to enhance grapevine growth while providing resistance to pathogens is arbuscular mycorrhizal fungi (AMF). These fungal symbionts can significantly improve plant growth by supplying essential soil nutrients to roots and controlling soilborne pathogens 17 . In this mutualistic interaction, the fungi provide soil nutrients such as nitrogen (N) and phosphorus (P) through their external mycelium in exchange for carbon from plant photosynthates released from the roots. In viticulture, AMF have been extensively studied for their beneficial nutritional traits 18 . Beyond nutrient uptake and pathogen inhibition, AMF are known to influence berry composition, enhancing their relevance for wine production 19 . It is common practice in nurseries to sell already mycorrhized grapevine plants to winegrowers. The combination of both AMF and PGPR represents a promising strategy for pathogen control and plant growth enhancement while producing high-quality fruits 20 . This methodology has been applied in strawberry plants 21 , and apple trees 22 , but no studies have investigated the responses of grapevine microbial communities when the host is subjected to the potential synergistic effect of PGPR and AMF. Microorganisms are the fundamental drivers of biogeochemical cycles in soil, which is the microbial and nutrient reservoir for plants. Microbial inoculants are applied to restore microbial dysbiosis and enhance the growth-promoting capacity of plants 23 . While biosafety concerns have been consistently evaluated concerning human healthcare and plant health, the impact of microbial applications on the indigenous microbiome and plant phenotypic traits is seldom considered 24 . The aim of this study was to characterize the plant growth-promoting activity of 200 rhizobacteria isolated from the rhizosphere of grapevines showing symptoms of decline, as well as from asymptomatic plants. Numerous traits, including nitrogen fixation, phosphate solubilization, siderophore synthesis, and the production of indole-3-acetic acid (IAA), ammonia, and 1-aminocyclopropane-1-carboxylate deaminase (ACCd) were first assessed. Phenotypic tests for screening of PGPR activity after in vitro inoculation of sprouted Lepidium sativum seeds (i.e., a fast-growing plant) and 1103 Paulsen (1103P) grapevine plantlets were then performed. The second objective was to test the ability of the best combination to promote grapevine plant growth in a soil with microbial dysbiosis (characterized by a higher abundance of latent fungal pathogens and potentially beneficial bacteria with lower diversity and richness compared to asymptomatic soil 25 ). Subsequently, the most effective PGPR combination was used to inoculate both mycorrhized and non-mycorrhized young grafted grapevines in a greenhouse to observe its effects on grapevine development. The rhizosphere and root endosphere microbial communities were investigated using metabarcoding analysis. Material & Methods Screening for in vitro PGP activities of bacterial isolates A subset of 200 isolates was randomly chosen from the initial pool of 800 rhizobacteria previously isolated 26 . This selection comprised 50 isolates from the rhizosphere of two rootstocks (Riparia Gloire de Montpellier or 1103 Paulsen) grown on soils from symptomatic or asymptomatic areas. These selected isolates underwent testing for some PGP activities including Indole-3-Acetic Acid (IAA) production, 1-aminocyclopropane-1-carboxylate deaminase (ACCd) deaminase production, ammonia production, siderophore synthesis, phosphate solubilization, and nitrogen fixation. IAA production The production of IAA was determined using the Salkowski reaction adapted from Gordon & Weber (1951) 27 . The bacterial isolates were grown in LB medium supplemented with 100 µg.ml − 1 l-tryptophan, acting as a precursor for IAA synthesis, for 48 hours at 28°C under continuous shaking at 200 rpm. Bacterial suspensions were centrifuged at 8000 g for 10 min at 4°C. One ml of the supernatant was then mixed with 4 ml of Salkowski reagent (1 ml of 0.5 M FeCl 3 in 50 ml of 35% HClO 4 ), followed by measuring the color changes using a spectrophotometer at 530 nm. The calibration curve for estimating auxin concentration was made with standards ranging from 10 to 100 µg.ml − 1 of IAA. Capacity to produce ACCd The presence of ACCd activity was determined using ACC as sole source of nitrogen, following the method adjusted from Penrose & Glick (2003) 29 , which estimates the amount of α-ketobutyrate produced. Cells initially grown in R2A medium were inoculated at OD600 = 0.1 in DF medium supplemented with 3 mM of ACC and incubated at 30°C for 48 hours. After centrifugation at 8000g, pellets were washed with 0.1 M Tris-HCl (pH 7.6) and resuspended in 600 µl of 0.1 M Tris-HCl (pH 8.5) amended with 30 µl of toluene, and vortexed for 30 s. The 200 µl of toluenized cells were gently mixed with 0.5 M ACC and incubated at 30°C for 15 minutes. The reaction was stopped by adding 1 ml of 0.56 M HCl and vortexed, followed by a 5-minute centrifugation at 16000 g. One ml of supernatant was mixed with 800 µl of 0.56 M HCl and 300 µl of 2,4 dinitrophenylhydrazine (0.2% in 2M HCl), and finally incubated at 30°C for 30 minutes. Colorimetric reactions occurred with the addition of 2 ml of 2 N NaOH and were measured at 540 nm. The calibration curve for estimating ACCd concentration was made with standards ranging from 0.1 to 1 µg.ml-1 of α-ketobutyrate. Ammonia production The production of ammonia for each rhizobacteria was assessed using the Nesslerization reaction described by Cappuccino & Sherman (1992) 31 . Each rhizobacterial isolate was grown in peptone water for 72 hours at 28°C at 200 rpm. Culture supernatant (200 ml) was mixed with 1 ml of Nessler’s reagent which was supplemented with 7.3 ml of ammonia-free water. The development of brown to yellow color indicating the ammonia production was spectrophotometrically monitored at 450 nm. The calibration curve for estimating ammonia concentration was made with standards ranging from 0.1 to 1 µmol.ml − 1 of ammonium sulphate. Siderophore synthesis The synthesis of siderophores was determined using the plating method based on Chrome-azurol S (CAS) medium adjusted from Schwyn & Neilands (1987) 33 . The CAS assay solution consisted in 6 ml of 10 mM HDTMA solution diluted up to 100 ml with distilled water and a mixture of 1.5 ml iron (III) solution (1 mM FeCl3·6H2O in 100 mM HCl) supplemented to 7.5 ml of 2 mM aqueous CAS solution which was added under stirring. Anhydrous piperazine (4.307 g) was dissolved in 30 mL of water, and 6.25 ml of HCl (37%) was carefully added to it. This buffer solution (pH 5.6) was adjusted to 100 ml and the CAS shuttle solution was obtained by adding 4 mM of 5-sulfosalicylic acid to the above solution. Bacterial isolates were plated on CAS agar and incubated for 72 hours at 28°C. Siderophore production was assessed by measuring the distance between the colony and the edge of its surrounding halo. Phosphate solubilization The ability of the rhizobacteria to solubilize phosphate was determined using the Pikovskaya medium 34 . Each bacterial isolate was plated on Pikovskaya agar (1% glucose, 0.5% Ca 3 (PO 4 ) 2 , 0.05% (NH 4 )SO 4 , 0.02% NaCl, 0.01% MgSO 4 .7H 2 O, 0.02% KCl, 0.0002% MnSO 4 .7H 2 O, 0.0002% FeSO 4 .7H 2 O, 0.05% yeast extract, 1.5% agar) supplemented with bromophenol blue to assess phosphate solubilization capacity. Plates were incubated for 72 hours at 28°C. Phosphate solubilization was assessed by measuring the distance between the colony and the edge of its surrounding halo. Nitrogen fixation The capacity of the isolates to fix nitrogen was assessed with the NfB solid medium adjusted from Döbereiner (1989) 36 . Each bacterial isolate was plated on pH 6.8 NfB (0.05% D-malic acid, 0.05% K 2 HPO 4 , 0.02% MgSO 4 , 0.01% NaCl, 1.5% agar) complemented with 2 ml of bromothymol blue (0.5% in 0.2M KOH), 1 ml of vitamin solution (per 100 ml: 10 mg biotin, 20 mg pyridoxine-HCl), and 2 ml of micronutrient solution (per litter: 40 mg CuSO 4 .5H 2 O, 120 mg ZnSO 4 .7H 2 O, 1.4g H 3 BO 3 , 1g Na 2 MoO 4 .2H 2 O, 1.5g MnSO 4 .H 2 O). Plates were incubated for 72 hours at 28°C. Nitrogen fixation was assessed by measuring the distance between the colony and the edge of its surrounding halo. Identification of the most promising strains Isolates exhibiting the most efficient PGP activities were subjected to sequencing of their 16S rRNA gene to confirm their identities, as previously obtained from MALDI-TOF MS analysis conducted by Darriaut et al. (2022a) 26 . DNA extraction from isolates was performed using the FTA® CloneSaver™ card (Whatman® BioScience, USA), as described by Zott et al. (2008) 39 . Extracted DNA contained in one FTA patch was used as template for PCR amplification specific primers for the 16S rRNA gene, namely 8F (5′-AGAGTTTGATCCTGGCTCAG-3′) and 1063R (5′-ACGGGCGGTGTGTRC-3′), as described by Martins et al., (2020) 41 . Obtained amplicons were sequenced using Sanger technology, and their sequences were aligned and compared to GenBank database, using the NCBI BLAST tool. The identification was considered valid when the identity of a contiguous sequence of 343 to 989 bp was at least 98%. The 16S rDNA sequences obtained were deposited in the GenBank Database under accession numbers ON159710 to ON159717. In vitro evaluation of growth promotion on Lepidium sativum Seeds of Lepidium sativum were surface sterilized by immersion in 2.5% sodium hypochlorite for 1 minute followed by an immersion in 3% H 2 O 2 for 1 minute. Afterwards, seeds were rinsed thrice with sterile distilled water. Sterilization was confirmed by macerating fifteen seeds in sterile 0.86% NaCl and plating 100 µl of the macerate on R2A medium. Subsequently, seeds were plated on water agar and incubated for 24 hours at 25°C. Fifteen pre-germinated seeds with uniform radicles length (1.5-2 mm) were then selected and plated on new water agar dishes. The eight selected PGPR were inoculated in single, dual, or triple combinations, resulting in 92 unique combinations in total, with 100 µL of water-solution at a final concentration of 10 9 CFUs.ml − 1 . The concentrations were estimated using a plating kinetics of 24 hours with 5 sampling points that were assigned with DO measurements. The control was considered as a treatment with sterile water only. Plates containing the inoculated pre-germinated sterilized seeds were then incubated for 72 hours at 25°C. To assess the capacity of PGPR to promote L. sativum growth, the length, as well as the fresh biomass, of stems and roots were measured. In vitro evaluation of growth promotion on 1103P plantlets Grapevine plantlets cv. 1103P ( Vitis berlandieri × Vitis rupestris ) were propagated in vitro on McCown Woody Plant Medium (Duchefa) supplemented with 3% sucrose, 0.27 µM 1-naphthalene acetic acid, and 0.75% agar. The propagation was conducted in a growth chamber set to a temperature of 25°C during the day and 20°C at night, with a photoperiod of 16 hours light and 8 hours dark with a light intensity of 145 µmol photons m – 2 s – 1 . After six weeks of growth, fifteen plantlets were transplanted into plant pots filled with McCown Woody Plant Medium containing 0.5% agar, without any additional supplements. The eight selected PGPR were inoculated in single or dual combinations, resulting in 36 unique combinations in total. Each combination was applied with 300 µL of water-solution at a final concentration of 10 9 CFUs.ml − 1 , targeting the basal part and root extremities of the plantlets. The control was treated with sterile water only. After inoculation, plantlets were returned to the growth chamber in their plant pots and kept for an additional 4 weeks. To evaluate the ability of PGPR to promote plantlet growth, both the length and fresh biomass of stems and roots were measured. Additionally, the number of primary and secondary roots was counted, and petiole length was measured. Plant material and microbial application used for the greenhouse experiment Eighty grapevines were obtained from the nursery Pépinière Guillaume (70700, Charcenne, France) as grafted V. vinife ra L. cv. Cabernet Sauvignon (CS) scion clone 169 onto 1103P, a rootstock characterized for its high vigor-conferred. This grapevine combination was produced from traditional bare root plants. Half of those plants were mycorrhized by the nursery with the commercial tablet AEGIS SYM™ from Atens (La Riera de Gaià, Spain). The inoculum consisted of a mixture of polysaccharides and Rhizophagus irregularis strain BEG72 (former Glomus intraradices ) and Funneliformis mosseae (former Glomus mosseae ). Approximatively 200 spores per plant were applied to the grapevine roots following the manufacturer’s instructions. Twenty mycorrhized plants and another twenty non-mycorrhized plants were separately inoculated by dipping the roots overnight in the bacterial solution. The inoculated bacterial solution, concentrated at 10 9 CFUs.ml − 1 in water, consisted of the most efficient combination of tested rhizobacteria aimed at promoting the growth of both L. sativum and 1103P plantlets. Greenhouse experimental design, plant phenotyping, and sampling The symptomatic soil from the inter-rows analyzed in Darriaut et al. (2022a) 26 was used as the matrix soil for the greenhouse experiment. The soil from the upper surface to approximately 30 cm deep was collected with a mini excavator, and then sieved to remove roots and gravel particles larger than 3 cm. Twenty plants from each treatment ( i.e ., untreated, mycorrhized, bacterized, and both mycorrhized and bacterized) were put in 7.5 L pots (diameter 26 cm, height 21 cm) filled with the excavated soil, supported with geotextile membrane, and amended with sterilized gravels. Those 20 pots per conditions, being untreated (control plant), bacterized (inoculated with rhizobacteria), mycorrhized (inoculated with commercially available mycorrhizal fungi), both mycorrhized and bacterized (inoculated with both mycorrhizal fungi and rhizobacteria), were placed in greenhouse at middle of April 2020 under ambient light and temperature. The plants were watered twice a week with 60 ml per pots with no nutrient supply. The shoots were tied with thread to stakes in order to let the plants grow on a fence in an upright position. A schematic summary of the disposal, samplings, and measurements conducted during this study is depicted in Supplementary Fig. S1 . Plants were harvested during the middle of September 2020, after 5 months of greenhouse experience on ten biological replicates per condition. Immediately after harvesting the samples, the following parameters were assessed: aerial fresh biomass, including leaves and shoots; fresh biomass of trunks and roots; and diameters and lengths of shoots and trunks. To evaluate the leaf greenness of the plants, chlorophyll contents of the top fourth and third leaves were measured using a portable chlorophyll meter (SPAD-502, Konica Minolta Sensing, Inc., Japan). Subsequently, the dry biomass of total leaves, stems, trunks, and roots was evaluated after drying at 70°C for 72 hours. In parallel with plant sampling for phenotypic measurements, roots, rhizosphere, and bulk soil were individually collected from each pot. Roots were removed from soil aggregates by manual shaking, then approximately 5 g of the roots were sampled in tubes containing sterile 0.85% NaCl solution and vortexed prior to 5,000 g centrifugation for 10 min to separate the rhizosphere from the roots. At this stage, half of each root samples was surface sterilized with 3% hypochlorite sodium for 1 minute subsequently to 3% H 2 O 2 for 1 minute and rinsed thrice using sterile water. Three randomly selected samples were merged to create a total of three pools. These sterilized roots were stored at -80°C prior to DNA extraction. The second half of roots was used at fresh state for staining to observe mycorrhizal structures. Rhizosphere samples obtained after centrifugation and separation with the roots were also separated into two subgroups. Similar to root pooling, three randomly selected rhizosphere samples were pooled to create a total of three pools. The first subgroup of rhizosphere samples was lyophilized for 48 h using Christ Alpha® 1–4 (Bioblock Scientific) and stored at -80°C prior to DNA extraction. The second subgroup was used for the potential metabolic diversity (PMD), the isolates quantification with plating method, as well as the isolates identification through MALDI-TOF MS. DNA extraction Total DNA was extracted from 250 mg of the lyophilized soils using the DNeasy PowerSoil Pro kit (Qiagen) according to the manufacturer recommendations excepted with an additional C5 washing step. Quantification of the extracted DNA samples were performed on a Qubit® 3.0 fluorometer (Thermo Fisher Scientific) using the Qubit™ dsDNA HS Assay Kit, while the quality checking was done with a NanoDrop™ 2000/2000c spectrophotometer (Thermo Fisher Scientific). DNA was then stored at − 20°C until further use. Potential metabolic diversity (PMD), quantification of rhizosphere microorganisms, and root mycorrhizal colonization PMD, quantification of cultivable bacteria and fungi from fresh rhizosphere, and quantitative PCR of bacterial 16S, archaeal 16S, and fungal 18S from lyophilized rhizosphere were performed according to Darriaut et al. (2021) 43 . This involved plating soil dilutions on R2A medium amended with 25 mg.l − 1 of nystatin to quantify the cultivable bacterial population while quantifying the fungal populations were on PDA medium supplemented with 500 mg.l − 1 of gentamicin and 50 mg.l − 1 of chloramphenicol. In parallel, PMD was evaluated on the three rhizosphere pools using Biolog Eco-Plates™ system (Biolog Inc., CA), by measuring 31 different substrates (i.e., amines, amino acids, carbohydrates, carboxylic acids, phenolic compounds, and polymers) consumed by present microorganisms, every 24 hours for 4 days. From the subgroup of fresh root samples that were not surface-sterilized, 30 subsamples of fresh roots were utilized to assess their colonization by mycorrhizal fungi. These roots were stained using the modified ink-KOH-H 2 O 2 method, and arbuscular mycorrhizal colonization was estimated as described by Darriaut et al. (2022a) 26 . From the DNA extracted in the lyophilized rhizosphere, quantitative PCR analyses based on absolute quantification were performed on the DNA extracted using three primers pairs to quantify bacterial (341F/515R) and archaeal 16S rRNA (Arch967F/Arch1060R) genes as well as the fungal 18S rRNA (FF390/FR1) gene, listed in Supplementary Table S1 . The efficiencies of the qPCR were ranging from 80–99% (R² > 0.99). Identification of cultivable rhizobacteria using MALDI-TOF MS The identification of bacterial isolates from the fresh pools of rhizosphere was performed using MALDI-TOF MS technology according to Darriaut et al. (2022a) 26 . Briefly, 50 isolates per pool were randomly selected and grown individually on new R2A plates. In total, 600 single fresh isolates were smeared on MSP96 target polished steel BC plate and overlaid with 1µl of 70% formic acid. Once dried at room temperature, samples were overlaid for crystallization with MALDI matrix consisting in 1 µl of 10 mg.ml − 1 α-cyano-4-hydroxycinnamic acid in 50% acetonitrile/2.5% trifluoroacetic acid. The target plate was submitted to MALDI-TOF MS analysis using Microflex MALDI-TOF (Bruker Daltonik GmbH, Leipzig, Germany) bench-top mass spectrometer scanned with laser wavelength at 337 nm and acceleration voltage of 20 kV. The analysis was performed using Flex Control, MTB Compass, and MALDI-Biotyper™ software (Bruker Daltonics, Germany) by comparing the mass profile of the isolates to mass profiles in the Biotyper database. Bacterial test standard was added to every plate in order to calibrate the mass spectral data performed by the MALDI-TOF MS. Results of the pattern-matching process were expressed with scores ranging from 0 to 3. Scores > 2.3 indicated highly probable species identification, score values between 1.7 and 2.0 generally indicated relationships at genus level, and a score < 1.7 indicated that the identification was not reliable. Pre-processing of 16S and 18S rRNA genes and ITS sequencing and bioinformatic analysis The DNA samples were randomized across plates and amplified using the universal primers, including