Global Analysis of the Apple Fruit Microbiome: Are All Apples the Same?

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Ahmed Abdelfattah, Shiri Freilich, Rotem Bartuv, V. Yeka Zhimo,, and 20 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-142742/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2021 Read the published version in Environmental Microbiology → Version 1 posted You are reading this latest preprint version Abstract Background: Apple is one of the most highly consumed fruits worldwide and is the largest fruit crop produced in temperate regions. Fruit quality, safety and long-term storage are issues that are important to growers, distributors, and consumers. We present the first worldwide study on the apple fruit microbiome that examines questions regarding the composition and the assembly of microbial communities on and in apple fruit. Results: Results revealed that the composition and structure of the fungal and bacterial communities associated with ‘Royal Gala’ apple fruit at harvest maturity vary and are highly dependent on geographical location. The study also confirmed that the spatial variation in the fungal and bacterial composition of different fruit tissues exists at a global level. Fungal diversity varied significantly in fruit harvested in different geographical locations and suggest a potential link between location and the type and rate of postharvest diseases that develop in each country. Although the geography, climatic conditions, and management practices may have a significant impact on the composition of fruit microbial communities, we were able to identify a 'core' microbiome that is shared in fruit across the globe. Conclusions: Results of this study provide foundational information about the apple fruit microbiome that can be utilized for the development of novel approaches for the management of fruit quality and safety, as well as for reducing losses due to the establishment and proliferation of postharvest pathogens. It also lays the groundwork for studying the complex microbial interactions that occur on apple fruit surfaces. General Microbiology Fruit microbiome Malus holobiont geographical location niche specialization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Developing a comprehensive understanding of the plant microbiome has identified as key for establishing a second green revolution [ 1 ]. In this regard, the sequencing of plant and microbial genomes has provided a wealth of information for developing new opportunities for crop improvement. Plants and their microbiomes have co-evolved as a meta-organism and the term ‘holobiont’ has been used to describe the inseparable relationship between higher organisms and their microbial communities [ 2 ]. A growing body of information indicates that the plant microbiome is involved in many host functions, directly or indirectly affecting host physiology, biochemistry, growth, disease resistance, stress tolerance, and quality, before and after harvest [ 3 ]. This field of research has already provided new applications with the ” microbiome factor” being included in breeding strategies, seed production, preharvest disease control, and the management of postharvest pathogens [ 3 – 5 ]. Domesticated apple ( Malus pumila Mill .) is one of the most popular edible fruits worldwide and is the largest fruit crop produced in temperate regions. The global production of apple has more than doubled in the past 20 years, from 41 million tons in 1990 to 86 million tons in 2018, with a total trading value of 7.53 billion USD [ 6 ]. Apples are often stored for several months and up to one year in cold storage in conjunction with different controlled atmosphere regimes. Preventing the proliferation and development of postharvest pathogens in storage is an important challenge for maintaining fruit quality and safety. Studying the temporal changes in the assembly and composition of microbial communities on and in fruit during storage and marketing is essential for controlling postharvest diseases and reducing losses and waste along the supply chain. Despite the existence of approximately 7,500 apple cultivars, which trace to the ancestral progenitor Malus sieversii (Ldb.) M. Roem about one tenth of this number have global prominence [ 7 ]. Among these apple cultivars, ‘Gala’, a cross developed in New Zealand between ‘Kidd’s Orange Red’ and ‘Golden Delicious’, represents a significant portion of global apple production. ‘Gala’ and its many sports, including ‘Royal Gala’ are grown extensively in all apple growing regions of the world and, thus, have major economic value [ 8 ]. Apple tree microbiome studies have shown, as in other tree crops, that its composition is influenced by genotype, management practices, rootstock, and soil properties [ 9 – 13 ]. The apple microbiome has been comprehensively reviewed [ 14 ]. However, relatively fewer studies have been conducted, on the pre- and postharvest fruit microbiome [ 14 ]. This is despite the fact that the use of various microbial antagonists has been pursued as an alternative to the use of synthetic chemicals to manage postharvest pathogens of apples. While postharvest biocontrol products using microbial antagonists, especially yeasts, have been commercialized, their wide sprayed use is limited due to problems with efficacy and regulatory hurdles. Other researchers have argued that a greater understanding of the fruit microbiome is needed to elucidate the factors involved in biocontrol systems and that this would facilitate the development of improved strategies that rely on the use of antagonistic microorganisms for managing postharvest diseases, and perhaps physiological disorders, that occur during the marketing and long-term storage of fruit crops [ 14 – 18 ]. Recent studies have shown that different apple fruit tissues (calyx-end, stem-end, peel, and mesocarp) harbor distinctly different fungal and bacterial communities that vary in diversity and abundance [ 10 – 12 , 14 ]. Those studies differed in several respects, although the same general patterns were observed. The question remains, however, whether the observed patterns of abundance and diversity in the different tissue-types is generally true at a global level, despite differences in climates, management practices, and cultivars. One objective of the current study was to begin to examine this question. Malus pumila and its derived cultivars have common ancestors ( Malus sieversii and Malus sylvestris ) that represent the primary progenitors of the modern apple [ 19 ]. Hologenome theory suggests that hosts and their microbiomes have co-evolved [ 2 ]. Therefore, we hypothesized that the fruit of a commercial cultivar, such as ‘Royal Gala’, would share a ‘core’ microbiome, regardless of the global location where the fruit is produced. We also hypothesized, that the high level of genetic diversity that exists in apple and its long history of domestication may have impacted the overall composition of the fruit microbiome in a regional or local manner. Additionally, biotic and abiotic conditions and management practices may have played an important role in influencing microbial community assemblages as apple production spread from its original site of origin and domestication. Determining the existence of a ‘core’ microbiome would provide important information on its impact on disease susceptibility and resistance and human health, as well as provide a more comprehensive understanding of fruit biology in light of the holobiont concept. A deeper understanding of the interactions between hosts and their resident microflora and how they are impacted by intrinsic (genetic) and extrinsic (management practices and the environment) can be used to develop novel approaches for the management of fruit quality, pre-and postharvest disease, and food safety. The main objectives of the present study were to determine: 1) if the spatial differences in microbial composition previously reported exist on a global scale, irrespective of where the fruit is grown; 2) how the structure of the fruit microbiome is affected by geographical location and general differences in climate, and; 3) if a core microbiome could be identified and if so how do the members of the core microbiome interact as a network. Results of the study provide a global perspective on the microbiome of apple fruit and provide a foundation for developing a better understanding of the interactions that potentially occur within the fruit microbial community, as well as the potential interactions that may occur between the fruit and its resident microflora in relation to postharvest diseases, fruit quality, and food safety. Materials And Methods ‘Royal Gala’ apple fruit harvested at commercial maturity were used in this study. Fruit were harvested in four regions (North America, South America, Europe, and the Middle East) that included 21 locations in 8 countries (USA, Canada, Uruguay, Italy, Spain, Switzerland, Israel, and Turkey). Fruit were harvested at commercial maturity using standard maturity indices. Harvesting occurred in July -September in the northern hemisphere and February-March in the southern hemisphere (Supplementary Table S1). A standardized protocol was used for sample collection and processing in all sampling locations across countries, then the extracted DNA was sent USDA-ARS, WV, USA, to avoid bias introduced by library preparation and sequencing. Briefly, in each locations/orchard, 8 trees (not adjacent to each other) were selected and 5 fruit/tree were sampled from around the circumference of the tree. Each tree consisted one replicate; total of 8 replicates per location/orchard. Five fruit from each tree are pooled to make 1 biological replicate (total 8 biological replicates/orchard). From each apple, 3 tissue types (peel, stem-end, and calyx-end) were sampled as previously described [ 12 ]. First, a sterile cork-borer was used to excise the fruit core and the top and bottom 1.5 cm were used as stem- and calyx-end, respectively. To collect the peel, a thin layer around the fruit equator with approximately 1.5 cm in width was obtained from each apple using a peeler. Samples from of the same fruit tissue from the same tree were pooled and considered a biological replicate making total of 8 replicate of each tissue site per orchard and a total of 505 samples globally. Samples were immediately frozen in liquid nitrogen, kept and − 20 or -80C until freeze-dried. Libraries and sequencing, Data processing, Downstream and statistical analysis Lyophilized samples were homogenized, and their DNA was extracted using DNeasy PowerLyzer PowerSoil Kit (Qiagen, Germantown, MD, USA). Initial tissue disruption of 250 mg was performed with a Qiagen PowerLyzer 24 Homogenizer (Qiagen, Germantown, MD USA). DNA extractions were automated using a Qiagen QiaCube (Qiagen, Germantown, MS, USA), using the processing routine recommended by the manufacturer for the PowerSoil kit. Extracted DNA was used as the template for amplicon PCR reactions that amplified the bacterial 16S ribosomal region and the fungal internal transcribed spacer (ITS) region. The V4 region of 16S rRNA was amplified using the universal primers 515F [ 20 ] and 806R [ 21 ] in conjunction with peptide nucleic acids (PNAs) (PNA Bio) added to inhibit amplification of ribosomal and mitochondrial sequences [ 22 ]. ITS amplicons were amplified using ITS3/KYO2 [ 23 ] and ITS4 [ 24 ] primers along with a custom-designed blocking oligo designed to inhibit amplification of the host DNA (5’ ATTGATATGCTTAAATTCAGCGGGTAACCCCGCCTGACCTGGGGTCGCGTT-C3 spacer 3’). All primers were modified to include the necessary Illumina adapters ( www.illumina.com ) for subsequent PCR addition of Illumina indexes for multiplexing. For bacteria, PCR reactions were conducted in a total volume of 25 µL containing 12.5 µL of KAPA HiFi HotStart ReadyMix (Kapa Biosystems), 1.0 µL of each primer (10 µM), 2.5 uL of mitochondrial PNA (5 uM), 2.5 uL of plastid PNA (5 uM), 2.5 µL of DNA template, and 3 µL nuclease-free water. Reactions were incubated in a T100 thermal cycler (BioRad) at 95 o C for 5 min followed by 30 cycles of 95 o C for 30 s, 78 o C for 5 s, 55 o C for 30 s, 72 o C for 30 s and a final extension at 72 o C for 5 min. For fungal (ITS) amplicon generation, 25 uL PCR reactions contained 12.5 µL of KAPA HiFi HotStart ReadyMix (Kapa Biosystems), 1.0 µL of each primer (10 µM), 1.0 uL of blocking oligo (10 uM), 2.5 µL of DNA template, and 7 µL nuclease-free water. Reactions were incubated in a T100 thermal cycler (BioRad) at 95 o C for 5 min followed by 30 cycles of 95 o C for 30 s, 55 o C for 30 s, 72 o C for 30 s a final extension at 72 o C for 5 min. Library preparation following amplicon PCR was performed as specified in the Illumina 16S Metagenomic Sequencing Library Preparation guide precisely as outlined in conjunction with the use of a Nextera Index Kit (Illumina) containing 96 indexes. Subsequent library size, quality, and confirmation of the absence of adapter dimers was performed on an Agilent 2100 Bioanalyzer (Agilent). Paired-end sequencing of amplicons was done on an Illumina MiSeq (Illumina) sequencer with a V3 600-cycle Reagent Kit (Illumina). Data Analysis Qiime2 [ 25 ] was used for demultiplexing, merging, quality filtering and trimming of reads, ASV table generation, and rarefaction to account for uneven sequencing depth. Taxonomic clustering of ASVs was done using a similarity threshold of 97% against the GreenGene [ 26 ] database for 16S reads and against the UNITE [ 27 ] database for ITS reads. MetagenomeSeq’s Cumulative Sum Scaling (CSS) [ 28 ] was used as a normalization method subsequent to community composition analyses, including the calculation of Bray–Curtis dissimilarity metrics [ 29 ], the construction of PCoA plots, and PERMANOVA analyses. Rarefaction to an even sequencing depth of 1,000 reads per sample was used to normalize ITS and 300 reads for the 16S features tables which were used to calculate Shannon diversity. Differences in community composition between the investigated countries, orchards and tissue types were tested using Permutational Multivariate Analysis of Variance Using adonis (~ PERMANOVA) in vegan R with 999 permutations [ 30 – 35 ]. The core microbiome was calculated based on genera present in at least 75% of the investigated samples using core function in Microbiome package [ 36 ]. Interactions between core and non-core species were limited to genera whose normalized relative abundance > 0.1% (average across replicas) in at least a single sample. Co-occurrences were described based on Spearman’s rho correlation coefficients between the distribution patterns of the genera joining the normalized bacterial and fungal tables. Scores were calculated using 'Pandas.corr' python package v1.1.0. Correlation matrix and visualized using 'seaborn.clustermap' python package v0.10.1. Co-occurrence networks were generated based on correlation scores. Network visualization and the positioning of the nodes were calculated according to the force-directed Fruchterman & Reingold algorithm used for calculating layouts of simple undirected graphs (Buchfink et al., 2014). The algorithm was implemented using nx.draw function via the ‘pos’ parameter in the 'NetworkX' python package v1.11. Node degree was calculated using the nx.degree function. Visualization was generated using 'Plotly' python package v4.9.0. Linear discriminant analysis effect size (LEfSe) [ 37 ]. was used for biomarker discovery to determine a list of taxa that best characterize each geographical location (Country). Higher LEfSe score indicate higher consistency of differences in relative abundance between taxa of each country. Significance in all the analyses was