Blueberry plants support a distinctive microbiome as a function of plant genetics and tissue

Blueberry plants support a distinctive microbiome as a function of plant genetics and tissue | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Blueberry plants support a distinctive microbiome as a function of plant genetics and tissue Matteo Giese, Erika Stefani, Simone Larger, Massimo Pindo, Brian Farneti, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5440144/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Fruits, such as blueberries, are critical for food production and ecosystem sustainability as they are largely consumed by humans and animals worldwide. The microbial communities (bacteria, archaea, and fungi) within and on the surface of these fruits play a key role in regulating food quality, alongside supporting crucial aspects of plant physiology and development. However, the specific factors shaping the microbiomes of blueberry fruits, as well as their relationship with other above-ground parts of the plant such as leaves and their stability over different years, remain poorly understood. Results We conducted a field experiment to characterize the taxonomic composition of fungal and bacterial communities colonizing the leaves and the surface and pulp of fruits on a collection of 10 different cultivars of blueberry over two consecutive years. We found that, independently from the sampling time, pulp of the fruit, surface and leaves harbors specific and distinct microbiomes. The major factor determining the structure of the microbiome of blueberry fruits and leaves was plant genetics, followed by tissue. We further identified the core microbiome for each plant tissue and demonstrated that core taxa account for the dominant fraction of the microbiota of each plant. Conclusions We showed that blueberries have a distinct microbiome associated with plant cultivar, and that this microbiome is consistent with time. We identified a tissue-specific core microbiome, with some genera shared among different tissues, and others consistently present only in specific tissues. As trade and production of blueberries is expanding globally, our results provide a foundation for advancing the development of targeted microbiome management strategies, with potential applications in enhancing plant health and productivity. Metabarcoding Blueberry Plant Microbiome Core Microbiome Endophyte Epiphyte Environmental microbiology Biodiversity Climate Change Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Plants are colonized by complex microbial communities that play an essential role in various aspects of their physiology and development through processes such as pathogenesis and nutrient cycling, ultimately contributing to plant health (Delgado-Baquerizo et al. 2018; Maestre et al. 2015 ; Trivedi et al. 2020 ; Msimbira and Smith 2020 ). Each plant tissue has different morphological and physiological characteristics that create a set of diverse micro-habitats (Zhen Wang et al. 2023 ), offering a multitude of distinct ecological niches (Chialva, Lanfranco, and Bonfante 2022; Leveau 2019 ). The structure and composition of plant microbiota depend on host-associated and environmental variables (Jian Li et al. 2023 ). These microbiomes are especially important when associated with food (e.g., fruits) consumed by humans and animals. Plant microbiomes are known to directly contribute to the formation and maintenance of human and animal microbiomes, with a larger effect at young age (Carlino et al. 2024 ). This is the case, for example, of blueberries, produced by plants belonging to the genus Vaccinium , which are today among the most consumed fruits worldwide. Among land plants, Vaccinium spp. are long-lived woody perennial members of the Ericaceae family (Jiangang Li et al. 2020 ). Despite their significant commercial importance (Chacón et al. 2022), little is known about the composition and functions of the microbiome of blueberry plants. The majority of existing studies have focused on rhizosphere and endosphere (Jiangang Li et al. 2020 ; Kawash et al. 2023; Perotto et al. 2022) and on their influence on plant adaptation against abiotic stress. Researchers have shown that blueberry plants can form ericoid mycorrhizal symbioses, enabling them to thrive in challenging environments (Ważny et al. 2022; Morvan et al. 2023). The factors that contribute to the formation of the microbial communities that colonize fruit and leaves of blueberry plants and how these two tissues differ and influence each other in terms of hosted microbial species have still not been fully understood. Blueberry fruit is exposed to several pathogens in its life cycle, such as mummy berry ( Monilinia vaccinii-corymbosi ) (Ashley and Annis 2024), Phytophthora (Pscheidt and Weiland 2015), Colletotrichum spp. and Botrytis cinerea , which are responsible for several diseases (Szymanski et al. 2023 ). Despite the potential for biocontrol, there is still a lack of studies that investigate the relationship between the microbial communities of different aboveground parts of blueberry plants, the impact of environmental factors, and the genetics of the plant. Moreover, the consistency of microbial communities of these perennial plants over different years has not been characterized. Finally, we lack a clear understanding of the relative importance of the core microbial species that are shared across plant cultivars and tissues and of the taxa that are specific to certain tissues, and of individual plants. To address these knowledge gaps, we explored the above-ground microbiome (fungi and bacteria) of cultivated Vaccinium s pp. using the metabarcoding technique in order to: i ) investigate the presence of specific compositional patterns across different tissues; ii ) determine if different tissues share the same set of species or differ due to their physical properties and identify organisms that are specific of each micro-niche; iii ) asses the major driving factors that influence the community composition, including host genetics; iv ) define the core microbiome specific for each micro-niche. Materials and Methods Plant materials and sampling Ten blueberry cultivars were chosen in the FEM blueberry germplasm collection, located in Trentino Alto Adige, Italy (46.0744° N, 11.2334° E), harvested in 2022 and 2023 and sampled in triplicate. For each cultivar, leaves and fruits were collected for a total of 30 samples for fruits and leaves, respectively. For each replicate, 25 fruits and 30 leaves were placed in separate sterile bags. Berries were harvested at stage 7 of the BBCH scale, visually homogeneous by size and showing a complete blue surface color; the white or red rings around the pedicel scar were considered to be genotype-specific. Fruits and leaves were collected using sterile tweezers and shears, placed in sterile plastic bags, then stored on ice and later stored at -20°C, until downstream analysis. DNA Extraction and Sequencing SAMPLE PREPARATION Epiphytic (Fruit and leaf Surface) Microbiome The epiphytic extraction was made using 150 ml of sterile washing solution (0.9% NaCl, 0.01% Tween 80) was added, and then incubated on an orbital shaker at 400 rpm for 1 hour (Behrens and Fischer 2022). At the end of the incubation, the water was filtered using sterile filtration apparatus (2GPU02RE Stericup Quick Release Millipore Express PLUS 0.22µm PES, 250 ml Merck Millipore) to recover as much biological material as possible. The filters were used for DNA extraction using DNeasy PowerWater kit QIAGEN according to the manufacturer’s protocol. Endophytic (Pulp) Microbiome The endophytic extraction required a different procedure (Bill et al. 2021 ). Fifteen fruits were randomly selected for each replicate and washed in 90% ethanol once and then rinsed three times in sterile Milli-Q water to minimize contamination from the surface of the fruit. The disinfected surface of fruit was aseptically peeled with a disinfected scalpel. The pulp tissue of the fruit portion was aseptically removed and cut into smaller pieces using a sterile scalpel blade, and used for DNA extraction using the DNeasy PowerSoil Pro Kit by QIAGEN, according to the manufacturer’s protocol. DNA was quantified using the NanoDrop™ 8000 Spectrophotometer (Thermo Fisher Scientific Inc. Waltham, Massachusetts, United States) according to the protocol provided by the manufacturer. The V3-V4 variable region of the 16S rDNA and Internal Transcribed Spacer (ITS) rDNA was used to analyze bacterial and fungal diversity, respectively. AMPLICON SEQUENCING For fungi, the ITS1 region was amplified using ITS1F (5’-CTT GGT CAT TTA GAG GAA GTAA-3’) and ITS2 (5’-GCT GCG TTC TTC ATC GAT GC-3’) primers (White et al., 1990; Gardes and Bruns 1993) with overhang Illumina adapters. The PCR reactions were carried out with a total volume of 25 µL, containing 1 µL of each primers (10 µM of each one), 0.25 µL of FastStart High Fidelity PCR System (Roche), 18,75 µL of nuclease-free water (Sigma–Aldrich), 2.5 [10X] Buffer, 0,5 µL dNTP and 1 µL of DNA (5–10 ng/ µL). PCR reactions were performed by using the GeneAmp PCR System 9700 (Thermo Fisher Scientific); the conditions were: initial denaturation at 95°C for 3 min, 35 cycles of denaturation at 95°C for 20 s, annealing at 50°C for 45 sec, extension at 72°C for 90 s, followed by a final extension at 72°C for 10 min. For bacteria, we amplified the sub-region V3-V4 of the 16S gene using the 341F (5’- CCT ACG GGN GGC WGC AG -3’) and 805R (5’- GAC TAC NVG GGT WTC TAA TCC − 3’) primers (Klindworth et al. 2013 ) with overhang Illumina adapters. The PCR reactions were carried out with a total volume of 25 µL, containing 5 µL of each primer (1µM), 12,5 µl MIX KAPA HiFi HotStart ReadyMix (Roche) and 2,5 µL of DNA (5–10 ng/µL). PCR reactions were performed by using the GeneAmp PCR System 9700 (Thermo Fisher Scientific); the conditions were: initial denaturation at 95°C for 5 min, 35 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, extension at 72°C for 30 s, followed by a final extension at 72°C for 5 min. The obtained amplicons were purified using the CleanNGS (CleanNA), following the manufacturer’s instructions. Afterward, a second PCR was used to apply dual indices and Illumina sequencing adapters Nextera XT Index Primer (Illumina), by 7 cycles PCR (16S Metagenomic Sequencing Library Preparation, Illumina). The amplicon libraries were purified using CleanNGS (CleanNA), and the quality control was performed on a Tapestation 4150 platform (Agilent Technologies, Santa Clara, CA, USA). Finally, all barcoded libraries were pooled in an equimolar way and sequenced on an Illumina® MiSeq (PE300) platform in pair ends (2 × 300 bp) (MiSeq Control Software 2.5.0.5 and Real-Time Analysis software 1.18.54.0). Library construction and NGS were performed at the sequencing platform of the Edmund Mach Foundation (San Michele all’Adige, Italy). BIOINFORMATIC ANALYSIS The raw reads, already demultiplexed, were analyzed using the MICCA (MICrobial Community Analysis) v1.7.2 bioinformatics pipeline (Albanese et al. 2015 ). Sequence pairs were merged to obtain consensus sequences. Primers were trimmed and sequences that did not contain these primers were discarded. Quality filtering was performed using an Expected Error Rate of 0.75 for ITS and 16S, with the minimum read length set to 200 for ITS and 400 for 16S. Reads were clustered into Amplicon Sequence Variants (ASVs) using the UNOISE protocol (Edgar 2016 ). Finally, taxonomy was assigned using the "UNITE 9.0" for the fungal component and "RDP classify 2.14" for the bacterial component. STATISTICAL ANALYSIS Alpha diversity and Beta diversity Due to the lower number of reads for endophytic communities, Alpha and Beta diversity analysis were carried out using different rarefactions depending on the analyzed tissues. Samples were rarefied to 3,000 reads for bacteria and 10,000 for fungi for the whole tissues (pulp, leaf and fruit surfaces) comparisons, while in comparisons including only epiphytic communities (leaf and fruit surfaces) the rarefaction was 10,000 reads for bacteria and 30,000 for fungi. Three alpha diversity indices were computed, namely Chao1, Shannon Diversity, and Simpson Dominance (Willis 2019 ; Finn 2024 ; Kim et al. 2017 ) using the "estimate_richness" function from the R package "Phyloseq version 1.46.0" (McMurdie and Holmes 2013); Chao1 indices are used to estimate richness; measurement of ASVs expected in samples given all the microbial species that were identified in the samples. The Shannon diversity index is a measure of species diversity in a community and taking into account the number of species present and their relative abundance, high values of this index indicate a higher biodiversity. The Dominance-Simpson index is another widely used diversity index in ecology; this ranges from 0 to 1, values close to zero indicate that all species are equally present, while a value close to 1 indicates that few species dominate the sample. The Wilcoxon-Mann-Whitney test is employed for the statistical analysis of alpha diversity in R Studio (Ripley 2001 ); the adjustment method used for P-value was Holm-Bonferroni. For beta diversity analysis, we use Bray-Curtis dissimilarity index to assess the difference between the plant tissues visualized with an PCoA. Statistical significance was assessed using PERMANOVA/Adonis with pairwiseAdonis v0.4 R package (Martinez-Villegas et al. 2024 ). The parameters considered in this analysis are difference in year, difference in tissues (for Epiphytes), and difference in plant cultivar compared to the reference set of pairwise distances between samples from the same year, plant cultivar and tissues. Enrichment of genera in the different tissues was assessed using the negative binomial distribution analysis implemented in R package DESeq2 (Love, Huber, and Anders 2014). Results RAW DATA ANALYSIS In total, after filtering for quality and discarding ribosomal and chloroplast reads, we generated 6,446,364 16S and 11,023,303 ITS quality filtered sequencing reads. Of these, 3,520,056 and 4,056,471 were from leaf bacterial and fungal epiphytes, respectively, and 2,559,002 and 4,402,685 from fruit bacterial and fungal epiphytes. Finally 367,306 and 2,564,147 from bacterial and fungal endophytes. It is noteworthy the lower number of reads that were obtained for endophytic communities, in particular for bacteria, due to the higher proportion of reads that were discarded at the filtering stage. The filtered reads were classified into 11,919 ASVs for 16S and 3,008 ASVs for ITS. Taxonomic profiles reveal compositional differences between Epiphytic and Endophytic Niches In all samples, the most prevalent fungal phyla were Ascomycota and Basidiomycota (Fig. 1 -A & 1 -B and Supp. Table 1, Supp. Table 2, Supp. Table 3) while Chytridiomycota and Zygomycota were found only in a few samples and at low relative abundance. At the genus level (Supp_Fig_1A&B and Supp. Table 4, Supp. Table 5, Supp. Table 6), Davidiella was the most abundant, followed by Phoma , Alternaria , Malassezia , Aureobasidium , Botrytis , Cryptococcus , Leptosphaerulina , Rhodotorula , Taphrina , and Mortierella . The compositional analysis of bacteria at the phylum level (Fig. 1 -C & 1 -D and Supp. Table 7, Supp. Table 8, Supp. Table 9) shows a wide variety of taxa present. The phylum Proteobacteria was dominant across all samples, followed by Actinobacteria, Bacteroidetes, and Firmicutes. We could identify compositional patterns that distinguish the endophytic and epiphytic communities, with certain taxa exclusively present in specific tissues. For instance, Acidobacteria were found on the leaf and fruit surfaces tissues but not in the pulp fruit, while Fusobacteria, which were present in small amounts in the pulp tissues, were absent in the leaf and fruit surface. At the genus level (SUPP_FIG 1-A & 1-B and Supp. Table 10, Supp. Table 11, Supp. Table 12), we found that the most abundant taxa were Massilia , Hymenobacter , Sphingomonas and Methylobacterium for the fruit and leaf surface while Micrococcus , Staphylococcus , Enhydrobacter and Corynebacterium was the most abundant in fruit pulp. To corroborate the observed qualitative differences in the taxonomic profiles of the different tissues, we analyzed the abundance data for both bacteria and fungi (Supp_fig_2 A-B) with DESeq2 (Debeljak and Baltar 2023). At phylum level we found a significant difference in fungal communities of fruit pulp and both fruit and leaf surface ( Supp. Table 13, Supp. Table 14, Supp. Table 15), while no statistical differences were found when comparing fruit and leaf surfaces. At genus level ( Supp. Table 16, Supp. Table 17, Supp. Table 18) we observed a significant difference in the comparison of fruit pulp with surface of leaf and fruit that involves several taxa such as: Leptosphaerulina and Rhodotorula presents only on the surface of fruit and leaves or Mortierella and Malassezia that are present only in the fruit pulp. Moreover we observed a significant difference also between fruit and leaf surface. Amongst the taxa that showed a significant difference, Davidiella and Botrytis had higher abundance on the fruit surface and Leptosphaerulina was more abundant on the leaf surface. For the bacterial communities, at phylum level (Supp. Table 19, Supp. Table 20,Supp. Table 21) we found that Acidobacteria, Fusobacteria and Actinobacteria were significantly less abundant in fruit pulp than on fruit and leaf surfaces, while the comparison between the surface of fruit and leaf showed a significant difference only for Deinococcus-Thermus and Bacteroidetes. At the genus level (Supp. Table 22, Supp. Table 23, Supp. Table 24) we found several genera with significant differences between fruit pulp and both surface tissues. Statistically significant differences were found in Sphingomonas , Deinococcus , Hymenobacter , Methylobacterium and Lactobacillus that were present only on fruit and leaf surface while taxa present only in the fruit pulp were Corynebacterium , Paracoccus , Enhydrobacter , Propionibacterium and Pseudomonas . The comparison of fruit and leaf surface showed fewer significant differences, some examples are: Methylobacterium , present mostly on leaf surface or Exiguobacterium predominant on the fruit surface. Alpha diversity reveals contrasting patterns for bacteria and fungi across plant niches To characterize the richness and diversity of the microbial communities in the different tissues, after rarefaction we computed the Chao1 estimator of species richness, the Shannon entropy index of diversity and the Simpson index of dominance. Fruit pulp had a significant lower richness than fruit and leaf surfaces both for bacteria and fungi (Supp. Table 25). Comparing fruit and leaf surfaces (Fig. 2 -A), we found for fungi significantly higher values for leaves than for fruits (Chao1, p-value = 0.008; Shannon, p-value = 0.008; Simpson, p-value = 0.005). Analyzing alpha diversity in relation to the year of sampling we found that Shannon and Simpson had significantly higher values in the first year of sampling in leaf communities, while for fruit epiphytes we observed a significant difference only in the Shannon index (supp_fig 3). For bacteria (Fig. 2 -B), we found no significant difference between the species richness measured by the Chao1 index (p-value = 0.54) between leaf and fruit epiphytic communities, while both the Shannon entropy and the Simpson dominance index were significantly higher in fruits than in leaves (Shannon, p-value = 0.046; Simpson, p-value = 0.0064). The analysis of alpha diversity comparing the different years of sampling showed an effect on richness of leaf epiphytes (supp fig_4). Indeed, while the Shannon and Simpson indexes did not vary significantly, we found that the Chao1 index for leaf epiphytes was significantly higher in the second year of sampling than in the first year. No significant differences were found for fruit epiphytes. Beta diversity shows that plant tissues and genotype have a significant impact on microbial communities. For Fungi (Fig. 2 -C), we found significant differences in the overall beta-diversity analysis (PERMANOVA/Adonis, p-Value = 0.001, R 2 = 0.21027). Similarly, a Pairwise-PERMANOVA (Supp. Table 26) highlighted that the greatest differences occurred between endophytic and epiphytic communities, but we detected a difference also among epiphytic communities due to plant variety and year of sampling (Year: p-Value = 0.005, R 2 = 0.0171; Plant Variety: p-Value = 0.001, R 2 = 0.13045). Focusing the analysis on epiphytes, we found statistically significant differences based on niche, year and plant variety (p-Value = 0.001). Taken together these variables account for 40.68% of the variability. In particular, we found that the most important factor was plant variety (p-Value = 0.001, R 2 = 0.26739), followed by niche (p-Value = 0.001, R 2 = 0.0962) and year (p-Value = 0.001, R 2 = 0.0437). For endophytic communities, we found significant differences considering year (p-Value = 0.015, R 2 = 0.0518) and plant variety (p-Value = 0.001, R 2 = 0.311). For Bacteria (Fig. 2 -D) we observed a division among the three niches, which was particularly evident when comparing fruit endophytic communities with epiphytic fruits and leaf communities. A PERMANOVA/Adonis statistical test on niches, year and variety of plant showed that these factors had a statistically significant effect (Niches: p-Value 0.001, R²=0.10414; Year: p-Value 0.001, R²=0.02001; Plant variety: p-Value 0.001, R²=0.10549). Most significant differences were observed between endophytic and epiphytic communities; however, there were also significant differences between the epiphytic communities of the fruit and leaf surface (Fig. 2 -E & 2 -F). Focussing on epiphytic communities, we found that plant variety, niche and year accounted for 21.7% of the variability (p-value = 0.001). Similarly to fungi, plant variety was the most relevant factor (p-Value = 0.001, R 2 = 0.151), followed by niche (p-Value = 0.001, R 2 = 0.03881) and year of sampling ( p-Value = 0.001, R 2 = 0.0264). Taken together, these data show that, although with some correction due to plant tissues and year, the plant variety was the dominating factor to determine the variability of the plant epiphytic bacterial communities. To further characterize the relative importance of the different factors (tissues, plant cultivar and year) that impact the structure of the microbial communities, we compared the distribution of the pairwise Bray-Curtis dissimilarities between samples in which only one factor varies. The boxplot (Fig. 3 ) represents the dissimilarity between microbial communities in relation to factors change. The analysis of fungal epiphytes shows significant dissimilarity when comparing the reference set that shares the same plant variety, year, and tissues. For the bacterial epiphytes, communities appear to be heterogeneous with values close to 1. Also here we found that plant variety, year and tissues have a significant influence on the heterogeneity of communities. For the fungal endophytic communities we found that plant variety and year show more heterogeneous communities while for bacterial endophytic communities we found that only year has an influence on the heterogeneity of the communities. Additionally, when comparing the communities, the fungal communities were found to be less heterogeneous than the bacterial ones, as confirmed by the Wilcoxon-Mann-Whitney test (Supp. Table 26), which revealed significant differences (P-value < 0.005). Prevalent taxa accounts for a large fraction of the microbiome reads in all tissues To correlate the relative abundance of different genera with their prevalence and quantify the relative importance of the sample-specific and shared taxa, for each analyzed tissue we calculated the fraction of the total number of reads belonging to genera with a specific prevalence (Fig. 4 ). The results showed that in all tissues, both for bacteria and fungi, the highest fraction of the communities is attributable to genera present in over 90% of the samples demonstrating that, despite differences in taxonomic composition amongst samples, the dominating part of the microbiome is composed by widely distributed taxa. This phenomenon is particularly noteworthy in the case of fungi on fruit and leaf surfaces, as the most prevalent genera constitute nearly 100% of the microbiota, while for bacteria the corresponding fraction was lower. Compared to the epiphytic communities, the endophytic microbiome was less homogeneous among the various samples, in particular for fungi. Interesting differences between bacteria and fungi appeared when we repeated the analysis at the ASV level (supp_fig_5). While for fungi the fraction of each sample attributable to prevalent ASVs was still well above 75% for both leaf and fruit surfaces, this fraction decreased drastically for bacteria. This finding was correlated to the greater genetic variability of bacterial genera compared to fungal genera. Indeed, we found a generally lower level of genetic complexity within fungal genera (supp_fig_6) than within bacterial genera (supp_fig_7). The most extreme example was the bacterial genus Hymenobacter that included more than 700 distinct ASV, while the most diverse fungal genus Mortierella included 20 distinct ASVs. Moreover, the within-genus variability was correlated with prevalence. Counting the number of samples in which a given genus was present and correlating this number to the number of distinct ASV that were classified in the same genus, we found that genera present in the largest number of samples also included the highest number of different ASVs (supp_fig_8, supp_fig_9), showing that while at higher taxonomic level there is a high degree of homogeneity across the different plants and tissues, this is accompanied by a large genomic variability at finer levels of taxonomic classification, with a large number of plant and tissues-specific strains for most prevalent taxa. The level of genetic diversity was particularly high for the most prevalent bacterial genera. Additionally, it is interesting to note the marked increase of bacterial taxa specific to a small number of samples when the analysis is conducted at the ASV level. Taken together, these results suggest that dispersal limitations have an impact on the colonization of plants by specific strains, while, at higher taxonomic level, each environment nonetheless selects a limited number of taxa. Microbial communities share a core of common microbial taxa across plant cultivars and tissues Given the dominant role of the most prevalent taxa in the microbiome of each tissues and the importance of plant variety in determining the structure of the microbiome, we explored if it was possible to define a tissues-specific core microbiome that included the taxa that were present in the specific tissues all plant genotypes. Moreover, leveraging on the availability of data over two consecutive years on the same plants, we asked if this core microbiome was conserved over time and, finally, if, given their close proximity, the endophytes and epiphyte tissues of fruit and leaf share the same set of core taxa, or if the different physical properties were sufficient to trigger a different composition. We thus constructed a presence/absence matrix at the genus level, where a genus was considered present if its abundance was greater than 0, using unrarefied data to increase the sensibility of the analysis. Using these data, we defined a taxa as “core” if it was present in a given year and tissues in at least 9/10 plant genotypes in one of