the specific overhang Illumina adapters from Darriaut et al. (2023) 45 listed in Supplementary Table S1 , specific to either the bacterial and archaeal 16S rRNA gene (785R/341F), the fungal 18S rRNA gene (AMV4.5Nf/AMDGr) or the fungal ITS1 region (ITS1F/ITS2). Each of the 25 µl reaction contained 5 µl of 5X GoTaq® Reaction Buffers (Promega, France), 15.875 µl of Nuclease-free water, 0.5 µl of mixed dNTPs (10 mM), 0.5 µl of each primer (10 µM), 2.5 µl of DNA template (5 ng/µl), and 0.125 µl of GoTaq® G2 DNA Polymerase (5 u/µl) (Promega, France). PCR amplifications were performed in three replicates for each gene. The cycling conditions of 16S rRNA gene differed from the fungal 18S rRNA gene and ITS amplifications, which were initiated with denaturation at 95°C for 5 minutes, followed by 25 and 30 cycles, respectively, consisted of a denaturation at 95°C for 30s, an annealing step at 55°C for 30s, followed by an extension step at 72°C for 30s and 45s, respectively. Further steps were carried out at the PGTB sequencing facility (Genome Transcriptome Facility of Bordeaux, Pierroton, France) using a V2 with 2 × 250 nucleotide paired reads protocol. The PCR products were purified with platform specific SPRI magnetic beads (1X ratio) and quantified using Quant-iT™ dsDNA Assay kit (ThermoFisher, France). MID and Illumina sequencing adapters were added. Libraries were pooled in equimolar amounts using a Hamilton Microlab STAR robot and sequenced on an Illumina MiSeq platform using the MiSeq Reagent Kit V2 (2 × 250 bp). Obtained sequences were demultiplexed with index search at the PGTB facility. The quality of the obtained sequences was first checked with FastQC v.0.11.8 46 . Sequences were quality filtered, trimmed, denoised, and clustered into Operational Taxonomy Units (OTUs) using FROGS pipeline from Galaxy instance v4.0.1 (2022/06) 47 , 48 . This involved assembling raw forward and reverse reads for each sample were into paired-ended reads with a minimum overlapping of 50 nucleotides and 0.1 mismatch using the VSEARCH tool 49 . Primers were removed using Cutadapt 50 , chimeras were detected and removed with UCHIME 51 , and clustering was performed using SWARM 52 in the FROGS pipeline. The minimum sequence abundance proportion was set at 5e − 5 to keep OTUs. Taxonomic assignments of 16S rRNA, ITS, and 18S rRNA-based OTUs were performed against silva138.1 (16S pintail100) 53 , Unite8.2 54 , and MaarJAM (18S 2019) 55 respectively, using Blast from Galaxy. Datasets were gathered and analyzed via phyloseq (1.38.0) 56 . Taxa related to mitochondrial and chloroplast OTUs were removed using the Arabido_TAIR10_Chl_Mito databank. Functional inferences of bacterial and fungal communities The phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) was used to predict the functional composition of the bacterial 16S rRNA marker 57 . PICRUSt2, which relies on OTUs, was run on the pipeline integrated within the Galaxy instance. SEPP was the placement tool used for insertion of sequences into the reference tree with the minimum alignment length set to 0.8 58 . The hidden-state prediction with maximum parsimony method was used to predict the functions abundances with KO (KEGG pathway) database The Nearest Sequenced Taxon Index (NSTI) cut-off was set to 0.5, excluding 367 clusters and keeping 2,269 clusters. Only the classifications related to “Celullar Processes”, “Metabolism”, and “Environmental Information Processing” were kept for statistical analyses, with their relative abundances assessed as a percentage of the total abundances. To taxonomically parse trophic modes and guilds of functional traits among fungal communities, based on OTUs, “funguild_assign” function from the FUNGuildR (0.2.0.9000) package 59 was used with the FUNGuild database 60 . Only the guild confidences classified as “Highly probable” and “Probable” were selected for statistical analyses. OTUs assigned to more than two trophic modes ( i.e ., pathotroph, saprotroph, symbiotroph) or more than two guilds ( i.e ., wood saprotroph, undefined saprotroph, plant saprotroph, orchid mycorrhizal, lichenized, epiphyte, plant pathogen, fungal parasite, endophyte, ectomycorrhizal, arbuscular mycorrhizal, animal pathogen) were classified as “Multi-affiliated”. Statistical analyses All analysis and graphs were performed on R (R-4.2.1) using RStudio (2022.07.1). Figures were generated with ggplot2 (3.5.0) and ggthemes (5.1) packages and arranged using ggpubr (0.6.0). One way ANOVA or Kruskal-Wallis and pairwise comparison using Student t or Wilcoxon tests were performed on the total biomasses and length from L. sativum and 1103P plantlets inoculated with the different bacterial consortia. To classify the growth potential of consortia, “hclust” function from stats (4.2.1) was performed to cluster groups together in a circular plot based on Euclidean distance with the Wards’s minimum variance method (Ward D2). Two-way Analysis of Variance (ANOVA) with treatment (untreated, bacterized, mycorrhized, both bacterized and mycorrhized) and compartment (bulk, rhizosphere, and root endosphere) factors were performed on enzymatic activities, cultivable, q-PCR, Eco-Plates measurements, and abundances of functional OTUs. Residuals were checked for their independency, normality, and variance homogeneity with the Durbin Watson, Shapiro-Wilk, and Bartlett tests, respectively. When assumptions for parametric tests were not respected, a multiple pairwise comparison using Wilcoxon test was performed subsequently to Kruskal–Wallis test using the multcomp (1.4–25) package. Principal Component Analysis (PCA) was performed using FactoMineR (2.9) and missMDA (1.19). Area under curve (AUC) of average color well development (AWCD) which better explain curve dynamics, was calculated with the trapezoidal method for each condition using caTools (1.18.2). Regarding amplicons analyses, shared OTUs were visualized with Venn diagrams generated with the VennDiagram (1.7.3) package. Richness and α-diversity metrics, represented by Chao1, Simpson’s diversity, and Bray-Curtis dissimilarity, respectively, were calculated through phyloseq (1.42.0) using “estimate_richness” and “distance” functions. In order to test for significant differences between the means of alpha diversity metrics by conditions, pairwise comparisons were used, based on either t or wilcoxon test, subsequently to homogeneity and normalization verifications using Levene and Shapiro tests. Non-metric multidimensional scaling (NMDS) was used to ordinate samples in two-dimensional space based on Bray-Curtis distance using ordinate function from phyloseq with “NMDS” method. Linear models and permutational multivariate analysis of variance (PERMANOVA), for richness and diversities metrics, were demonstrated using the formula: variable ~ Soil status × Compartment. Type-II ANOVAs were performed using car (3.1-2) on Chao1 and Simpson’s diversity metrics while PERMANOVAs were assessed on Bray-Curtis dissimilarity using “adonis2” function from vegan with 999 permutations. Functions “ggeffectsize” and “ggdiffbox” from MicrobiotaProcess (1.10.3) were used to discriminate significantly different taxa across conditions. This process was set with Kruskal (α = 0.05) test based on linear discriminant analysis (LDA) effect size (LEfSe) and Wilcox (α = 0.05), corrected with False Discovery Rate (FDR). Co-occurrence networks were set up for 16S rRNA and ITS communities within root system (both rhizosphere and root endosphere) using “trans_network” from microeco (1.4.0). Spearman’s correlation was estimated using WGCNA (1.72-5) with a 0.001 threshold. The “cor_optimization” function retrieved the optimal coefficient threshold and “COR_p_thres” value was set up to 0.05. Ecological modules, a cluster of nodes highly interconnected, were determined using the greedy optimization from “cal_module”. The networks were visualized using Gephi software (0.10.1). Network properties were extracted with “cal_network_attr”, and network stability indexes were estimated using “robustness” (10 run) and “vulnerability”. The Z-score evaluating within module connectivity, and P-score assessing among module connectivity, of the nodes were calculated using “plot_taxa_roles” to identify keystone species. The hubs defined as module hub nodes (Z-score > 2.5 and P-score ≤ 0.62) and connector nodes (Z-score ≤ 2.5 and P-score > 0.62) were considered as keystone taxa. Results Diversity and functional characteristics of rhizobacterial isolates Of the 800 rhizobacteria isolated and screened for identification via MALDI-TOF MS by Darriaut et al., (2022a) 26 , 200 isolates were randomly selected and evaluated for biochemical tests associated with PGP traits. The rhizobacterial isolates belonged to 17 genera as follows: Bacillus (24%), Pseudomonas (11%), Rahnella (7%), Enterobacter (4%), and Buttiauxella (4%), while 35% were not identified (Fig. 1 . A ). Minority isolates were namely Paenibacillus (2%), Burkholderia (2%), Lysinibacillus (2%), Ralstonia (2%), Serratia (2%), Streptomyces (2%), Brevibacillus (1%), Amycolaptosis (1%), Cupriavidus (1%), Dyella (1%), and Staphylococcus (1%). All the isolated rhizobacteria exhibited at least one PGP trait, including ammonia production, siderophore synthesis, phosphate solubilization, nitrogen fixation, or indole-3-acetic acid (IAA) production. The identified genera with functional abilities to contribute to plant growth promotion in each PGP trait included Bacillus , Burkholderia , Enterobacter , Pseudomonas , and Rhizobium . ACC deaminase was the least common PGP trait (15%) found in the isolates tested, while the most common was siderophore production (55.5%), followed by nitrogen fixation (55%), ammonia production (54.5%), IAA synthesis (49%), and phosphate solubilization (48%). After this biochemical screening, the most efficient isolate for each of the PGP traits, along with two isolates effective for all traits tested, were selected (Fig. 1 . B ). Their 16S rRNA sequencing confirmed MALDI-TOF MS identification as two Pseudomonas veronii (labelled as A and F isolates), one Enterobacter cloacae (isolate B), Pseudomonas brassicacearum (isolate C), Pseudomonas sp. (isolate D), Enterobacter asburiae (isolate G), and Rhizobium radiobacter (isolate H). Growth promotion effects on Lepidium sativum and Vitis vinifera plantlets Lepidium sativum seeds and 1103P plantlets were inoculated by the selected isolates. Phenotypic traits related to their growth were measured and compared to water treatment, corresponding to the negative control. The inoculates were applied in single inoculation, double inoculation ( i.e ., isolate X × isolate Y), or triple inoculation ( i.e ., isolate X × isolate Y × isolate Z), with the latter combination exclusively performed on L. sativum . For L . sativum , among the 92 combinations tested, only 2% inhibited stem mass, and 4% inhibited root length, while 24% inhibited root mass, and 39% inhibited stem length ( Supplementary Table S2 ). Regarding 1103P, among the 36 combinations tested, 39% inhibited leaf and stem mass, while 53% reduced stem length, and 70% reduced petiole length ( Supplementary Table S3 ). Regarding the root system in grapevine plantlets, 78% promoted root mass, while 70% of the tested combinations promoted the length of secondary roots and 39% increased the length of primary roots. To compare phenotypic traits between L. sativum and 1103P plantlets, only single and double combination effects on total biomass and length compared to water treatment are presented in Fig. 2 . A . Significant groups were detected across the different inoculates. The significantly best-performing single or double combinations in terms of total biomass promotion of L. sativum were A×C, B×D, C, C×H, C×G, F, B×F, and A×H. Regarding the promotion of root and stem lengths, A×C, B×F, A×H, and C×H combinations were the most effective. Similarly, significant groups were distinguished in the growth traits of 1103P plantlets. The combinations C×G, D×F, B×E, A×G, F×H, and A×C induced a significantly greater biomass gain compared to water treatment, while only A×C and A×B combinations were significantly different to the water control in terms of total length promotion. Biplot PCA was used to visualize the effects of inoculates on the phenotypic traits measured for L. sativum , and V. vinifera , distinctly (Fig. 2 . B ). The first two dimensions (Dim1 and Dim2) accounted for 88.4%, and 72.3% of the total variance in L. sativum and 1103P plantlet PCA, respectively. For L. sativum PCA, Dim1 was positively correlated with all the traits measured ( i.e ., root and stem mass, and root and stem length). Dim2 was positively correlated with both root and stem mass while negatively correlated with root and stem length. The combinations comprising single or double isolates in L. sativum samples were on the positive side of Dim2, whereas the combinations composed of triple isolates were on the negative side of Dim2. Regarding grapevine plantlet PCA, Dim1 was positively correlated to root biomass and petiole length, as well as secondary root length and number, while aerial biomass and stem length were negatively correlated to stem length and biomass of the aerial system. In contrast, Dim1 was positively correlated to each of the measured variables except the length of the primary root and the number of secondary roots. As with the L. sativum PCA, combinations including double isolates were predominantly on the positive side of Dim2 while the single inoculates were on the negative side. The HCA dendrogram depicts the combinations of inoculates tested on L. sativum and 1103P, and identifies seven and five clusters respectively, based on the phenotypic similarities of the samples (Fig. 2 . C ). The combination with the greatest positive effects on the development of L. sativum was A×C, while A×C and A×B were the most positive for the grapevine plantlet. Taking this into account, the A×C consortium, consisting of P . veronii and P . brassicacearum , was employed as the bacterized treatment during the greenhouse experiment on young grapevine bare-rooted plants potted with the symptomatic soil experiencing microbial dysbiosis described in Darriaut et al. (2024) 25 . Rhizosphere microbial profiles of the greenhouse experiment using cultivable and q-PCR measurements After two months in the greenhouse, the grapevines did not present any growth differences in the aerial or root system across the four conditions ( Supplementary Table S4 ). However, five months after treatment, a significantly higher branch diameter was observed in untreated vines and vines treated with rhizobacteria compared to the mycorrhized plants (χ 2 = 21.0, P < 0.001). The mycorrhized plants inoculated with the rhizobacteria also displayed a significantly greater dry root biomass compared to bacterized- mycorrhized conditions (F(3, 36) = 2.27, P = 0.047). Consequently, the microbial profile of the rhizosphere was analyzed in each treatment five months after application. Using MALDI-TOF MS, 63 different species were identified ( Supplementary Figure S2 ) belonging to 26 distinct genera, while unidentified genera accounted for 28% of the isolates (Fig. 3 . A ). The less abundant genera were identified as Burkholderia , Dyella , Acidovorax , Aquincola , Methylobacterium , Microbacterium , Flavobacterium , Kitasatospora , Ralstonia , Ramosus , Rhizobium , Rhodococcus , Sinomonas , Sphingomonas , Stenotrophomonas , and Variovorax . The four treatments exhibited six core genera, belonging to Bacillus (22%), Pseudarthrobacter (9%), Pseudomonas (8%), Paenarthrobacter (2.5%), and Paraburkholderia (5%) (Fig. 3 . B ). Some genera such as Rhizobium , Methylobacterium , and Microbacterium were detected only in mycorrhized conditions, while Lysinibacillus was only found in conditions inoculated with rhizobacteria, and Flavobacterium was unique to untreated conditions. The diversity of cultivable rhizobacteria, represented by the Simpson and Shannon indexes (Fig. 3 . A ), was lowest when grapevines were inoculated with both mycorrhizal fungi and rhizobacteria (Fig. 3 . C ). Conversely, the highest diversity was found in bacterized, followed by mycorrhized and untreated conditions. The samples treated with the combined inoculation presented significantly lower levels of cultivable bacteria compared to other single inoculation and untreated conditions, and cultivable fungi in the untreated samples were significantly higher compared to the other treatments (Fig. 3 . C ). No significant differences between conditions were found in terms of mycorrhizal intensity on roots. In contrast, the mycorrhized condition exhibited significantly higher mycorrhizal intensity compared to the bacterized condition. Total DNA extracted from the rhizosphere was significantly higher in bacterized samples compared to the mycorrhized condition (Fig. 3 . D ). Among the three tested amplicons for q-PCR, only fungal 18S was significantly different, with the lowest copy number in samples treated exclusively with mycorrhizal fungi. Treatments did not have a strong influence on the composition of belowground microbial communities Sequencing was performed in the greenhouse experiment on the root endosphere, rhizosphere, and bulk soil, accounting for 36 samples. A total of 5,878,244 raw sequences were generated from the libraries run. After chimera removal, paired-end sequences were clustered into 2,684 16S rRNA, 860 ITS, and 275 18S rRNA operational taxonomic units (OTUs). OTUs shared between the bulk, rhizosphere, and root endosphere compartments were 77%, 69% and 27% OTUs across the 16S rRNA, ITS, and 18S rRNA sequencing respectively ( Supplementary Figure S3 ). Likewise, 89%, 75%, and 33%, respectively, were shared among the four treatments ( i.e ., untreated, mycorrhized, bacterized, and mycorrhized-bacterized). Independently of the compartment or treatment, Proteobacteria (37%), Actinobacteriota (22.3%), Acidobacteria (9.7%), Firmicutes (7.9%), Chloforexi (6.4%), Bacteroidota (4.2%), Verrucomicrobiota (3.8%), Planctomycetota (3.2%), Gemmatimonadota (2.2%), and Myxococcota (1.6%) were the most abundant bacterial phyla (Fig. 4 . A ). The less abundant phyla categorized as ‘Others’ consisted of Patescibacteria , Nitrospirota , Desulfobacterota , Fibrobacterota , Bdellovibrionota , GAL15 , Latescibacterota , WS2 , Methylomirabilota , Nanoarchaeota , RCP2-54 , and MBNT15 . Regarding ITS sequences, Ascomycota (56.7%), Basidiomycota (29.5%), Glomeromycota (6.3%), Rozellomycota (2.2%), and Mortierellomycota (1.9%) were the predominant phyla, while unaffiliated OTUs accounted for 2.8% (Fig. 4 . A ). The ‘Others’ group comprised Chytridiomycota , Monoblepharomycota , Kickxellomycota , Calcarisporiellomycota , Blastocladiomycota, Zoopagomycota , and Olpidiomycota . Regarding 18S rRNA gene sequences, 5.5% were unaffiliated. The predominant genera detected were Glomus (90%), Acaulospora (3.4%), Paraglomus (0.6%), and Claroideoglomus (0.3%), accounting for 94.3% of the total sequences (Fig. 4 . A ). The ‘Others’ group was composed of Scutellospora , Archaeospora , and Ambispora . Richness (Fig. 4 . B ) and diversity (Fig. 4 . C ), represented by the Chao1 and Simpson indexes respectively, were higher in the rhizosphere compared to the roots for 16S rRNA, 18S rRNA genes, and ITS communities. Similarly, β-diversity based on Bray-Curtis dissimilarities revealed a compartment effect (Fig. 4 . D ). Although treatments did not have any significant effects on α and β diversities, significant differences were observed between conditions, notably the lower richness of 16S rRNA communities in mycorrhized-bacterized treated roots, or the lower richness of ITS communities within the mycorrhized rhizosphere (Fig. 4 . B ). In addition, the root endosphere samples treated with both mycorrhizal fungi and rhizobacteria displayed significantly lower diversity in ITS communities compared to the bacterized treatment. Microbial phyla present in the bulk soil were similar to those found in the rhizosphere and root compartments ( Supplementary Figure S4 ). Within the bulk soil, no significant effects of treatments were detected on the richness or diversity of the bacteria, fungi, or Glomeromycotan communities. Furthermore, the LEfSe (P 1.5) detected enriched genera within rhizosphere ( Supplementary Figure S5.A ) and root ( Supplementary Figure S5.B ) compartments for both 16S rRNA and ITS-based communities, while none were related to 18S rRNA communities. In the rhizosphere, five bacterial genera were enriched among the four conditions, with Alsobacter and Symbiobacterium detected in the mycorrhized treatment ( Supplementary Figure S5.A ). From the five enriched fungal genera in the rhizosphere, three were detected in the untreated condition from the Ascomycota phylum ( Pseudallescheria , unidentified genera from Sordariomycetes and Ascobolaceae ). In the roots, 16 bacterial genera were enriched with Rhizobium and Pseudomonas in the combined mycorrhized and bacterized treatment, while none were detected for the untreated condition. Similarly, nine genera were enriched in the mycorrhized root endosphere ( e.g ., Candidatus Solibacter , Phenylobacterium , Gemmatimonas , Frateuria , and unidentified genera from Gaiellales , Acidobacteriales ) and five in the bacterized root endosphere ( Dongia , Puia , and unidentified genera from Alphaproteobacteria and Ilumatobacteraceae ) ( Supplementary Figure S5.B ). Regarding the fungal genera within roots, Botrytis cinerea was significantly enriched in the untreated condition while Paraglomus was more abundant in the bacterized treatment. Treatments exhibited distinct microbial co-occurrence networks within root systems Co-occurrence networks of bacteria and fungi within the root system, encompassing the rhizosphere and root endosphere, provide insights into the impact of microorganisms additions on bacterial (Fig. 5 . A ) and fungal (Fig. 5 . C ) interactions. The network was simpler for the fungal communities (coefficient clustering: 0.777 to 0.850) compared to the bacterial ones (coefficient clustering: 0.598 to 0.803) (Table 1 ). Among the bacterial communities, the interaction networks increased in complexity, represented by the nodes and edges, when the grapevines were bacterized (+ 0%, + 64%), mycorrhized (+ 9%, + 230%), and both bacterized and mycorrhized (+ 13%, + 559%), compared to untreated samples. Although the nodes and edges of fungal co-occurrence networks were simpler in mycorrhized (-21%, -58%), and bacterized (+ 7%, -16%) conditions compared to untreated samples, they gained in complexity (+ 36%, + 22%) in the mycorrhized-bacterized condition. Network stability, evaluated by robustness and vulnerability, was greater in bacterial communities, especially in mycorrhized-bacterized conditions compared to other conditions ( Supplementary Figure S6 ). However, fungal network stability presented high robustness and high vulnerability for the untreated condition compared to treated samples. Irrespective of the treatment, based on Z-scores (within-module connectivity) and P-scores (among-module connectivity), 2 and 27 OTUs within these co-occurrence networks were identified as module hubs and connectors, respectively, for bacterial communities (Fig. 5 . B ), and 0 and 9 OTUs for fungal communities (Fig. 5 . D ). The Zi score identified Fonticella and Acidobacteriales as keystone taxa for the untreated condition and combined treatment of both mycorrhiza and bacteria respectively. The Pi score detected more keystone taxa within both bacterial and fungal networks in the mycorrhized- bacterized condition compared to the untreated one. Samples treated only with mycorrhizal fungi presented 10 bacterial OTUs as connectors while none were detected for fungal networks. Furthermore, no keystone taxa were detected for the bacterized treatment. Table 1 Topological features of the 16S rRNA and ITS-based communities in the root system (root and rhizosphere) in untreated, mycorrhized (Myc), bacterized (Bac), and both mycorrhized and bacterized (MycBac) conditions. 16S rRNA ITS Untreated MycBac Myc Bac Untreated MycBac Myc Bac Nodes 166 187 181 166 67 91 53 72 Edges 584 3847 1926 959 128 156 54 108 Degree 7.036 41.144 21.281 11.554 3.821 3.429 2.038 3.000 Path length 6.325 2.102 2.752 4.770 4.869 2.433 1.238 1.877 Network diameter 18 5 8 12 14 8 3 5 Clustering coefficient 0.797 0.644 0.598 0.803 0.777 0.793 0.850 0.816 Density 0.043 0.221 0.118 0.070 0.058 0.038 0.039 0.042 Heterogeneity 0.785 0.470 0.611 0.709 0.569 0.560 0.726 0.847 Centralization 0.085 0.209 0.137 0.148 0.078 0.040 0.076 0.113 Modularity 0.772 0.289 0.463 0.708 0.771 0.857 0.838 0.754 Microbial addition had an impact on the metabolic functions of microbial communities EcoPlates measurements based on the consumption of various carbon substrates revealed an increase in global metabolic activity of the mycorrhized-bacterized rhizosphere (Fig. 6 . A ). From the consumed substrates, after 96 hours incubation of the EcoPlates, it appeared that the increased metabolic activities were mostly due to carbohydrates and carboxylic acids, with enhanced activity for the combined mycorrhized and bacterized treatment compared to the untreated condition. Potential metabolic pathways based on 16S rRNA sequences were estimated using PICRUSt2. The 2,269 predicted clusters formed 19 selected pathways among the ‘Cellular Processes’, ‘Metabolism’ and ‘Environmental Information Processing’ classifications (Fig. 6 . B ). Significant differences were detected in both root and rhizosphere compartments for the metabolism of xenobiotics, carbohydrates, and amino acids. Functional inference based on ITS sequences was performed using FUNGuild, which detected four trophic modes (multi-affiliation, pathotroph, saprotroph and symbiotroph), and ten guilds (wood saprotroph, undefined saprotroph, plant saprotroph, plant pathogen, orchid mycorrhizal, multi-affiliation, fungal parasite, endophyte, arbuscular mycorrhizal, and animal pathogen) (Fig. 6 . C ). Significant differences were detected in the rhizosphere compartment, such as reduced abundances of wood saprotroph, undefined saprotroph, plant pathogen, and orchid mycorrhizal fungi in the combined mycorrhized and bacterized treatment compared to other conditions. Discussion Stressed plants serve as a potential reservoir for identifying promising rhizobacteria which exhibit PGP traits In addition to signaling compounds exudated from roots, environmental stimuli are able to modulate the biochemical functions of microorganisms. For example, the composition and production of exopolysaccharides or anti-oxidative enzymes in cyanobacteria under salt stress are modified 62 , 63 . In previous studies, the metabolic diversity measured by EcoPlates technology was greater in the symptomatic bulk 42 and rhizosphere 61 soils experiencing grapevine decline compared to asymptomatic ones. Therefore, one hypothesis would be that the grapevine under decline produces compounds stimulating the microbial communities in the surrounding soil, and that the high abundance of fungi potentially associated with grapevine diseases creates a niche for beneficial bacteria. Ethylene, being a plant hormone that coordinates stress signaling within the host, is produced under various environmental stimuli 64 . Among the different PGP traits, ACC deaminase is known to alleviate the ethylene-negative effects on plant development 65 . Strains possessing the highest efficiency in ACC deaminase have been isolated from some nutrient-poor and alkaline areas 66 . Similarly, the best siderophore producers have been isolated in the rhizosphere of tolerant cultivar under iron stress 67 . The best candidates for phosphate solubilization, nitrogen fixation, siderophore, and IAA synthesis, identified as Pseudomonas syringae , Pseudomonas koreensis , Pseudomonas veronii , and Enterobacter cloacae respectively, were all isolated from symptomatic soils 37 . Studying isolates in stressed environments could be an interesting goal to pursue, especially in the root endosphere or the rhizosphere of symptomatic plants which may harbor highly active and beneficial microbes. From laboratory to greenhouse, an essential step in the development of microbial additives Exploring grapevine and soil microbiomes not only highlights the mechanisms governing the assembly and dynamics of plant-associated microbial communities but also offers strategic guidance to enhance grapevine fitness and promote sustainable viticulture based on microbiome engineering 68 . Transposing results from artificial environments to living host plants is essential to the success of probiotic development. Deployment of consortia may confer more efficient growth promotion than single strain application 69 , which is consistent with our findings. The selection of bacterial treatment was based on the increased root biomass and length in the fast-growing plant L. sativum (124% and 47%) and in grapevine plantlets 1103P (41% and 75%). Similarly, this bacterial inoculate increased the root biomass of 1103P rootstock compared to the untreated condition by 26%. Pseudomonas species have already been reported to promote growth of crop roots in laboratory 70 and greenhouse 71 environments. Although few grapevine studies exist in vivo 72 , the exploration of plant growth enhancement is not as extensively developed as the research on biological control 73 . Pseudomonas isolated from wood tissues had antagonistic effects on various GTD fungal pathogens related to Botryosphaeria , Eutypa , and Esca/Petri diseases 74 . Apart from the root growth promotion triggered by the Pseudomonas treatment and mycorrhizal fungi, LEfSe analysis identified a higher abundance of Botrytis in the untreated root endosphere, suggesting an additional potential biocontrol property. Similarly, this result raises questions about the ability of bacteria to colonize plant roots, either by penetrating the root cortex as endophytes or by establishing themselves in the strict rhizosphere or on the root surface as epiphytes. Several PGPR acting as grapevine endophytes were explored for their growth promotion, such as Pseudomonas protegens MP12 75 , and Burkholderia phytofirmans PsJN 76 , 77 . The processes governing the fate and persistence of biological inoculants in soil can be intricate, as they may result from the interplay of numerous variables, making them challenging to comprehend and predict 78 . Among the two species inoculated, only Pseudomonas brassicacearum was detected using MALDI-TOF MS in bacterized and mycorrhized-bacterized treatments after five months, but not in the other treatments. It has been proposed, with the use of resistant mutants to antibiotics, that certain inoculants with PGP traits can persist in soils for up to seven weeks 69 . Moreover, using microsatellite markers, the biocontrol fungus Beauveria brongniartii was still present 14 years after application in fields, and even coexisted with indigenous strains 79 . The combined addition of bacteria and mycorrhiza altered the microbial structure and functioning within the root system Berg et al. (2021) 81 reviewed the different effects of inoculants on indigenous plant microbiomes encompassing transient microbiome shifts, stabilization or increase of microbial diversity and evenness, restoration of a dysbiosis, targeted triggering of host beneficial microbes, and control of pathogens. In our case and based on amplicon sequencing results, we might have reduced the potential fungal pathogen Botrytis and functional pathotrophs (wood and undefined guild) while increasing potentially beneficial bacteria in the rhizosphere, such as Pseudomonas 82 , Rhizobium 83 in the mycorrhized-bacterized treatment, and Candidatus Solibacter 84 , Phenylobacterium 85 , Gemmatimonas 86 in the mycorrhized treatment. Cardinale et al. (2022) 88 similarly reported enrichments of beneficial bacteria but non-hub taxa within root endosphere after inoculation of mycorrhizal fungi on 1103P ( Burkholderiaceae , Rhizobiaceae , Methylophilaceae , Bacillus , Massilia , and Streptomyces ), which is consistent with our findings. Bona et al. (2019) 90 employed a metaproteome approach to investigate the rhizosphere of V. vinifera cv. Pinot Noir, and identified bacteria belonging to Streptomyces , Bacillus , Bradyrhizobium , Burkholderia , and Pseudomonas with highly active protein expression, primarily involved in phosphorus and nitrogen metabolism. These genera were also detected using MALDI-TOF MS within the rhizosphere of different conditions in this study. As with plants, introducing any microbial inoculant into the soil can be regarded as a disturbance to the native microbiota. These inoculation-induced changes in soil microbial composition can inherently alter soil functioning 91 . Functional redundancy, which is the presumption that several taxa provide the same ecological function within a microbial community 92 , could explain the ecosystem’s resilience despite low microbial diversity and richness 93 . This theory would justify the lower level of cultivable fungal and bacterial diversity observed in the rhizosphere of treated conditions compared to the untreated one, while having greater metabolic diversity measured using EcoPlates and PICRUSt2. The increased activity measured with EcoPlates was related to carbohydrates and carboxylic acid. Kohler et al. (2006) 95 reported an increase in carbohydrates in the rhizosphere of lettuce plants inoculated with Pseudomonas mendocina and R. irregularis , which was presumed to be linked to soil stabilization. Moreover, R. irregularis is known to alter the carbohydrate content in the rhizosphere 96 . In a similar manner, R. irregularis is known to increase the levels of low-molecular-weight organic acids in the rhizosphere to mobilize P content bound to Fe oxides, which represents one of the key exchanges from the symbiont to the host in return for photosynthetic carbon 97 . The co-inoculation of F. mosseae and the growth-promoting rhizobacteria Ensifer meliloti on V. vinifera cv. Cabernet Sauvignon greatly increased the abundance of volatile organic compounds within roots 98 . As AMF do not acquire carbon from the soil due to their symbiotic relationship with the host, this could explain the lower carbohydrate metabolism inferred by PICRUSt2 within roots that were both mycorrhized and bacterized, compared to untreated roots. The treatments synergistically complexified the bacterial co-occurrence within root systems and had limited impact on fungal network Co-occurrence networks offer a snapshot of the intricate relationships among microbial communities in various environments, including soil and plant-associated habitats 99 , 100 . These networks, associated with the functional stability of microbial communities, and characterized by their modularity, connectivity and other topological features, are constructed by identifying significant patterns of co-occurrence between microbial taxa, providing insights into potential interactions 101 . A higher degree of network complexity is thought to reflect a more robust and resilient community, which can serve as a proxy for assessing soil quality 102 , and plant health 103 . It had been previously reported that microbial inoculation of plants resulted in more complex and compact associations within their associated microbiomes. For instance, soybeans inoculated with Rhizobium resulted in an increased number of connections within the fungal community network 104 . Although this was not observed in the fungal network, an important shift in the microbiome was detected in this study. Similarly, inoculation of R . irregularis increased the bacterial network complexity within the root endosphere of maize 105 . Regarding vineyard soil, Torres et al. (2021) 107 unveiled the increase of network complexity and stability due to AMF inoculation. In our study, a notable increase in the complexity of nodes and edges within the bacterial community network was reported in the root system treated with both mycorrhiza and bacteria, compared to untreated samples. The shifts observed in the microbial network are most likely linked to the change of microbial functionality and chemical compounds occurring within the root system. In co-occurrence networks, keystone taxa play a crucial role in community structure and function irrespective of their abundance 108 . Identifying a keystone species solely based on its topological role within the network is not sufficient unless its major ecological role within the ecosystem can be determined. Acidobacteriales being keystone taxa detected as a peripheral node in the co-inoculation condition has already been determined as such in soil fertility for rice 109 and soil respiration 110 , and is primarily involved in carbon cycling and soil aggregation 111 . In addition, the combined mycorrhized and bacterized condition revealed the presence of Mesorhizobium , a keystone bacterial genus involved in soil nitrogen metabolism 112 , Blastocatellaceae , directly involved in the stability of soil microbial-mediated functions 113 , and even Onygenales , an important group of fungal decomposers in soil 114 . Based on detected keystone taxa, it is possible to obtain beneficial microbes for a synthetic community design by targeting specific species while avoiding large PGP screening 115 . Conclusion Multifunctional plant growth-promoting rhizobacteria were isolated from symptomatic and asymptomatic grapevines, with the most effective isolates originating from the rhizosphere of stressed grapevines. After growth induction in fast-growing L. sativum and 1103P plantlets in vitro , a selected bacterial treatment consisting of P. veronii and P. brassicacearum was inoculated on grapevines grown in a greenhouse, either singly or in combination with commercial mycorrhizal fungi R . irregularis and F . mosseae . In addition to root biomass enhancement, the composition and functionality of the indigenous microbiome within the root system of treated conditions were altered, compared to untreated samples. Increased metabolic activity, such as carbohydrate metabolism, was accompanied by taxonomic shifts, including an enrichment of potentially beneficial bacteria and a reduction in fungal pathogens. Furthermore, the microbial community structure, as revealed by co-occurrence networks, increased in complexity upon treatment with microbial applications, unveiling keystone taxa within the microbial interactions. This work provides insights on the promising role of isolates from stressed environments that could alleviate microbiome dysbiosis linked to crop decline. Declarations Funding This work was supported by FranceAgrimer/CNIV funded as part of the program ‘Plan National Dépérissement du Vignoble’ within the project Vitirhizobiome (grant number 2018–52537). Authors' contributions RD, VLau, IM-P, and NO conceived the study. RD and JW performed the enzymatic assays and the in vitro bioassays with Lepidum and grapevine. RD managed the greenhouse experiment. RD, GM, PB, IM-P, NO, and VLai contributed to the sampling, the data collection and analysis. RD and VLai performed DNA extraction and metabarcoding analyses. RD and JT did the bioinformatic analysis. RD prepared the figures and tables. RD wrote the manuscript and VLau supervised the writing. All authors critically reviewed and edited the manuscript. Competing interests The authors declare no competing interests. Acknowledgements This work was supported by FranceAgrimer/CNIV and funded as part of the “Plan National Dépérissement du Vignoble” program of the Vitirhizobiome project (grant number FAM no. 22001206). The authors would like to thank the vineyard owners for their permission to sample the soil used as matrix for the greenhouse experiment. The authors also thank the Genotoul bioinformatics platform Toulouse Midi-Pyrenees and Sigenae group for providing storage resources via Galaxy instance. The authors thank Marc Meynadier for his help in the choice of the bioinformatics pipelines. Data availability Raw sequences have been deposited in Sequence Read Archive (NCBI), Bioproject PRJNA826314 https://www.ncbi.nlm.nih.gov/bioproject/PRJNA826314 References Bernardo, S., Dinis, L.-T., Machado, N. & Moutinho-Pereira, J. Grapevine abiotic stress assessment and search for sustainable adaptation strategies in Mediterranean-like climates. A review. Agronomy for Sustainable Development 38 , 66 (2018). Azevedo-Nogueira, F. et al. The road to molecular identification and detection of fungal grapevine trunk diseases. Frontiers in Plant Science 13 , 1–18 (2022). Sanmartin, C. et al. Restoration of an old vineyard by replanting of missing vines: Effects on grape production and wine quality. Agrochimica 61 , 154–163 (2017). Vives-Peris, V., de Ollas, C., Gómez-Cadenas, A. & Pérez-Clemente, R. M. Root exudates: from plant to rhizosphere and beyond. Plant Cell Reports 39 , 3–17 (2020). 