determined using 999 Monte Carlo permutations, and Benjamini–Hochberg (FDR) corrections were used to adjust the calculated p values. All statistical analyses were done using R version 3.6.2 [ 38 ] in RStudio version 1.1.453 [ 39 ] and the packages vegan version 2.5-6, lme4 version 1.1–21, multcomp version 1.4–13, phyloseq version 1.32.0 [ 31 , 32 , 34 ]. Results Microbial diversity associated with Royal Gala apple After removal of low-quality sequences and plant sequences, 6.117.315 16S and 48.528.735 ITS2 reads were obtained and assigned to 20.072 bacterial and 16.241 fungal ASVs, respectively. The ASVs corresponded to 25 bacterial and 6 fungal phyla, which in turn were assigned to 558 bacterial and 822 fungal genera. The apple fungal community across the investigated countries was dominated by Ascomycota (79.8%) and Basidiomycota (9.3%), although, other phyla such as Chytridiomycota, Entomophthoromycota, Mortierellomycota , and Mucoromycota were also detected at a lower relative abundance (data not shown). Within the Ascomycota, genera such as Aureobasidium (29.00%), Cladosporium (16.60%), and unidentified groups of Capnodiales (8.80%) and Pleosporaceae (8.50%) represented more than 60% of the total fungal community (Supplementary Table 2). Sporobolomyces (5.70%), Filobasidium (4.20%), and Vishniacozyma (1.60%) were the predominant Basidomycota . Regarding bacteria, Proteobacteria (65.1%), Firmicutes (15.8%) Actinobacteria (15.1%), and Bacteroidetes (2.3%) were the most prevalent bacterial phyla, representing 98.3% of the entire bacterial community. The abundance distribution of the bacterial phyla was consistent across countries, except in Turkey where Firmicutes were more abundant than Proteobacteria compared to the other countries. Sphingomonas (12.40%), Erwinia (11.30%), Pseudomonas (9.30%), Bacillus (7.10%), unidentified Oxalobacteraceae (6.80%), Methylobacterium (6.20%) and unidentified Microbacteriaceae (5.90%) were the most abundant bacterial genera. (Supplementary Table 2). Results of the linear discriminant analysis (LEfSe) revealed 90 fungal and 57 bacterial taxa characterized each of the investigated countries (Fig. 1 ). Turkey had the highest number of fungal genera (25), which included Penicillium , Zasmidium , and Pseudomicrostroma . In contrast, Spain had the lowest number of fungal genera (5), which included Monilinia, Vishniacozyma, and Bensingtonia (Fig. 1 a). Israel and the western USA had the highest number of unique bacterial taxa, while only one bacterial taxon, identified as within the Paenibacillaceae was observed in samples collected in Uruguay (Fig. 1 b). The effect of growing region on the microbial diversity of apple fruit The geographical location in which apples were sampled had a significant effect on the microbial diversity associated with the fruit (Table 1 ). For example, country of origin (including location within a country) had a significant effect on the diversity of fungi ( F = 44.06, P 2 × 10 − 16 ). Similarly, although to a lesser extent, the effect of orchard on fungi ( F = 30.49, P > 2 × 10 − 16 ) and bacteria ( F = 5.491, P = 1.09 × 10 − 8 ) was also statistically significantly. Pairwise comparison between Shannon diversity of the investigated countries indicated that both fungal and bacterial diversity differed significantly between locations and orchards within a location (Supplementary Table 3). Italy had the highest fungal diversity, followed by Turkey and then Israel (Fig. 2 a). The highest bacterial diversity was observed in apples collected from Italy, the USA, and Switzerland (Fig. 2 b). Table 1 Model results using anova test on the effects of location, orchard, tissue, and their interactions with the Shannon diversity of bacteria and fungi on apple fruits. Shannon Df Sum Sq Mean Sq F value Pr(> F) Fungi Country 8 26.98 3.372 44.06 < 2 × 10 − 16 Orchard 12 28 2.334 30.49 < 2 × 10 − 16 Tissue 2 0.38 0.188 2.45 0.0875 Country × Tissue 16 14.62 0.913 11.93 < 2e-16 Orchard × Tissue 24 4.81 0.201 2.62 6.09E-05 Residuals 428 32.76 0.077 Bacteria Country 8 46.09 5.761 22.993 < 2 × 10 − 16 Orchard 12 16.51 1.376 5.491 1.09 × 10 − 8 Tissue 2 34.47 17.236 68.794 < 2 × 10 − 16 Country × Tissue 16 18.96 1.185 4.729 8.22E-09 Orchard × Tissue 24 22.11 0.921 3.678 3.03E-08 Residuals 399 99.97 0.251 Community composition of apple across countries The geographical location of the investigated sites had a significant effect on shaping the community composition of the tested apples. While the “country effect” had a significant impact on the overall apple microbiome ( P = 0.001), it was more evident in the fungal community ( R 2 = 0.375) than in the bacterial community ( R 2 = 0.152). This was also evident in the PCoA analysis based on Bray Curtis dissimilarity test (Fig. 3 a&c). An effect of orchard was also observed, explaining less variation, however, in fungal ( R 2 = 0.136, P = 0.001) and bacterial ( R 2 = 0.048, P = 0.001) communities relative to country (Table 2 ). Hierarchal clustering revealed that European apples (Switzerland, Italy, and Spain) had a bacterial community that was more similar to each other, relative to sites in eastern North America and South America (eastern USA, Canada, and Uruguay) which formed a separate cluster (Fig. 3 d). Turkish and Israeli apples appeared to harbor a distinct bacterial community. Hierarchal clustering of the fungal community composition revealed that apples collected from the western USA, Italy, Spain, and Israel formed a separate cluster from a cluster formed by the eastern USA, Canada, Uruguay, and Switzerland (Fig. 3 b). Furthermore, orchards within the same country appeared to have similar microbial communities than those sampled from another country. These results were more evident, however, in fungal communities than in bacterial communities (Supplementary Fig. 1). Table 2 PERMANOVA results on testing the effects of Location, Orchard, tissue, and their interactions on bacterial and fungal communities of apple fruits. The comparisons were based on Bray Curtis dissimilarity, and p-values were calculated using the adonis function in vegan and corrected using the FDR method. Df Sums of Sqs Mean Sqs F. Model R 2 Pr(> F) Fungi Country 8 48.079 6.0098 59.229 0.37528 0.001 Orchard 12 17.478 1.4565 14.354 0.13643 0.001 Tissue type 2 3.408 1.7038 16.792 0.0266 0.001 Country × Tissue type 16 8.892 0.5558 5.477 0.06941 0.001 Orchard × Tissue type 24 6.829 0.2845 2.804 0.0533 0.001 Residuals 428 43.428 0.1015 0.33898 Total 490 128.113 1 Bacteria Country 8 30.649 3.8311 12.5466 0.15272 0.001 Orchard 12 9.741 0.8117 2.6583 0.04853 0.001 Tissue type 2 10.419 5.2093 17.0602 0.05191 0.001 Country × Tissue type 16 16.21 1.0131 3.3178 0.08077 0.001 Orchard × Tissue type 24 11.841 0.4934 1.6157 0.059 0.001 Residuals 399 121.835 0.3054 0.60707 Total 461 200.694 1 Spatial variation in the apple microbiome The effect of tissue types on fungal diversity (Shannon) was not statistically significant when tissue samples from all countries were grouped together ( F = 2.45, P = 0.0875). The interaction between country and tissue type, as well as between orchard and tissue type, however, were significant (Table 1 ). In the majority of the orchards, calyx-end tissue exhibited a higher fungal diversity, followed by peel and stem-end tissues, with a few exceptions observed in samples collected from Uruguay, Turkey, and Spain (Fig. 4 a). In contrast, tissue type had a significant effect on bacterial diversity, regardless of the sampling location ( F = 68.794, P = 2 × 10 − 16 ), as well as in the interaction between country and tissue, as well as orchard and tissue (Table 1 ). Stem-end tissues harbored the highest bacterial diversity relative to fruit peel and calyx-end tissues, except in the New Brunswick, Canada samples (Fig. 4 b). PERMANOVA analysis indicated that tissue type, as well as the interaction between tissue type and country, and tissue type and orchard, had a significant effect on fungal community composition (Table 2 ). This effect was observed in all of the investigated orchards in all countries, based on the results of the PCoA analysis where samples collected from apple calyx-end, stem-end, and peel, tissues clustered separately from each other (Fig. 5 a). Similar results were also found for the bacterial community which differed significantly in all orchards, (Fig. 5 b). The core microbiome of Royal Gala apple The global core of the apple microbiome, defined at taxa present in at least 75% of the samples, consisted of six fungal genera, namely: Aureobasidium, Cladosporium, Alternaria, Filobasidium, Vishniacozyma , and Sporobolomyces and two bacterial genera namely: Sphingomonas and Methylobacterium. While none of the bacterial genera were found to be prevalent in 90% of the samples, the fungal genera Aureobasidium , and Cladosporium were found in up to 96% of the samples. Interestingly, the community composition of Sphingomonas was sufficient to distinguish between most of the investigated countries and showed niche specialization within the fruit i.e. stem-end, calyx-end, and peel tissues harbored different Sphingomonas communities (Supplementary Fig. 2). Similar results were also observed for Aureobasidium , a core fungal genus, however, species variability was limited, and differences were attributed to niche specialization in the different tissue-types (data not shown). In order to detect potential interactions between core and non-core groups we depicted co-occurrences by constructing a correlation matrix based on normalized distribution patterns of bacterial and fungal genera (Fig. 6 a). Clustering pattern indicates that genera can be divided into five key groups of co-occurring species mixing bacterial and fungal genera. Core species are distributed in two clusters, each hosting one of the two most dominant Ascomycota genera - Aureobasidium (green) and Cladosporium (purple). Microbiomes with a high relative abundance of Aureobasidium and a low abundance of Cladosporium were characterized in Switzerland, USA and Canada; alternatively, high numbers of Cladosporium and low numbers of Aureobasidium were described in Israel and Turkey (Supplementary Fig. 1). Considering the significant negative and positive interactions between genera, core species were found to have a significantly higher number of interactions in comparison to non-core species with an average node degree of 19.125 neighbors in comparison to 12.23 in none core species (Supplementary Fig. 3). A network formed by the interactions of core genera with core and non-core goops is consistent of 142 edges and connects 8 and 60 core and non-core genera, respectively (Fig. 6 b). The highest number of interactions − 30– was recorded for one core genus – Sphingomonas . Using the network, we could identify potentially useful relationships among and between core and non-core genera within the microbial community (Fig. 6 b). For example, the core genera Methylobacterium is positively associated with Burkholderiales – a group that includes reported biocontrol agents (Angeli et al., 2019), and a negative association with a known apple pathogen Podosphaera . These co-occurrence associations can be indicative of cooperative and competitive interactions, respectively, and can serve the design of experiments to assess these interactions in vitro and on the fruit. Discussion This is the first study to provide a global analysis of the apple fruit (‘Royal Gala’) microbiome and determine the structure and diversity of microbial communities on and in different fruit tissues at harvest. A core microbiome shared between apple samples in all locations was identified suggesting that the members of the core microbiome may have co-evolved with the domestication of apple and potentially may play an essential role in defining fruit traits related to disease resistance and fruit quality. We characterized the microbial communities associated with ‘Royal Gala’ apple fruit at harvest maturity stage and assessed the effect of geographical location on both large-scale spatial variations, i.e. across different countries, and small-scale spatial variations, i.e. within a fruit. While the microbiome associated with plants has been extensively studied, knowledge about the fruit microbiome is still rather limited relative to rhizosphere, endophyte, and phyllosphere studies [ 14 , 15 ]. Additionally, information about the role of the fruit microbiome on pre- and postharvest diseases, as well as fruit physiology, is also lacking. This is despite the importance of postharvest losses in reducing the economic return from fruit production, especially after so many resources have already been expended to produce a harvestable crop. Apples also encounter losses in storage, transit, markets, and homes due to postharvest pathogens [ 40 ]. For over 30 years, there has been considerable research focus on the development of biological control strategies based on naturally-occurring microorganisms [ 16 , 41 ]. Especially with the use of yeast antagonists, has been an active area of research. Several postharvest biocontrol products based on single antagonists have been developed and registered. The large scale commercial use of these products have been limited a due to inconsistent performance under commercial conditions [ 42 ]. In this regard, Droby et al. (2018) have indicated that a new paradigm is needed for postharvest biocontrol to achieve commercial success and that understanding the naturally-occurring microbiome of fruit surfaces and its function, will lead to the development of new biological strategies for postharvest disease control. Several studies have reported on the population dynamics of biocontrol agents on intact and wounded fruit over the course of low-temperature storage. A wide array of mechanisms has also been demonstrated for postharvest biocontrol agents that involve yeast antagonist, the pathogen, and the host. This study and others are providing the foundation for understanding the structure and function of the carposphere microbiome. Such information is an essential step towards the development of effective biological approaches to postharvest disease management. For example, efforts to modulate the gut microbiome for improved human health have moved from simple inoculations with beneficial bacteria (probiotics) to supplements that contain specific metabolites that provide a resource that can be selectively utilized by beneficial bacteria (prebiotics) to combinations of probiotics and prebiotics (synbiotics) that can more effectively shift the composition of an existing host community [ 43 ]. Similarly, in the apple rhizosphere, efforts to manipulate the soil microbiome to treat apple replant disease have shown that directed changes to the resource environment (e.g. through selective soil amendments) are more successful at controlling disease than inoculations with single strains or simple consortia of beneficial microbes [ 44 – 46 ]. Research designed to identify, quantify, and elucidate the metabolic networks constructed by microbial populations on harvested fruit is a fundamental need. Such studies will improve our understanding of the mechanisms that regulate the assembly of beneficial microbial communities, and lead to the development of strategies for beneficially manipulating microbial communities in situ . Geographical Location Apples represent a major item of export and are shipped globally. Therefore, it is of importance to determine if the structure of the apple fruit microbiome is relatively uniform regardless of where the fruit is produced. Rather than the presence of a uniform microbiome, the present study revealed that geographical location is a principle factor determining the structure of the apple