the triplicates (Fig. 5 ). As expected given the lower number of sequenced reads, we found both for bacteria and fungi a smaller core for the endophytic tissues (11 and 12 taxa for the first and second year for fungi, 19 and 14 genera taxa for the first and second year for bacteria, respectively) that for the fruit and leaf surface (56, 60 for fruit and 77, 85 for leaf core genera in the first and second year for fungi and 104, 104 for fruits and 89, 127 for leaf core genera for the first and second year for bacteria, respectively). Comparing the tissues-specific core microbiome across the two years, we found that each tissue exhibited a core similarity greater than 50% between the two years (Supp. Table 27). Moreover, comparing the set of core taxa across tissues, we found that for both fungi (supp_fig_10) and bacteria (supp_fig_11) most of the core taxa were shared across the epiphytic tissues, while the core taxa of the endophytic fruit tissues were a subset of the taxa present on the surface of fruits and leaves. In the core analysis (Supp. Table 28, Supp. Table 29), we detected the presence of ubiquitous genera across different tissues, while others were tissue-specific. Fungal genera such as Davidiella , Leptosphaerulina , and Botrytis were present in all tissues, though with varying abundance percentages. Some genera, such as Taphrina and Pleospora , were found exclusively in the surface tissues of fruit and leaf, while Dioszegia and Myrothecium were specific to the leaf tissue, and Udeniomyces to the fruit. We also identified a core microbiome for bacteria, highlighting widely distributed genera in all the tissues such as Methylobacterium, Micrococcus and Massilia , with notable variations in abundance, similar to what was observed for fungal genera. Other bacterial genera showed tissue specificity: Gaiella and Rathayibacter were found only in surface tissues, Exiguobacterium and Aridibacter were exclusive to the fruit, and Micromonospora was exclusive to the leaf. Discussion We conducted one of the first field experiments over consecutive years to investigate the microbiome of blueberry fruits and leaves and we found that blueberries have a distinctive microbiome, consistent across years of study, and mostly influenced by plant genetics and tissues. This work reports the first characterization of the factors that drive the biodiversity of the microbiota that colonize the above ground tissues of a relevant perennial fruit plant of considerable economic value. The Blueberry microbiome Using targeted metagenomics, we performed a comprehensive comparison of the structure and composition of both bacterial and fungal components of the plant-associated microbiota across above-ground tissues over two consecutive years. Considering the fungal component, we found that the fruit pulp was dominated by the fungal phylum Basidiomycetes, in contrast to what has been observed in other studies on Vaccinium myrtillus L. (Nguyen et al. 2024 ), while leaf and fruit surfaces were dominated by Ascomycetes as was observed for Vitis vinifera and Vaccinium spp. (Behrens and Fischer 2022; M. Li et al. 2022 ). At finer taxonomic level (i.e. genera), Davidiella and Botrytis were found to be shared between all the tissues, as already observed in grapevine (Rincón and Neelam 2021; Sivakumar et al. 2020 ; Bolívar-Anillo, Garrido, and Collado 2020). Although generally considered pathogenic, some taxa from the Davidiella genus (e.g. D. tassiana ) have been isolated from apparently healthy vines (Pancher et al. 2012 ). We also found that Mortierella and Malassezia were consistently present in fruit pulp, while Rhodotorula and Leptosphaerulina were present on fruit and leaf surfaces. Similar results were observed in plants such as grapevine, tomato, apples, strawberries and white clover (H. Zhang et al. 2014 ; Victoria et al. 2020 ). For Bacteria, we found taxa from the phylum Proteobacteria present in all the tissues. This was expected as this phylum is the largest and most diverse lineage within the bacteria domain, which also includes plant pathogens and parasites (Orellana et al. 2022 ). We also found that, in line with another study on V. corymbosum , all tissues were colonized by taxa from the phyla Actinobacteria and Firmicutes , although at lower relative abundance. Differently, we found that taxa from the phylum Acidobacteria were present both in fruit and leaf surface, but not in the pulp, while Fusobacteria were present only in the fruit pulp. At the genus level, Massilia , which is commonly isolated from cultivated plants (Raths, Peta, and Bücking 2020 ), was found across all the three tissues while Hymenobacter, Sphingomonas and Methylobacterium were found only in fruit and leaf surfaces. Moreover, Corynebacterium, Enhydrobacter, Paracoccus and Pseudomonas , that have been found able to promote plant growth through enhanced nutrition, were found only in the fruit pulp in line with other studies (Y. Li, Tao, and Liang 2024; Liu et al. 2024 ; Chakraborty and Ramteke 2023). In terms of microbiota richness and diversity, the fruit pulp hosted a simpler microbiota, including a significantly lower richness compared to the surface of fruit and leaf. This result is consistent with a previous study on blueberries (Szymanski et al. 2023 ; Farwell et al. 2024 ). Smaller, but nevertheless significant differences could be found also in diversity and richness between leaf and fruit surfaces and across different plant cultivars. In particular, in fungi epiphytic communities we found a higher diversity in leaves that might indicate a more favorable habitat able to sustain a more diverse community (Gomes et al. 2018 ). For bacteria communities, only Shannon and Simpson indexes were significantly different between leaf and fruit surface. Within-species plant genetic diversity shapes the microbiome of blueberries Beyond microbiota composition, the importance of host-associated and environmental factors shaping microbial richness and diversity in such habitats are still not fully understood (Hamonts et al. 2018 ; Dastogeer et al. 2020 ; Bashir et al. 2022 ). In some cases, such as different Vitis vinifera cultivars, host genotype is identified as the primary driver (Qian et al. 2020 ; Gong and Xin 2021), while other studies report other drivers such as treatment, growing stage, plant health status or seasonal variation (Szymanski et al. 2023 ; Bao et al. 2020 ; Trivedi et al. 2020 ; M. Li et al. 2022 ; Bashir et al. 2022 ). Due to the complexity of this system, it has been proposed that both host species and environment might be the co-determinants of microbial communities in the phyllosphere (Jian Li et al. 2023 ). In our study, we found that the tissue type is the main determinant for the microbial composition in most cases. The analysis of β-diversity showed that the interior of the fruit was colonized by a microbiome distinct from that of the surface of fruits and leaves, that were much more similar to each other. Focusing only on epiphytic communities, the major factor driving the differences between the structure of the microbial communities was host variety, followed by tissues, while year of sampling was usually the least important factor. To gain a deeper understanding of the heterogeneity within microbial communities in response to different factors, we analyzed in detail the Bray-Curtis dissimilarity matrix as a function of the different factors. This analysis confirmed that fungal and bacterial fruit and leaf surface communities were significantly influenced by host plant cultivar, year, and tissues. While we found a significant effect of plant cultivar and year of sampling on fungal endophytic communities, bacterial endophytes were influenced only by year of sampling. We further identified a core of taxa that are shared across the different plants and tissues. Several studies had demonstrated the presence of a core microbiome in the roots and rhizosphere (Wang et al. 2023 ; Yang et al. 2024 ), but also in the phyllosphere across different species, genotypes, growing stage, season and geographical locations (Almario et al. 2022 ; M. Li et al. 2022 ; Sohrabi et al. 2023 ). We provide the first quantitative evidence of the dominance of the core microbial taxa in the above ground microbiome of a perennial plant showing that, despite the large number of microbial taxa present and the tissue specific features, the largest fraction of the microbiota is composed by taxa that are widely shared across plant cultivars and time. In some cases these taxa are specific to a given tissue, while in others they are shared across tissues. For fungi, we found that Davidiella is present in all tissues; this genus has been reported as a beneficial taxon involved in promoting growth, stress tolerance and pathogens resistance (Garello et al. 2023 ; Kulišová et al. 2021 ; Zhu et al. 2021 ; Xiang et al. 2023 ). Alternaria, Leptosphaerulina and Botrytis , that were found to be part of the core fungal microbiota of all the tissues, are known as fungal pathogens or parasites (Bell et al. 2021; Umagiliyage et al. 2017 ; Jia Li et al. 2024 ; Yuan et al. 2020 ). Aureobasidium , also part of the core microbiota shared across the tissues, has been shown to have antagonistic properties against pathogens (Sanzani et al. 2021 ; Ramudingana et al. 2024). Cryptococcus , present in the core fungal microbiota, is a genus involved in biocontrol for blue mold in apples and postharvest decay (Neto et al. 2021 ; Hashem et al. 2014 ; Hammami et al. 2022 ). Sporobolomyces also is shared across the tissues and is known for its role as a natural antagonist for B.cinerea (Carmichael et al. 2019 ; Ianiri et al. 2017 ). Beyond the genera shared across all tissues, we identified tissue-specific taxa: specifically, Taphrina and Pleospora , known as plant pathogen (Christita et al. 2021 ; Vaghefi et al. 2019 ), were found to be exclusive to the fruit and leaf surface. Dioszegia and Myrothecium , well known as plant pathogens and found on leaves in other studies (Qiao et al. 2024 ; Fujinawa et al. 2020) were present only on leaf surfaces, while Udeniomyces is exclusive in fruit surface. As for fungi, the bacterial core microbiome showed several genera shared across all three tissues. The genera Massilia was shared between all the tissues and was detected also in other plants such as tomatoes and grapevine (Runge et al. 2023; Singh et al. 2019 ). Methylobacterium , also part of the core microbiome, is involved in regulation of phytohormone levels, in growth-promoting processes and exhibits biocontrol activities against pathogens (C. Zhang et al. 2021 ; Md Gulzar and Mazumder 2024 ). Micrococcus , belonging to the core microbiome, was proposed as a biocontrol agent against the post-harvest pathogen such as B. cinerea , P. expansum and A. uvarum (Ranade et al. 2021 ; Perrone and Gallo 2017). The core microbiomes of fruit and leaf surfaces are characterized by Gaiella and Rathayibacter; the latter is a plant pathogen (Stoll et al. 2023 ). Moreover, we identified specific genera present only on the fruit surface such as Exiguobacterium . Some strains of this genus have been proposed as biocontrol agents against Peronophythora (Huang et al. 2024 )While Micromonospora was detected in our study only on leaves, other studies demonstrated that this genus is able to colonize different tissues of plants from the roots to the leaves (Benito et al. 2022 ; Riesco et al. 2022 ). These differences between the core microbiome composition in the different tissues might be explained by the different chemical and physical properties of the tissues. The functions associated with the core microbiome could significantly contribute to health, productivity and resistance to plant pathogens (Yeoh et al. 2017 ; Lemanceau et al. 2017 ; Hamonts et al. 2018 ). Despite the overall healthy condition of the plants analyzed, we observed not only well-known plant pathogens such as Botrytis or Leptosphaerulina but also microorganisms that may enhance plant health by coping with biotic and abiotic stress such as Methylobacterium or Micrococcus (Karwa and Saini 2023; Upadhayay et al. 2023 ). A deeper understanding of the core exhibited by different tissues provides valuable insight into consistently present taxa and transient ones, highlighting potential key players which may be exploited for developing effective approaches to enhance productivity under biotic and abiotic stress conditions (Roman-Reyna et al. 2019 ; Fitzpatrick et al. 2018 ; Trivedi et al. 2020 ). Conclusion This study provides a comprehensive analysis of the microbial communities associated with Vaccinium spp., revealing the complex relationships between plant tissues and their resident microbes. Our results highlight that different plant tissues—leaf surfaces, fruit surfaces, and fruit pulp—support distinct microbial communities, with tissue type proving to be a key factor in determining community composition. The diversity of fungal and bacterial communities is influenced by factors such as plant variety, environmental conditions, and temporal changes, demonstrating the complexity of these communities. The core microbiome identified across tissues points to key genera that play crucial roles in plant health and interactions. Beneficial fungi like Davidiella and Malassezia , and bacteria such as Propionibacterium and Staphylococcus were consistently present, suggesting their importance in promoting plant growth and resilience. Interestingly, several taxa demonstrated tissue specificity: genera such as Taphrina and Pleospora were found exclusively on the surface tissues of fruit and leaf, while Dioszegia and Myrothecium were exclusive to leaf tissues, and Udeniomyces to fruit. Likewise, bacterial genera like Gaiella and Rathayibacter were restricted to surface tissues, while Exiguobacterium and Aridibacter were found only in the fruit, and Micromonospora was unique to leaf tissues. Our two-year data set allowed us to observe that core microbiome composition was stable across years, despite minor shifts in abundance. These results underscore the complex nature of plant-associated microbiomes and the need for further research to explore the functional implications of these microbial interactions and the mechanisms that drive plant microbiome assembly. By understanding the tissue-specific composition patterns, the factor that shapes microbial communities, the core microbiota we can better leverage microbial communities to enhance plant health and productivity, potentially reducing reliance on chemical inputs, fostering more sustainable agricultural practices and address challenges associated with climate change, such as drought, pests, and extreme weather events. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials Metagenomes raw data are available under the ENA accession PRJEB82098 Competing interests The authors declare that they have no competing interests. Funding This paper and related research have been conducted during and with the support of the Italian national inter-university PhD course in Sustainable Development and Climate change (link: http://www.phd-sdc.it). C.C. is supported by the European Commission under the H2020 Marie Skłodowska-Curie Actions Grant Agreement No. 702057 (DRYLIFE). This study was supported by the Agritech National Research Center and received funding from the European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR)—MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4—D.D. 1032 17/06/2022, CN00000022). Authors' contributions C.C, L.G. and C.D. designed the study. M.G., B.F., M.A., M.C. collected samples. M.G., E.S., S.L. and M.P. produced metagenomic sequences. M.G., C.C., M.D-B., C.D. analyzed data. M.G. prepared figures and tables. M.G, L.G, M.D-B., C.C. and C.D. wrote the main manuscript text. All authors reviewed the manuscript. References Albanese, Davide, Paolo Fontana, Carlotta De Filippo, Duccio Cavalieri, and Claudio Donati. 2015. “MICCA: A Complete and Accurate Software for Taxonomic Profiling of Metagenomic Data.” Scientific Reports 5 (May):9743. 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Yuan, Xiao-Long, Min Cao, Guo-Ming Shen, Huai-Bao Zhang, Yong-Mei Du, Zhong-Feng Zhang, Qian Li, et al. 2020. “Characterization of Nuclear and Mitochondrial Genomes of Two Tobacco Endophytic Fungi Leptosphaerulina Chartarum and Curvularia Trifolii and Their Contributions to Phylogenetic Implications in the Pleosporales.” International Journal of Molecular Sciences 21 (7). https://doi.org/10.3390/ijms21072461. Zhang, Cong, Meng-Ying Wang, Naeem Khan, Ling-Ling Tan, and Song Yang. 2021. “Potentials, Utilization, and Bioengineering of Plant Growth-Promoting Methylobacterium for Sustainable Agriculture.” Sustainability: Science Practice and Policy 13 (7): 3941. Zhang, Hongyin, Lingling Ge, Keping Chen, Lina Zhao, and Xiaoyun Zhang. 2014. “Enhanced Biocontrol Activity of Rhodotorula Mucilaginosa Cultured in Media Containing Chitosan against Postharvest Diseases in Strawberries: Possible Mechanisms Underlying the Effect.” Journal of Agricultural and Food Chemistry 62 (18): 4214–24. Zhu, Lihua, Tian Li, Xiaoyu Xu, Xuewei Shi, and Bin Wang. 2021. “Succession of Fungal Communities at Different Developmental Stages of Cabernet Sauvignon Grapes From an Organic Vineyard in Xinjiang.” Frontiers in Microbiology 12 (August):718261. Additional Declarations No competing interests reported. Supplementary Files Supp.Tables.xlsx SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5440144","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":383974590,"identity":"2e3e6dcb-054b-4505-b722-50f337378434","order_by":0,"name":"Matteo Giese","email":"","orcid":"","institution":"University School for Advanced Studies IUSS Pavia","correspondingAuthor":false,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Giese","suffix":""},{"id":383974591,"identity":"6363ba4e-27e8-4e2c-8d3a-1581f363d84e","order_by":1,"name":"Erika Stefani","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Erika","middleName":"","lastName":"Stefani","suffix":""},{"id":383974592,"identity":"4a97ea23-7fa1-495b-8f4a-2edec5297de9","order_by":2,"name":"Simone Larger","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Simone","middleName":"","lastName":"Larger","suffix":""},{"id":383974593,"identity":"fc236470-efa3-4623-896f-e92fdcbdea2b","order_by":3,"name":"Massimo Pindo","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Massimo","middleName":"","lastName":"Pindo","suffix":""},{"id":383974594,"identity":"c5bed353-e6d9-4127-9c58-a11d815eba97","order_by":4,"name":"Brian Farneti","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Brian","middleName":"","lastName":"Farneti","suffix":""},{"id":383974595,"identity":"500c52e1-14a8-42f4-98af-70ae6d4c62c3","order_by":5,"name":"Matteo Ajelli","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Matteo","middleName":"","lastName":"Ajelli","suffix":""},{"id":383974596,"identity":"8678599a-2ad8-4371-a3cb-44eca6f1dfcf","order_by":6,"name":"Monica Cattani","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Monica","middleName":"","lastName":"Cattani","suffix":""},{"id":383974597,"identity":"043e1213-1968-4f76-a0a3-ff2964d3c9e5","order_by":7,"name":"Manuel Delgado-Baquerizo","email":"","orcid":"","institution":"Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS), CSIC","correspondingAuthor":false,"prefix":"","firstName":"Manuel","middleName":"","lastName":"Delgado-Baquerizo","suffix":""},{"id":383974598,"identity":"1da10549-6136-4f15-a37a-2cf2a7b5f5a5","order_by":8,"name":"Lara Giongo","email":"","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":false,"prefix":"","firstName":"Lara","middleName":"","lastName":"Giongo","suffix":""},{"id":383974601,"identity":"f136a85b-3e88-49e7-a4d5-442cb66cf759","order_by":9,"name":"Claudia Coleine","email":"","orcid":"","institution":"University of Tuscia","correspondingAuthor":false,"prefix":"","firstName":"Claudia","middleName":"","lastName":"Coleine","suffix":""},{"id":383974603,"identity":"67716f1e-bd91-48db-af00-3a7ce7ad5199","order_by":10,"name":"Claudio Donati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYDACdiDmMQASB0A8Axt+IMkGZODRwoys5YBBmmQDRAsePWAtDDAtDIehWvBYw9/M/EziTYENA9/x3oOfPxScl9BtP/zsAUPBH5xaJA6zmUnOMUhjkDxzLlnigMFtCbMzaeYG+BxmwMxgJs1jcJjB4EaOAUhLndmBHDYJ/FrYv8G0GP84YHBOwuz8G0JaeOC2mAFtOSBhdoOALRKHeYotgX7hkTxzxszijEEyUMszM4kEA2OcWvjb2zfeePPHRo7veI/xjYo/dkCHJT+T+PBHDqcWGOBB5SYQ1DAKRsEoGAWjAB8AAGKaTQrsILlNAAAAAElFTkSuQmCC","orcid":"","institution":"Fondazione Edmund Mach","correspondingAuthor":true,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Donati","suffix":""}],"badges":[],"createdAt":"2024-11-12 13:53:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5440144/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5440144/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70329942,"identity":"829a5afc-472c-4254-943d-fab558376229","added_by":"auto","created_at":"2024-12-02 08:18:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":229436,"visible":true,"origin":"","legend":"\u003cp\u003ePhylum-Level Composition of Fungal and Bacterial Communities in Blueberry Cultivars, showing only the ten most abundant taxa for each part of cultivars, a total of 4 different taxa for fungi and 11 different taxa for bacteria. \u003cstrong\u003eA)\u003c/strong\u003eStacked bar charts showing the relative abundance of fungal phyla across different blueberry cultivars and tissue types for 2022 and 2023; \u003cstrong\u003eB)\u003c/strong\u003e Box plots illustrating the relative abundance of fungal phyla in different tissues for the same years, highlighting variability between tissue types and years; \u003cstrong\u003eC)\u003c/strong\u003e Stacked bar charts depicting the relative abundance of bacterial phyla across blueberry cultivars and tissue types for 2022 and 2023; \u003cstrong\u003eD)\u003c/strong\u003e Box plots showing the relative abundance of bacterial phyla in different tissues for the same years, indicating differences in bacterial community composition between tissue types and years.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5440144/v1/b29ba32c2844a939108496c7.png"},{"id":70329425,"identity":"e6d07a46-97d3-4025-9755-fcb703307f67","added_by":"auto","created_at":"2024-12-02 08:10:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123177,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003eAlpha Diversity Indices of Fungi communities in Fruit Epiphyte (Blue) and Leaf Epiphyte Tissues (Green); \u003cstrong\u003eB) \u003c/strong\u003eAlpha Diversity Indices of Bacteria communities in Fruit Epiphyte (Blue) and Leaf Epiphyte Tissues (Green); \u003cstrong\u003eC)\u003c/strong\u003e PCoA plot showing fungal communities, with distinct clustering of samples from fruit endophytes, fruit epiphytes, and leaf epiphytes; \u003cstrong\u003eD)\u003c/strong\u003e PCoA plot depicting bacterial communities, with clear separation between fruit endophytes and the epiphytic tissues; \u003cstrong\u003eE)\u003c/strong\u003e PCoA plot focusing on fungal communities in epiphytic tissues, indicating some overlap but distinct clustering between fruit epiphytes and leaf epiphytes; \u003cstrong\u003eF)\u003c/strong\u003e PCoA plot of bacterial communities in epiphytic tissues, showing more pronounced overlap between fruit epiphytes and leaf epiphytes.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5440144/v1/9710f040d27ec780fb7aba06.png"},{"id":70329431,"identity":"ff4ab2e5-3e79-4db4-8ed5-11186d3bbc69","added_by":"auto","created_at":"2024-12-02 08:10:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40475,"visible":true,"origin":"","legend":"\u003cp\u003eBray-Curtis Dissimilarity Analysis for fungi and bacteria. The figure presents the variation in dissimilarity values across different parameters: different variety, different tissues, different year, and no differences. \u003cstrong\u003eA)\u003c/strong\u003eThe dissimilarity for epiphytic communities of fungi; \u003cstrong\u003eB)\u003c/strong\u003e The dissimilarity for epiphytic communities of bacteria; \u003cstrong\u003eC)\u003c/strong\u003e The dissimilarity for endophytic communities of fungi; \u003cstrong\u003eD)\u003c/strong\u003e The dissimilarity for endophytic communities of bacteria.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5440144/v1/e9fd5b3936e02b01c637a822.png"},{"id":70329941,"identity":"2ec5fb2e-790c-4fbc-bb81-b31444455575","added_by":"auto","created_at":"2024-12-02 08:18:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":134256,"visible":true,"origin":"","legend":"\u003cp\u003eBox plots of the relative abundance of Genus in blueberry endophytic and epiphytic niches, summarized by their frequency of occurrence across samples in order to analyze the prevalent taxa; each panel represents a specific tissue: A) Endophytic fungi communities in fruits B) Epiphytic fungi communities on leaves C) Epiphytic fungi communities on fruits D) Endophytic bacteria communities in fruits E) Epiphytic bacteria communities on leaves F) Epiphytic bacteria communities on fruits.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5440144/v1/8463136e89006ef4b8acf996.png"},{"id":70329427,"identity":"78259b50-f15f-4242-8bfc-60598a317789","added_by":"auto","created_at":"2024-12-02 08:10:18","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":528187,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eTree of fungal microbial core at the genus level in witch are considered only the genera that appear in 9 out of 10 plants; the labels are at the Phylum level, surrounded by six matrices: E1 first year fruit endophytes, E2 second year fruit endophytes, F1 first year fruit epiphytes, F2 second year fruit epiphytes, L1 first year leaf epiphytes, L2 second year leaf epiphytes that indicate the presence/absence of the genus in cultivars analyzed; \u003cstrong\u003eB) \u003c/strong\u003eTree of bacteria microbial core at the genus level in witch are considered only the genera that appear in 9 out of 10 plants; the labels are at the Phylum level, surrounded by six matrices: E1 first year fruit endophytes, E2 second year fruit endophytes, F1 first year fruit epiphytes, F2 second year fruit epiphytes, L1 first year leaf epiphytes, L2 second year leaf epiphytes, that indicate the presence/absence of the genus in each one plant in cultivars variety analyzed\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5440144/v1/7f56085b3dd81d628be34e55.png"},{"id":75832335,"identity":"1537231e-2256-459c-b314-c7ead5016281","added_by":"auto","created_at":"2025-02-09 13:01:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1879378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5440144/v1/369d66e1-9162-4f1b-90b4-45d73a4553ac.pdf"},{"id":70329428,"identity":"9de6a6ca-6db4-4009-a823-5340dcfb3295","added_by":"auto","created_at":"2024-12-02 08:10:18","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1180500,"visible":true,"origin":"","legend":"","description":"","filename":"Supp.Tables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5440144/v1/7c4bc6a7de74ae1834a5ab26.xlsx"},{"id":70329432,"identity":"dc9c4f2b-4cf6-43af-a42e-cad59cba939e","added_by":"auto","created_at":"2024-12-02 08:10:18","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4621468,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5440144/v1/5ec8beba9dde66049bf51e32.