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Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Tran","suffix":""},{"id":406761425,"identity":"4fee518f-d43b-41d5-b462-4fb83d7fd1a2","order_by":4,"name":"Guilherme Martins","email":"","orcid":"","institution":"Université de Bordeaux, INRAE, Bordeaux INP, Bordeaux Sciences Agro, UMR OEnologie 1366, ISVV","correspondingAuthor":false,"prefix":"","firstName":"Guilherme","middleName":"","lastName":"Martins","suffix":""},{"id":406761426,"identity":"32509c3e-1d92-42cb-a7f4-7dcb143f036e","order_by":5,"name":"Patricia Ballestra","email":"","orcid":"","institution":"Université de Bordeaux, INRAE, Bordeaux INP, Bordeaux Sciences Agro, UMR OEnologie 1366, ISVV","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Ballestra","suffix":""},{"id":406761427,"identity":"2b716723-ad76-4ad4-8a83-b017462d41c3","order_by":6,"name":"Isabelle Masneuf-Pomarède","email":"","orcid":"","institution":"Université de Bordeaux, INRAE, Bordeaux INP, Bordeaux Sciences Agro, UMR OEnologie 1366, ISVV","correspondingAuthor":false,"prefix":"","firstName":"Isabelle","middleName":"","lastName":"Masneuf-Pomarède","suffix":""},{"id":406761428,"identity":"ca3dbbbe-b7f5-4d33-8f7f-e3fbf56c2a06","order_by":7,"name":"Nathalie Ollat","email":"","orcid":"","institution":"EGFV, Univ. 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Bordeaux, Bordeaux Sciences Agro, INRAE, ISVV","correspondingAuthor":true,"prefix":"","firstName":"Virginie","middleName":"","lastName":"Lauvergeat","suffix":""}],"badges":[],"createdAt":"2025-01-22 11:17:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5880310/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5880310/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-12673-5","type":"published","date":"2025-07-30T16:21:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":74938507,"identity":"5b5d0c04-6cc1-48fe-9453-a81a58992672","added_by":"auto","created_at":"2025-01-28 13:43:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":622501,"visible":true,"origin":"","legend":"\u003cp\u003ePlant growth-promoting (PGP) capacity of the 200 bacteria isolates from the grapevine rhizosphere to produce ammonia, indole acetic acid, ACC deaminase, and siderophore, to solubilize phosphate, and to fix atmospheric nitrogen. \u003cstrong\u003e(A)\u003c/strong\u003e Distribution of the PGP activities according to the genera previously identified using MALDI-TOF MS. When a PGP activity was detected, it was considered as an occurrence within the distribution graph (i.e., one isolate can have several PGP activities). \u003cstrong\u003e(B)\u003c/strong\u003eThe eight most promising isolates identified using sequencing, and selected for in vitro growth promotion assays on Lepidium sativum and 1103P plantlets. (+++) indicates that the isolate was the most effective within the considered PGP function. (++) indicates that the isolate was among the top 50% most effective. (+) indicates that the isolate was among the 50% least effective. (-) indicates a non-effective isolate in the corresponding PGP function.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5880310/v1/5186261e2fa9e43336ad980c.png"},{"id":74938755,"identity":"736520b6-37cc-4ff5-bb89-fe2011214ac3","added_by":"auto","created_at":"2025-01-28 13:51:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1478300,"visible":true,"origin":"","legend":"\u003cp\u003eGrowth promotion effects of potentially beneficial isolates on Lepidium sativum sprouted seeds (n = 15) and 1103P plantlets (n = 15). \u003cstrong\u003e(A)\u003c/strong\u003eInfluence on the total length and biomass (stem and root) of single inoculants and consortia composed of two or three different isolates compared to water treatment. Different letters indicate that the groups have a mean difference that is statistically significant (P \u0026lt; 0.05). \u003cstrong\u003e(B)\u003c/strong\u003e Biplot PCA of several growth traits from aerial and root systems. PCA individuals were colored according to their inoculation type (single, double, triple, water, or mix, with the latter combination standing for the eight-isolate consortium). \u003cstrong\u003e(C)\u003c/strong\u003e HCA of the different consortia based on phenotypic measurements. Clusters were color-coded based on the gradient of efficiency of isolates in influencing growth, with symbols (+), (++), (+++), and (++++) denoting promotion, and (-) and (--) indicating inhibition compared to conditions treated with water. (Water-like) indicates that the cluster inoculants had the same effect on plant growth as water treatment.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5880310/v1/5f8897fbdb56df37bf01b2cb.png"},{"id":74938504,"identity":"01e10c14-fccd-4457-bb6b-d1b8e5ce70e9","added_by":"auto","created_at":"2025-01-28 13:43:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":869140,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial profile of the rhizosphere compartment of grapevines grown five months in a greenhouse, under untreated, mycorrhized, bacterized, and mycorrhized-bacterized conditions. \u003cstrong\u003e(A)\u003c/strong\u003e MALDI-TOF MS identification of the top ten most abundant rhizobacteria, associated with the Simpson and Shannon diversity indexes (n = 150). The less abundant genera were grouped in ‘Others’. \u003cstrong\u003e(B)\u003c/strong\u003e Venn diagram illustrating the overlap of the identified genera. \u003cstrong\u003e(C)\u003c/strong\u003eCultivable dependent methods analysis related to the level of cultivable bacteria and fungi, and to the mycorrhizal associations with roots under microscopic observations. \u003cstrong\u003e(D)\u003c/strong\u003e Molecular dependent methods analysis related to the extracted DNA associated with the number of gene copies (i.e., 18S, archaeal and bacterial 16S). Different letters indicate that the groups have a mean difference that is statistically significant (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5880310/v1/1adaf1c3c421398a27e56c6f.png"},{"id":74938512,"identity":"98b16451-4534-4c04-8dba-93b01d16101e","added_by":"auto","created_at":"2025-01-28 13:43:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1149476,"visible":true,"origin":"","legend":"\u003cp\u003eMicrobial composition metrics of bacterial (16S rRNA gene), fungal (ITS), and Glomeromycota (18S rRNA gene) communities across the root and rhizosphere compartments in untreated, mycorrhized (Myc), bacterized (Bac), and mycorrhized-bacterized (Myc+Bac) samples. (\u003cstrong\u003eA\u003c/strong\u003e) Relative abundances, (\u003cstrong\u003eB\u003c/strong\u003e) Chao1 richness, (\u003cstrong\u003eC\u003c/strong\u003e) Simpson’s diversity, and (\u003cstrong\u003eD\u003c/strong\u003e) NMDS based on Bray-Curtis dissimilarities with the associated ANOVA and PERMANOVA (perm = 999) tests. ANOVA and PERMANOVA tests are displayed with treatment (= T) and compartment (= C) effects. Different letters indicate that the groups have a mean difference that is statistically significant (P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5880310/v1/c4332d14f8d62e79e2f2bd0b.png"},{"id":74938514,"identity":"47d608b6-8412-4d10-afd2-56ec5d18e8b7","added_by":"auto","created_at":"2025-01-28 13:43:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":9095250,"visible":true,"origin":"","legend":"\u003cp\u003eEcological network analysis for bacterial and fungal communities within root systems (root and rhizosphere combined). Co-occurrence networks of the (\u003cstrong\u003eA\u003c/strong\u003e) 16S rRNA and (\u003cstrong\u003eC\u003c/strong\u003e) ITS-based communities among untreated, mycorrhized, bacterized, and mycorrhized-bacterized conditions. Different node colors refer to different OTUs according to their phyla, and edge colors represent significant positive (red) and negative (green) correlations (r \u0026gt; 0.70; P\u0026lt; 0.05). The role of OTUs in the network of (\u003cstrong\u003eB\u003c/strong\u003e) bacterial and (\u003cstrong\u003eD\u003c/strong\u003e) fungal communities was determined according to within-module connectivity (Zi) and among-module connectivity (Pi). Keystone taxa were determined when Pi \u0026gt; 0.62 (connector node) and Zi \u0026gt; 2.5 (module hub node).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5880310/v1/8df6b068f0743c11e52d13f2.png"},{"id":74938515,"identity":"18925266-0e90-45cd-b330-ac7ed27474fe","added_by":"auto","created_at":"2025-01-28 13:43:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2783588,"visible":true,"origin":"","legend":"\u003cp\u003ePotential activities of the root and rhizosphere microbiomes in untreated, mycorrhized, bacterized, and mycorrhized-bacterized samples. (\u003cstrong\u003eA\u003c/strong\u003e) EcoPlates measurements in the rhizosphere compartment, associated with the different family compounds measured 96 hours after incubation. Functional inference of the (\u003cstrong\u003eB\u003c/strong\u003e) 16S rRNA gene using PICRUSt2 and (\u003cstrong\u003eC\u003c/strong\u003e) ITS gene using the FUNGuild database. Different letters indicate that the groups have a mean difference that is statistically significant (P \u0026lt; 0.05)\u003cem\u003e.\u003c/em\u003eSignificant \u003cem\u003eP\u003c/em\u003e values of the ANOVA are displayed in bold.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5880310/v1/99220c9e6c85662e739ce3d8.png"},{"id":88268240,"identity":"75cf3809-ba05-4b5f-9cfb-5b42e9889c6d","added_by":"auto","created_at":"2025-08-04 16:50:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14876693,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5880310/v1/c55e3851-8580-464b-880b-db33a8172bad.pdf"},{"id":74938509,"identity":"7e686504-b52b-4042-8575-1932547c4b2e","added_by":"auto","created_at":"2025-01-28 13:43:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1390708,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5880310/v1/dead3e1fb360a20259f22405.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rhizobacteria from vineyard and commercial mycorrhizal fungi induce synergistic microbiome shifts within grapevine root systems","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe cultivated grapevine, \u003cem\u003eVitis vinifera\u003c/em\u003e L., is a perennial plant of significant global economic importance, typically propagated through grafting on \u003cem\u003eVitis\u003c/em\u003e rootstocks. However, this species encounters various challenges from both abiotic and biotic stressors, ultimately compromising crop yield and grape berry quality. Among the abiotic factors, drought and salinity have become increasingly prominent, exerting intensified pressure in the ongoing context of climate change \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In terms of biotic stresses, grapevine trunk diseases (GTDs), along with pests and viruses, represent major threats to viticulture due to the limited effectiveness of available countermeasures \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These factors collectively exert a detrimental impact on grapevine growth and productivity.\u003c/p\u003e \u003cp\u003eIn response, a common practice involves replacing dead or unproductive vines with new, young ones, which requires at least 3 to 10 years post-establishment to become profitable \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. During this period, young plants are exposed to environmental constraints that can significantly influence their health and development. Grapevines, like other plants, acquire most of their associated microbiota from the soil through chemoattractants exuded by the roots \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Soilborne pathogenic microorganisms, such as species from the \u003cem\u003eBotryosphaeriaceae\u003c/em\u003e family \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e or the \u003cem\u003ePhaeoacremonium\u003c/em\u003e genus \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, are members of the microbial community which are attracted to and infect the root systems of both young and mature grapevines. It has also been observed that grapevine plants obtained from nurseries may harbor fungal pathogens, contributing to the decline of young vines. The infection process often occurs during the cutting and grafting preparation stages, which create numerous wounds that facilitate fungal colonization \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Infected plant material is sometimes provided by nurseries due to inadequate quality control and assessment criteria for grapevine propagation \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Besides these well-known incidences of GTDs originating from nurseries, some defaults affecting the vigor and longevity of young grapevines have also been reported \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Collectively, these low-quality grapevines do not last long, and need to be replaced shortly after planting.\u003c/p\u003e \u003cp\u003eNo comprehensive solutions currently exist to fully control soilborne pathogens that infect grapevine roots. However, several biological control strategies have been developed. The rhizosphere, defined as the narrow region of soil closely surrounding the roots, is a critical hotspot for microbe-plant interactions. Plant growth-promoting rhizobacteria (PGPR) can enhance plant development through direct nutrient transfer, hormonal regulation, or by controlling phytopathogens \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In grapevines, rhizobacteria have primarily been isolated and tested for their capacity to reduce the incidence of GTDs \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The plant growth-promoting (PGP) activities of rhizobacteria in grapevines have been assessed \u003cem\u003ein vitro\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, in greenhouse conditions \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and in field conditions \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Interestingly, the ability of grapevine rootstocks to attract rhizobacteria with PGP traits appears to be a fundamental function, independent of vineyard location \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and rootstock genotype \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAnother noteworthy group of microorganisms with the potential to enhance grapevine growth while providing resistance to pathogens is arbuscular mycorrhizal fungi (AMF). These fungal symbionts can significantly improve plant growth by supplying essential soil nutrients to roots and controlling soilborne pathogens \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In this mutualistic interaction, the fungi provide soil nutrients such as nitrogen (N) and phosphorus (P) through their external mycelium in exchange for carbon from plant photosynthates released from the roots. In viticulture, AMF have been extensively studied for their beneficial nutritional traits \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Beyond nutrient uptake and pathogen inhibition, AMF are known to influence berry composition, enhancing their relevance for wine production \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. It is common practice in nurseries to sell already mycorrhized grapevine plants to winegrowers.\u003c/p\u003e \u003cp\u003eThe combination of both AMF and PGPR represents a promising strategy for pathogen control and plant growth enhancement while producing high-quality fruits \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. This methodology has been applied in strawberry plants \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and apple trees \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, but no studies have investigated the responses of grapevine microbial communities when the host is subjected to the potential synergistic effect of PGPR and AMF. Microorganisms are the fundamental drivers of biogeochemical cycles in soil, which is the microbial and nutrient reservoir for plants. Microbial inoculants are applied to restore microbial dysbiosis and enhance the growth-promoting capacity of plants \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. While biosafety concerns have been consistently evaluated concerning human healthcare and plant health, the impact of microbial applications on the indigenous microbiome and plant phenotypic traits is seldom considered \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe aim of this study was to characterize the plant growth-promoting activity of 200 rhizobacteria isolated from the rhizosphere of grapevines showing symptoms of decline, as well as from asymptomatic plants. Numerous traits, including nitrogen fixation, phosphate solubilization, siderophore synthesis, and the production of indole-3-acetic acid (IAA), ammonia, and 1-aminocyclopropane-1-carboxylate deaminase (ACCd) were first assessed. Phenotypic tests for screening of PGPR activity after \u003cem\u003ein vitro\u003c/em\u003e inoculation of sprouted \u003cem\u003eLepidium sativum\u003c/em\u003e seeds (i.e., a fast-growing plant) and 1103 Paulsen (1103P) grapevine plantlets were then performed. The second objective was to test the ability of the best combination to promote grapevine plant growth in a soil with microbial dysbiosis (characterized by a higher abundance of latent fungal pathogens and potentially beneficial bacteria with lower diversity and richness compared to asymptomatic soil \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e). Subsequently, the most effective PGPR combination was used to inoculate both mycorrhized and non-mycorrhized young grafted grapevines in a greenhouse to observe its effects on grapevine development. The rhizosphere and root endosphere microbial communities were investigated using metabarcoding analysis.\u003c/p\u003e"},{"header":"Material \u0026 Methods","content":"\u003cp\u003e \u003cb\u003eScreening for\u003c/b\u003e \u003cb\u003ein vitro\u003c/b\u003e \u003cb\u003ePGP activities of bacterial isolates\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA subset of 200 isolates was randomly chosen from the initial pool of 800 rhizobacteria previously isolated \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. This selection comprised 50 isolates from the rhizosphere of two rootstocks (Riparia Gloire de Montpellier or 1103 Paulsen) grown on soils from symptomatic or asymptomatic areas. These selected isolates underwent testing for some PGP activities including Indole-3-Acetic Acid (IAA) production, 1-aminocyclopropane-1-carboxylate deaminase (ACCd) deaminase production, ammonia production, siderophore synthesis, phosphate solubilization, and nitrogen fixation.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIAA production\u003c/h2\u003e \u003cp\u003eThe production of IAA was determined using the Salkowski reaction adapted from Gordon \u0026amp; Weber (1951) \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The bacterial isolates were grown in LB medium supplemented with 100 \u0026micro;g.ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e l-tryptophan, acting as a precursor for IAA synthesis, for 48 hours at 28\u0026deg;C under continuous shaking at 200 rpm. Bacterial suspensions were centrifuged at 8000 g for 10 min at 4\u0026deg;C. One ml of the supernatant was then mixed with 4 ml of Salkowski reagent (1 ml of 0.5 M FeCl\u003csub\u003e3\u003c/sub\u003e in 50 ml of 35% HClO\u003csub\u003e4\u003c/sub\u003e), followed by measuring the color changes using a spectrophotometer at 530 nm. The calibration curve for estimating auxin concentration was made with standards ranging from 10 to 100 \u0026micro;g.ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of IAA.