# Taxonomic and functional diversity of bacteria and fungi in the rye root environment is affected by landrace genotype

Martino Adamo, Gianluca Grasso, Valeria Bianciotto, Roland Marmeisse, Marco Mucciarelli

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Martino Adamo, Gianluca Grasso, Valeria Bianciotto, Roland Marmeisse, Marco Mucciarelli. Taxonomic and functional diversity of bacteria and fungi in the rye root environment is affected by landrace genotype. Applied Soil Ecology, 2025, 217, pp.106581. 10.1016/j.apsoil.2025.106581. hal-05364736

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Research paper

# Taxonomic and functional diversity of bacteria and fungi in the rye root environment is affected by landrace genotype

Martino Adamo<sup>a, *, c</sup>, Gianluca Grasso<sup>a, b, c</sup>, Valeria Bianciotto<sup>c</sup>, Roland Marmeisse<sup>b, c, 1</sup>, Marco Mucciarelli<sup>a, 1</sup>

## ARTICLE INFO

### Keywords:

Plants genetic diversity

Secale cereale

Landraces

Microbiome

Metabarcoding

## ABSTRACT

The plant microbiome is essential for plant life, with root compartments hosting diverse bacterial and fungal communities that support plant survival and productivity. This study investigates whether rye (Secale cereale L.) landraces, which may reflect local adaptations, harbor distinct root-associated microbiomes compared to modern varieties. Using metabarcoding (bacterial 16S rRNA and fungal ITS2), we characterized microbial communities in the rhizosphere and root endosphere of three local landraces and two modern varieties cultivated in two sites of the Italian Western Alps. Root compartment and growing site emerged as the main factors shaping microbial community structure, but genotype-specific differences were also observed in endospheric communities. Local landraces formed distinct microbial clusters compared to modern varieties, with differential abundances of key taxa such as *Bradyrhizobium* and *Mortierella*. Functional predictions further indicated significant variation in several bacterial metabolic pathways, suggesting that host genotype affects microbial adaptation. These findings highlight that, beyond environmental filtering, plant genotype contributes to root microbiome assembly, offering perspectives for improving agricultural resilience through genotype-informed microbiome management.

## 1. Introduction

The plant microbiome encompasses a diversified community of microorganisms that colonize every plant compartment, including roots, stems, leaves, flowers, and fruits (Compant et al., 2011; Lwindow and Brandl, 2003; Vorholt, 2012). Beneficial plant-associated fungi and bacteria consist of different taxonomic and functional groups of microorganisms that coevolved with plants for millions of years. These communities contribute to numerous aspects of plant biology as diverse as nutrient acquisition, modulation of plant hormone production, or protection against biotic and abiotic stresses (Bulgarelli et al., 2012; Hassani et al., 2013; Liu et al., 2019; Ritpitakphong et al., 2016). This plant-microbes association results in a complex network of functional interactions which has been often referred to the ‘holobiont’ or meta-organism, or a single biological entity which expresses original extended phenotypes that the plant alone could not express (Chialva and Bonfante, 2018; Vandenkoornhuyse et al., 2015). Numerous studies on

plant microbiomes have focused on roots and rhizospheric soil compartments, due to their importance in nutrient acquisition, water uptake and disease resistance (Donn et al., 2015; Edwards et al., 2015; Hamayun et al., 2010; Yeoh et al., 2016). The belowground plant compartment is central for the development of a sustainable agriculture that maximizes the use of local soil resources while minimizing the use of chemical fertilizers and pesticides. Thus, manipulation of the plant microbiome has been proposed as one of the approaches to maximize crop production (Chialva et al., 2020, 2022; Geddes et al., 2015; Qiu et al., 2019). A prerequisite to the development of such an approach is to understand how microbiomes assemble and what factors control their recruitment (Berg et al., 2017; Singh and Trivedi, 2017).