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Blueberry plants support a distinctive microbiome as a function of plant genetics and tissue","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlants are colonized by complex microbial communities that play an essential role in various aspects of their physiology and development through processes such as pathogenesis and nutrient cycling, ultimately contributing to plant health (Delgado-Baquerizo et al. 2018; Maestre et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Trivedi et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Msimbira and Smith \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Each plant tissue has different morphological and physiological characteristics that create a set of diverse micro-habitats (Zhen Wang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), offering a multitude of distinct ecological niches (Chialva, Lanfranco, and Bonfante 2022; Leveau \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The structure and composition of plant microbiota depend on host-associated and environmental variables (Jian Li et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These microbiomes are especially important when associated with food (e.g., fruits) consumed by humans and animals. Plant microbiomes are known to directly contribute to the formation and maintenance of human and animal microbiomes, with a larger effect at young age (Carlino et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This is the case, for example, of blueberries, produced by plants belonging to the genus \u003cem\u003eVaccinium\u003c/em\u003e, which are today among the most consumed fruits worldwide.\u003c/p\u003e \u003cp\u003eAmong land plants, \u003cem\u003eVaccinium spp.\u003c/em\u003e are long-lived woody perennial members of the Ericaceae family (Jiangang Li et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Despite their significant commercial importance (Chac\u0026oacute;n et al. 2022), little is known about the composition and functions of the microbiome of blueberry plants. The majority of existing studies have focused on rhizosphere and endosphere (Jiangang Li et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kawash et al. 2023; Perotto et al. 2022) and on their influence on plant adaptation against abiotic stress. Researchers have shown that blueberry plants can form ericoid mycorrhizal symbioses, enabling them to thrive in challenging environments (Ważny et al. 2022; Morvan et al. 2023). The factors that contribute to the formation of the microbial communities that colonize fruit and leaves of blueberry plants and how these two tissues differ and influence each other in terms of hosted microbial species have still not been fully understood. Blueberry fruit is exposed to several pathogens in its life cycle, such as mummy berry (\u003cem\u003eMonilinia vaccinii-corymbosi\u003c/em\u003e) (Ashley and Annis 2024), \u003cem\u003ePhytophthora\u003c/em\u003e (Pscheidt and Weiland 2015), \u003cem\u003eColletotrichum\u003c/em\u003e spp. and \u003cem\u003eBotrytis cinerea\u003c/em\u003e, which are responsible for several diseases (Szymanski et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Despite the potential for biocontrol, there is still a lack of studies that investigate the relationship between the microbial communities of different aboveground parts of blueberry plants, the impact of environmental factors, and the genetics of the plant. Moreover, the consistency of microbial communities of these perennial plants over different years has not been characterized. Finally, we lack a clear understanding of the relative importance of the core microbial species that are shared across plant cultivars and tissues and of the taxa that are specific to certain tissues, and of individual plants.\u003c/p\u003e \u003cp\u003eTo address these knowledge gaps, we explored the above-ground microbiome (fungi and bacteria) of cultivated \u003cem\u003eVaccinium\u003c/em\u003e s\u003cem\u003epp.\u003c/em\u003e using the metabarcoding technique in order to: \u003cem\u003ei\u003c/em\u003e) investigate the presence of specific compositional patterns across different tissues; \u003cem\u003eii\u003c/em\u003e) determine if different tissues share the same set of species or differ due to their physical properties and identify organisms that are specific of each micro-niche; \u003cem\u003eiii\u003c/em\u003e) asses the major driving factors that influence the community composition, including host genetics; \u003cem\u003eiv\u003c/em\u003e) define the core microbiome specific for each micro-niche.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003ePlant materials and sampling\u003c/p\u003e \u003cp\u003eTen blueberry cultivars were chosen in the FEM blueberry germplasm collection, located in Trentino Alto Adige, Italy (46.0744\u0026deg; N, 11.2334\u0026deg; E), harvested in 2022 and 2023 and sampled in triplicate. For each cultivar, leaves and fruits were collected for a total of 30 samples for fruits and leaves, respectively. For each replicate, 25 fruits and 30 leaves were placed in separate sterile bags. Berries were harvested at stage 7 of the BBCH scale, visually homogeneous by size and showing a complete blue surface color; the white or red rings around the pedicel scar were considered to be genotype-specific. Fruits and leaves were collected using sterile tweezers and shears, placed in sterile plastic bags, then stored on ice and later stored at -20\u0026deg;C, until downstream analysis.\u003c/p\u003e \u003cp\u003eDNA Extraction and Sequencing\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSAMPLE PREPARATION\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eEpiphytic (Fruit and leaf Surface) Microbiome\u003c/h2\u003e \u003cp\u003eThe epiphytic extraction was made using 150 ml of sterile washing solution (0.9% NaCl, 0.01% Tween 80) was added, and then incubated on an orbital shaker at 400 rpm for 1 hour (Behrens and Fischer 2022). At the end of the incubation, the water was filtered using sterile filtration apparatus (2GPU02RE Stericup Quick Release Millipore Express PLUS 0.22\u0026micro;m PES, 250 ml Merck Millipore) to recover as much biological material as possible. The filters were used for DNA extraction using DNeasy PowerWater kit QIAGEN according to the manufacturer\u0026rsquo;s protocol.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eEndophytic (Pulp) Microbiome\u003c/h3\u003e\n\u003cp\u003eThe endophytic extraction required a different procedure (Bill et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fifteen fruits were randomly selected for each replicate and washed in 90% ethanol once and then rinsed three times in sterile Milli-Q water to minimize contamination from the surface of the fruit. The disinfected surface of fruit was aseptically peeled with a disinfected scalpel. The pulp tissue of the fruit portion was aseptically removed and cut into smaller pieces using a sterile scalpel blade, and used for DNA extraction using the DNeasy PowerSoil Pro Kit by QIAGEN, according to the manufacturer\u0026rsquo;s protocol. DNA was quantified using the NanoDrop\u0026trade; 8000 Spectrophotometer (Thermo Fisher Scientific Inc. Waltham, Massachusetts, United States) according to the protocol provided by the manufacturer. The V3-V4 variable region of the 16S rDNA and Internal Transcribed Spacer (ITS) rDNA was used to analyze bacterial and fungal diversity, respectively.\u003c/p\u003e\n\u003ch3\u003eAMPLICON SEQUENCING\u003c/h3\u003e\n\u003cp\u003eFor fungi, the ITS1 region was amplified using ITS1F (5\u0026rsquo;-CTT GGT CAT TTA GAG GAA GTAA-3\u0026rsquo;) and ITS2 (5\u0026rsquo;-GCT GCG TTC TTC ATC GAT GC-3\u0026rsquo;) primers (White et al., 1990; Gardes and Bruns 1993) with overhang Illumina adapters. The PCR reactions were carried out with a total volume of 25 \u0026micro;L, containing 1 \u0026micro;L of each primers (10 \u0026micro;M of each one), 0.25 \u0026micro;L of FastStart High Fidelity PCR System (Roche), 18,75 \u0026micro;L of nuclease-free water (Sigma\u0026ndash;Aldrich), 2.5 [10X] Buffer, 0,5 \u0026micro;L dNTP and 1 \u0026micro;L of DNA (5\u0026ndash;10 ng/ \u0026micro;L). PCR reactions were performed by using the GeneAmp PCR System 9700 (Thermo Fisher Scientific); the conditions were: initial denaturation at 95\u0026deg;C for 3 min, 35 cycles of denaturation at 95\u0026deg;C for 20 s, annealing at 50\u0026deg;C for 45 sec, extension at 72\u0026deg;C for 90 s, followed by a final extension at 72\u0026deg;C for 10 min.\u003c/p\u003e \u003cp\u003eFor bacteria, we amplified the sub-region V3-V4 of the 16S gene using the 341F (5\u0026rsquo;- CCT ACG GGN GGC WGC AG -3\u0026rsquo;) and 805R (5\u0026rsquo;- GAC TAC NVG GGT WTC TAA TCC \u0026minus;\u0026thinsp;3\u0026rsquo;) primers (Klindworth et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) with overhang Illumina adapters. The PCR reactions were carried out with a total volume of 25 \u0026micro;L, containing 5 \u0026micro;L of each primer (1\u0026micro;M), 12,5 \u0026micro;l MIX KAPA HiFi HotStart ReadyMix (Roche) and 2,5 \u0026micro;L of DNA (5\u0026ndash;10 ng/\u0026micro;L).\u003c/p\u003e \u003cp\u003ePCR reactions were performed by using the GeneAmp PCR System 9700 (Thermo Fisher Scientific); the conditions were: initial denaturation at 95\u0026deg;C for 5 min, 35 cycles of denaturation at 95\u0026deg;C for 30 s, annealing at 55\u0026deg;C for 30 s, extension at 72\u0026deg;C for 30 s, followed by a final extension at 72\u0026deg;C for 5 min.\u003c/p\u003e \u003cp\u003eThe obtained amplicons were purified using the CleanNGS (CleanNA), following the manufacturer\u0026rsquo;s instructions. Afterward, a second PCR was used to apply dual indices and Illumina sequencing adapters Nextera XT Index Primer (Illumina), by 7 cycles PCR (16S Metagenomic Sequencing Library Preparation, Illumina). The amplicon libraries were purified using CleanNGS (CleanNA), and the quality control was performed on a Tapestation 4150 platform (Agilent Technologies, Santa Clara, CA, USA). Finally, all barcoded libraries were pooled in an equimolar way and sequenced on an Illumina\u0026reg; MiSeq (PE300) platform in pair ends (2 \u0026times; 300 bp) (MiSeq Control Software 2.5.0.5 and Real-Time Analysis software 1.18.54.0). Library construction and NGS were performed at the sequencing platform of the Edmund Mach Foundation (San Michele all\u0026rsquo;Adige, Italy).\u003c/p\u003e\n\u003ch3\u003eBIOINFORMATIC ANALYSIS\u003c/h3\u003e\n\u003cp\u003eThe raw reads, already demultiplexed, were analyzed using the MICCA (MICrobial Community Analysis) v1.7.2 bioinformatics pipeline (Albanese et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Sequence pairs were merged to obtain consensus sequences. Primers were trimmed and sequences that did not contain these primers were discarded. Quality filtering was performed using an Expected Error Rate of 0.75 for ITS and 16S, with the minimum read length set to 200 for ITS and 400 for 16S. Reads were clustered into Amplicon Sequence Variants (ASVs) using the UNOISE protocol (Edgar \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Finally, taxonomy was assigned using the \"UNITE 9.0\" for the fungal component and \"RDP classify 2.14\" for the bacterial component.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSTATISTICAL ANALYSIS\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eAlpha diversity and Beta diversity\u003c/h2\u003e \u003cp\u003eDue to the lower number of reads for endophytic communities, Alpha and Beta diversity analysis were carried out using different rarefactions depending on the analyzed tissues. Samples were rarefied to 3,000 reads for bacteria and 10,000 for fungi for the whole tissues (pulp, leaf and fruit surfaces) comparisons, while in comparisons including only epiphytic communities (leaf and fruit surfaces) the rarefaction was 10,000 reads for bacteria and 30,000 for fungi. Three alpha diversity indices were computed, namely Chao1, Shannon Diversity, and Simpson Dominance (Willis \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Finn \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kim et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) using the \"estimate_richness\" function from the R package \"Phyloseq version 1.46.0\" (McMurdie and Holmes 2013); Chao1 indices are used to estimate richness; measurement of ASVs expected in samples given all the microbial species that were identified in the samples. The Shannon diversity index is a measure of species diversity in a community and taking into account the number of species present and their relative abundance, high values of this index indicate a higher biodiversity. The Dominance-Simpson index is another widely used diversity index in ecology; this ranges from 0 to 1, values close to zero indicate that all species are equally present, while a value close to 1 indicates that few species dominate the sample. The Wilcoxon-Mann-Whitney test is employed for the statistical analysis of alpha diversity in R Studio (Ripley \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e); the adjustment method used for P-value was Holm-Bonferroni. For beta diversity analysis, we use Bray-Curtis dissimilarity index to assess the difference between the plant tissues visualized with an PCoA. Statistical significance was assessed using PERMANOVA/Adonis with pairwiseAdonis v0.4 R package (Martinez-Villegas et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The parameters considered in this analysis are difference in year, difference in tissues (for Epiphytes), and difference in plant cultivar compared to the reference set of pairwise distances between samples from the same year, plant cultivar and tissues. Enrichment of genera in the different tissues was assessed using the negative binomial distribution analysis implemented in R package DESeq2 (Love, Huber, and Anders 2014).