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCapacity to produce ACCd\u003c/h3\u003e\n\u003cp\u003eThe presence of ACCd activity was determined using ACC as sole source of nitrogen, following the method adjusted from Penrose \u0026amp; Glick (2003)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, which estimates the amount of α-ketobutyrate produced. Cells initially grown in R2A medium were inoculated at OD600\u0026thinsp;=\u0026thinsp;0.1 in DF medium supplemented with 3 mM of ACC and incubated at 30\u0026deg;C for 48 hours. After centrifugation at 8000g, pellets were washed with 0.1 M Tris-HCl (pH 7.6) and resuspended in 600 \u0026micro;l of 0.1 M Tris-HCl (pH 8.5) amended with 30 \u0026micro;l of toluene, and vortexed for 30 s. The 200 \u0026micro;l of toluenized cells were gently mixed with 0.5 M ACC and incubated at 30\u0026deg;C for 15 minutes. The reaction was stopped by adding 1 ml of 0.56 M HCl and vortexed, followed by a 5-minute centrifugation at 16000 g. One ml of supernatant was mixed with 800 \u0026micro;l of 0.56 M HCl and 300 \u0026micro;l of 2,4 dinitrophenylhydrazine (0.2% in 2M HCl), and finally incubated at 30\u0026deg;C for 30 minutes. Colorimetric reactions occurred with the addition of 2 ml of 2 N NaOH and were measured at 540 nm. The calibration curve for estimating ACCd concentration was made with standards ranging from 0.1 to 1 \u0026micro;g.ml-1 of α-ketobutyrate.\u003c/p\u003e\n\u003ch3\u003eAmmonia production\u003c/h3\u003e\n\u003cp\u003eThe production of ammonia for each rhizobacteria was assessed using the Nesslerization reaction described by Cappuccino \u0026amp; Sherman (1992) \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Each rhizobacterial isolate was grown in peptone water for 72 hours at 28\u0026deg;C at 200 rpm. Culture supernatant (200 ml) was mixed with 1 ml of Nessler\u0026rsquo;s reagent which was supplemented with 7.3 ml of ammonia-free water. The development of brown to yellow color indicating the ammonia production was spectrophotometrically monitored at 450 nm. The calibration curve for estimating ammonia concentration was made with standards ranging from 0.1 to 1 \u0026micro;mol.ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of ammonium sulphate.\u003c/p\u003e\n\u003ch3\u003eSiderophore synthesis\u003c/h3\u003e\n\u003cp\u003eThe synthesis of siderophores was determined using the plating method based on Chrome-azurol S (CAS) medium adjusted from Schwyn \u0026amp; Neilands (1987) \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The CAS assay solution consisted in 6 ml of 10 mM HDTMA solution diluted up to 100 ml with distilled water and a mixture of 1.5 ml iron (III) solution (1 mM FeCl3\u0026middot;6H2O in 100 mM HCl) supplemented to 7.5 ml of 2 mM aqueous CAS solution which was added under stirring. Anhydrous piperazine (4.307 g) was dissolved in 30 mL of water, and 6.25 ml of HCl (37%) was carefully added to it. This buffer solution (pH 5.6) was adjusted to 100 ml and the CAS shuttle solution was obtained by adding 4 mM of 5-sulfosalicylic acid to the above solution. Bacterial isolates were plated on CAS agar and incubated for 72 hours at 28\u0026deg;C. Siderophore production was assessed by measuring the distance between the colony and the edge of its surrounding halo.\u003c/p\u003e\n\u003ch3\u003ePhosphate solubilization\u003c/h3\u003e\n\u003cp\u003eThe ability of the rhizobacteria to solubilize phosphate was determined using the Pikovskaya medium \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Each bacterial isolate was plated on Pikovskaya agar (1% glucose, 0.5% Ca\u003csub\u003e3\u003c/sub\u003e(PO\u003csub\u003e4\u003c/sub\u003e)\u003csub\u003e2\u003c/sub\u003e, 0.05% (NH\u003csub\u003e4\u003c/sub\u003e)SO\u003csub\u003e4\u003c/sub\u003e, 0.02% NaCl, 0.01% MgSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 0.02% KCl, 0.0002% MnSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 0.0002% FeSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 0.05% yeast extract, 1.5% agar) supplemented with bromophenol blue to assess phosphate solubilization capacity. Plates were incubated for 72 hours at 28\u0026deg;C. Phosphate solubilization was assessed by measuring the distance between the colony and the edge of its surrounding halo.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNitrogen fixation\u003c/h2\u003e \u003cp\u003eThe capacity of the isolates to fix nitrogen was assessed with the NfB solid medium adjusted from D\u0026ouml;bereiner (1989) \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Each bacterial isolate was plated on pH 6.8 NfB (0.05% D-malic acid, 0.05% K\u003csub\u003e2\u003c/sub\u003eHPO\u003csub\u003e4\u003c/sub\u003e, 0.02% MgSO\u003csub\u003e4\u003c/sub\u003e, 0.01% NaCl, 1.5% agar) complemented with 2 ml of bromothymol blue (0.5% in 0.2M KOH), 1 ml of vitamin solution (per 100 ml: 10 mg biotin, 20 mg pyridoxine-HCl), and 2 ml of micronutrient solution (per litter: 40 mg CuSO\u003csub\u003e4\u003c/sub\u003e.5H\u003csub\u003e2\u003c/sub\u003eO, 120 mg ZnSO\u003csub\u003e4\u003c/sub\u003e.7H\u003csub\u003e2\u003c/sub\u003eO, 1.4g H\u003csub\u003e3\u003c/sub\u003eBO\u003csub\u003e3\u003c/sub\u003e, 1g Na\u003csub\u003e2\u003c/sub\u003eMoO\u003csub\u003e4\u003c/sub\u003e.2H\u003csub\u003e2\u003c/sub\u003eO, 1.5g MnSO\u003csub\u003e4\u003c/sub\u003e.H\u003csub\u003e2\u003c/sub\u003eO). Plates were incubated for 72 hours at 28\u0026deg;C. Nitrogen fixation was assessed by measuring the distance between the colony and the edge of its surrounding halo.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification of the most promising strains\u003c/h3\u003e\n\u003cp\u003eIsolates exhibiting the most efficient PGP activities were subjected to sequencing of their 16S rRNA gene to confirm their identities, as previously obtained from MALDI-TOF MS analysis conducted by Darriaut et al. (2022a)\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. DNA extraction from isolates was performed using the FTA\u0026reg; CloneSaver\u0026trade; card (Whatman\u0026reg; BioScience, USA), as described by Zott et al. (2008) \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eExtracted DNA contained in one FTA patch was used as template for PCR amplification specific primers for the 16S rRNA gene, namely 8F (5\u0026prime;-AGAGTTTGATCCTGGCTCAG-3\u0026prime;) and 1063R (5\u0026prime;-ACGGGCGGTGTGTRC-3\u0026prime;), as described by Martins et al., (2020) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Obtained amplicons were sequenced using Sanger technology, and their sequences were aligned and compared to GenBank database, using the NCBI BLAST tool. The identification was considered valid when the identity of a contiguous sequence of 343 to 989 bp was at least 98%. The 16S rDNA sequences obtained were deposited in the GenBank Database under accession numbers ON159710 to ON159717.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003eevaluation of growth promotion on\u003c/b\u003e \u003cb\u003eLepidium sativum\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSeeds of \u003cem\u003eLepidium sativum\u003c/em\u003e were surface sterilized by immersion in 2.5% sodium hypochlorite for 1 minute followed by an immersion in 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for 1 minute. Afterwards, seeds were rinsed thrice with sterile distilled water. Sterilization was confirmed by macerating fifteen seeds in sterile 0.86% NaCl and plating 100 \u0026micro;l of the macerate on R2A medium. Subsequently, seeds were plated on water agar and incubated for 24 hours at 25\u0026deg;C. Fifteen pre-germinated seeds with uniform radicles length (1.5-2 mm) were then selected and plated on new water agar dishes. The eight selected PGPR were inoculated in single, dual, or triple combinations, resulting in 92 unique combinations in total, with 100 \u0026micro;L of water-solution at a final concentration of 10\u003csup\u003e9\u003c/sup\u003e CFUs.ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The concentrations were estimated using a plating kinetics of 24 hours with 5 sampling points that were assigned with DO measurements. The control was considered as a treatment with sterile water only. Plates containing the inoculated pre-germinated sterilized seeds were then incubated for 72 hours at 25\u0026deg;C. To assess the capacity of PGPR to promote \u003cem\u003eL. sativum\u003c/em\u003e growth, the length, as well as the fresh biomass, of stems and roots were measured.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn vitro\u003c/b\u003e \u003cb\u003eevaluation of growth promotion on 1103P plantlets\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGrapevine plantlets cv. 1103P (\u003cem\u003eVitis berlandieri\u003c/em\u003e \u0026times; \u003cem\u003eVitis rupestris\u003c/em\u003e) were propagated in vitro on McCown Woody Plant Medium (Duchefa) supplemented with 3% sucrose, 0.27 \u0026micro;M 1-naphthalene acetic acid, and 0.75% agar. The propagation was conducted in a growth chamber set to a temperature of 25\u0026deg;C during the day and 20\u0026deg;C at night, with a photoperiod of 16 hours light and 8 hours dark with a light intensity of 145 \u0026micro;mol photons m\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e s\u003csup\u003e\u0026ndash;\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. After six weeks of growth, fifteen plantlets were transplanted into plant pots filled with McCown Woody Plant Medium containing 0.5% agar, without any additional supplements. The eight selected PGPR were inoculated in single or dual combinations, resulting in 36 unique combinations in total. Each combination was applied with 300 \u0026micro;L of water-solution at a final concentration of 10\u003csup\u003e9\u003c/sup\u003e CFUs.ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, targeting the basal part and root extremities of the plantlets. The control was treated with sterile water only. After inoculation, plantlets were returned to the growth chamber in their plant pots and kept for an additional 4 weeks. To evaluate the ability of PGPR to promote plantlet growth, both the length and fresh biomass of stems and roots were measured. Additionally, the number of primary and secondary roots was counted, and petiole length was measured.\u003c/p\u003e\n\u003ch3\u003ePlant material and microbial application used for the greenhouse experiment\u003c/h3\u003e\n\u003cp\u003eEighty grapevines were obtained from the nursery P\u0026eacute;pini\u0026egrave;re Guillaume (70700, Charcenne, France) as grafted \u003cem\u003eV. vinife\u003c/em\u003era L. cv. Cabernet Sauvignon (CS) scion clone 169 onto 1103P, a rootstock characterized for its high vigor-conferred. This grapevine combination was produced from traditional bare root plants. Half of those plants were mycorrhized by the nursery with the commercial tablet AEGIS SYM\u0026trade; from Atens (La Riera de Gai\u0026agrave;, Spain). The inoculum consisted of a mixture of polysaccharides and \u003cem\u003eRhizophagus irregularis\u003c/em\u003e strain BEG72 (former \u003cem\u003eGlomus intraradices\u003c/em\u003e) and \u003cem\u003eFunneliformis mosseae\u003c/em\u003e (former \u003cem\u003eGlomus mosseae\u003c/em\u003e). Approximatively 200 spores per plant were applied to the grapevine roots following the manufacturer\u0026rsquo;s instructions. Twenty mycorrhized plants and another twenty non-mycorrhized plants were separately inoculated by dipping the roots overnight in the bacterial solution. The inoculated bacterial solution, concentrated at 10\u003csup\u003e9\u003c/sup\u003e CFUs.ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in water, consisted of the most efficient combination of tested rhizobacteria aimed at promoting the growth of both \u003cem\u003eL. sativum\u003c/em\u003e and 1103P plantlets.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGreenhouse experimental design, plant phenotyping, and sampling\u003c/h2\u003e \u003cp\u003eThe symptomatic soil from the inter-rows analyzed in Darriaut et al. (2022a) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e was used as the matrix soil for the greenhouse experiment. The soil from the upper surface to approximately 30 cm deep was collected with a mini excavator, and then sieved to remove roots and gravel particles larger than 3 cm. Twenty plants from each treatment (\u003cem\u003ei.e\u003c/em\u003e., untreated, mycorrhized, bacterized, and both mycorrhized and bacterized) were put in 7.5 L pots (diameter 26 cm, height 21 cm) filled with the excavated soil, supported with geotextile membrane, and amended with sterilized gravels. Those 20 pots per conditions, being untreated (control plant), bacterized (inoculated with rhizobacteria), mycorrhized (inoculated with commercially available mycorrhizal fungi), both mycorrhized and bacterized (inoculated with both mycorrhizal fungi and rhizobacteria), were placed in greenhouse at middle of April 2020 under ambient light and temperature. The plants were watered twice a week with 60 ml per pots with no nutrient supply. The shoots were tied with thread to stakes in order to let the plants grow on a fence in an upright position.\u003c/p\u003e \u003cp\u003eA schematic summary of the disposal, samplings, and measurements conducted during this study is depicted in \u003cb\u003eSupplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. Plants were harvested during the middle of September 2020, after 5 months of greenhouse experience on ten biological replicates per condition. Immediately after harvesting the samples, the following parameters were assessed: aerial fresh biomass, including leaves and shoots; fresh biomass of trunks and roots; and diameters and lengths of shoots and trunks. To evaluate the leaf greenness of the plants, chlorophyll contents of the top fourth and third leaves were measured using a portable chlorophyll meter (SPAD-502, Konica Minolta Sensing, Inc., Japan). Subsequently, the dry biomass of total leaves, stems, trunks, and roots was evaluated after drying at 70\u0026deg;C for 72 hours.\u003c/p\u003e \u003cp\u003eIn parallel with plant sampling for phenotypic measurements, roots, rhizosphere, and bulk soil were individually collected from each pot. Roots were removed from soil aggregates by manual shaking, then approximately 5 g of the roots were sampled in tubes containing sterile 0.85% NaCl solution and vortexed prior to 5,000 g centrifugation for 10 min to separate the rhizosphere from the roots.\u003c/p\u003e \u003cp\u003eAt this stage, half of each root samples was surface sterilized with 3% hypochlorite sodium for 1 minute subsequently to 3% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for 1 minute and rinsed thrice using sterile water. Three randomly selected samples were merged to create a total of three pools. These sterilized roots were stored at -80\u0026deg;C prior to DNA extraction. The second half of roots was used at fresh state for staining to observe mycorrhizal structures.\u003c/p\u003e \u003cp\u003eRhizosphere samples obtained after centrifugation and separation with the roots were also separated into two subgroups. Similar to root pooling, three randomly selected rhizosphere samples were pooled to create a total of three pools. The first subgroup of rhizosphere samples was lyophilized for 48 h using Christ Alpha\u0026reg; 1\u0026ndash;4 (Bioblock Scientific) and stored at -80\u0026deg;C prior to DNA extraction. The second subgroup was used for the potential metabolic diversity (PMD), the isolates quantification with plating method, as well as the isolates identification through MALDI-TOF MS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction\u003c/h2\u003e \u003cp\u003eTotal DNA was extracted from 250 mg of the lyophilized soils using the DNeasy PowerSoil Pro kit (Qiagen) according to the manufacturer recommendations excepted with an additional C5 washing step. Quantification of the extracted DNA samples were performed on a Qubit\u0026reg; 3.0 fluorometer (Thermo Fisher Scientific) using the Qubit\u0026trade; dsDNA HS Assay Kit, while the quality checking was done with a NanoDrop\u0026trade; 2000/2000c spectrophotometer (Thermo Fisher Scientific). DNA was then stored at \u0026minus;\u0026thinsp;20\u0026deg;C until further use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePotential metabolic diversity (PMD), quantification of rhizosphere microorganisms, and root mycorrhizal colonization\u003c/h2\u003e \u003cp\u003ePMD, quantification of cultivable bacteria and fungi from fresh rhizosphere, and quantitative PCR of bacterial 16S, archaeal 16S, and fungal 18S from lyophilized rhizosphere were performed according to Darriaut et al. (2021) \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. This involved plating soil dilutions on R2A medium amended with 25 mg.l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of nystatin to quantify the cultivable bacterial population while quantifying the fungal populations were on PDA medium supplemented with 500 mg.l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of gentamicin and 50 mg.l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of chloramphenicol.\u003c/p\u003e \u003cp\u003eIn parallel, PMD was evaluated on the three rhizosphere pools using Biolog Eco-Plates\u0026trade; system (Biolog Inc., CA), by measuring 31 different substrates (i.e., amines, amino acids, carbohydrates, carboxylic acids, phenolic compounds, and polymers) consumed by present microorganisms, every 24 hours for 4 days.\u003c/p\u003e \u003cp\u003eFrom the subgroup of fresh root samples that were not surface-sterilized, 30 subsamples of fresh roots were utilized to assess their colonization by mycorrhizal fungi. These roots were stained using the modified ink-KOH-H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e method, and arbuscular mycorrhizal colonization was estimated as described by Darriaut et al. (2022a) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFrom the DNA extracted in the lyophilized rhizosphere, quantitative PCR analyses based on absolute quantification were performed on the DNA extracted using three primers pairs to quantify bacterial (341F/515R) and archaeal 16S rRNA (Arch967F/Arch1060R) genes as well as the fungal 18S rRNA (FF390/FR1) gene, listed in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. The efficiencies of the qPCR were ranging from 80\u0026ndash;99% (R\u0026sup2; \u0026gt; 0.99).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of cultivable rhizobacteria using MALDI-TOF MS\u003c/h2\u003e \u003cp\u003eThe identification of bacterial isolates from the fresh pools of rhizosphere was performed using MALDI-TOF MS technology according to Darriaut et al. (2022a) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Briefly, 50 isolates per pool were randomly selected and grown individually on new R2A plates. In total, 600 single fresh isolates were smeared on MSP96 target polished steel BC plate and overlaid with 1\u0026micro;l of 70% formic acid. Once dried at room temperature, samples were overlaid for crystallization with MALDI matrix consisting in 1 \u0026micro;l of 10 mg.ml\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e α-cyano-4-hydroxycinnamic acid in 50% acetonitrile/2.5% trifluoroacetic acid. The target plate was submitted to MALDI-TOF MS analysis using Microflex MALDI-TOF (Bruker Daltonik GmbH, Leipzig, Germany) bench-top mass spectrometer scanned with laser wavelength at 337 nm and acceleration voltage of 20 kV. The analysis was performed using Flex Control, MTB Compass, and MALDI-Biotyper\u0026trade; software (Bruker Daltonics, Germany) by comparing the mass profile of the isolates to mass profiles in the Biotyper database. Bacterial test standard was added to every plate in order to calibrate the mass spectral data performed by the MALDI-TOF MS. Results of the pattern-matching process were expressed with scores ranging from 0 to 3. Scores\u0026thinsp;\u0026gt;\u0026thinsp;2.3 indicated highly probable species identification, score values between 1.7 and 2.0 generally indicated relationships at genus level, and a score\u0026thinsp;\u0026lt;\u0026thinsp;1.7 indicated that the identification was not reliable.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePre-processing of 16S and 18S rRNA genes and ITS sequencing and bioinformatic analysis\u003c/h2\u003e \u003cp\u003eThe DNA samples were randomized across plates and amplified using the universal primers, including the specific overhang Illumina adapters from Darriaut et al. (2023) \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e listed in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, specific to either the bacterial and archaeal 16S rRNA gene (785R/341F), the fungal 18S rRNA gene (AMV4.5Nf/AMDGr) or the fungal ITS1 region (ITS1F/ITS2).