fruit microbiome. Fungal communities, however, were more affected by geographical location (country and site within a country) than bacterial communities. The stability of the fruit-associated bacterial community, relative to their fungal counterparts, has been previously reported in stored apples [ 11 , 47 ]. The higher level of variation in the fungal community may be potentially attributed to the fact that fungal assemblages appear to be derived from regional fungal pools with limited dispersal capability [ 48 ]. In addition, we observed that as the variation in the microbial communities among sites was positively correlated with the distance between those locations, especially for fungi. For example, variations in fungal and bacterial communities associated with apple fruit were lower at a local scale, i.e. among orchards within the same geographical location, sites within a country e.g. eastern and western USA and Canada and increased at the country level. Furthermore, a continental pattern can be drawn especially for the bacterial community which in one hand indicates adaptation of the apple microbiome to local environments, and on the other hand it may be explained by the metacommunity theory. A metacommunity is defined as a set of local communities that are linked by dispersal of multiple potentially interacting species [ 49 ]. However, the present study had an insufficient distribution of samples to evaluate this premise. Nevertheless, the geographical location has been previously reported to be one of the most important determinants of the structure of the plant microbiome [ 50 , 51 ]. A study of the maize rhizosphere found that location had a higher impact on the plant microbiome than genotype [ 52 ]. Similarly, a study of the global citrus rhizosphere microbiome reported large variations in community structure that were attributed to geographical location (samples collected in different countries) [ 53 ]. The large-scale variations between countries, together with the similarity observed among apple microbial communities within a country or region within a country, suggests that the structure of the microbial community on apple fruit is locally-adapted to local environmental conditions that influence microbial diversity and composition [ 54 ]. In this regard, it is also commonly recognized that the humid, wet conditions present in the eastern portions of the USA and Canada, present a much greater disease and pest challenge than the dry conditions present in the western USA and Canada. This is especially supported by the differences in diversity levels between these two contrasting environments, although more evident for the fungal community (e.g. Figure 3 ). Tissue type Plants tissues provide a variety of niches that can harbor distinct microbial communities. Plant roots, leaves, flowers, fruit, as well as other organs, represent different microhabitats, each with specific features that favor the growth of specific microorganisms in these organs. Different tissue types within the same organ, have been previously reported to exhibit spatial variations in microbial community structure. For example, the upper and lower leaf sides, as well as the peel and pulp of various fruits, including apple, have been reported to exhibit differences in microbial community structure [ 11 , 12 , 55 , 56 ]. The experimental design used in the present study was selected to determine if spatial variations within a fruit is global, i.e. will be evident regardless of geographical location and the variety of environmental conditions present in the different sites. Results indicated that the effect of fruit tissue-type on the composition of the microbial community was rather limited, R 2 = 0.0266 for fungi and R 2 = 0.05191 for bacteria, yet significant i.e. P = 0.001. A larger effect was observed, however, when individual orchards were analyzed separately (Figs. 5 and 6 ). Spatial variations in fungal and bacterial community composition and Shannon diversity due to tissue-type was consistently observed in all of the investigated orchards. These results, along with previous studies, confirms that spatial variation in the structure of the microbial community exist between tissue-types (calyx-end, stem-end. and peel) at a global level. Since geographical location, is the main factor shaping the structure of the apple microbiome, however, the effect of tissue-type is greatly reduced when samples of tissue-types are pooled across countries. Notably, the association of a distinct microbiome with such a small environmental niche (tissue-type) suggests specialized adaptation and function to those microhabitats. We suggest that the conditions (morphological, nutrient, and environmental) present in each of these microhabitats (tissue-types) could play an important role in determining community structure. For instance, the calyx-end is an open site that may create special niche for specialized fungi such as Alternaria and other fungal pathogens which can cause internal rots. Interestingly, Erwinia species were found at higher abundance in the Calyx-end tissue compared to the other tissue types, especially in Canadian apples. This can be explained by the fact that the calyx contains floral residues which are most affected by Erwinia amylvora , the cause of fire blight disease of pome fruit. Core microbiome A core microbiome is a set of microbes consistently present over time on a specific host and is likely to be critical to host development, health, and functioning [ 57 ]. Defining the core microbiome enables researchers to filter out transient associations and focus on stable taxa with a greater likelihood of influencing host phenotype and is therefore essential in exploring the potential for pre/probiotic treatments that support host health [ 57 ]. In this study the core microbiome of apple fruit was defined as fungal and bacterial taxa present in at least 75% of all samples. We found two bacterial genera, namely Sphingomonas and Methylobacterium , and six fungal genera i.e. Aureobasidium, Cladosporium, Alternaria, Filobasidium, Vishniacozyma, and Sporobolomyces . This is a considerably low number of taxa, relative to other reported core microbiomes in plants [ 58 ]. However, this can be attributed to the high number of samples in the present study; which lowers the probability that same taxon will be present in all samples and the evaluation of samples from different countries and tissue-types. Sphingomonas , a gram-negative, non-motile, aerobic bacterial genus, is known for its bioremediation of heavy metals and biodegradation of polycyclic aromatic hydrocarbons, and is associated with plant growth promotion through its ability to produce gibberellins and indole acetic acid in response to different abiotic stress conditions, such as drought, salinity, and heavy metal stresses [ 59 ]. Interestingly, those phytohormones are also involved in fruit maturation, development, and quality. For example, fruit-set in tomato ( Solanum lycopersicum ) depends on gibberellins and auxins [ 60 , 61 ]. Similarly, Methylobacterium is a gram-negative, aerobic, motile bacterial genus with plant growth-promoting properties [ 62 ]. Sphingomonas and Methylobacterium have been previously reported as a component of the apple microbiome and as two of their predominate genera [ 9 – 11 , 47 ], as well as a component of the core microbiome in several other plant species [ 50 , 63 – 65 ]. Aureobasidium and Cladosporium have also been reported as a common member of the microbiome of apple [ 12 , 47 ] and other plants [ 66 – 68 ]. These taxa can be found as endophytes or epiphytes in association with various plant organs e.g. leaves, flowers, fruit, seed etc. Although the core microbiome is typically considered to have a high level of specificity between species, the common reporting of these taxa suggests the possibility of a core microbiome that is shared between different plant species. This commonality is expected to exist at the level of genus and that some degree of species specificity may exist. In this regard, we found that the core bacterial genera, Sphingomonas and Methylobacterium accounted for a considerable fraction of the observed variation between the investigated locations, as well as tissue types. For example, the community composition of either Sphingomonas and Methylobacterium was sufficient to distinguish between most of the investigated countries. Similar results were also observed for Aureobasidium , a core fungal genus, however, species variability was limited, and differences were attributed to niche specialization in the different tissue-types. Notably, both bacterial genera appeared to be distinct in tissue types. The geographical location demonstrated to be an important determinants of the Methylobacterium community composition in the plant phyllosphere [ 69 ]. The presence of distinct Sphingomonas community in different fruit tissue-types suggests site-specialization to these microhabitats. Interestingly, the majority of the fungal core microbiome was represented by yeasts with known antagonistic activity against pre- and postharvest pathogens. Despite being one of the most common fungi associated with apples, Penicillium , the causal agent of the most important apple postharvest disease, blue mold [ 70 – 72 ], was not found to be a component of the core microbiome. Penicillium species are able to grow and proliferate at low temperatures during cold storage, giving them an advantage over other fungal species [ 11 ]. In this regard and considering samples in the present study were collected immediately after harvest, it can explain the low prevalence and the absence of Penicillium from the apple core microbiome. Information about the core microbiome can be further used to develop biological control strategies against apple diseases and disorders. Though core species, by definition, are detected across all samples, their relative abundance pattern vary and, in some cases, forms characteristic groups of microorganisms. Dissecting the microbiome into co-occurrence modules can serve the construction of synthetic communities with distinct function [ 73 ]. For example, such associations can serve the design of multiple-species synthetic communities for achieving an efficient biocontrol activity. Alternatively, dissecting the microbiome into microbial modules can allow limiting the search for a single efficient antagonist agent. In the context of the apple fruit microbiome, co-occurrence patterns have stratified the fruit microbiome into five key groups with core genera located in two of them: one cluster with Aureobasidium , and the second with Cladosporium , the two most abundant Ascomycota genera. Though most of the significant interactions detected in the network are positive, some negative associations allow formulating predictions for potential biocontrol agents against pathogens. Based on the network view, experimental design of potential biocontrol agent could compare the activity of a single microorganism vs consortium representing a native co-occurring module. Potential biocontrol strategies can hence benefit from the network view of microbiome interactions allow to go beyond the single biocontrol agent to the educated design of a biocontrol consortium. Conclusions Recent studies have demonstrated the critical role that the plant microbiome plays in plant health, fitness and productivity. Less attention, however, has been given to studies on the carposphere, compared to the rhizosphere, and phyllosphere. Apple fruit were recently reported to host a high microbial diversity with niche specialization exhibited in calyx-end, stem-end, and peel tissues. Whether this niche specialization is consistent in different apple-production areas globally and whether a “core” microbiome exists, regardless of geographic location, as has been reported for the rhizosphere of other fruit crops has not been determined. In the present study, the microbial communities associated with ‘Royal Gala’ apple were characterized using amplicon-based high-throughput sequencing to assess both large- and small-scale spatial variations and to determine the presence of a core microbiome and hub microbes. Such information is critical for understanding the role of microbiome in the susceptibility of apple fruit to pre- and postharvest diseases, fruit safety, and potentially fruit quality during long-term storage. Here we demonstrated that the microbiome of the apple fruit collected from similar climates, within a continent or hemisphere, exhibiting the highest degree of similarity. Notably, fungal communities were more variable than bacterial communities in terms of diversity and abundance. In addition, we showed that the distinct composition of the different tissue-types is a global feature of the apple microbiome. Six fungal genera ( Aureobasidium, Cladosporium, Alternaria, Filobasidium, Vishniacozyma , and Sporobolomyces ) and two bacterial genera ( Sphingomonas and Methylobacterium ) were defined as representing the core microbiome. Overall, the findings in the present study may suggest local adaptations of the apple microbiome to local environment. Regarding the spatial variations within the fruit, we suggest for future apple microbiome studies to consider these variations during their experimental design and sampling strategies by either analyzing different fruit tissues separately or including the whole fruit to minimize discrepancies between studies. In addition, it would be interesting for future fruit microbiome works to investigate whether the variations among fruit tissue types can be generalized to all fruits. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and material The datasets generated and/or analyzed during the current study are available in the [SRA NCBI] repository, and can be accessed from the following link Competing interests The authors declare no conflict or competing interests. Funding This research was funded by BARD, Israel- US Binational Agricultural Research and Development Fund, (IS-5040-17) awarded to S.D. and M.W. European Union’s Horizon2020 under “Nurturing excellence by means of cross-border and cross-sector mobility” program for MSCA-IF-2018-Individual Fellowships, grant agreement 844114 [A.A.]. Author Contributions S.D and M.W. conceptualized and designed the experiments. Y.V.Z., A.K., A.B. 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Supplementary Files Fig.S1.tif Hierarchical clustering showing the similarity among apple fungal (a) and bacterial (b) communities composition collected from different countries i.e. Canada, Turkey, Israel, Italy, Uruguay, USA West, USA East, Switzerland, and Spain. Fig.S2.tif A) a phylogenetic tree of the most prevalent Sphingomonas ASVs which were at least present with 0.1%. B) hierarchical clustering of Sphingomonas community. C) PCA ordination showing the variation in Sphingomonas (the core genus) community between fruit tissue types in all investigated orchards. Fig.S3.tif Distribution of node-degree of core and none-core species in co-occurrence network. Most core species have multiple links (>13). Node degree of core species is significantly higher than non-core species (Wilcoxon p=0.0039). SupplementaryTable1.xlsx A list summarizing the information about the apple samples included in the present study. SupplementaryTable2.xlsx Taxonomy and relative abundance of the most prevalent fungal and bacterial taxa detected on apple fruit in each of the investigated countries (Canada, Turkey, Israel, Italy, Uruguay, USA West, USA East, Switzerland, and Spain). SupplementaryTable3.docx Pairwise comparisons of the fungal and bacterial diversity (based on Shannon index) between the sampling locations (Canada, Turkey, Israel, Italy, Uruguay, USA West, USA East, Switzerland, and Spain) using Wilcox test and corrected using FDR method. P values less than 0.05 were considered significant. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2021 Read the published version in Environmental Microbiology → 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. 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Station","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Dardick","suffix":""},{"id":7740193,"identity":"6913b91d-1bed-4ebc-969e-4fb29a8cdca2","order_by":10,"name":"Jia Liu","email":"","orcid":"","institution":"Chongqing University of Arts and Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Liu","suffix":""},{"id":7740194,"identity":"1ecb5b7d-935a-4f2d-b63d-fe9894617d54","order_by":11,"name":"Awais Khan","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Awais","middleName":"","lastName":"Khan","suffix":""},{"id":7740195,"identity":"8e868e55-a9fd-4551-836b-fdbadac543b3","order_by":12,"name":"Walid Ellouze","email":"","orcid":"","institution":"Agriculture et Agroalimentaire Canada: Agriculture and Agri-Food Canada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Walid","middleName":"","lastName":"Ellouze","suffix":""},{"id":7740196,"identity":"723b62c9-d775-460f-a0f9-6b64582f548b","order_by":13,"name":"Shawkat Ali","email":"","orcid":"","institution":"Agriculture and Agri-Food Canada","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shawkat","middleName":"","lastName":"Ali","suffix":""},{"id":7740197,"identity":"78f7fac2-e340-45b3-bfc6-8cd6e3af1a97","order_by":14,"name":"Davide Spadaro","email":"","orcid":"","institution":"University of Turin: Universita degli Studi di Torino","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Davide","middleName":"","lastName":"Spadaro","suffix":""},{"id":7740198,"identity":"66f53e92-1f9e-49ee-be2c-abe696653bbc","order_by":15,"name":"Rosario Torres","email":"","orcid":"","institution":"IRTA: Institut de Recerca i Tecnologia Agroalimentaries","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rosario","middleName":"","lastName":"Torres","suffix":""},{"id":7740199,"identity":"733431a0-7095-4a3d-950a-b82cf2261008","order_by":16,"name":"Neus Teixido","email":"","orcid":"","institution":"IRTA: Institut de Recerca i Tecnologia Agroalimentaries","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Neus","middleName":"","lastName":"Teixido","suffix":""},{"id":7740200,"identity":"973bc844-028f-446a-881b-ea152abe54bc","order_by":17,"name":"Okan Ozkaya","email":"","orcid":"","institution":"Cukurova University: Cukurova Universitesi","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Okan","middleName":"","lastName":"Ozkaya","suffix":""},{"id":7740201,"identity":"36b8ab36-0bf9-4112-bbd2-3fb98c0c88dd","order_by":18,"name":"Andreas Buehlmann","email":"","orcid":"","institution":"Agroscope Standort Wädenswil: Agroscope Standort Wadenswil","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Andreas","middleName":"","lastName":"Buehlmann","suffix":""},{"id":7740202,"identity":"9f06dbe3-5d7b-45b1-873c-f6c0f17ad161","order_by":19,"name":"Silvana Vero","email":"","orcid":"","institution":"Universidad La República: Universidad La Republica","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Silvana","middleName":"","lastName":"Vero","suffix":""},{"id":7740203,"identity":"f971960e-febf-4d28-b1ff-9a6a0d078a9f","order_by":20,"name":"Pedro Mondino","email":"","orcid":"","institution":"Universidad de la República Uruguay: Universidad de la Republica Uruguay","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Pedro","middleName":"","lastName":"Mondino","suffix":""},{"id":7740204,"identity":"07ea94ef-ac34-47c0-a892-8d8afd60e10e","order_by":21,"name":"Gabriele Berg","email":"","orcid":"","institution":"Graz University of Technology: Technische Universitat Graz","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gabriele","middleName":"","lastName":"Berg","suffix":""},{"id":7740205,"identity":"810fb0f5-ed7e-492e-b53a-f95a4064a471","order_by":22,"name":"Michael Wisniewski","email":"","orcid":"","institution":"Virginia Tech: Virginia Polytechnic Institute and State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Wisniewski","suffix":""},{"id":7740206,"identity":"b95dc569-ee8b-42b2-be1d-e2c76750168f","order_by":23,"name":"Samir Droby","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie2QMUsDMRiG3xDILUHXK5XLX4gUXDz1hziFQrdCp3Jj5OC6FLrq1L/Q4h84yehxroWbDmeHokNBB5PqUiS1o0ieIYSP78n7EiAQ+KOUYCkQuat0B9HbMYv2KgOA/lDovhxmvpUdPIqYPCqTjZ/UjNL2dTSCENdGk7fM4MijyGq4eKjqRt3lrNe9lThd1krTk8p4i0lY5aZoetJwdLkEWU6Jpp1i4FXE7MUptVPou1WuflWw2qaUiVWYS1FzTjRZF6m/2Mqm6LqfdHJ2ds5l3F9YxaBKub/Y8H6tx5f8OMqfG/6RXsynUdtusjgRE+1ptksMWQL2K8AP2v/KtW+TzeH7gUAg8P/5BLD3U9xICxXiAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2727-8697","institution":"ARO, the Volcani Center","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Samir","middleName":"","lastName":"Droby","suffix":""}],"badges":[],"createdAt":"2021-01-07 15:04:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-142742/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-142742/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1111/1462-2920.15469","type":"published","date":"2021-03-18T13:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":4872923,"identity":"2f56b6ae-d0ab-47d3-b21f-28c57606bec3","added_by":"auto","created_at":"2021-01-11 20:18:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":947213,"visible":true,"origin":"","legend":"Circular barplot of the LDA scores showing a list of a) fungal and b) bacterial taxa that best characterize each geographical location (Country). Higher LEfSe score indicate higher consistency of differences in relative abundance between taxa of each country.","description":"","filename":"OnlineFig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/5a60024e1389a05c23c15c44.png"},{"id":4872731,"identity":"206922c5-c7e7-4649-84aa-a577d048edd5","added_by":"auto","created_at":"2021-01-11 20:15:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":283349,"visible":true,"origin":"","legend":"Box plots showing the bacterial fungal diversity (Shannon index) in apple growing orchards in different countries.","description":"","filename":"OnlineFig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/4de03037f2d3a0006b52067d.png"},{"id":4872738,"identity":"3b333077-8b81-4f66-9e72-7aedb68e4fd6","added_by":"auto","created_at":"2021-01-11 20:15:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":350042,"visible":true,"origin":"","legend":"PCoA plots of the fungal (a) and bacterial (c) communities composition based on Bray-Curtis dissimilarity distances. Dendrogram of hierarchical clustering showing the similarity between apple fungal (b) and bacterial (d) communities collected from different countries i.e. Canada, Turkey, Israel, Italy, Uruguay, USA West, USA East, Switzerland, and Spain. The hierarchical clustering was based Bray Curtis dissimilarity metric using “average clustering UPGMA” and k mean = 4 as implemented in vegan R, where branches colors correspond to clusters.","description":"","filename":"OnlineFig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/203a0e912d8dc25c9f0d1729.png"},{"id":4873050,"identity":"e3510386-e5b8-4c5d-8b44-2d5af8c87fbd","added_by":"auto","created_at":"2021-01-11 20:21:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":541227,"visible":true,"origin":"","legend":"boxplots of fungal (top) and bacterial (bottom) Shannon diversity among apple tissues (Calyx, stem and peel) in the investigated countries.","description":"","filename":"OnlineFig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/43b60dafce35a11d5d396519.png"},{"id":4872740,"identity":"b93d8f0f-a85e-494f-8db4-b155ff8fe7c1","added_by":"auto","created_at":"2021-01-11 20:15:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":445349,"visible":true,"origin":"","legend":"PCoA plots showing the variation in fungal (a) and bacterial (b) community composition among apple tissue types (Calyx, stem and peel). Analysis were based on Bray Curtis dissimilarity metric of CSS normalized OTU table. ","description":"","filename":"OnlineFig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/1c74397381ed9d41787d64c4.png"},{"id":4872925,"identity":"426b0a60-c39a-4dd1-b5b5-328dbd921715","added_by":"auto","created_at":"2021-01-11 20:18:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":688214,"visible":true,"origin":"","legend":"Correlation matrix (based on Spearman's rank correlation coefficient) of the abundance profiles of core and non-core genera from the apple fruit microbiome (27 bacterial and 70 fungal species). ‘Average’ linkage was used for the hierarchical clustering. Dendrogram was divided into five groups by cutting the tree at h=0.8. Black squares on the diagonal line indicate core species also labeled in red font (a). Co-occurrence network presenting interactions involving core species. Core and non-core species are represented by star and circle-shaped nodes, respectively. Green and red lines (i.e., edges) represent significant positive (r \u003e 0.4, p \u003c 0.01) and negative (r \u003c 0.4, p \u003c 0.01) correlation between two nodes, respectively. The size of each node is proportional to nodes' degree (the number of edges associated with the node). Colors are corresponding to the five key clusters in panel. The black frame highlights positive and negative interaction between a core bacteria (Methylobacterium) and potential biocontrol agent (Burkholderiales) and pathogen, respectively (Podosphaera) (b). An interactive version of the network is available in Supplementary Data 4.","description":"","filename":"OnlineFig.6.png","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/7170eed3fa3ac67272172866.png"},{"id":13644917,"identity":"184a25be-8ed3-45e0-a5c0-12f17fa996fc","added_by":"auto","created_at":"2021-09-17 09:17:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4421607,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/8dc8c622-4e3c-4704-9c65-a30c1b4d4f87.pdf"},{"id":4872924,"identity":"2638b570-0817-4a1d-90aa-2156bdc5f3b0","added_by":"auto","created_at":"2021-01-11 20:18:36","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2686316,"visible":true,"origin":"","legend":"Hierarchical clustering showing the similarity among apple fungal (a) and bacterial (b) communities composition collected from different countries i.e. Canada, Turkey, Israel, Italy, Uruguay, USA West, USA East, Switzerland, and Spain.","description":"","filename":"Fig.S1.tif","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/644c873fe45974164e8d88a3.tif"},{"id":4872742,"identity":"6b63374c-72c7-4333-a49b-c3d7f8ad6f8d","added_by":"auto","created_at":"2021-01-11 20:15:36","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":7677852,"visible":true,"origin":"","legend":"A) a phylogenetic tree of the most prevalent Sphingomonas ASVs which were at least present with 0.1%. B) hierarchical clustering of Sphingomonas community. C) PCA ordination showing the variation in Sphingomonas (the core genus) community between fruit tissue types in all investigated orchards.","description":"","filename":"Fig.S2.tif","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/3ee93407a1770bbd6217f059.tif"},{"id":4872741,"identity":"61ceb652-14ac-4a22-b7f5-98bfd02b3901","added_by":"auto","created_at":"2021-01-11 20:15:36","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2909172,"visible":true,"origin":"","legend":"Distribution of node-degree of core and none-core species in co-occurrence network. Most core species have multiple links (\u003e13). Node degree of core species is significantly higher than non-core species (Wilcoxon p=0.0039).","description":"","filename":"Fig.S3.tif","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/3e3b8849cf580edae6206bd1.tif"},{"id":4872739,"identity":"8ffe178b-17c3-4d3a-8d6d-dff2e638821c","added_by":"auto","created_at":"2021-01-11 20:15:36","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":18034,"visible":true,"origin":"","legend":"A list summarizing the information about the apple samples included in the present study.","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/2012aa489341e1670aadc9f3.xlsx"},{"id":4872926,"identity":"0de9e5b3-02a4-4f9a-849b-9a5b3c31c0bc","added_by":"auto","created_at":"2021-01-11 20:18:36","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":36506,"visible":true,"origin":"","legend":"Taxonomy and relative abundance of the most prevalent fungal and bacterial taxa detected on apple fruit in each of the investigated countries (Canada, Turkey, Israel, Italy, Uruguay, USA West, USA East, Switzerland, and Spain). ","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/9daf36840f449a7b870e8552.xlsx"},{"id":4872928,"identity":"4761a243-0a18-4840-a459-9a7ac8d44d9f","added_by":"auto","created_at":"2021-01-11 20:18:36","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":16324,"visible":true,"origin":"","legend":"Pairwise comparisons of the fungal and bacterial diversity (based on Shannon index) between the sampling locations (Canada, Turkey, Israel, Italy, Uruguay, USA West, USA East, Switzerland, and Spain) using Wilcox test and corrected using FDR method. P values less than 0.05 were considered significant.","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-142742/v1/8b19d578af940f174f2cf5f3.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eGlobal Analysis of the Apple Fruit Microbiome: Are All Apples the Same?\u003c/p\u003e","fulltext":[{"header":"Background","content":" \u003cp\u003eDeveloping a comprehensive understanding of the plant microbiome has identified as key for establishing a second green revolution [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In this regard, the sequencing of plant and microbial genomes has provided a wealth of information for developing new opportunities for crop improvement. Plants and their microbiomes have co-evolved as a meta-organism and the term \u0026lsquo;holobiont\u0026rsquo; has been used to describe the inseparable relationship between higher organisms and their microbial communities [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A growing body of information indicates that the plant microbiome is involved in many host functions, directly or indirectly affecting host physiology, biochemistry, growth, disease resistance, stress tolerance, and quality, before and after harvest [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This field of research has already provided new applications with the \u0026rdquo; microbiome factor\u0026rdquo; being included in breeding strategies, seed production, preharvest disease control, and the management of postharvest pathogens [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDomesticated apple (\u003cem\u003eMalus pumila\u003c/em\u003e \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eMill\u003c/span\u003e.) is one of the most popular edible fruits worldwide and is the largest fruit crop produced in temperate regions. The global production of apple has more than doubled in the past 20 years, from 41\u0026nbsp;million tons in 1990 to 86\u0026nbsp;million tons in 2018, with a total trading value of 7.53\u0026nbsp;billion USD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Apples are often stored for several months and up to one year in cold storage in conjunction with different controlled atmosphere regimes. Preventing the proliferation and development of postharvest pathogens in storage is an important challenge for maintaining fruit quality and safety. Studying the temporal changes in the assembly and composition of microbial communities on and in fruit during storage and marketing is essential for controlling postharvest diseases and reducing losses and waste along the supply chain.