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRAW DATA ANALYSIS\u003c/h2\u003e \u003cp\u003eIn total, after filtering for quality and discarding ribosomal and chloroplast reads, we generated 6,446,364 16S and 11,023,303 ITS quality filtered sequencing reads. Of these, 3,520,056 and 4,056,471 were from leaf bacterial and fungal epiphytes, respectively, and 2,559,002 and 4,402,685 from fruit bacterial and fungal epiphytes. Finally 367,306 and 2,564,147 from bacterial and fungal endophytes. It is noteworthy the lower number of reads that were obtained for endophytic communities, in particular for bacteria, due to the higher proportion of reads that were discarded at the filtering stage. The filtered reads were classified into 11,919 ASVs for 16S and 3,008 ASVs for ITS.\u003c/p\u003e \u003cp\u003eTaxonomic profiles reveal compositional differences between Epiphytic and Endophytic Niches\u003c/p\u003e \u003cp\u003eIn all samples, the most prevalent fungal phyla were Ascomycota and Basidiomycota (Fig.\u0026nbsp;\u0026lt;link rid=\"fig1\"\u0026gt;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u0026lt;/link\u0026gt;\u003c/span\u003e-A \u0026amp; \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-B and Supp. Table\u0026nbsp;1, Supp. Table\u0026nbsp;2, Supp. Table\u0026nbsp;3) while Chytridiomycota and Zygomycota were found only in a few samples and at low relative abundance. At the genus level (Supp_Fig_1A\u0026amp;B and Supp. Table\u0026nbsp;4, Supp. Table\u0026nbsp;5, Supp. Table\u0026nbsp;6), \u003cem\u003eDavidiella\u003c/em\u003e was the most abundant, followed by \u003cem\u003ePhoma\u003c/em\u003e, \u003cem\u003eAlternaria\u003c/em\u003e, \u003cem\u003eMalassezia\u003c/em\u003e, \u003cem\u003eAureobasidium\u003c/em\u003e, \u003cem\u003eBotrytis\u003c/em\u003e, \u003cem\u003eCryptococcus\u003c/em\u003e, \u003cem\u003eLeptosphaerulina\u003c/em\u003e, \u003cem\u003eRhodotorula\u003c/em\u003e, \u003cem\u003eTaphrina\u003c/em\u003e, and \u003cem\u003eMortierella\u003c/em\u003e. The compositional analysis of bacteria at the phylum level (Fig.\u0026nbsp;\u0026lt;link rid=\"fig1\"\u0026gt;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u0026lt;/link\u0026gt;\u003c/span\u003e-C \u0026amp; \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-D and Supp. Table\u0026nbsp;7, Supp. Table\u0026nbsp;8, Supp. Table\u0026nbsp;9) shows a wide variety of taxa present. The phylum Proteobacteria was dominant across all samples, followed by Actinobacteria, Bacteroidetes, and Firmicutes. We could identify compositional patterns that distinguish the endophytic and epiphytic communities, with certain taxa exclusively present in specific tissues. For instance, Acidobacteria were found on the leaf and fruit surfaces tissues but not in the pulp fruit, while Fusobacteria, which were present in small amounts in the pulp tissues, were absent in the leaf and fruit surface. At the genus level (SUPP_FIG 1-A \u0026amp; 1-B and Supp. Table\u0026nbsp;10, Supp. Table\u0026nbsp;11, Supp. Table\u0026nbsp;12), we found that the most abundant taxa were \u003cem\u003eMassilia\u003c/em\u003e, \u003cem\u003eHymenobacter\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e for the fruit and leaf surface while \u003cem\u003eMicrococcus\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eEnhydrobacter and Corynebacterium\u003c/em\u003e was the most abundant in fruit pulp.\u003c/p\u003e \u003cp\u003eTo corroborate the observed qualitative differences in the taxonomic profiles of the different tissues, we analyzed the abundance data for both bacteria and fungi (Supp_fig_2 A-B) with DESeq2 (Debeljak and Baltar 2023). At phylum level we found a significant difference in fungal communities of fruit pulp and both fruit and leaf surface ( Supp. Table\u0026nbsp;13, Supp. Table\u0026nbsp;14, Supp. Table\u0026nbsp;15), while no statistical differences were found when comparing fruit and leaf surfaces. At genus level ( Supp. Table\u0026nbsp;16, Supp. Table\u0026nbsp;17, Supp. Table\u0026nbsp;18) we observed a significant difference in the comparison of fruit pulp with surface of leaf and fruit that involves several taxa such as: \u003cem\u003eLeptosphaerulina\u003c/em\u003e and \u003cem\u003eRhodotorula\u003c/em\u003e presents only on the surface of fruit and leaves or \u003cem\u003eMortierella\u003c/em\u003e and \u003cem\u003eMalassezia\u003c/em\u003e that are present only in the fruit pulp. Moreover we observed a significant difference also between fruit and leaf surface. Amongst the taxa that showed a significant difference, \u003cem\u003eDavidiella\u003c/em\u003e and \u003cem\u003eBotrytis\u003c/em\u003e had higher abundance on the fruit surface and \u003cem\u003eLeptosphaerulina\u003c/em\u003e was more abundant on the leaf surface.\u003c/p\u003e \u003cp\u003eFor the bacterial communities, at phylum level (Supp. Table\u0026nbsp;19, Supp. Table\u0026nbsp;20,Supp. Table\u0026nbsp;21) we found that Acidobacteria, Fusobacteria and Actinobacteria were significantly less abundant in fruit pulp than on fruit and leaf surfaces, while the comparison between the surface of fruit and leaf showed a significant difference only for Deinococcus-Thermus and Bacteroidetes. At the genus level (Supp. Table\u0026nbsp;22, Supp. Table\u0026nbsp;23, Supp. Table\u0026nbsp;24) we found several genera with significant differences between fruit pulp and both surface tissues. Statistically significant differences were found in \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eDeinococcus\u003c/em\u003e, \u003cem\u003eHymenobacter\u003c/em\u003e, \u003cem\u003eMethylobacterium\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e that were present only on fruit and leaf surface while taxa present only in the fruit pulp were \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eParacoccus\u003c/em\u003e, \u003cem\u003eEnhydrobacter\u003c/em\u003e, \u003cem\u003ePropionibacterium\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e. The comparison of fruit and leaf surface showed fewer significant differences, some examples are: \u003cem\u003eMethylobacterium\u003c/em\u003e, present mostly on leaf surface or \u003cem\u003eExiguobacterium\u003c/em\u003e predominant on the fruit surface.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAlpha diversity reveals contrasting patterns for bacteria and fungi across plant niches\u003c/p\u003e \u003cp\u003eTo characterize the richness and diversity of the microbial communities in the different tissues, after rarefaction we computed the Chao1 estimator of species richness, the Shannon entropy index of diversity and the Simpson index of dominance. Fruit pulp had a significant lower richness than fruit and leaf surfaces both for bacteria and fungi (Supp. Table\u0026nbsp;25). Comparing fruit and leaf surfaces (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A), we found for fungi significantly higher values for leaves than for fruits (Chao1, p-value\u0026thinsp;=\u0026thinsp;0.008; Shannon, p-value\u0026thinsp;=\u0026thinsp;0.008; Simpson, p-value\u0026thinsp;=\u0026thinsp;0.005). Analyzing alpha diversity in relation to the year of sampling we found that Shannon and Simpson had significantly higher values in the first year of sampling in leaf communities, while for fruit epiphytes we observed a significant difference only in the Shannon index (supp_fig 3). For bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B), we found no significant difference between the species richness measured by the Chao1 index (p-value\u0026thinsp;=\u0026thinsp;0.54) between leaf and fruit epiphytic communities, while both the Shannon entropy and the Simpson dominance index were significantly higher in fruits than in leaves (Shannon, p-value\u0026thinsp;=\u0026thinsp;0.046; Simpson, p-value\u0026thinsp;=\u0026thinsp;0.0064). The analysis of alpha diversity comparing the different years of sampling showed an effect on richness of leaf epiphytes (supp fig_4). Indeed, while the Shannon and Simpson indexes did not vary significantly, we found that the Chao1 index for leaf epiphytes was significantly higher in the second year of sampling than in the first year. No significant differences were found for fruit epiphytes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBeta diversity shows that plant tissues and genotype have a significant impact on microbial communities.\u003c/p\u003e \u003cp\u003eFor Fungi (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-C), we found significant differences in the overall beta-diversity analysis (PERMANOVA/Adonis, p-Value\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.21027). Similarly, a Pairwise-PERMANOVA (Supp. Table\u0026nbsp;26) highlighted that the greatest differences occurred between endophytic and epiphytic communities, but we detected a difference also among epiphytic communities due to plant variety and year of sampling (Year: p-Value\u0026thinsp;=\u0026thinsp;0.005, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0171; Plant Variety: p-Value\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.13045).\u003c/p\u003e \u003cp\u003eFocusing the analysis on epiphytes, we found statistically significant differences based on niche, year and plant variety (p-Value\u0026thinsp;=\u0026thinsp;0.001). Taken together these variables account for 40.68% of the variability. In particular, we found that the most important factor was plant variety (p-Value\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.26739), followed by niche (p-Value\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0962) and year (p-Value\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0437). For endophytic communities, we found significant differences considering year (p-Value\u0026thinsp;=\u0026thinsp;0.015, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0518) and plant variety (p-Value\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.311).\u003c/p\u003e \u003cp\u003eFor Bacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-D) we observed a division among the three niches, which was particularly evident when comparing fruit endophytic communities with epiphytic fruits and leaf communities. A PERMANOVA/Adonis statistical test on niches, year and variety of plant showed that these factors had a statistically significant effect (Niches: p-Value 0.001, R\u0026sup2;=0.10414; Year: p-Value 0.001, R\u0026sup2;=0.02001; Plant variety: p-Value 0.001, R\u0026sup2;=0.10549). Most significant differences were observed between endophytic and epiphytic communities; however, there were also significant differences between the epiphytic communities of the fruit and leaf surface (Fig.\u0026nbsp;\u0026lt;link rid=\"fig2\"\u0026gt;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u0026lt;/link\u0026gt;\u003c/span\u003e-E \u0026amp; \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-F). Focussing on epiphytic communities, we found that plant variety, niche and year accounted for 21.7% of the variability (p-value\u0026thinsp;=\u0026thinsp;0.001). Similarly to fungi, plant variety was the most relevant factor (p-Value\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.151), followed by niche (p-Value\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.03881) and year of sampling ( p-Value\u0026thinsp;=\u0026thinsp;0.001, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0264). Taken together, these data show that, although with some correction due to plant tissues and year, the plant variety was the dominating factor to determine the variability of the plant epiphytic bacterial communities.\u003c/p\u003e \u003cp\u003eTo further characterize the relative importance of the different factors (tissues, plant cultivar and year) that impact the structure of the microbial communities, we compared the distribution of the pairwise Bray-Curtis dissimilarities between samples in which only one factor varies. The boxplot (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) represents the dissimilarity between microbial communities in relation to factors change. The analysis of fungal epiphytes shows significant dissimilarity when comparing the reference set that shares the same plant variety, year, and tissues. For the bacterial epiphytes, communities appear to be heterogeneous with values close to 1. Also here we found that plant variety, year and tissues have a significant influence on the heterogeneity of communities. For the fungal endophytic communities we found that plant variety and year show more heterogeneous communities while for bacterial endophytic communities we found that only year has an influence on the heterogeneity of the communities. Additionally, when comparing the communities, the fungal communities were found to be less heterogeneous than the bacterial ones, as confirmed by the Wilcoxon-Mann-Whitney test (Supp. Table\u0026nbsp;26), which revealed significant differences (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.005).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePrevalent taxa accounts for a large fraction of the microbiome reads in all tissues\u003c/p\u003e \u003cp\u003eTo correlate the relative abundance of different genera with their prevalence and quantify the relative importance of the sample-specific and shared taxa, for each analyzed tissue we calculated the fraction of the total number of reads belonging to genera with a specific prevalence (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results showed that in all tissues, both for bacteria and fungi, the highest fraction of the communities is attributable to genera present in over 90% of the samples demonstrating that, despite differences in taxonomic composition amongst samples, the dominating part of the microbiome is composed by widely distributed taxa. This phenomenon is particularly noteworthy in the case of fungi on fruit and leaf surfaces, as the most prevalent genera constitute nearly 100% of the microbiota, while for bacteria the corresponding fraction was lower. Compared to the epiphytic communities, the endophytic microbiome was less homogeneous among the various samples, in particular for fungi.