\u003c/p\u003e \u003cp\u003eEach of the 25 \u0026micro;l reaction contained 5 \u0026micro;l of 5X GoTaq\u0026reg; Reaction Buffers (Promega, France), 15.875 \u0026micro;l of Nuclease-free water, 0.5 \u0026micro;l of mixed dNTPs (10 mM), 0.5 \u0026micro;l of each primer (10 \u0026micro;M), 2.5 \u0026micro;l of DNA template (5 ng/\u0026micro;l), and 0.125 \u0026micro;l of GoTaq\u0026reg; G2 DNA Polymerase (5 u/\u0026micro;l) (Promega, France). PCR amplifications were performed in three replicates for each gene. The cycling conditions of 16S rRNA gene differed from the fungal 18S rRNA gene and ITS amplifications, which were initiated with denaturation at 95\u0026deg;C for 5 minutes, followed by 25 and 30 cycles, respectively, consisted of a denaturation at 95\u0026deg;C for 30s, an annealing step at 55\u0026deg;C for 30s, followed by an extension step at 72\u0026deg;C for 30s and 45s, respectively. Further steps were carried out at the PGTB sequencing facility (Genome Transcriptome Facility of Bordeaux, Pierroton, France) using a V2 with 2 \u0026times; 250 nucleotide paired reads protocol. The PCR products were purified with platform specific SPRI magnetic beads (1X ratio) and quantified using Quant-iT\u0026trade; dsDNA Assay kit (ThermoFisher, France). MID and Illumina sequencing adapters were added. Libraries were pooled in equimolar amounts using a Hamilton Microlab STAR robot and sequenced on an Illumina MiSeq platform using the MiSeq Reagent Kit V2 (2 \u0026times; 250 bp). Obtained sequences were demultiplexed with index search at the PGTB facility.\u003c/p\u003e \u003cp\u003eThe quality of the obtained sequences was first checked with FastQC v.0.11.8 \u003csup\u003e46\u003c/sup\u003e. Sequences were quality filtered, trimmed, denoised, and clustered into Operational Taxonomy Units (OTUs) using FROGS pipeline from Galaxy instance v4.0.1 (2022/06) \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. This involved assembling raw forward and reverse reads for each sample were into paired-ended reads with a minimum overlapping of 50 nucleotides and 0.1 mismatch using the VSEARCH tool \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Primers were removed using Cutadapt \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, chimeras were detected and removed with UCHIME \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and clustering was performed using SWARM \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e in the FROGS pipeline. The minimum sequence abundance proportion was set at 5e\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e to keep OTUs. Taxonomic assignments of 16S rRNA, ITS, and 18S rRNA-based OTUs were performed against silva138.1 (16S pintail100) \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, Unite8.2 \u003csup\u003e54\u003c/sup\u003e, and MaarJAM (18S 2019) \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e respectively, using Blast from Galaxy. Datasets were gathered and analyzed via \u003cem\u003ephyloseq\u003c/em\u003e (1.38.0) \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Taxa related to mitochondrial and chloroplast OTUs were removed using the Arabido_TAIR10_Chl_Mito databank.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFunctional inferences of bacterial and fungal communities\u003c/h2\u003e \u003cp\u003eThe phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) was used to predict the functional composition of the bacterial 16S rRNA marker \u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. PICRUSt2, which relies on OTUs, was run on the pipeline integrated within the Galaxy instance. SEPP was the placement tool used for insertion of sequences into the reference tree with the minimum alignment length set to 0.8 \u003csup\u003e58\u003c/sup\u003e. The hidden-state prediction with maximum parsimony method was used to predict the functions abundances with KO (KEGG pathway) database The Nearest Sequenced Taxon Index (NSTI) cut-off was set to 0.5, excluding 367 clusters and keeping 2,269 clusters. Only the classifications related to \u0026ldquo;Celullar Processes\u0026rdquo;, \u0026ldquo;Metabolism\u0026rdquo;, and \u0026ldquo;Environmental Information Processing\u0026rdquo; were kept for statistical analyses, with their relative abundances assessed as a percentage of the total abundances.\u003c/p\u003e \u003cp\u003eTo taxonomically parse trophic modes and guilds of functional traits among fungal communities, based on OTUs, \u0026ldquo;funguild_assign\u0026rdquo; function from the \u003cem\u003eFUNGuildR\u003c/em\u003e (0.2.0.9000) package \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e was used with the FUNGuild database \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Only the guild confidences classified as \u0026ldquo;Highly probable\u0026rdquo; and \u0026ldquo;Probable\u0026rdquo; were selected for statistical analyses. OTUs assigned to more than two trophic modes (\u003cem\u003ei.e\u003c/em\u003e., pathotroph, saprotroph, symbiotroph) or more than two guilds (\u003cem\u003ei.e\u003c/em\u003e., wood saprotroph, undefined saprotroph, plant saprotroph, orchid mycorrhizal, lichenized, epiphyte, plant pathogen, fungal parasite, endophyte, ectomycorrhizal, arbuscular mycorrhizal, animal pathogen) were classified as \u0026ldquo;Multi-affiliated\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eAll analysis and graphs were performed on R (R-4.2.1) using RStudio (2022.07.1). Figures were generated with \u003cem\u003eggplot2\u003c/em\u003e (3.5.0) and \u003cem\u003eggthemes\u003c/em\u003e (5.1) packages and arranged using \u003cem\u003eggpubr\u003c/em\u003e (0.6.0).\u003c/p\u003e \u003cp\u003eOne way ANOVA or Kruskal-Wallis and pairwise comparison using Student t or Wilcoxon tests were performed on the total biomasses and length from \u003cem\u003eL. sativum\u003c/em\u003e and 1103P plantlets inoculated with the different bacterial consortia. To classify the growth potential of consortia, \u0026ldquo;hclust\u0026rdquo; function from \u003cem\u003estats\u003c/em\u003e (4.2.1) was performed to cluster groups together in a circular plot based on Euclidean distance with the Wards\u0026rsquo;s minimum variance method (Ward D2).\u003c/p\u003e \u003cp\u003eTwo-way Analysis of Variance (ANOVA) with treatment (untreated, bacterized, mycorrhized, both bacterized and mycorrhized) and compartment (bulk, rhizosphere, and root endosphere) factors were performed on enzymatic activities, cultivable, q-PCR, Eco-Plates measurements, and abundances of functional OTUs. Residuals were checked for their independency, normality, and variance homogeneity with the Durbin Watson, Shapiro-Wilk, and Bartlett tests, respectively. When assumptions for parametric tests were not respected, a multiple pairwise comparison using Wilcoxon test was performed subsequently to Kruskal\u0026ndash;Wallis test using the \u003cem\u003emultcomp\u003c/em\u003e (1.4\u0026ndash;25) package. Principal Component Analysis (PCA) was performed using \u003cem\u003eFactoMineR\u003c/em\u003e (2.9) and \u003cem\u003emissMDA\u003c/em\u003e (1.19). Area under curve (AUC) of average color well development (AWCD) which better explain curve dynamics, was calculated with the trapezoidal method for each condition using \u003cem\u003ecaTools\u003c/em\u003e (1.18.2).\u003c/p\u003e \u003cp\u003eRegarding amplicons analyses, shared OTUs were visualized with Venn diagrams generated with the \u003cem\u003eVennDiagram\u003c/em\u003e (1.7.3) package. Richness and α-diversity metrics, represented by Chao1, Simpson\u0026rsquo;s diversity, and Bray-Curtis dissimilarity, respectively, were calculated through \u003cem\u003ephyloseq\u003c/em\u003e (1.42.0) using \u0026ldquo;estimate_richness\u0026rdquo; and \u0026ldquo;distance\u0026rdquo; functions. In order to test for significant differences between the means of alpha diversity metrics by conditions, pairwise comparisons were used, based on either t or wilcoxon test, subsequently to homogeneity and normalization verifications using Levene and Shapiro tests. Non-metric multidimensional scaling (NMDS) was used to ordinate samples in two-dimensional space based on Bray-Curtis distance using ordinate function from \u003cem\u003ephyloseq\u003c/em\u003e with \u0026ldquo;NMDS\u0026rdquo; method. Linear models and permutational multivariate analysis of variance (PERMANOVA), for richness and diversities metrics, were demonstrated using the formula: variable\u0026thinsp;~\u0026thinsp;Soil status \u0026times; Compartment. Type-II ANOVAs were performed using \u003cem\u003ecar\u003c/em\u003e (3.1-2) on Chao1 and Simpson\u0026rsquo;s diversity metrics while PERMANOVAs were assessed on Bray-Curtis dissimilarity using \u0026ldquo;adonis2\u0026rdquo; function from \u003cem\u003evegan\u003c/em\u003e with 999 permutations. Functions \u0026ldquo;ggeffectsize\u0026rdquo; and \u0026ldquo;ggdiffbox\u0026rdquo; from \u003cem\u003eMicrobiotaProcess\u003c/em\u003e (1.10.3) were used to discriminate significantly different taxa across conditions. This process was set with Kruskal (α\u0026thinsp;=\u0026thinsp;0.05) test based on linear discriminant analysis (LDA) effect size (LEfSe) and Wilcox (α\u0026thinsp;=\u0026thinsp;0.05), corrected with False Discovery Rate (FDR). Co-occurrence networks were set up for 16S rRNA and ITS communities within root system (both rhizosphere and root endosphere) using \u0026ldquo;trans_network\u0026rdquo; from \u003cem\u003emicroeco\u003c/em\u003e (1.4.0). Spearman\u0026rsquo;s correlation was estimated using \u003cem\u003eWGCNA\u003c/em\u003e (1.72-5) with a 0.001 threshold. The \u0026ldquo;cor_optimization\u0026rdquo; function retrieved the optimal coefficient threshold and \u0026ldquo;COR_p_thres\u0026rdquo; value was set up to 0.05. Ecological modules, a cluster of nodes highly interconnected, were determined using the greedy optimization from \u0026ldquo;cal_module\u0026rdquo;. The networks were visualized using Gephi software (0.10.1). Network properties were extracted with \u0026ldquo;cal_network_attr\u0026rdquo;, and network stability indexes were estimated using \u0026ldquo;robustness\u0026rdquo; (10 run) and \u0026ldquo;vulnerability\u0026rdquo;. The Z-score evaluating within module connectivity, and P-score assessing among module connectivity, of the nodes were calculated using \u0026ldquo;plot_taxa_roles\u0026rdquo; to identify keystone species. The hubs defined as module hub nodes (Z-score\u0026thinsp;\u0026gt;\u0026thinsp;2.5 and P-score\u0026thinsp;\u0026le;\u0026thinsp;0.62) and connector nodes (Z-score\u0026thinsp;\u0026le;\u0026thinsp;2.5 and P-score\u0026thinsp;\u0026gt;\u0026thinsp;0.62) were considered as keystone taxa.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eDiversity and functional characteristics of rhizobacterial isolates\u003c/h2\u003e \u003cp\u003eOf the 800 rhizobacteria isolated and screened for identification via MALDI-TOF MS by Darriaut et al., (2022a) \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, 200 isolates were randomly selected and evaluated for biochemical tests associated with PGP traits. The rhizobacterial isolates belonged to 17 genera as follows: \u003cem\u003eBacillus\u003c/em\u003e (24%), \u003cem\u003ePseudomonas\u003c/em\u003e (11%), \u003cem\u003eRahnella\u003c/em\u003e (7%), \u003cem\u003eEnterobacter\u003c/em\u003e (4%), and \u003cem\u003eButtiauxella\u003c/em\u003e (4%), while 35% were not identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e). Minority isolates were namely \u003cem\u003ePaenibacillus\u003c/em\u003e (2%), \u003cem\u003eBurkholderia\u003c/em\u003e (2%), \u003cem\u003eLysinibacillus\u003c/em\u003e (2%), \u003cem\u003eRalstonia\u003c/em\u003e (2%), \u003cem\u003eSerratia\u003c/em\u003e (2%), \u003cem\u003eStreptomyces\u003c/em\u003e (2%), \u003cem\u003eBrevibacillus\u003c/em\u003e (1%), \u003cem\u003eAmycolaptosis\u003c/em\u003e (1%), \u003cem\u003eCupriavidus\u003c/em\u003e (1%), \u003cem\u003eDyella\u003c/em\u003e (1%), and \u003cem\u003eStaphylococcus\u003c/em\u003e (1%). All the isolated rhizobacteria exhibited at least one PGP trait, including ammonia production, siderophore synthesis, phosphate solubilization, nitrogen fixation, or indole-3-acetic acid (IAA) production. The identified genera with functional abilities to contribute to plant growth promotion in each PGP trait included \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eBurkholderia\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003eRhizobium\u003c/em\u003e. ACC deaminase was the least common PGP trait (15%) found in the isolates tested, while the most common was siderophore production (55.5%), followed by nitrogen fixation (55%), ammonia production (54.5%), IAA synthesis (49%), and phosphate solubilization (48%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter this biochemical screening, the most efficient isolate for each of the PGP traits, along with two isolates effective for all traits tested, were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e). Their 16S rRNA sequencing confirmed MALDI-TOF MS identification as two \u003cem\u003ePseudomonas veronii\u003c/em\u003e (labelled as A and F isolates), one \u003cem\u003eEnterobacter cloacae\u003c/em\u003e (isolate B), \u003cem\u003ePseudomonas brassicacearum\u003c/em\u003e (isolate C), \u003cem\u003ePseudomonas\u003c/em\u003e sp. (isolate D), \u003cem\u003eEnterobacter asburiae\u003c/em\u003e (isolate G), and \u003cem\u003eRhizobium radiobacter\u003c/em\u003e (isolate H).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGrowth promotion effects on\u003c/b\u003e \u003cb\u003eLepidium sativum\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eVitis vinifera\u003c/b\u003e \u003cb\u003eplantlets\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cem\u003eLepidium sativum\u003c/em\u003e seeds and 1103P plantlets were inoculated by the selected isolates. Phenotypic traits related to their growth were measured and compared to water treatment, corresponding to the negative control. The inoculates were applied in single inoculation, double inoculation (\u003cem\u003ei.e\u003c/em\u003e., isolate X \u0026times; isolate Y), or triple inoculation (\u003cem\u003ei.e\u003c/em\u003e., isolate X \u0026times; isolate Y \u0026times; isolate Z), with the latter combination exclusively performed on \u003cem\u003eL. sativum\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eL\u003c/em\u003e. \u003cem\u003esativum\u003c/em\u003e, among the 92 combinations tested, only 2% inhibited stem mass, and 4% inhibited root length, while 24% inhibited root mass, and 39% inhibited stem length (\u003cb\u003eSupplementary Table S2\u003c/b\u003e). Regarding 1103P, among the 36 combinations tested, 39% inhibited leaf and stem mass, while 53% reduced stem length, and 70% reduced petiole length (\u003cb\u003eSupplementary Table S3\u003c/b\u003e). Regarding the root system in grapevine plantlets, 78% promoted root mass, while 70% of the tested combinations promoted the length of secondary roots and 39% increased the length of primary roots.\u003c/p\u003e \u003cp\u003eTo compare phenotypic traits between \u003cem\u003eL. sativum\u003c/em\u003e and 1103P plantlets, only single and double combination effects on total biomass and length compared to water treatment are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e. Significant groups were detected across the different inoculates. The significantly best-performing single or double combinations in terms of total biomass promotion of \u003cem\u003eL. sativum\u003c/em\u003e were A\u0026times;C, B\u0026times;D, C, C\u0026times;H, C\u0026times;G, F, B\u0026times;F, and A\u0026times;H. Regarding the promotion of root and stem lengths, A\u0026times;C, B\u0026times;F, A\u0026times;H, and C\u0026times;H combinations were the most effective. Similarly, significant groups were distinguished in the growth traits of 1103P plantlets. The combinations C\u0026times;G, D\u0026times;F, B\u0026times;E, A\u0026times;G, F\u0026times;H, and A\u0026times;C induced a significantly greater biomass gain compared to water treatment, while only A\u0026times;C and A\u0026times;B combinations were significantly different to the water control in terms of total length promotion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBiplot PCA was used to visualize the effects of inoculates on the phenotypic traits measured for \u003cem\u003eL. sativum\u003c/em\u003e, and \u003cem\u003eV. vinifera\u003c/em\u003e, distinctly (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e). The first two dimensions (Dim1 and Dim2) accounted for 88.4%, and 72.3% of the total variance in \u003cem\u003eL. sativum\u003c/em\u003e and 1103P plantlet PCA, respectively. For \u003cem\u003eL. sativum\u003c/em\u003e PCA, Dim1 was positively correlated with all the traits measured (\u003cem\u003ei.e\u003c/em\u003e., root and stem mass, and root and stem length). Dim2 was positively correlated with both root and stem mass while negatively correlated with root and stem length. The combinations comprising single or double isolates in \u003cem\u003eL. sativum\u003c/em\u003e samples were on the positive side of Dim2, whereas the combinations composed of triple isolates were on the negative side of Dim2.\u003c/p\u003e \u003cp\u003eRegarding grapevine plantlet PCA, Dim1 was positively correlated to root biomass and petiole length, as well as secondary root length and number, while aerial biomass and stem length were negatively correlated to stem length and biomass of the aerial system. In contrast, Dim1 was positively correlated to each of the measured variables except the length of the primary root and the number of secondary roots. As with the \u003cem\u003eL. sativum\u003c/em\u003e PCA, combinations including double isolates were predominantly on the positive side of Dim2 while the single inoculates were on the negative side.\u003c/p\u003e \u003cp\u003eThe HCA dendrogram depicts the combinations of inoculates tested on \u003cem\u003eL. sativum\u003c/em\u003e and 1103P, and identifies seven and five clusters respectively, based on the phenotypic similarities of the samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003cb\u003eC\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eThe combination with the greatest positive effects on the development of \u003cem\u003eL. sativum\u003c/em\u003e was A\u0026times;C, while A\u0026times;C and A\u0026times;B were the most positive for the grapevine plantlet. Taking this into account, the A\u0026times;C consortium, consisting of \u003cem\u003eP\u003c/em\u003e. \u003cem\u003everonii\u003c/em\u003e and \u003cem\u003eP\u003c/em\u003e. \u003cem\u003ebrassicacearum\u003c/em\u003e, was employed as the bacterized treatment during the greenhouse experiment on young grapevine bare-rooted plants potted with the symptomatic soil experiencing microbial dysbiosis described in Darriaut et al. (2024) \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRhizosphere microbial profiles of the greenhouse experiment using cultivable and q-PCR measurements\u003c/h2\u003e \u003cp\u003eAfter two months in the greenhouse, the grapevines did not present any growth differences in the aerial or root system across the four conditions (\u003cb\u003eSupplementary Table S4\u003c/b\u003e). However, five months after treatment, a significantly higher branch diameter was observed in untreated vines and vines treated with rhizobacteria compared to the mycorrhized plants (χ 2\u0026thinsp;=\u0026thinsp;21.0, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The mycorrhized plants inoculated with the rhizobacteria also displayed a significantly greater dry root biomass compared to bacterized- mycorrhized conditions (F(3, 36)\u0026thinsp;=\u0026thinsp;2.27, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047).