\u003c/p\u003e \u003cp\u003eDespite the existence of approximately 7,500 apple cultivars, which trace to the ancestral progenitor \u003cem\u003eMalus sieversii\u003c/em\u003e (Ldb.) \u003cspan type=\"SmallCaps\" class=\"SmallCaps\" name=\"Emphasis\"\u003eM. Roem\u003c/span\u003e about one tenth of this number have global prominence [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Among these apple cultivars, \u0026lsquo;Gala\u0026rsquo;, a cross developed in New Zealand between \u0026lsquo;Kidd\u0026rsquo;s Orange Red\u0026rsquo; and \u0026lsquo;Golden Delicious\u0026rsquo;, represents a significant portion of global apple production. \u0026lsquo;Gala\u0026rsquo; and its many sports, including \u0026lsquo;Royal Gala\u0026rsquo; are grown extensively in all apple growing regions of the world and, thus, have major economic value [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eApple tree microbiome studies have shown, as in other tree crops, that its composition is influenced by genotype, management practices, rootstock, and soil properties [\u003cspan additionalcitationids=\"CR10 CR11 CR12\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The apple microbiome has been comprehensively reviewed [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, relatively fewer studies have been conducted, on the pre- and postharvest fruit microbiome [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This is despite the fact that the use of various microbial antagonists has been pursued as an alternative to the use of synthetic chemicals to manage postharvest pathogens of apples. While postharvest biocontrol products using microbial antagonists, especially yeasts, have been commercialized, their wide sprayed use is limited due to problems with efficacy and regulatory hurdles. Other researchers have argued that a greater understanding of the fruit microbiome is needed to elucidate the factors involved in biocontrol systems and that this would facilitate the development of improved strategies that rely on the use of antagonistic microorganisms for managing postharvest diseases, and perhaps physiological disorders, that occur during the marketing and long-term storage of fruit crops [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have shown that different apple fruit tissues (calyx-end, stem-end, peel, and mesocarp) harbor distinctly different fungal and bacterial communities that vary in diversity and abundance [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Those studies differed in several respects, although the same general patterns were observed. The question remains, however, whether the observed patterns of abundance and diversity in the different tissue-types is generally true at a global level, despite differences in climates, management practices, and cultivars. One objective of the current study was to begin to examine this question. \u003cem\u003eMalus pumila\u003c/em\u003e and its derived cultivars have common ancestors (\u003cem\u003eMalus sieversii\u003c/em\u003e and \u003cem\u003eMalus sylvestris\u003c/em\u003e) that represent the primary progenitors of the modern apple [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Hologenome theory suggests that hosts and their microbiomes have co-evolved [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, we hypothesized that the fruit of a commercial cultivar, such as \u0026lsquo;Royal Gala\u0026rsquo;, would share a \u0026lsquo;core\u0026rsquo; microbiome, regardless of the global location where the fruit is produced. We also hypothesized, that the high level of genetic diversity that exists in apple and its long history of domestication may have impacted the overall composition of the fruit microbiome in a regional or local manner. Additionally, biotic and abiotic conditions and management practices may have played an important role in influencing microbial community assemblages as apple production spread from its original site of origin and domestication.\u003c/p\u003e \u003cp\u003eDetermining the existence of a \u0026lsquo;core\u0026rsquo; microbiome would provide important information on its impact on disease susceptibility and resistance and human health, as well as provide a more comprehensive understanding of fruit biology in light of the holobiont concept. A deeper understanding of the interactions between hosts and their resident microflora and how they are impacted by intrinsic (genetic) and extrinsic (management practices and the environment) can be used to develop novel approaches for the management of fruit quality, pre-and postharvest disease, and food safety. The main objectives of the present study were to determine: 1) if the spatial differences in microbial composition previously reported exist on a global scale, irrespective of where the fruit is grown; 2) how the structure of the fruit microbiome is affected by geographical location and general differences in climate, and; 3) if a core microbiome could be identified and if so how do the members of the core microbiome interact as a network. Results of the study provide a global perspective on the microbiome of apple fruit and provide a foundation for developing a better understanding of the interactions that potentially occur within the fruit microbial community, as well as the potential interactions that may occur between the fruit and its resident microflora in relation to postharvest diseases, fruit quality, and food safety.\u003c/p\u003e "},{"header":"Materials And Methods","content":"\u003cp\u003e\u0026lsquo;Royal Gala\u0026rsquo; apple fruit harvested at commercial maturity were used in this study. Fruit were harvested in four regions (North America, South America, Europe, and the Middle East) that included 21 locations in 8 countries (USA, Canada, Uruguay, Italy, Spain, Switzerland, Israel, and Turkey). Fruit were harvested at commercial maturity using standard maturity indices. Harvesting occurred in July -September in the northern hemisphere and February-March in the southern hemisphere (Supplementary Table S1). A standardized protocol was used for sample collection and processing in all sampling locations across countries, then the extracted DNA was sent USDA-ARS, WV, USA, to avoid bias introduced by library preparation and sequencing. Briefly, in each locations/orchard, 8 trees (not adjacent to each other) were selected and 5 fruit/tree were sampled from around the circumference of the tree. Each tree consisted one replicate; total of 8 replicates per location/orchard. Five fruit from each tree are pooled to make 1 biological replicate (total 8 biological replicates/orchard). From each apple, 3 tissue types (peel, stem-end, and calyx-end) were sampled as previously described [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. First, a sterile cork-borer was used to excise the fruit core and the top and bottom 1.5\u0026nbsp;cm were used as stem- and calyx-end, respectively. To collect the peel, a thin layer around the fruit equator with approximately 1.5\u0026nbsp;cm in width was obtained from each apple using a peeler. Samples from of the same fruit tissue from the same tree were pooled and considered a biological replicate making total of 8 replicate of each tissue site per orchard and a total of 505 samples globally. Samples were immediately frozen in liquid nitrogen, kept and \u0026minus;\u0026thinsp;20 or -80C until freeze-dried.\u003c/p\u003e\n\u003ch2 style=\"text-align: justify;\"\u003eLibraries and sequencing, Data processing, Downstream and statistical analysis\u003c/h2\u003e\n\u003cp\u003eLyophilized samples were homogenized, and their DNA was extracted using DNeasy PowerLyzer PowerSoil Kit (Qiagen, Germantown, MD, USA). Initial tissue disruption of 250\u0026nbsp;mg was performed with a Qiagen PowerLyzer 24 Homogenizer (Qiagen, Germantown, MD USA). DNA extractions were automated using a Qiagen QiaCube (Qiagen, Germantown, MS, USA), using the processing routine recommended by the manufacturer for the PowerSoil kit. Extracted DNA was used as the template for amplicon PCR reactions that amplified the bacterial 16S ribosomal region and the fungal internal transcribed spacer (ITS) region. The V4 region of 16S rRNA was amplified using the universal primers 515F [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e] and 806R [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e] in conjunction with peptide nucleic acids (PNAs) (PNA Bio) added to inhibit amplification of ribosomal and mitochondrial sequences [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. ITS amplicons were amplified using ITS3/KYO2 [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e] and ITS4 [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e] primers along with a custom-designed blocking oligo designed to inhibit amplification of the host DNA (5\u0026rsquo; ATTGATATGCTTAAATTCAGCGGGTAACCCCGCCTGACCTGGGGTCGCGTT-C3 spacer 3\u0026rsquo;). All primers were modified to include the necessary Illumina adapters (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.illumina.com\" target=\"_blank\"\u003ewww.illumina.com\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e) for subsequent PCR addition of Illumina indexes for multiplexing.\u003c/p\u003e\n\u003cp\u003eFor bacteria, PCR reactions were conducted in a total volume of 25 \u0026micro;L containing 12.5 \u0026micro;L of KAPA HiFi HotStart ReadyMix (Kapa Biosystems), 1.0 \u0026micro;L of each primer (10\u0026nbsp;\u0026micro;M), 2.5 uL of mitochondrial PNA (5 uM), 2.5 uL of plastid PNA (5 uM), 2.5 \u0026micro;L of DNA template, and 3 \u0026micro;L nuclease-free water. Reactions were incubated in a T100 thermal cycler (BioRad) at 95\u003csup\u003eo\u003c/sup\u003eC for 5\u0026nbsp;min followed by 30 cycles of 95\u003csup\u003eo\u003c/sup\u003eC for 30\u0026nbsp;s, 78\u003csup\u003eo\u003c/sup\u003eC for 5\u0026nbsp;s, 55\u003csup\u003eo\u003c/sup\u003eC for 30\u0026nbsp;s, 72\u003csup\u003eo\u003c/sup\u003eC for 30\u0026nbsp;s and a final extension at 72\u003csup\u003eo\u003c/sup\u003eC for 5\u0026nbsp;min. For fungal (ITS) amplicon generation, 25 uL PCR reactions contained 12.5 \u0026micro;L of KAPA HiFi HotStart ReadyMix (Kapa Biosystems), 1.0 \u0026micro;L of each primer (10\u0026nbsp;\u0026micro;M), 1.0 uL of blocking oligo (10 uM), 2.5 \u0026micro;L of DNA template, and 7 \u0026micro;L nuclease-free water. Reactions were incubated in a T100 thermal cycler (BioRad) at 95\u003csup\u003eo\u003c/sup\u003eC for 5\u0026nbsp;min followed by 30 cycles of 95\u003csup\u003eo\u003c/sup\u003eC for 30\u0026nbsp;s, 55\u003csup\u003eo\u003c/sup\u003eC for 30\u0026nbsp;s, 72\u003csup\u003eo\u003c/sup\u003eC for 30\u0026nbsp;s a final extension at 72\u003csup\u003eo\u003c/sup\u003eC for 5\u0026nbsp;min. Library preparation following amplicon PCR was performed as specified in the Illumina 16S Metagenomic Sequencing Library Preparation guide precisely as outlined in conjunction with the use of a Nextera Index Kit (Illumina) containing 96 indexes. Subsequent library size, quality, and confirmation of the absence of adapter dimers was performed on an Agilent 2100 Bioanalyzer (Agilent). Paired-end sequencing of amplicons was done on an Illumina MiSeq (Illumina) sequencer with a V3 600-cycle Reagent Kit (Illumina).\u003c/p\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eData Analysis\u003c/h2\u003e\n\u003cp\u003eQiime2 [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] was used for demultiplexing, merging, quality filtering and trimming of reads, ASV table generation, and rarefaction to account for uneven sequencing depth. Taxonomic clustering of ASVs was done using a similarity threshold of 97% against the GreenGene [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] database for 16S reads and against the UNITE [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] database for ITS reads. MetagenomeSeq\u0026rsquo;s Cumulative Sum Scaling (CSS) [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] was used as a normalization method subsequent to community composition analyses, including the calculation of Bray\u0026ndash;Curtis dissimilarity metrics [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], the construction of PCoA plots, and PERMANOVA analyses. Rarefaction to an even sequencing depth of 1,000 reads per sample was used to normalize ITS and 300 reads for the 16S features tables which were used to calculate Shannon diversity. Differences in community composition between the investigated countries, orchards and tissue types were tested using Permutational Multivariate Analysis of Variance Using adonis (~\u0026thinsp;PERMANOVA) in vegan R with 999 permutations [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eThe core microbiome was calculated based on genera present in at least 75% of the investigated samples using \u003cem\u003ecore\u003c/em\u003e function in \u003cem\u003eMicrobiome\u003c/em\u003e package [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. Interactions between core and non-core species were limited to genera whose normalized relative abundance\u0026thinsp;\u0026gt;\u0026thinsp;0.1% (average across replicas) in at least a single sample. Co-occurrences were described based on Spearman\u0026rsquo;s rho correlation coefficients between the distribution patterns of the genera joining the normalized bacterial and fungal tables. Scores were calculated using 'Pandas.corr' python package v1.1.0. Correlation matrix and visualized using 'seaborn.clustermap' python package v0.10.1. Co-occurrence networks were generated based on correlation scores. Network visualization and the positioning of the nodes were calculated according to the force-directed Fruchterman \u0026amp; Reingold algorithm used for calculating layouts of simple undirected graphs (Buchfink et al., 2014). The algorithm was implemented using nx.draw function via the \u0026lsquo;pos\u0026rsquo; parameter in the 'NetworkX' python package v1.11. Node degree was calculated using the nx.degree function. Visualization was generated using 'Plotly' python package v4.9.0. Linear discriminant analysis effect size (LEfSe) [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. was used for biomarker discovery to determine a list of taxa that best characterize each geographical location (Country). Higher LEfSe score indicate higher consistency of differences in relative abundance between taxa of each country. Significance in all the analyses was determined using 999 Monte Carlo permutations, and Benjamini\u0026ndash;Hochberg (FDR) corrections were used to adjust the calculated p values. All statistical analyses were done using R version 3.6.2 [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e] in RStudio version 1.1.453 [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] and the packages vegan version 2.5-6, lme4 version 1.1\u0026ndash;21, multcomp version 1.4\u0026ndash;13, phyloseq version 1.32.0 [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003eMicrobial diversity associated with Royal Gala apple\u003c/h2\u003e\n\u003cp\u003eAfter removal of low-quality sequences and plant sequences, 6.117.315 16S and 48.528.735 ITS2 reads were obtained and assigned to 20.072 bacterial and 16.241 fungal ASVs, respectively. The ASVs corresponded to 25 bacterial and 6 fungal phyla, which in turn were assigned to 558 bacterial and 822 fungal genera. The apple fungal community across the investigated countries was dominated by \u003cem\u003eAscomycota\u003c/em\u003e (79.8%) and \u003cem\u003eBasidiomycota\u003c/em\u003e (9.3%), although, other phyla such as \u003cem\u003eChytridiomycota, Entomophthoromycota, Mortierellomycota\u003c/em\u003e, and \u003cem\u003eMucoromycota\u003c/em\u003e were also detected at a