\u003c/p\u003e \u003cp\u003eInteresting differences between bacteria and fungi appeared when we repeated the analysis at the ASV level (supp_fig_5). While for fungi the fraction of each sample attributable to prevalent ASVs was still well above 75% for both leaf and fruit surfaces, this fraction decreased drastically for bacteria. This finding was correlated to the greater genetic variability of bacterial genera compared to fungal genera. Indeed, we found a generally lower level of genetic complexity within fungal genera (supp_fig_6) than within bacterial genera (supp_fig_7). The most extreme example was the bacterial genus \u003cem\u003eHymenobacter\u003c/em\u003e that included more than 700 distinct ASV, while the most diverse fungal genus \u003cem\u003eMortierella\u003c/em\u003e included 20 distinct ASVs. Moreover, the within-genus variability was correlated with prevalence. Counting the number of samples in which a given genus was present and correlating this number to the number of distinct ASV that were classified in the same genus, we found that genera present in the largest number of samples also included the highest number of different ASVs (supp_fig_8, supp_fig_9), showing that while at higher taxonomic level there is a high degree of homogeneity across the different plants and tissues, this is accompanied by a large genomic variability at finer levels of taxonomic classification, with a large number of plant and tissues-specific strains for most prevalent taxa. The level of genetic diversity was particularly high for the most prevalent bacterial genera. Additionally, it is interesting to note the marked increase of bacterial taxa specific to a small number of samples when the analysis is conducted at the ASV level. Taken together, these results suggest that dispersal limitations have an impact on the colonization of plants by specific strains, while, at higher taxonomic level, each environment nonetheless selects a limited number of taxa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMicrobial communities share a core of common microbial taxa across plant cultivars and tissues\u003c/p\u003e \u003cp\u003eGiven the dominant role of the most prevalent taxa in the microbiome of each tissues and the importance of plant variety in determining the structure of the microbiome, we explored if it was possible to define a tissues-specific core microbiome that included the taxa that were present in the specific tissues all plant genotypes. Moreover, leveraging on the availability of data over two consecutive years on the same plants, we asked if this core microbiome was conserved over time and, finally, if, given their close proximity, the endophytes and epiphyte tissues of fruit and leaf share the same set of core taxa, or if the different physical properties were sufficient to trigger a different composition. We thus constructed a presence/absence matrix at the genus level, where a genus was considered present if its abundance was greater than 0, using unrarefied data to increase the sensibility of the analysis. Using these data, we defined a taxa as \u0026ldquo;core\u0026rdquo; if it was present in a given year and tissues in at least 9/10 plant genotypes in one of the triplicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As expected given the lower number of sequenced reads, we found both for bacteria and fungi a smaller core for the endophytic tissues (11 and 12 taxa for the first and second year for fungi, 19 and 14 genera taxa for the first and second year for bacteria, respectively) that for the fruit and leaf surface (56, 60 for fruit and 77, 85 for leaf core genera in the first and second year for fungi and 104, 104 for fruits and 89, 127 for leaf core genera for the first and second year for bacteria, respectively). Comparing the tissues-specific core microbiome across the two years, we found that each tissue exhibited a core similarity greater than 50% between the two years (Supp. Table\u0026nbsp;27). Moreover, comparing the set of core taxa across tissues, we found that for both fungi (supp_fig_10) and bacteria (supp_fig_11) most of the core taxa were shared across the epiphytic tissues, while the core taxa of the endophytic fruit tissues were a subset of the taxa present on the surface of fruits and leaves.\u003c/p\u003e \u003cp\u003eIn the core analysis (Supp. Table\u0026nbsp;28, Supp. Table\u0026nbsp;29), we detected the presence of ubiquitous genera across different tissues, while others were tissue-specific. Fungal genera such as \u003cem\u003eDavidiella\u003c/em\u003e, \u003cem\u003eLeptosphaerulina\u003c/em\u003e, and \u003cem\u003eBotrytis\u003c/em\u003e were present in all tissues, though with varying abundance percentages. Some genera, such as \u003cem\u003eTaphrina\u003c/em\u003e and \u003cem\u003ePleospora\u003c/em\u003e, were found exclusively in the surface tissues of fruit and leaf, while \u003cem\u003eDioszegia\u003c/em\u003e and \u003cem\u003eMyrothecium\u003c/em\u003e were specific to the leaf tissue, and \u003cem\u003eUdeniomyces\u003c/em\u003e to the fruit. We also identified a core microbiome for bacteria, highlighting widely distributed genera in all the tissues such as \u003cem\u003eMethylobacterium, Micrococcus\u003c/em\u003e and \u003cem\u003eMassilia\u003c/em\u003e, with notable variations in abundance, similar to what was observed for fungal genera. Other bacterial genera showed tissue specificity: \u003cem\u003eGaiella\u003c/em\u003e and \u003cem\u003eRathayibacter\u003c/em\u003e were found only in surface tissues, \u003cem\u003eExiguobacterium\u003c/em\u003e and \u003cem\u003eAridibacter\u003c/em\u003e were exclusive to the fruit, and \u003cem\u003eMicromonospora\u003c/em\u003e was exclusive to the leaf.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe conducted one of the first field experiments over consecutive years to investigate the microbiome of blueberry fruits and leaves and we found that blueberries have a distinctive microbiome, consistent across years of study, and mostly influenced by plant genetics and tissues. This work reports the first characterization of the factors that drive the biodiversity of the microbiota that colonize the above ground tissues of a relevant perennial fruit plant of considerable economic value.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe Blueberry microbiome\u003c/h2\u003e \u003cp\u003eUsing targeted metagenomics, we performed a comprehensive comparison of the structure and composition of both bacterial and fungal components of the plant-associated microbiota across above-ground tissues over two consecutive years. Considering the fungal component, we found that the fruit pulp was dominated by the fungal phylum Basidiomycetes, in contrast to what has been observed in other studies on \u003cem\u003eVaccinium myrtillus L.\u003c/em\u003e (Nguyen et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), while leaf and fruit surfaces were dominated by Ascomycetes as was observed for \u003cem\u003eVitis vinifera\u003c/em\u003e and \u003cem\u003eVaccinium spp.\u003c/em\u003e (Behrens and Fischer 2022; M. Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At finer taxonomic level (i.e. genera), \u003cem\u003eDavidiella\u003c/em\u003e and \u003cem\u003eBotrytis\u003c/em\u003e were found to be shared between all the tissues, as already observed in grapevine (Rinc\u0026oacute;n and Neelam 2021; Sivakumar et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bol\u0026iacute;var-Anillo, Garrido, and Collado 2020). Although generally considered pathogenic, some taxa from the \u003cem\u003eDavidiella\u003c/em\u003e genus (e.g. \u003cem\u003eD. tassiana\u003c/em\u003e) have been isolated from apparently healthy vines (Pancher et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We also found that \u003cem\u003eMortierella\u003c/em\u003e and \u003cem\u003eMalassezia\u003c/em\u003e were consistently present in fruit pulp, while \u003cem\u003eRhodotorula\u003c/em\u003e and \u003cem\u003eLeptosphaerulina\u003c/em\u003e were present on fruit and leaf surfaces. Similar results were observed in plants such as grapevine, tomato, apples, strawberries and white clover (H. Zhang et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Victoria et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor Bacteria, we found taxa from the phylum \u003cem\u003eProteobacteria\u003c/em\u003e present in all the tissues. This was expected as this phylum is the largest and most diverse lineage within the bacteria domain, which also includes plant pathogens and parasites (Orellana et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). We also found that, in line with another study on \u003cem\u003eV. corymbosum\u003c/em\u003e, all tissues were colonized by taxa from the phyla \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e, although at lower relative abundance. Differently, we found that taxa from the phylum \u003cem\u003eAcidobacteria\u003c/em\u003e were present both in fruit and leaf surface, but not in the pulp, while \u003cem\u003eFusobacteria\u003c/em\u003e were present only in the fruit pulp. At the genus level, \u003cem\u003eMassilia\u003c/em\u003e, which is commonly isolated from cultivated plants (Raths, Peta, and B\u0026uuml;cking \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), was found across all the three tissues while \u003cem\u003eHymenobacter, Sphingomonas\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e were found only in fruit and leaf surfaces. Moreover, \u003cem\u003eCorynebacterium, Enhydrobacter, Paracoccus and Pseudomonas\u003c/em\u003e, that have been found able to promote plant growth through enhanced nutrition, were found only in the fruit pulp in line with other studies (Y. Li, Tao, and Liang 2024; Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Chakraborty and Ramteke 2023).\u003c/p\u003e \u003cp\u003eIn terms of microbiota richness and diversity, the fruit pulp hosted a simpler microbiota, including a significantly lower richness compared to the surface of fruit and leaf. This result is consistent with a previous study on blueberries (Szymanski et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Farwell et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Smaller, but nevertheless significant differences could be found also in diversity and richness between leaf and fruit surfaces and across different plant cultivars. In particular, in fungi epiphytic communities we found a higher diversity in leaves that might indicate a more favorable habitat able to sustain a more diverse community (Gomes et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For bacteria communities, only Shannon and Simpson indexes were significantly different between leaf and fruit surface.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eWithin-species plant genetic diversity shapes the microbiome of blueberries\u003c/h2\u003e \u003cp\u003eBeyond microbiota composition, the importance of host-associated and environmental factors shaping microbial richness and diversity in such habitats are still not fully understood (Hamonts et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dastogeer et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Bashir et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In some cases, such as different \u003cem\u003eVitis vinifera\u003c/em\u003e cultivars, host genotype is identified as the primary driver (Qian et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gong and Xin 2021), while other studies report other drivers such as treatment, growing stage, plant health status or seasonal variation (Szymanski et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bao et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Trivedi et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; M. Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bashir et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Due to the complexity of this system, it has been proposed that both host species and environment might be the co-determinants of microbial communities in the phyllosphere (Jian Li et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, we found that the tissue type is the main determinant for the microbial composition in most cases. The analysis of β-diversity showed that the interior of the fruit was colonized by a microbiome distinct from that of the surface of fruits and leaves, that were much more similar to each other. Focusing only on epiphytic communities, the major factor driving the differences between the structure of the microbial communities was host variety, followed by tissues, while year of sampling was usually the least important factor. To gain a deeper understanding of the heterogeneity within microbial communities in response to different factors, we analyzed in detail the Bray-Curtis dissimilarity matrix as a function of the different factors. This analysis confirmed that fungal and bacterial fruit and leaf surface communities were significantly influenced by host plant cultivar, year, and tissues. While we found a significant effect of plant cultivar and year of sampling on fungal endophytic communities, bacterial endophytes were influenced only by year of sampling.\u003c/p\u003e \u003cp\u003eWe further identified a core of taxa that are shared across the different plants and tissues. Several studies had demonstrated the presence of a core microbiome in the roots and rhizosphere (Wang et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), but also in the phyllosphere across different species, genotypes, growing stage, season and geographical locations (Almario et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; M. Li et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sohrabi et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We provide the first quantitative evidence of the dominance of the core microbial taxa in the above ground microbiome of a perennial plant showing that, despite the large number of microbial taxa present and the tissue specific features, the largest fraction of the microbiota is composed by taxa that are widely shared across plant cultivars and time. In some cases these taxa are specific to a given tissue, while in others they are shared across tissues.