\u003c/p\u003e \u003cp\u003eConsequently, the microbial profile of the rhizosphere was analyzed in each treatment five months after application. Using MALDI-TOF MS, 63 different species were identified (\u003cb\u003eSupplementary Figure S2\u003c/b\u003e) belonging to 26 distinct genera, while unidentified genera accounted for 28% of the isolates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e). The less abundant genera were identified as \u003cem\u003eBurkholderia\u003c/em\u003e, \u003cem\u003eDyella\u003c/em\u003e, \u003cem\u003eAcidovorax\u003c/em\u003e, \u003cem\u003eAquincola\u003c/em\u003e, \u003cem\u003eMethylobacterium\u003c/em\u003e, \u003cem\u003eMicrobacterium\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eKitasatospora\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eRamosus\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eRhodococcus\u003c/em\u003e, \u003cem\u003eSinomonas\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eStenotrophomonas\u003c/em\u003e, and \u003cem\u003eVariovorax\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe four treatments exhibited six core genera, belonging to \u003cem\u003eBacillus\u003c/em\u003e (22%), \u003cem\u003ePseudarthrobacter\u003c/em\u003e (9%), \u003cem\u003ePseudomonas\u003c/em\u003e (8%), \u003cem\u003ePaenarthrobacter\u003c/em\u003e (2.5%), and \u003cem\u003eParaburkholderia\u003c/em\u003e (5%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e). Some genera such as \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eMethylobacterium\u003c/em\u003e, and \u003cem\u003eMicrobacterium\u003c/em\u003e were detected only in mycorrhized conditions, while \u003cem\u003eLysinibacillus\u003c/em\u003e was only found in conditions inoculated with rhizobacteria, and \u003cem\u003eFlavobacterium\u003c/em\u003e was unique to untreated conditions.\u003c/p\u003e \u003cp\u003eThe diversity of cultivable rhizobacteria, represented by the Simpson and Shannon indexes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e), was lowest when grapevines were inoculated with both mycorrhizal fungi and rhizobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eC\u003c/b\u003e). Conversely, the highest diversity was found in bacterized, followed by mycorrhized and untreated conditions.\u003c/p\u003e \u003cp\u003eThe samples treated with the combined inoculation presented significantly lower levels of cultivable bacteria compared to other single inoculation and untreated conditions, and cultivable fungi in the untreated samples were significantly higher compared to the other treatments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eC\u003c/b\u003e). No significant differences between conditions were found in terms of mycorrhizal intensity on roots. In contrast, the mycorrhized condition exhibited significantly higher mycorrhizal intensity compared to the bacterized condition.\u003c/p\u003e \u003cp\u003eTotal DNA extracted from the rhizosphere was significantly higher in bacterized samples compared to the mycorrhized condition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003cb\u003eD\u003c/b\u003e). Among the three tested amplicons for q-PCR, only fungal 18S was significantly different, with the lowest copy number in samples treated exclusively with mycorrhizal fungi.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eTreatments did not have a strong influence on the composition of belowground microbial communities\u003c/h2\u003e \u003cp\u003eSequencing was performed in the greenhouse experiment on the root endosphere, rhizosphere, and bulk soil, accounting for 36 samples. A total of 5,878,244 raw sequences were generated from the libraries run. After chimera removal, paired-end sequences were clustered into 2,684 16S rRNA, 860 ITS, and 275 18S rRNA operational taxonomic units (OTUs).\u003c/p\u003e \u003cp\u003eOTUs shared between the bulk, rhizosphere, and root endosphere compartments were 77%, 69% and 27% OTUs across the 16S rRNA, ITS, and 18S rRNA sequencing respectively (\u003cb\u003eSupplementary Figure S3\u003c/b\u003e). Likewise, 89%, 75%, and 33%, respectively, were shared among the four treatments (\u003cem\u003ei.e\u003c/em\u003e., untreated, mycorrhized, bacterized, and mycorrhized-bacterized).\u003c/p\u003e \u003cp\u003eIndependently of the compartment or treatment, \u003cem\u003eProteobacteria\u003c/em\u003e (37%), \u003cem\u003eActinobacteriota\u003c/em\u003e (22.3%), \u003cem\u003eAcidobacteria\u003c/em\u003e (9.7%), \u003cem\u003eFirmicutes\u003c/em\u003e (7.9%), \u003cem\u003eChloforexi\u003c/em\u003e (6.4%), \u003cem\u003eBacteroidota\u003c/em\u003e (4.2%), \u003cem\u003eVerrucomicrobiota\u003c/em\u003e (3.8%), \u003cem\u003ePlanctomycetota\u003c/em\u003e (3.2%), \u003cem\u003eGemmatimonadota\u003c/em\u003e (2.2%), and \u003cem\u003eMyxococcota\u003c/em\u003e (1.6%) were the most abundant bacterial phyla (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e). The less abundant phyla categorized as \u0026lsquo;Others\u0026rsquo; consisted of \u003cem\u003ePatescibacteria\u003c/em\u003e, \u003cem\u003eNitrospirota\u003c/em\u003e, \u003cem\u003eDesulfobacterota\u003c/em\u003e, \u003cem\u003eFibrobacterota\u003c/em\u003e, \u003cem\u003eBdellovibrionota\u003c/em\u003e, \u003cem\u003eGAL15\u003c/em\u003e, \u003cem\u003eLatescibacterota\u003c/em\u003e, \u003cem\u003eWS2\u003c/em\u003e, \u003cem\u003eMethylomirabilota\u003c/em\u003e, \u003cem\u003eNanoarchaeota\u003c/em\u003e, \u003cem\u003eRCP2-54\u003c/em\u003e, and \u003cem\u003eMBNT15\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding ITS sequences, \u003cem\u003eAscomycota\u003c/em\u003e (56.7%), \u003cem\u003eBasidiomycota\u003c/em\u003e (29.5%), \u003cem\u003eGlomeromycota\u003c/em\u003e (6.3%), \u003cem\u003eRozellomycota\u003c/em\u003e (2.2%), and \u003cem\u003eMortierellomycota\u003c/em\u003e (1.9%) were the predominant phyla, while unaffiliated OTUs accounted for 2.8% (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e). The \u0026lsquo;Others\u0026rsquo; group comprised \u003cem\u003eChytridiomycota\u003c/em\u003e, \u003cem\u003eMonoblepharomycota\u003c/em\u003e, \u003cem\u003eKickxellomycota\u003c/em\u003e, \u003cem\u003eCalcarisporiellomycota\u003c/em\u003e, \u003cem\u003eBlastocladiomycota, Zoopagomycota\u003c/em\u003e, and \u003cem\u003eOlpidiomycota\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eRegarding 18S rRNA gene sequences, 5.5% were unaffiliated. The predominant genera detected were \u003cem\u003eGlomus\u003c/em\u003e (90%), \u003cem\u003eAcaulospora\u003c/em\u003e (3.4%), \u003cem\u003eParaglomus\u003c/em\u003e (0.6%), and \u003cem\u003eClaroideoglomus\u003c/em\u003e (0.3%), accounting for 94.3% of the total sequences (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e). The \u0026lsquo;Others\u0026rsquo; group was composed of \u003cem\u003eScutellospora\u003c/em\u003e, \u003cem\u003eArchaeospora\u003c/em\u003e, and \u003cem\u003eAmbispora\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eRichness (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e) and diversity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eC\u003c/b\u003e), represented by the Chao1 and Simpson indexes respectively, were higher in the rhizosphere compared to the roots for 16S rRNA, 18S rRNA genes, and ITS communities. Similarly, β-diversity based on Bray-Curtis dissimilarities revealed a compartment effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eD\u003c/b\u003e). Although treatments did not have any significant effects on α and β diversities, significant differences were observed between conditions, notably the lower richness of 16S rRNA communities in mycorrhized-bacterized treated roots, or the lower richness of ITS communities within the mycorrhized rhizosphere (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e). In addition, the root endosphere samples treated with both mycorrhizal fungi and rhizobacteria displayed significantly lower diversity in ITS communities compared to the bacterized treatment. Microbial phyla present in the bulk soil were similar to those found in the rhizosphere and root compartments (\u003cb\u003eSupplementary Figure S4\u003c/b\u003e). Within the bulk soil, no significant effects of treatments were detected on the richness or diversity of the bacteria, fungi, or Glomeromycotan communities.\u003c/p\u003e \u003cp\u003eFurthermore, the LEfSe (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR, LDA\u0026thinsp;\u0026gt;\u0026thinsp;1.5) detected enriched genera within rhizosphere (\u003cb\u003eSupplementary Figure S5.A\u003c/b\u003e) and root (\u003cb\u003eSupplementary Figure S5.B\u003c/b\u003e) compartments for both 16S rRNA and ITS-based communities, while none were related to 18S rRNA communities. In the rhizosphere, five bacterial genera were enriched among the four conditions, with \u003cem\u003eAlsobacter\u003c/em\u003e and \u003cem\u003eSymbiobacterium\u003c/em\u003e detected in the mycorrhized treatment (\u003cb\u003eSupplementary Figure S5.A\u003c/b\u003e). From the five enriched fungal genera in the rhizosphere, three were detected in the untreated condition from the \u003cem\u003eAscomycota\u003c/em\u003e phylum (\u003cem\u003ePseudallescheria\u003c/em\u003e, unidentified genera from \u003cem\u003eSordariomycetes\u003c/em\u003e and \u003cem\u003eAscobolaceae\u003c/em\u003e). In the roots, 16 bacterial genera were enriched with \u003cem\u003eRhizobium\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e in the combined mycorrhized and bacterized treatment, while none were detected for the untreated condition. Similarly, nine genera were enriched in the mycorrhized root endosphere (\u003cem\u003ee.g\u003c/em\u003e., \u003cem\u003eCandidatus Solibacter\u003c/em\u003e, \u003cem\u003ePhenylobacterium\u003c/em\u003e, \u003cem\u003eGemmatimonas\u003c/em\u003e, \u003cem\u003eFrateuria\u003c/em\u003e, and unidentified genera from \u003cem\u003eGaiellales\u003c/em\u003e, \u003cem\u003eAcidobacteriales\u003c/em\u003e) and five in the bacterized root endosphere (\u003cem\u003eDongia\u003c/em\u003e, \u003cem\u003ePuia\u003c/em\u003e, and unidentified genera from \u003cem\u003eAlphaproteobacteria\u003c/em\u003e and \u003cem\u003eIlumatobacteraceae\u003c/em\u003e) (\u003cb\u003eSupplementary Figure S5.B\u003c/b\u003e). Regarding the fungal genera within roots, \u003cem\u003eBotrytis cinerea\u003c/em\u003e was significantly enriched in the untreated condition while \u003cem\u003eParaglomus\u003c/em\u003e was more abundant in the bacterized treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eTreatments exhibited distinct microbial co-occurrence networks within root systems\u003c/h2\u003e \u003cp\u003eCo-occurrence networks of bacteria and fungi within the root system, encompassing the rhizosphere and root endosphere, provide insights into the impact of microorganisms additions on bacterial (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e) and fungal (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cb\u003eC\u003c/b\u003e) interactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe network was simpler for the fungal communities (coefficient clustering: 0.777 to 0.850) compared to the bacterial ones (coefficient clustering: 0.598 to 0.803) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the bacterial communities, the interaction networks increased in complexity, represented by the nodes and edges, when the grapevines were bacterized (+\u0026thinsp;0%, + 64%), mycorrhized (+\u0026thinsp;9%, +\u0026thinsp;230%), and both bacterized and mycorrhized (+\u0026thinsp;13%, +\u0026thinsp;559%), compared to untreated samples. Although the nodes and edges of fungal co-occurrence networks were simpler in mycorrhized (-21%, -58%), and bacterized (+\u0026thinsp;7%, -16%) conditions compared to untreated samples, they gained in complexity (+\u0026thinsp;36%, +\u0026thinsp;22%) in the mycorrhized-bacterized condition.\u003c/p\u003e \u003cp\u003eNetwork stability, evaluated by robustness and vulnerability, was greater in bacterial communities, especially in mycorrhized-bacterized conditions compared to other conditions (\u003cb\u003eSupplementary Figure S6\u003c/b\u003e). However, fungal network stability presented high robustness and high vulnerability for the untreated condition compared to treated samples. Irrespective of the treatment, based on Z-scores (within-module connectivity) and P-scores (among-module connectivity), 2 and 27 OTUs within these co-occurrence networks were identified as module hubs and connectors, respectively, for bacterial communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e), and 0 and 9 OTUs for fungal communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003cb\u003eD\u003c/b\u003e). The Zi score identified \u003cem\u003eFonticella\u003c/em\u003e and \u003cem\u003eAcidobacteriales\u003c/em\u003e as keystone taxa for the untreated condition and combined treatment of both mycorrhiza and bacteria respectively. The Pi score detected more keystone taxa within both bacterial and fungal networks in the mycorrhized- bacterized condition compared to the untreated one. Samples treated only with mycorrhizal fungi presented 10 bacterial OTUs as connectors while none were detected for fungal networks. Furthermore, no keystone taxa were detected for the bacterized treatment.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTopological features of the 16S rRNA and ITS-based communities in the root system (root and rhizosphere) in untreated, mycorrhized (Myc), bacterized (Bac), and both mycorrhized and bacterized (MycBac) conditions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e16S rRNA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eITS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUntreated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMycBac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMyc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUntreated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMycBac\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMyc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBac\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNodes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEdges\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.554\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork diameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClustering coefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeterogeneity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentralization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModularity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.754\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMicrobial addition had an impact on the metabolic functions of microbial communities\u003c/h2\u003e \u003cp\u003eEcoPlates measurements based on the consumption of various carbon substrates revealed an increase in global metabolic activity of the mycorrhized-bacterized rhizosphere (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cb\u003eA\u003c/b\u003e). From the consumed substrates, after 96 hours incubation of the EcoPlates, it appeared that the increased metabolic activities were mostly due to carbohydrates and carboxylic acids, with enhanced activity for the combined mycorrhized and bacterized treatment compared to the untreated condition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePotential metabolic pathways based on 16S rRNA sequences were estimated using PICRUSt2. The 2,269 predicted clusters formed 19 selected pathways among the \u0026lsquo;Cellular Processes\u0026rsquo;, \u0026lsquo;Metabolism\u0026rsquo; and \u0026lsquo;Environmental Information Processing\u0026rsquo; classifications (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cb\u003eB\u003c/b\u003e). Significant differences were detected in both root and rhizosphere compartments for the metabolism of xenobiotics, carbohydrates, and amino acids.\u003c/p\u003e \u003cp\u003eFunctional inference based on ITS sequences was performed using FUNGuild, which detected four trophic modes (multi-affiliation, pathotroph, saprotroph and symbiotroph), and ten guilds (wood saprotroph, undefined saprotroph, plant saprotroph, plant pathogen, orchid mycorrhizal, multi-affiliation, fungal parasite, endophyte, arbuscular mycorrhizal, and animal pathogen) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003cb\u003eC\u003c/b\u003e). Significant differences were detected in the rhizosphere compartment, such as reduced abundances of wood saprotroph, undefined saprotroph, plant pathogen, and orchid mycorrhizal fungi in the combined mycorrhized and bacterized treatment compared to other conditions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eStressed plants serve as a potential reservoir for identifying promising rhizobacteria which exhibit PGP traits\u003c/h2\u003e \u003cp\u003eIn addition to signaling compounds exudated from roots, environmental stimuli are able to modulate the biochemical functions of microorganisms. For example, the composition and production of exopolysaccharides or anti-oxidative enzymes in cyanobacteria under salt stress are modified \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. In previous studies, the metabolic diversity measured by EcoPlates technology was greater in the symptomatic bulk \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e and rhizosphere \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e soils experiencing grapevine decline compared to asymptomatic ones. Therefore, one hypothesis would be that the grapevine under decline produces compounds stimulating the microbial communities in the surrounding soil, and that the high abundance of fungi potentially associated with grapevine diseases creates a niche for beneficial bacteria. Ethylene, being a plant hormone that coordinates stress signaling within the host, is produced under various environmental stimuli \u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Among the different PGP traits, ACC deaminase is known to alleviate the ethylene-negative effects on plant development \u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Strains possessing the highest efficiency in ACC deaminase have been isolated from some nutrient-poor and alkaline areas \u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Similarly, the best siderophore producers have been isolated in the rhizosphere of tolerant cultivar under iron stress \u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. The best candidates for phosphate solubilization, nitrogen fixation, siderophore, and IAA synthesis, identified as \u003cem\u003ePseudomonas syringae\u003c/em\u003e, \u003cem\u003ePseudomonas koreensis\u003c/em\u003e, \u003cem\u003ePseudomonas veronii\u003c/em\u003e, and \u003cem\u003eEnterobacter cloacae\u003c/em\u003e respectively, were all isolated from symptomatic soils \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Studying isolates in stressed environments could be an interesting goal to pursue, especially in the root endosphere or the rhizosphere of symptomatic plants which may harbor highly active and beneficial microbes.