lower relative abundance (data not shown). Within the Ascomycota, genera such as \u003cem\u003eAureobasidium\u003c/em\u003e (29.00%), \u003cem\u003eCladosporium\u003c/em\u003e (16.60%), and unidentified groups of \u003cem\u003eCapnodiales\u003c/em\u003e (8.80%) and \u003cem\u003ePleosporaceae\u003c/em\u003e (8.50%) represented more than 60% of the total fungal community (Supplementary Table\u0026nbsp;2). \u003cem\u003eSporobolomyces\u003c/em\u003e (5.70%), \u003cem\u003eFilobasidium\u003c/em\u003e (4.20%), and \u003cem\u003eVishniacozyma\u003c/em\u003e (1.60%) were the predominant \u003cem\u003eBasidomycota\u003c/em\u003e. Regarding bacteria, \u003cem\u003eProteobacteria\u003c/em\u003e (65.1%), \u003cem\u003eFirmicutes\u003c/em\u003e (15.8%) \u003cem\u003eActinobacteria\u003c/em\u003e (15.1%), and \u003cem\u003eBacteroidetes\u003c/em\u003e (2.3%) were the most prevalent bacterial phyla, representing 98.3% of the entire bacterial community. The abundance distribution of the bacterial phyla was consistent across countries, except in Turkey where \u003cem\u003eFirmicutes\u003c/em\u003e were more abundant than \u003cem\u003eProteobacteria\u003c/em\u003e compared to the other countries. \u003cem\u003eSphingomonas\u003c/em\u003e (12.40%), \u003cem\u003eErwinia\u003c/em\u003e (11.30%), \u003cem\u003ePseudomonas\u003c/em\u003e (9.30%), \u003cem\u003eBacillus\u003c/em\u003e (7.10%), unidentified \u003cem\u003eOxalobacteraceae\u003c/em\u003e (6.80%), \u003cem\u003eMethylobacterium\u003c/em\u003e (6.20%) and unidentified \u003cem\u003eMicrobacteriaceae\u003c/em\u003e (5.90%) were the most abundant bacterial genera. (Supplementary Table\u0026nbsp;2). Results of the linear discriminant analysis (LEfSe) revealed 90 fungal and 57 bacterial taxa characterized each of the investigated countries (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Turkey had the highest number of fungal genera (25), which included \u003cem\u003ePenicillium\u003c/em\u003e, \u003cem\u003eZasmidium\u003c/em\u003e, and \u003cem\u003ePseudomicrostroma\u003c/em\u003e. In contrast, Spain had the lowest number of fungal genera (5), which included \u003cem\u003eMonilinia, Vishniacozyma, and Bensingtonia\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003ea). Israel and the western USA had the highest number of unique bacterial taxa, while only one bacterial taxon, identified as within the \u003cem\u003ePaenibacillaceae\u003c/em\u003e was observed in samples collected in Uruguay (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e\n\u003ch2\u003eThe effect of growing region on the microbial diversity of apple fruit\u003c/h2\u003e\n\u003cp\u003eThe geographical location in which apples were sampled had a significant effect on the microbial diversity associated with the fruit (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For example, country of origin (including location within a country) had a significant effect on the diversity of fungi (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;44.06, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e) and bacteria (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;22.993, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e). Similarly, although to a lesser extent, the effect of orchard on fungi (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;30.49, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e) and bacteria (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.491, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;1.09\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) was also statistically significantly. Pairwise comparison between Shannon diversity of the investigated countries indicated that both fungal and bacterial diversity differed significantly between locations and orchards within a location (Supplementary Table\u0026nbsp;3). Italy had the highest fungal diversity, followed by Turkey and then Israel (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). The highest bacterial diversity was observed in apples collected from Italy, the USA, and Switzerland (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eModel results using anova test on the effects of location, orchard, tissue, and their interactions with the \u003cstrong\u003eShannon\u003c/strong\u003e diversity of bacteria and fungi on apple fruits.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eShannon\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSum Sq\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean Sq\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF value\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePr(\u0026gt;\u0026thinsp;F)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eFungi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.372\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e44.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrchard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.334\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.188\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0875\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCountry\u0026thinsp;\u0026times;\u0026thinsp;Tissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.913\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;2e-16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrchard\u0026thinsp;\u0026times;\u0026thinsp;Tissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.201\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.09E-05\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResiduals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"6\" align=\"left\"\u003e\n\u003cp\u003eBacteria\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.761\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e22.993\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrchard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16.51\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.376\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.491\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.09\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.236\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e68.794\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCountry\u0026thinsp;\u0026times;\u0026thinsp;Tissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.96\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.185\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e4.729\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.22E-09\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrchard\u0026thinsp;\u0026times;\u0026thinsp;Tissue\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.921\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.678\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.03E-08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResiduals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.251\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eCommunity composition of apple across countries\u003c/h2\u003e\n\u003cp\u003eThe geographical location of the investigated sites had a significant effect on shaping the community composition of the tested apples. While the \u0026ldquo;country effect\u0026rdquo; had a significant impact on the overall apple microbiome (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), it was more evident in the fungal community (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;0.375) than in the bacterial community (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.152). This was also evident in the PCoA analysis based on Bray Curtis dissimilarity test (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea\u0026amp;c). An effect of orchard was also observed, explaining less variation, however, in fungal (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.136, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001) and bacterial (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.048, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001) communities relative to country (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eHierarchal clustering revealed that European apples (Switzerland, Italy, and Spain) had a bacterial community that was more similar to each other, relative to sites in eastern North America and South America (eastern USA, Canada, and Uruguay) which formed a separate cluster (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed). Turkish and Israeli apples appeared to harbor a distinct bacterial community. Hierarchal clustering of the fungal community composition revealed that apples collected from the western USA, Italy, Spain, and Israel formed a separate cluster from a cluster formed by the eastern USA, Canada, Uruguay, and Switzerland (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). Furthermore, orchards within the same country appeared to have similar microbial communities than those sampled from another country. These results were more evident, however, in fungal communities than in bacterial communities (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePERMANOVA results on testing the effects of Location, Orchard, tissue, and their interactions on bacterial and fungal communities of apple fruits. The comparisons were based on Bray Curtis dissimilarity, and p-values were calculated using the adonis function in vegan and corrected using the FDR method.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eDf\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSums of Sqs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean Sqs\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eF. Model\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePr(\u0026gt;\u0026thinsp;F)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eFungi\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e48.079\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.0098\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e59.229\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.37528\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrchard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.478\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.4565\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e14.354\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.13643\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTissue type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.408\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.7038\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.792\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCountry\u0026thinsp;\u0026times;\u0026thinsp;Tissue type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8.892\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.5558\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.477\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.06941\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrchard\u0026thinsp;\u0026times;\u0026thinsp;Tissue type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e6.829\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.2845\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.804\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.0533\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResiduals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e43.428\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.1015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33898\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e490\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e128.113\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"7\" align=\"left\"\u003e\n\u003cp\u003eBacteria\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCountry\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e30.649\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.8311\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12.5466\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.15272\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrchard\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e9.741\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.8117\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.6583\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.04853\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTissue type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.419\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.2093\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e17.0602\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.05191\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCountry\u0026thinsp;\u0026times;\u0026thinsp;Tissue type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e16.21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.0131\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.3178\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.08077\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOrchard\u0026thinsp;\u0026times;\u0026thinsp;Tissue type\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.841\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.4934\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.6157\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.059\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eResiduals\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e121.835\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.3054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.60707\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e461\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e200.694\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eSpatial variation in the apple microbiome\u003c/h2\u003e\n\u003cp\u003eThe effect of tissue types on fungal diversity (Shannon) was not statistically significant when tissue samples from all countries were grouped together (\u003cem\u003eF\u0026thinsp;=\u003c/em\u003e\u0026thinsp;2.45, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.0875). The interaction between country and tissue type, as well as between orchard and tissue type, however, were significant (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In the majority of the orchards, calyx-end tissue exhibited a higher fungal diversity, followed by peel and stem-end tissues, with a few exceptions observed in samples collected from Uruguay, Turkey, and Spain (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea). In contrast, tissue type had a significant effect on bacterial diversity, regardless of the sampling location (\u003cem\u003eF\u0026thinsp;=\u003c/em\u003e\u0026thinsp;68.794, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;2\u0026thinsp;\u0026times;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e), as well as in the interaction between country and tissue, as well as orchard and tissue (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Stem-end tissues harbored the highest bacterial diversity relative to fruit peel and calyx-end tissues, except in the New Brunswick, Canada samples (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb). PERMANOVA analysis indicated that tissue type, as well as the interaction between tissue type and country, and tissue type and orchard, had a significant effect on fungal community composition (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). This effect was observed in all of the investigated orchards in all countries, based on the results of the PCoA analysis where samples collected from apple calyx-end, stem-end, and peel, tissues clustered separately from each other (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). Similar results were also found for the bacterial community which differed significantly in all orchards, (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eThe core microbiome of Royal Gala apple\u003c/h2\u003e\n\u003cp\u003eThe global core of the apple microbiome, defined at taxa present in at least 75% of the samples, consisted of six fungal genera, namely: \u003cem\u003eAureobasidium, Cladosporium, Alternaria, Filobasidium, Vishniacozyma\u003c/em\u003e, and \u003cem\u003eSporobolomyces\u003c/em\u003e and two bacterial genera namely: \u003cem\u003eSphingomonas\u003c/em\u003e and \u003cem\u003eMethylobacterium.