\u003c/p\u003e \u003cp\u003eFor fungi, we found that \u003cem\u003eDavidiella\u003c/em\u003e is present in all tissues; this genus has been reported as a beneficial taxon involved in promoting growth, stress tolerance and pathogens resistance (Garello et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Kulišov\u0026aacute; et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xiang et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eAlternaria, Leptosphaerulina\u003c/em\u003e and \u003cem\u003eBotrytis\u003c/em\u003e, that were found to be part of the core fungal microbiota of all the tissues, are known as fungal pathogens or parasites (Bell et al. 2021; Umagiliyage et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jia Li et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Yuan et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). \u003cem\u003eAureobasidium\u003c/em\u003e, also part of the core microbiota shared across the tissues, has been shown to have antagonistic properties against pathogens (Sanzani et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ramudingana et al. 2024). \u003cem\u003eCryptococcus\u003c/em\u003e, present in the core fungal microbiota, is a genus involved in biocontrol for blue mold in apples and postharvest decay (Neto et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hashem et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hammami et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eSporobolomyces\u003c/em\u003e also is shared across the tissues and is known for its role as a natural antagonist for \u003cem\u003eB.cinerea\u003c/em\u003e (Carmichael et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ianiri et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Beyond the genera shared across all tissues, we identified tissue-specific taxa: specifically, \u003cem\u003eTaphrina\u003c/em\u003e and \u003cem\u003ePleospora\u003c/em\u003e, known as plant pathogen (Christita et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vaghefi et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), were found to be exclusive to the fruit and leaf surface. \u003cem\u003eDioszegia\u003c/em\u003e and \u003cem\u003eMyrothecium\u003c/em\u003e, well known as plant pathogens and found on leaves in other studies (Qiao et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Fujinawa et al. 2020) were present only on leaf surfaces, while \u003cem\u003eUdeniomyces\u003c/em\u003e is exclusive in fruit surface.\u003c/p\u003e \u003cp\u003eAs for fungi, the bacterial core microbiome showed several genera shared across all three tissues. The genera \u003cem\u003eMassilia\u003c/em\u003e was shared between all the tissues and was detected also in other plants such as tomatoes and grapevine (Runge et al. 2023; Singh et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). \u003cem\u003eMethylobacterium\u003c/em\u003e, also part of the core microbiome, is involved in regulation of phytohormone levels, in growth-promoting processes and exhibits biocontrol activities against pathogens (C. Zhang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Md Gulzar and Mazumder \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). \u003cem\u003eMicrococcus\u003c/em\u003e, belonging to the core microbiome, was proposed as a biocontrol agent against the post-harvest pathogen such as \u003cem\u003eB. cinerea\u003c/em\u003e, \u003cem\u003eP. expansum\u003c/em\u003e and \u003cem\u003eA. uvarum\u003c/em\u003e (Ranade et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Perrone and Gallo 2017). The core microbiomes of fruit and leaf surfaces are characterized by \u003cem\u003eGaiella\u003c/em\u003e and \u003cem\u003eRathayibacter;\u003c/em\u003e the latter is a plant pathogen (Stoll et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, we identified specific genera present only on the fruit surface such as \u003cem\u003eExiguobacterium\u003c/em\u003e. Some strains of this genus have been proposed as biocontrol agents against \u003cem\u003ePeronophythora\u003c/em\u003e (Huang et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)While \u003cem\u003eMicromonospora\u003c/em\u003e was detected in our study only on leaves, other studies demonstrated that this genus is able to colonize different tissues of plants from the roots to the leaves (Benito et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Riesco et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese differences between the core microbiome composition in the different tissues might be explained by the different chemical and physical properties of the tissues. The functions associated with the core microbiome could significantly contribute to health, productivity and resistance to plant pathogens (Yeoh et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lemanceau et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hamonts et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite the overall healthy condition of the plants analyzed, we observed not only well-known plant pathogens such as \u003cem\u003eBotrytis\u003c/em\u003e or \u003cem\u003eLeptosphaerulina\u003c/em\u003e but also microorganisms that may enhance plant health by coping with biotic and abiotic stress such as \u003cem\u003eMethylobacterium\u003c/em\u003e or \u003cem\u003eMicrococcus\u003c/em\u003e (Karwa and Saini 2023; Upadhayay et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). A deeper understanding of the core exhibited by different tissues provides valuable insight into consistently present taxa and transient ones, highlighting potential key players which may be exploited for developing effective approaches to enhance productivity under biotic and abiotic stress conditions (Roman-Reyna et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fitzpatrick et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Trivedi et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides a comprehensive analysis of the microbial communities associated with \u003cem\u003eVaccinium\u003c/em\u003e spp., revealing the complex relationships between plant tissues and their resident microbes. Our results highlight that different plant tissues\u0026mdash;leaf surfaces, fruit surfaces, and fruit pulp\u0026mdash;support distinct microbial communities, with tissue type proving to be a key factor in determining community composition. The diversity of fungal and bacterial communities is influenced by factors such as plant variety, environmental conditions, and temporal changes, demonstrating the complexity of these communities.\u003c/p\u003e \u003cp\u003eThe core microbiome identified across tissues points to key genera that play crucial roles in plant health and interactions. Beneficial fungi like \u003cem\u003eDavidiella\u003c/em\u003e and \u003cem\u003eMalassezia\u003c/em\u003e, and bacteria such as \u003cem\u003ePropionibacterium\u003c/em\u003e and \u003cem\u003eStaphylococcus\u003c/em\u003e were consistently present, suggesting their importance in promoting plant growth and resilience. Interestingly, several taxa demonstrated tissue specificity: genera such as \u003cem\u003eTaphrina\u003c/em\u003e and \u003cem\u003ePleospora\u003c/em\u003e were found exclusively on the surface tissues of fruit and leaf, while \u003cem\u003eDioszegia\u003c/em\u003e and \u003cem\u003eMyrothecium\u003c/em\u003e were exclusive to leaf tissues, and \u003cem\u003eUdeniomyces\u003c/em\u003e to fruit. Likewise, bacterial genera like \u003cem\u003eGaiella\u003c/em\u003e and \u003cem\u003eRathayibacter\u003c/em\u003e were restricted to surface tissues, while \u003cem\u003eExiguobacterium\u003c/em\u003e and \u003cem\u003eAridibacter\u003c/em\u003e were found only in the fruit, and \u003cem\u003eMicromonospora\u003c/em\u003e was unique to leaf tissues. Our two-year data set allowed us to observe that core microbiome composition was stable across years, despite minor shifts in abundance.\u003c/p\u003e \u003cp\u003eThese results underscore the complex nature of plant-associated microbiomes and the need for further research to explore the functional implications of these microbial interactions and the mechanisms that drive plant microbiome assembly. By understanding the tissue-specific composition patterns, the factor that shapes microbial communities, the core microbiota we can better leverage microbial communities to enhance plant health and productivity, potentially reducing reliance on chemical inputs, fostering more sustainable agricultural practices and address challenges associated with climate change, such as drought, pests, and extreme weather events.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetagenomes raw data are available under the ENA accession PRJEB82098\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper and related research have been conducted during and with the support of the Italian national inter-university PhD course in Sustainable Development and Climate change (link: http://www.phd-sdc.it).\u003c/p\u003e\n\u003cp\u003eC.C. is supported by the European Commission under the H2020 Marie Skłodowska-Curie Actions Grant Agreement No. 702057 (DRYLIFE).\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Agritech National Research Center and received funding from the European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR)—MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4—D.D. 1032 17/06/2022, CN00000022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eC.C, L.G. and C.D. designed the study.\u003c/p\u003e\n\u003cp\u003eM.G., B.F., M.A., M.C. collected samples.\u003c/p\u003e\n\u003cp\u003eM.G., E.S., S.L. and M.P. produced metagenomic sequences.\u003c/p\u003e\n\u003cp\u003eM.G., C.C., M.D-B., C.D. analyzed data.\u003c/p\u003e\n\u003cp\u003eM.G. prepared figures and tables.\u003c/p\u003e\n\u003cp\u003eM.G, L.G, M.D-B., C.C. and C.D. wrote the main manuscript text.\u003c/p\u003e\n\u003cp\u003eAll authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlbanese, Davide, Paolo Fontana, Carlotta De Filippo, Duccio Cavalieri, and Claudio Donati. 2015. \u0026ldquo;MICCA: A Complete and Accurate Software for Taxonomic Profiling of Metagenomic Data.\u0026rdquo; \u003cem\u003eScientific Reports\u003c/em\u003e 5 (May):9743.\u003c/li\u003e\n \u003cli\u003eAlmario, Juliana, Maryam Mahmoudi, Samuel Kroll, Mathew Agler, Aleksandra Placzek, Alfredo Mari, and Eric Kemen. 2022. \u0026ldquo;The Leaf Microbiome of Arabidopsis Displays Reproducible Dynamics and Patterns throughout the Growing Season.\u0026rdquo; \u003cem\u003emBio\u003c/em\u003e, April. https://doi.org/10.1128/mbio.02825-21.\u003c/li\u003e\n \u003cli\u003eBao, Lijun, Likun Gu, Bo Sun, Wenyang Cai, Shiwei Zhang, Guoqiang Zhuang, Zhihui Bai, and Xuliang Zhuang. 2020. \u0026ldquo;Seasonal Variation of Epiphytic Bacteria in the Phyllosphere of Gingko Biloba, Pinus Bungeana and Sabina Chinensis.\u0026rdquo; \u003cem\u003eFEMS Microbiology Ecology\u003c/em\u003e 96 (3). https://doi.org/10.1093/femsec/fiaa017.\u003c/li\u003e\n \u003cli\u003eBashir, Iqra, Aadil Farooq War, Iflah Rafiq, Zafar A. 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The microbial communities (bacteria, archaea, and fungi) within and on the surface of these fruits play a key role in regulating food quality, alongside supporting crucial aspects of plant physiology and development. However, the specific factors shaping the microbiomes of blueberry fruits, as well as their relationship with other above-ground parts of the plant such as leaves and their stability over different years, remain poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a field experiment to characterize the taxonomic composition of fungal and bacterial communities colonizing the leaves and the surface and pulp of fruits on a collection of 10 different cultivars of blueberry over two consecutive years. We found that, independently from the sampling time, pulp of the fruit, surface and leaves harbors specific and distinct microbiomes. The major factor determining the structure of the microbiome of blueberry fruits and leaves was plant genetics, followed by tissue. We further identified the core microbiome for each plant tissue and demonstrated that core taxa account for the dominant fraction of the microbiota of each plant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe showed that blueberries have a distinct microbiome associated with plant cultivar, and that this microbiome is consistent with time. We identified a tissue-specific core microbiome, with some genera shared among different tissues, and others consistently present only in specific tissues. As trade and production of blueberries is expanding globally, our results provide a foundation for advancing the development of targeted microbiome management strategies, with potential applications in enhancing plant health and productivity.\u003c/p\u003e","manuscriptTitle":"Blueberry plants support a distinctive microbiome as a function of plant genetics and tissue","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 08:10:13","doi":"10.21203/rs.3.rs-5440144/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"84607f6e-db83-45e4-a811-56b97a7f749b","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-09T12:53:33+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 08:10:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5440144","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5440144","identity":"rs-5440144","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}