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eFrom laboratory to greenhouse, an essential step in the development of microbial additives\u003c/h2\u003e \u003cp\u003eExploring grapevine and soil microbiomes not only highlights the mechanisms governing the assembly and dynamics of plant-associated microbial communities but also offers strategic guidance to enhance grapevine fitness and promote sustainable viticulture based on microbiome engineering \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Transposing results from artificial environments to living host plants is essential to the success of probiotic development. Deployment of consortia may confer more efficient growth promotion than single strain application \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e, which is consistent with our findings. The selection of bacterial treatment was based on the increased root biomass and length in the fast-growing plant \u003cem\u003eL. sativum\u003c/em\u003e (124% and 47%) and in grapevine plantlets 1103P (41% and 75%). Similarly, this bacterial inoculate increased the root biomass of 1103P rootstock compared to the untreated condition by 26%. \u003cem\u003ePseudomonas\u003c/em\u003e species have already been reported to promote growth of crop roots in laboratory \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e and greenhouse \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e environments. Although few grapevine studies exist \u003cem\u003ein vivo\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, the exploration of plant growth enhancement is not as extensively developed as the research on biological control \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003ePseudomonas\u003c/em\u003e isolated from wood tissues had antagonistic effects on various GTD fungal pathogens related to \u003cem\u003eBotryosphaeria\u003c/em\u003e, \u003cem\u003eEutypa\u003c/em\u003e, and \u003cem\u003eEsca/Petri\u003c/em\u003e diseases \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. Apart from the root growth promotion triggered by the \u003cem\u003ePseudomonas\u003c/em\u003e treatment and mycorrhizal fungi, LEfSe analysis identified a higher abundance of \u003cem\u003eBotrytis\u003c/em\u003e in the untreated root endosphere, suggesting an additional potential biocontrol property. Similarly, this result raises questions about the ability of bacteria to colonize plant roots, either by penetrating the root cortex as endophytes or by establishing themselves in the strict rhizosphere or on the root surface as epiphytes. Several PGPR acting as grapevine endophytes were explored for their growth promotion, such as \u003cem\u003ePseudomonas protegens\u003c/em\u003e MP12 \u003csup\u003e75\u003c/sup\u003e, and \u003cem\u003eBurkholderia phytofirmans\u003c/em\u003e PsJN \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe processes governing the fate and persistence of biological inoculants in soil can be intricate, as they may result from the interplay of numerous variables, making them challenging to comprehend and predict \u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e. Among the two species inoculated, only \u003cem\u003ePseudomonas brassicacearum\u003c/em\u003e was detected using MALDI-TOF MS in bacterized and mycorrhized-bacterized treatments after five months, but not in the other treatments. It has been proposed, with the use of resistant mutants to antibiotics, that certain inoculants with PGP traits can persist in soils for up to seven weeks \u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. Moreover, using microsatellite markers, the biocontrol fungus \u003cem\u003eBeauveria brongniartii\u003c/em\u003e was still present 14 years after application in fields, and even coexisted with indigenous strains \u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe combined addition of bacteria and mycorrhiza altered the microbial structure and functioning within the root system\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBerg et al. (2021) \u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e reviewed the different effects of inoculants on indigenous plant microbiomes encompassing transient microbiome shifts, stabilization or increase of microbial diversity and evenness, restoration of a dysbiosis, targeted triggering of host beneficial microbes, and control of pathogens. In our case and based on amplicon sequencing results, we might have reduced the potential fungal pathogen \u003cem\u003eBotrytis\u003c/em\u003e and functional pathotrophs (wood and undefined guild) while increasing potentially beneficial bacteria in the rhizosphere, such as \u003cem\u003ePseudomonas\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eRhizobium\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e in the mycorrhized-bacterized treatment, and \u003cem\u003eCandidatus Solibacter\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003ePhenylobacterium\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eGemmatimonas\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e in the mycorrhized treatment. Cardinale et al. (2022) \u003csup\u003e\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e similarly reported enrichments of beneficial bacteria but non-hub taxa within root endosphere after inoculation of mycorrhizal fungi on 1103P (\u003cem\u003eBurkholderiaceae\u003c/em\u003e, \u003cem\u003eRhizobiaceae\u003c/em\u003e, \u003cem\u003eMethylophilaceae\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eMassilia\u003c/em\u003e, and \u003cem\u003eStreptomyces\u003c/em\u003e), which is consistent with our findings. Bona et al. (2019) \u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e\u003c/sup\u003e employed a metaproteome approach to investigate the rhizosphere of \u003cem\u003eV. vinifera\u003c/em\u003e cv. Pinot Noir, and identified bacteria belonging to \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eBacillus\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eBurkholderia\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e with highly active protein expression, primarily involved in phosphorus and nitrogen metabolism. These genera were also detected using MALDI-TOF MS within the rhizosphere of different conditions in this study.\u003c/p\u003e \u003cp\u003eAs with plants, introducing any microbial inoculant into the soil can be regarded as a disturbance to the native microbiota. These inoculation-induced changes in soil microbial composition can inherently alter soil functioning \u003csup\u003e\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e. Functional redundancy, which is the presumption that several taxa provide the same ecological function within a microbial community \u003csup\u003e\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e, could explain the ecosystem\u0026rsquo;s resilience despite low microbial diversity and richness \u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e. This theory would justify the lower level of cultivable fungal and bacterial diversity observed in the rhizosphere of treated conditions compared to the untreated one, while having greater metabolic diversity measured using EcoPlates and PICRUSt2. The increased activity measured with EcoPlates was related to carbohydrates and carboxylic acid. Kohler et al. (2006) \u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u003c/sup\u003e reported an increase in carbohydrates in the rhizosphere of lettuce plants inoculated with \u003cem\u003ePseudomonas mendocina\u003c/em\u003e and \u003cem\u003eR. irregularis\u003c/em\u003e, which was presumed to be linked to soil stabilization. Moreover, \u003cem\u003eR. irregularis\u003c/em\u003e is known to alter the carbohydrate content in the rhizosphere \u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e. In a similar manner, \u003cem\u003eR. irregularis\u003c/em\u003e is known to increase the levels of low-molecular-weight organic acids in the rhizosphere to mobilize P content bound to Fe oxides, which represents one of the key exchanges from the symbiont to the host in return for photosynthetic carbon \u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. The co-inoculation of \u003cem\u003eF. mosseae\u003c/em\u003e and the growth-promoting rhizobacteria \u003cem\u003eEnsifer meliloti\u003c/em\u003e on \u003cem\u003eV. vinifera\u003c/em\u003e cv. Cabernet Sauvignon greatly increased the abundance of volatile organic compounds within roots \u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. As AMF do not acquire carbon from the soil due to their symbiotic relationship with the host, this could explain the lower carbohydrate metabolism inferred by PICRUSt2 within roots that were both mycorrhized and bacterized, compared to untreated roots.\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe treatments synergistically complexified the bacterial co-occurrence within root systems and had limited impact on fungal network\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCo-occurrence networks offer a snapshot of the intricate relationships among microbial communities in various environments, including soil and plant-associated habitats \u003csup\u003e\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e,\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. These networks, associated with the functional stability of microbial communities, and characterized by their modularity, connectivity and other topological features, are constructed by identifying significant patterns of co-occurrence between microbial taxa, providing insights into potential interactions \u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. A higher degree of network complexity is thought to reflect a more robust and resilient community, which can serve as a proxy for assessing soil quality \u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u003c/sup\u003e, and plant health \u003csup\u003e\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. It had been previously reported that microbial inoculation of plants resulted in more complex and compact associations within their associated microbiomes. For instance, soybeans inoculated with \u003cem\u003eRhizobium\u003c/em\u003e resulted in an increased number of connections within the fungal community network \u003csup\u003e\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. Although this was not observed in the fungal network, an important shift in the microbiome was detected in this study. Similarly, inoculation of \u003cem\u003eR\u003c/em\u003e. \u003cem\u003eirregularis\u003c/em\u003e increased the bacterial network complexity within the root endosphere of maize \u003csup\u003e\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e\u003c/sup\u003e. Regarding vineyard soil, Torres et al. (2021) \u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e unveiled the increase of network complexity and stability due to AMF inoculation. In our study, a notable increase in the complexity of nodes and edges within the bacterial community network was reported in the root system treated with both mycorrhiza and bacteria, compared to untreated samples. The shifts observed in the microbial network are most likely linked to the change of microbial functionality and chemical compounds occurring within the root system. In co-occurrence networks, keystone taxa play a crucial role in community structure and function irrespective of their abundance \u003csup\u003e\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e. Identifying a keystone species solely based on its topological role within the network is not sufficient unless its major ecological role within the ecosystem can be determined. \u003cem\u003eAcidobacteriales\u003c/em\u003e being keystone taxa detected as a peripheral node in the co-inoculation condition has already been determined as such in soil fertility for rice \u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e and soil respiration \u003csup\u003e\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e, and is primarily involved in carbon cycling and soil aggregation \u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e. In addition, the combined mycorrhized and bacterized condition revealed the presence of \u003cem\u003eMesorhizobium\u003c/em\u003e, a keystone bacterial genus involved in soil nitrogen metabolism \u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eBlastocatellaceae\u003c/em\u003e, directly involved in the stability of soil microbial-mediated functions \u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e, and even \u003cem\u003eOnygenales\u003c/em\u003e, an important group of fungal decomposers in soil \u003csup\u003e\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e\u003c/sup\u003e. Based on detected keystone taxa, it is possible to obtain beneficial microbes for a synthetic community design by targeting specific species while avoiding large PGP screening \u003csup\u003e\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eMultifunctional plant growth-promoting rhizobacteria were isolated from symptomatic and asymptomatic grapevines, with the most effective isolates originating from the rhizosphere of stressed grapevines. After growth induction in fast-growing \u003cem\u003eL. sativum\u003c/em\u003e and 1103P plantlets \u003cem\u003ein vitro\u003c/em\u003e, a selected bacterial treatment consisting of \u003cem\u003eP. veronii\u003c/em\u003e and \u003cem\u003eP. brassicacearum\u003c/em\u003e was inoculated on grapevines grown in a greenhouse, either singly or in combination with commercial mycorrhizal fungi \u003cem\u003eR\u003c/em\u003e. \u003cem\u003eirregularis\u003c/em\u003e and \u003cem\u003eF\u003c/em\u003e. \u003cem\u003emosseae\u003c/em\u003e. In addition to root biomass enhancement, the composition and functionality of the indigenous microbiome within the root system of treated conditions were altered, compared to untreated samples. Increased metabolic activity, such as carbohydrate metabolism, was accompanied by taxonomic shifts, including an enrichment of potentially beneficial bacteria and a reduction in fungal pathogens. Furthermore, the microbial community structure, as revealed by co-occurrence networks, increased in complexity upon treatment with microbial applications, unveiling keystone taxa within the microbial interactions. This work provides insights on the promising role of isolates from stressed environments that could alleviate microbiome dysbiosis linked to crop decline.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThis work was supported by FranceAgrimer/CNIV funded as part of the program \u0026lsquo;Plan National D\u0026eacute;p\u0026eacute;rissement du Vignoble\u0026rsquo; within the project Vitirhizobiome (grant number 2018\u0026ndash;52537).\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eRD, VLau, IM-P, and NO conceived the study. RD and JW performed the enzymatic assays and the in vitro bioassays with \u003cem\u003eLepidum\u003c/em\u003e and grapevine. RD managed the greenhouse experiment. RD, GM, PB, IM-P, NO, and VLai contributed to the sampling, the data collection and analysis. RD and VLai performed DNA extraction and metabarcoding analyses. RD and JT did the bioinformatic analysis. RD prepared the figures and tables. RD wrote the manuscript and VLau supervised the writing. All authors critically reviewed and edited the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis work was supported by FranceAgrimer/CNIV and funded as part of the \u0026ldquo;Plan National D\u0026eacute;p\u0026eacute;rissement du Vignoble\u0026rdquo; program of the Vitirhizobiome project (grant number FAM no. 22001206). The authors would like to thank the vineyard owners for their permission to sample the soil used as matrix for the greenhouse experiment. The authors also thank the Genotoul bioinformatics platform Toulouse Midi-Pyrenees and Sigenae group for providing storage resources via Galaxy instance. The authors thank Marc Meynadier for his help in the choice of the bioinformatics pipelines.\u003c/p\u003e\n\u003ch2\u003eData availability\u003c/h2\u003e\n\u003cp\u003eRaw sequences have been deposited in Sequence Read Archive (NCBI), Bioproject PRJNA826314 https://www.ncbi.nlm.nih.gov/bioproject/PRJNA826314 \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBernardo, S., Dinis, L.-T., Machado, N. \u0026amp; Moutinho-Pereira, J. Grapevine abiotic stress assessment and search for sustainable adaptation strategies in Mediterranean-like climates. 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K. \u0026amp; Delgado-Baquerizo, M. Humidity and low pH boost occurrence of Onygenales fungi in soil at global scale. \u003cem\u003eSoil Biology and Biochemistry\u003c/em\u003e \u003cstrong\u003e167\u003c/strong\u003e, 108617 (2022).\u003c/li\u003e\n\u003cli\u003eZheng, Y. \u003cem\u003eet al.\u003c/em\u003e Exploring Biocontrol Agents From Microbial Keystone Taxa Associated to Suppressive Soil: A New Attempt for a Biocontrol Strategy. \u003cem\u003eFrontiers in Plant Science\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, (2021).\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":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PGPR screening, metabarcoding inference, microbiome engineering, grapevine dysbiosis, microbial network, metabolic activities","lastPublishedDoi":"10.21203/rs.3.rs-5880310/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5880310/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe addition of bacteria and arbuscular mycorrhizal fungi (AMF) is a strategy used to protect plants against disease and improve their growth and yield, known as biocontrol and biostimulation, respectively. In viticulture, the plant growth promotion (PGP) potential of bacteria endemic to vineyard soil has been underexplored. Furthermore, most research about microbial biostimulants focuses on the effect on the plant, but little is known on how their application modify the soil and root microbial ecosystem, which may have an impact on plant growth and resistance. The objectives of this work were 1) to identify bacteria present in vineyard soils with functional PGP traits, 2) to test their PGP activity on young grapevines, in combination with AMF, 3) to assess the impact on the microbial communities and their inferred functions in the rhizosphere and plant roots.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eTwo hundred bacteria were isolated from vineyards and characterized for their biochemical PGP activities. The most efficient were tested \u003cem\u003ein vitro\u003c/em\u003e, both singly and in combination, on \u003cem\u003eLepidium sativum\u003c/em\u003e and grapevine plantlets. Two \u003cem\u003ePseudomonas\u003c/em\u003e species particularly increased \u003cem\u003ein vitro\u003c/em\u003e growth and were selected for further testing, with and without two \u003cem\u003eGlomus\u003c/em\u003e species, on grapevines planted in soil experiencing microbial dysbiosis in a greenhouse setting. After five months of growth, the co-application of PGP rhizobacteria and AMF significantly enhanced root biomass and increased the abundance of potentially beneficial bacterial genera in the roots, compared to untreated conditions and single inoculum treatments. Additionally, the prevalence of Botr\u003cem\u003eytis cinerea\u003c/em\u003e, associated with grapevine diseases, decreased in the root endosphere. The combined inoculation of bacteria and AMF resulted in a more complex bacterial network with higher metabolic functionality than single inoculation treatments.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eTo our knowledge, this is the first study to examine and apply bacterial strains derived from soils of the same vineyard plot in co-application with commercialized fungi. The results show a remodeling of microbial communities and their functions associated with a beneficial effect on the plant in terms of growth and presence of pathogens. The observed synergistic effect of bacteria and arbuscular mycorrhizal fungi indicates that it is important to consider the combined effects of individuals from synthetic communities applied in the field.\u003c/p\u003e","manuscriptTitle":"Rhizobacteria from vineyard and commercial mycorrhizal fungi induce synergistic microbiome shifts within grapevine root systems","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-28 13:43:06","doi":"10.21203/rs.3.rs-5880310/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-09T08:10:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-11T12:01:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291420070438464663880330420206399745601","date":"2025-05-01T15:18:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300175222595027891689951353830787617773","date":"2025-04-30T23:23:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T13:38:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42283733011239720784578020361262624032","date":"2025-04-11T02:56:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262225412610609344823896458018568322627","date":"2025-02-15T16:44:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300703632791954793864660520328478091412","date":"2025-02-15T14:42:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180379931858116816025199337489188852226","date":"2025-02-08T09:15:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-05T21:44:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-28T21:14:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-01-28T13:06:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-01-24T15:39:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-01-22T11:05:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4059e290-6727-4d28-b648-0f4dd87dfa0f","owner":[],"postedDate":"January 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-04T16:41:23+00:00","versionOfRecord":{"articleIdentity":"rs-5880310","link":"https://doi.org/10.1038/s41598-025-12673-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-30 16:21:09","publishedOnDateReadable":"July 30th, 2025"},"versionCreatedAt":"2025-01-28 13:43:06","video":"","vorDoi":"10.1038/s41598-025-12673-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-12673-5","workflowStages":[]},"version":"v1","identity":"rs-5880310","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5880310","identity":"rs-5880310","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}