\u003c/em\u003e While none of the bacterial genera were found to be prevalent in 90% of the samples, the fungal genera \u003cem\u003eAureobasidium\u003c/em\u003e, and \u003cem\u003eCladosporium\u003c/em\u003e were found in up to 96% of the samples. Interestingly, the community composition of \u003cem\u003eSphingomonas\u003c/em\u003e was sufficient to distinguish between most of the investigated countries and showed niche specialization within the fruit i.e. stem-end, calyx-end, and peel tissues harbored different Sphingomonas communities (Supplementary Fig.\u0026nbsp;2). Similar results were also observed for \u003cem\u003eAureobasidium\u003c/em\u003e, a core fungal genus, however, species variability was limited, and differences were attributed to niche specialization in the different tissue-types (data not shown).\u003c/p\u003e\n\u003cp\u003eIn order to detect potential interactions between core and non-core groups we depicted co-occurrences by constructing a correlation matrix based on normalized distribution patterns of bacterial and fungal genera (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003ea). Clustering pattern indicates that genera can be divided into five key groups of co-occurring species mixing bacterial and fungal genera. Core species are distributed in two clusters, each hosting one of the two most dominant Ascomycota genera - \u003cem\u003eAureobasidium\u003c/em\u003e (green) and \u003cem\u003eCladosporium\u003c/em\u003e (purple). Microbiomes with a high relative abundance of \u003cem\u003eAureobasidium\u003c/em\u003e and a low abundance of Cladosporium were characterized in Switzerland, USA and Canada; alternatively, high numbers of \u003cem\u003eCladosporium\u003c/em\u003e and low numbers of \u003cem\u003eAureobasidium\u003c/em\u003e were described in Israel and Turkey (Supplementary Fig.\u0026nbsp;1). Considering the significant negative and positive interactions between genera, core species were found to have a significantly higher number of interactions in comparison to non-core species with an average node degree of 19.125 neighbors in comparison to 12.23 in none core species (Supplementary Fig.\u0026nbsp;3). A network formed by the interactions of core genera with core and non-core goops is consistent of 142 edges and connects 8 and 60 core and non-core genera, respectively (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). The highest number of interactions \u0026minus;\u0026thinsp;30\u0026ndash; was recorded for one core genus \u0026ndash; \u003cem\u003eSphingomonas\u003c/em\u003e. Using the network, we could identify potentially useful relationships among and between core and non-core genera within the microbial community (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eb). For example, the core genera \u003cem\u003eMethylobacterium\u003c/em\u003e is positively associated with \u003cem\u003eBurkholderiales\u003c/em\u003e \u0026ndash; a group that includes reported biocontrol agents (Angeli et al., 2019), and a negative association with a known apple pathogen \u003cem\u003ePodosphaera\u003c/em\u003e. These co-occurrence associations can be indicative of cooperative and competitive interactions, respectively, and can serve the design of experiments to assess these interactions \u003cem\u003ein vitro\u003c/em\u003e and on the fruit.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to provide a global analysis of the apple fruit (\u0026lsquo;Royal Gala\u0026rsquo;) microbiome and determine the structure and diversity of microbial communities on and in different fruit tissues at harvest. A core microbiome shared between apple samples in all locations was identified suggesting that the members of the core microbiome may have co-evolved with the domestication of apple and potentially may play an essential role in defining fruit traits related to disease resistance and fruit quality. We characterized the microbial communities associated with \u0026lsquo;Royal Gala\u0026rsquo; apple fruit at harvest maturity stage and assessed the effect of geographical location on both large-scale spatial variations, i.e. across different countries, and small-scale spatial variations, i.e. within a fruit. While the microbiome associated with plants has been extensively studied, knowledge about the fruit microbiome is still rather limited relative to rhizosphere, endophyte, and phyllosphere studies [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, information about the role of the fruit microbiome on pre- and postharvest diseases, as well as fruit physiology, is also lacking. This is despite the importance of postharvest losses in reducing the economic return from fruit production, especially after so many resources have already been expended to produce a harvestable crop. Apples also encounter losses in storage, transit, markets, and homes due to postharvest pathogens [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]. For over 30 years, there has been considerable research focus on the development of biological control strategies based on naturally-occurring microorganisms [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. Especially with the use of yeast antagonists, has been an active area of research. Several postharvest biocontrol products based on single antagonists have been developed and registered. The large scale commercial use of these products have been limited a due to inconsistent performance under commercial conditions [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. In this regard, Droby et al. (2018) have indicated that a new paradigm is needed for postharvest biocontrol to achieve commercial success and that understanding the naturally-occurring microbiome of fruit surfaces and its function, will lead to the development of new biological strategies for postharvest disease control. Several studies have reported on the population dynamics of biocontrol agents on intact and wounded fruit over the course of low-temperature storage. A wide array of mechanisms has also been demonstrated for postharvest biocontrol agents that involve yeast antagonist, the pathogen, and the host. This study and others are providing the foundation for understanding the structure and function of the carposphere microbiome. Such information is an essential step towards the development of effective biological approaches to postharvest disease management. For example, efforts to modulate the gut microbiome for improved human health have moved from simple inoculations with beneficial bacteria (probiotics) to supplements that contain specific metabolites that provide a resource that can be selectively utilized by beneficial bacteria (prebiotics) to combinations of probiotics and prebiotics (synbiotics) that can more effectively shift the composition of an existing host community [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. Similarly, in the apple rhizosphere, efforts to manipulate the soil microbiome to treat apple replant disease have shown that directed changes to the resource environment (e.g. through selective soil amendments) are more successful at controlling disease than inoculations with single strains or simple consortia of beneficial microbes [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. Research designed to identify, quantify, and elucidate the metabolic networks constructed by microbial populations on harvested fruit is a fundamental need. Such studies will improve our understanding of the mechanisms that regulate the assembly of beneficial microbial communities, and lead to the development of strategies for beneficially manipulating microbial communities \u003cem\u003ein situ\u003c/em\u003e.\u003c/p\u003e\n\u003ch2\u003eGeographical Location\u003c/h2\u003e\n\u003cp\u003eApples represent a major item of export and are shipped globally. Therefore, it is of importance to determine if the structure of the apple fruit microbiome is relatively uniform regardless of where the fruit is produced. Rather than the presence of a uniform microbiome, the present study revealed that geographical location is a principle factor determining the structure of the apple fruit microbiome. Fungal communities, however, were more affected by geographical location (country and site within a country) than bacterial communities. The stability of the fruit-associated bacterial community, relative to their fungal counterparts, has been previously reported in stored apples [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. The higher level of variation in the fungal community may be potentially attributed to the fact that fungal assemblages appear to be derived from regional fungal pools with limited dispersal capability [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. In addition, we observed that as the variation in the microbial communities among sites was positively correlated with the distance between those locations, especially for fungi. For example, variations in fungal and bacterial communities associated with apple fruit were lower at a local scale, i.e. among orchards within the same geographical location, sites within a country e.g. eastern and western USA and Canada and increased at the country level. Furthermore, a continental pattern can be drawn especially for the bacterial community which in one hand indicates adaptation of the apple microbiome to local environments, and on the other hand it may be explained by the metacommunity theory. A metacommunity is defined as a set of local communities that are linked by dispersal of multiple potentially interacting species [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. However, the present study had an insufficient distribution of samples to evaluate this premise. Nevertheless, the geographical location has been previously reported to be one of the most important determinants of the structure of the plant microbiome [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e]. A study of the maize rhizosphere found that location had a higher impact on the plant microbiome than genotype [\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similarly, a study of the global citrus rhizosphere microbiome reported large variations in community structure that were attributed to geographical location (samples collected in different countries) [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. The large-scale variations between countries, together with the similarity observed among apple microbial communities within a country or region within a country, suggests that the structure of the microbial community on apple fruit is locally-adapted to local environmental conditions that influence microbial diversity and composition [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e]. In this regard, it is also commonly recognized that the humid, wet conditions present in the eastern portions of the USA and Canada, present a much greater disease and pest challenge than the dry conditions present in the western USA and Canada. This is especially supported by the differences in diversity levels between these two contrasting environments, although more evident for the fungal community (e.g. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003ch2\u003eTissue type\u003c/h2\u003e\n\u003cp\u003ePlants tissues provide a variety of niches that can harbor distinct microbial communities. Plant roots, leaves, flowers, fruit, as well as other organs, represent different microhabitats, each with specific features that favor the growth of specific microorganisms in these organs. Different tissue types within the same organ, have been previously reported to exhibit spatial variations in microbial community structure. For example, the upper and lower leaf sides, as well as the peel and pulp of various fruits, including apple, have been reported to exhibit differences in microbial community structure [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. The experimental design used in the present study was selected to determine if spatial variations within a fruit is global, i.e. will be evident regardless of geographical location and the variety of environmental conditions present in the different sites. Results indicated that the effect of fruit tissue-type on the composition of the microbial community was rather limited, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0266 for fungi and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.05191 for bacteria, yet significant i.e. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001. A larger effect was observed, however, when individual orchards were analyzed separately (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Spatial variations in fungal and bacterial community composition and Shannon diversity due to tissue-type was consistently observed in all of the investigated orchards. These results, along with previous studies, confirms that spatial variation in the structure of the microbial community exist between tissue-types (calyx-end, stem-end. and peel) at a global level. Since geographical location, is the main factor shaping the structure of the apple microbiome, however, the effect of tissue-type is greatly reduced when samples of tissue-types are pooled across countries. Notably, the association of a distinct microbiome with such a small environmental niche (tissue-type) suggests specialized adaptation and function to those microhabitats. We suggest that the conditions (morphological, nutrient, and environmental) present in each of these microhabitats (tissue-types) could play an important role in determining community structure. For instance, the calyx-end is an open site that may create special niche for specialized fungi such as \u003cem\u003eAlternaria\u003c/em\u003e and other fungal pathogens which can cause internal rots. Interestingly, \u003cem\u003eErwinia\u003c/em\u003e species were found at higher abundance in the Calyx-end tissue compared to the other tissue types, especially in Canadian apples. This can be explained by the fact that the calyx contains floral residues which are most affected by \u003cem\u003eErwinia amylvora\u003c/em\u003e, the cause of fire blight disease of pome fruit.\u003c/p\u003e\n\u003ch2\u003eCore microbiome\u003c/h2\u003e\n\u003cp\u003eA core microbiome is a set of microbes consistently present over time on a specific host and is likely to be critical to host development, health, and functioning [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. Defining the core microbiome enables researchers to filter out transient associations and focus on stable taxa with a greater likelihood of influencing host phenotype and is therefore essential in exploring the potential for pre/probiotic treatments that support host health [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e]. In this study the core microbiome of apple fruit was defined as fungal and bacterial taxa present in at least 75% of all samples. We found two bacterial genera, namely \u003cem\u003eSphingomonas\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e, and six fungal genera i.e. \u003cem\u003eAureobasidium, Cladosporium, Alternaria, Filobasidium, Vishniacozyma, and Sporobolomyces\u003c/em\u003e. This is a considerably low number of taxa, relative to other reported core microbiomes in plants [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e]. However, this can be attributed to the high number of samples in the present study; which lowers the probability that same taxon will be present in all samples and the evaluation of samples from different countries and tissue-types.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSphingomonas\u003c/em\u003e, a gram-negative, non-motile, aerobic bacterial genus, is known for its bioremediation of heavy metals and biodegradation of polycyclic aromatic hydrocarbons, and is associated with plant growth promotion through its ability to produce gibberellins and indole acetic acid in response to different abiotic stress conditions, such as drought, salinity, and heavy metal stresses [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e]. Interestingly, those phytohormones are also involved in fruit maturation, development, and quality. For example, fruit-set in tomato (\u003cem\u003eSolanum lycopersicum\u003c/em\u003e) depends on gibberellins and auxins [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e]. Similarly, \u003cem\u003eMethylobacterium\u003c/em\u003e is a gram-negative, aerobic, motile bacterial genus with plant growth-promoting properties [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e]. \u003cem\u003eSphingomonas\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e have been previously reported as a component of the apple microbiome and as two of their predominate genera [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e], as well as a component of the core microbiome in several other plant species [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e]. \u003cem\u003eAureobasidium\u003c/em\u003e and \u003cem\u003eCladosporium\u003c/em\u003e have also been reported as a common member of the microbiome of apple [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e] and other plants [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. These taxa can be found as endophytes or epiphytes in association with various plant organs e.g. leaves, flowers, fruit, seed etc. Although the core microbiome is typically considered to have a high level of specificity between species, the common reporting of these taxa suggests the possibility of a core microbiome that is shared between different plant species. This commonality is expected to exist at the level of genus and that some degree of species specificity may exist. In this regard, we found that the core bacterial genera, \u003cem\u003eSphingomonas\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e accounted for a considerable fraction of the observed variation between the investigated locations, as well as tissue types. For example, the community composition of either \u003cem\u003eSphingomonas\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e was sufficient to distinguish between most of the investigated countries. Similar results were also observed for \u003cem\u003eAureobasidium\u003c/em\u003e, a core fungal genus, however, species variability was limited, and differences were attributed to niche specialization in the different tissue-types. Notably, both bacterial genera appeared to be distinct in tissue types. The geographical location demonstrated to be an important determinants of the \u003cem\u003eMethylobacterium\u003c/em\u003e community composition in the plant phyllosphere [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e]. The presence of distinct \u003cem\u003eSphingomonas\u003c/em\u003e community in different fruit tissue-types suggests site-specialization to these microhabitats. Interestingly, the majority of the fungal core microbiome was represented by yeasts with known antagonistic activity against pre- and postharvest pathogens. Despite being one of the most common fungi associated with apples, \u003cem\u003ePenicillium\u003c/em\u003e, the causal agent of the most important apple postharvest disease, blue mold [\u003cspan class=\"CitationRef\"\u003e70\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e72\u003c/span\u003e], was not found to be a component of the core microbiome. \u003cem\u003ePenicillium\u003c/em\u003e species are able to grow and proliferate at low temperatures during cold storage, giving them an advantage over other fungal species [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this regard and considering samples in the present study were collected immediately after harvest, it can explain the low prevalence and the absence of \u003cem\u003ePenicillium\u003c/em\u003e from the apple core microbiome. Information about the core microbiome can be further used to develop biological control strategies against apple diseases and disorders. Though core species, by definition, are detected across all samples, their relative abundance pattern vary and, in some cases, forms characteristic groups of microorganisms. Dissecting the microbiome into co-occurrence modules can serve the construction of synthetic communities with distinct function [\u003cspan class=\"CitationRef\"\u003e73\u003c/span\u003e]. For example, such associations can serve the design of multiple-species synthetic communities for achieving an efficient biocontrol activity. Alternatively, dissecting the microbiome into microbial modules can allow limiting the search for a single efficient antagonist agent. In the context of the apple fruit microbiome, co-occurrence patterns have stratified the fruit microbiome into five key groups with core genera located in two of them: one cluster with \u003cem\u003eAureobasidium\u003c/em\u003e, and the second with \u003cem\u003eCladosporium\u003c/em\u003e, the two most abundant Ascomycota genera. Though most of the significant interactions detected in the network are positive, some negative associations allow formulating predictions for potential biocontrol agents against pathogens. Based on the network view, experimental design of potential biocontrol agent could compare the activity of a single microorganism vs consortium representing a native co-occurring module. Potential biocontrol strategies can hence benefit from the network view of microbiome interactions allow to go beyond the single biocontrol agent to the educated design of a biocontrol consortium.\u003c/p\u003e"},{"header":"Conclusions","content":" \u003cp\u003eRecent studies have demonstrated the critical role that the plant microbiome plays in plant health, fitness and productivity. Less attention, however, has been given to studies on the carposphere, compared to the rhizosphere, and phyllosphere. Apple fruit were recently reported to host a high microbial diversity with niche specialization exhibited in calyx-end, stem-end, and peel tissues. Whether this niche specialization is consistent in different apple-production areas globally and whether a \u0026ldquo;core\u0026rdquo; microbiome exists, regardless of geographic location, as has been reported for the rhizosphere of other fruit crops has not been determined. In the present study, the microbial communities associated with \u0026lsquo;Royal Gala\u0026rsquo; apple were characterized using amplicon-based high-throughput sequencing to assess both large- and small-scale spatial variations and to determine the presence of a core microbiome and hub microbes. Such information is critical for understanding the role of microbiome in the susceptibility of apple fruit to pre- and postharvest diseases, fruit safety, and potentially fruit quality during long-term storage.\u003c/p\u003e \u003cp\u003eHere we demonstrated that the microbiome of the apple fruit collected from similar climates, within a continent or hemisphere, exhibiting the highest degree of similarity. Notably, fungal communities were more variable than bacterial communities in terms of diversity and abundance. In addition, we showed that the distinct composition of the different tissue-types is a global feature of the apple microbiome. Six fungal genera (\u003cem\u003eAureobasidium, Cladosporium, Alternaria, Filobasidium, Vishniacozyma\u003c/em\u003e, and \u003cem\u003eSporobolomyces\u003c/em\u003e) and two bacterial genera (\u003cem\u003eSphingomonas\u003c/em\u003e and \u003cem\u003eMethylobacterium\u003c/em\u003e) were defined as representing the core microbiome. Overall, the findings in the present study may suggest local adaptations of the apple microbiome to local environment. Regarding the spatial variations within the fruit, we suggest for future apple microbiome studies to consider these variations during their experimental design and sampling strategies by either analyzing different fruit tissues separately or including the whole fruit to minimize discrepancies between studies. In addition, it would be interesting for future fruit microbiome works to investigate whether the variations among fruit tissue types can be generalized to all fruits.\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 material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available in the [SRA NCBI] repository, and can be accessed from the following link\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict or competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by BARD, Israel- US Binational Agricultural Research and Development Fund, (IS-5040-17) awarded to S.D. and M.W. European Union\u0026rsquo;s Horizon2020 under \u0026ldquo;Nurturing excellence by means of cross-border and cross-sector mobility\u0026rdquo; program for MSCA-IF-2018-Individual Fellowships, grant agreement 844114 [A.A.].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u003c/strong\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.D and M.W. conceptualized and designed the experiments. Y.V.Z., A.K., A.B. S.S., O.F., E.B. performed the experiments; A.A, S.F., R.B. analyzed the data. C.D., J.L., A.K., W.E., S.A., D.S., R.T., N.T., O.O., A.B., S.V. P.D. sampled the fruit in different countries and extracted DNA from fruit tissues. A.A. wrote the first draft, and M.W. and S.D. made a major contribution to the final version. S.D. and M.W. supervision and project administration. G.B. analysis of the data and critically read the manuscript. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNational Academies of Sciences E, Medicine: \u003cem\u003eScience breakthroughs to advance food and agricultural research by 2030.\u003c/em\u003e National Academies Press; 2019.\u003c/li\u003e\n\u003cli\u003eZilber-Rosenberg I, Rosenberg E: \u003cstrong\u003eRole of microorganisms in the evolution of animals and plants: the hologenome theory of evolution.\u003c/strong\u003e \u003cem\u003eFEMS microbiology reviews \u003c/em\u003e2008, \u003cstrong\u003e32:\u003c/strong\u003e723-735.\u003c/li\u003e\n\u003cli\u003eBerg G, Rybakova D, Grube M, K\u0026ouml;berl M: \u003cstrong\u003eThe plant microbiome explored: implications for experimental botany.\u003c/strong\u003e \u003cem\u003eJournal of Experimental Botany \u003c/em\u003e2016, \u003cstrong\u003e67:\u003c/strong\u003e995-1002.\u003c/li\u003e\n\u003cli\u003eWei Z, Jousset A: \u003cstrong\u003ePlant breeding goes microbial.\u003c/strong\u003e \u003cem\u003eTrends in Plant Science \u003c/em\u003e2017, \u003cstrong\u003e22:\u003c/strong\u003e555-558.\u003c/li\u003e\n\u003cli\u003eGopal M, Gupta A: \u003cstrong\u003eMicrobiome selection could spur next-generation plant breeding strategies.\u003c/strong\u003e \u003cem\u003eFrontiers in microbiology \u003c/em\u003e2016, \u003cstrong\u003e7:\u003c/strong\u003e1971.\u003c/li\u003e\n\u003cli\u003eFAOSTAT: \u003cstrong\u003eFood and Agriculture Organization of the United Nations.\u003c/strong\u003e 2020.\u003c/li\u003e\n\u003cli\u003eCornille A, Gladieux P, Smulders MJM, Rold\u0026aacute;n-Ruiz I, Laurens F, Le Cam B, Nersesyan A, Clavel J, Olonova M, Feugey L, et al: \u003cstrong\u003eNew Insight into the History of Domesticated Apple: Secondary Contribution of the European Wild Apple to the Genome of Cultivated Varieties.\u003c/strong\u003e \u003cem\u003ePLOS Genetics \u003c/em\u003e2012, \u003cstrong\u003e8:\u003c/strong\u003ee1002703.\u003c/li\u003e\n\u003cli\u003eBair J: \u003cstrong\u003eApple Production, Exports Up for 2019 Crop, Says USApple.\u003c/strong\u003e USA: U.S. Apple Association; 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Oxford: Academic Press; 2014: 471-481\u003c/li\u003e\n\u003cli\u003eVannier N, Agler M, Hacquard S: \u003cstrong\u003eMicrobiota-mediated disease resistance in plants.\u003c/strong\u003e \u003cem\u003ePLOS Pathogens \u003c/em\u003e2019, \u003cstrong\u003e15:\u003c/strong\u003ee1007740.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fruit microbiome, Malus, holobiont, geographical location, niche specialization","lastPublishedDoi":"10.21203/rs.3.rs-142742/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-142742/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Apple is one of the most highly consumed fruits worldwide and is the largest fruit crop produced in temperate regions. Fruit quality, safety and long-term storage are issues that are important to growers, distributors, and consumers. We present the first worldwide study on the apple fruit microbiome that examines questions regarding the composition and the assembly of microbial communities on and in apple fruit. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Results revealed that the composition and structure of the fungal and bacterial communities associated with ‘Royal Gala’ apple fruit at harvest maturity vary and are highly dependent on geographical location. The study also confirmed that the spatial variation in the fungal and bacterial composition of different fruit tissues exists at a global level. Fungal diversity varied significantly in fruit harvested in different geographical locations and suggest a potential link between location and the type and rate of postharvest diseases that develop in each country. Although the geography, climatic conditions, and management practices may have a significant impact on the composition of fruit microbial communities, we were able to identify a 'core' microbiome that is shared in fruit across the globe. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Results of this study provide foundational information about the apple fruit microbiome that can be utilized for the development of novel approaches for the management of fruit quality and safety, as well as for reducing losses due to the establishment and proliferation of postharvest pathogens. It also lays the groundwork for studying the complex microbial interactions that occur on apple fruit surfaces.\u003c/p\u003e","manuscriptTitle":"Global Analysis of the Apple Fruit Microbiome: Are All Apples the Same?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-01-11 20:15:33","doi":"10.21203/rs.3.rs-142742/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":"b92920a2-96fc-4183-8875-ea472ddc72a2","owner":[],"postedDate":"January 11th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":1816427,"name":"General Microbiology"}],"tags":[],"updatedAt":"2021-04-15T13:26:53+00:00","versionOfRecord":{"articleIdentity":"rs-142742","link":"https://doi.org/10.1111/1462-2920.15469","journal":{"identity":"environmental-microbiology","isVorOnly":true,"title":"Environmental Microbiology"},"publishedOn":"2021-03-18 13:00:00","publishedOnDateReadable":"March 18th, 2021"},"versionCreatedAt":"2021-01-11 20:15:33","video":"","vorDoi":"10.1111/1462-2920.15469","vorDoiUrl":"https://doi.org/10.1111/1462-2920.15469","workflowStages":[]},"version":"v1","identity":"rs-142742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-142742","identity":"rs-142742","version":["v1"]},"buildId":"eB-D7MK2yqyqIWuf3Ze0-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}