Among all factors, a very important driver of microbiome diversity is the soil, one of the richest and most diversified pools of microorganisms on earth (Bardgett and Van Der Putten, 2014) in which most plants are anchored. Plants recruit horizontally from the soil matrix most of their microbiomes, whose diversity is higher in below-ground plant

<footnote><sup>a</sup> Department of Life Sciences and Systems Biology (DBIOS), Università degli Studi di Torino, 10125, Torino, Italy</footnote>

<footnote><sup>b</sup> Institut de Systématique, Evolution, Biodiversité (ISYEB), Muséum National d'Histoire Naturelle, CNRS, Sorbonne Université, EPHE, Université Des Antilles, 75005, Paris, France</footnote>

<footnote><sup>c</sup> Institute for Sustainable Plant Protection (IPSP), National Research Council (CNR), 10125, Torino, Italy</footnote>

<footnote>* Corresponding author at: Orto Botanico di Torino, Viale Pier Andrea Mattioli 25, 10125, Torino, Italy.
E-mail address: martino.adamo@unito.it (M. Adamo).</footnote>

<footnote n="1">¹ These authors have contributed equally to this work and share last authorship</footnote>
compartments (rhizosphere, roots) compared to aerial ones far away from soil (Xiong et al., 2021a). Plant microbiome recruitment is strongly influenced by various factors and is not a random phenomenon. Soil-borne microorganisms can be attracted or repelled by seed exudates, like carbohydrates or amino acids and secondary metabolites, even before its germination (Meneses et al., 2016; Ritpitakphong et al., 2016; Weston and Mathesius, 2013). Moreover, several beneficial or pathogenic microorganisms have evolved mechanisms that allow them to bypass the plant's immune systems to durably interact with plants (Doornbos et al., 2012; Zhalnina et al., 2018).

Several studies have demonstrated that plant microbiome diversity is also influenced by other factors including geography (Coleman-Derr et al., 2016; Hassani et al., 2018), plant species or cultivars (Fitzpatrick et al., 2018), plant developmental stage (Ajilogba et al., 2022; Xiong et al., 2021), plant health and plant compartments such as leaves, stalks, or roots (Berendsen et al., 2012; Cregger et al., 2018; Dastogeer et al., 2020; De Souza et al., 2016; Fonseca-García et al., 2016; Gao et al., 2021). Plant identity has a strong impact on microbiome composition; it affects some essential plant characteristics such as root architecture, growth patterns or exudate production (Birt et al., 2022; Hu et al., 2018; Koprivova and Kopriva, 2022; Kudjordjie et al., 2021; Sasse et al., 2018; Seitz et al., 2022; Zhalnina et al., 2018). The impact of plant genetic identity on its microbiomes has been reported for the model plant Arabidopsis thaliana (Bulgarelli et al., 2012; Lundberg et al., 2012; Schlaeppi et al., 2014) and for several crop species like maize (Aira et al., 2010), sorghum (Schlemper et al., 2017), barley (Bulgarelli et al., 2015) and rice (Edwards et al., 2015).

The present study investigates local co-evolution between a crop plant and its microbiome. We suggest that landraces recruit in their original local environment similar microbiomes distinct from those recruited by modern varieties that originate in distant geographic areas. To test this, we compared the microbial communities of three local landraces and two modern rye varieties cultivated in the Western Italian Alps. Our study aims at elucidating how plant history and local adaptation contribute to microbiome assembly, providing insights into the ecological and agricultural significance of plant–microbe interactions.

## 2. Materials and methods

### 2.1. Rye accessions

Experimental plots were established to examine genetic variability and agronomic performances of three local mountain landraces and two modern rye varieties (Adamo et al., 2021; Sardella et al., 2023). A landrace refers to a traditional crop variety bred and cultivated over a long time in a specific region. It could thus be assimilated to a plant ecotype defined as a locally-adapted population as a result of the action of natural selection (Villa et al., 2005). Landraces Exilles (EXI), Marmora (MAR) and Ormea (ORM) represent three local accessions maintained by local farmers of the Western Italian Alps. All three are indeed “family-owned” landraces originating from the Susa Valley (TO), Maira Valley (CN) and Tanaro Valley (CN), respectively, and which were found to be genetically distinct in a RAD-seq survey (Adamo et al., 2021). ANT (cv. Antoninskie) and SUN (cv. Su Nasri) were chosen as modern varieties. These are accessions conserved by Poznań ska Hodowla Roślin *Sp.* z.o.o. and Saaten-Union GmbH, respectively. ANT is commercially available at the Seed Bank of the IPK Leibniz Institute (Gatersleben, Germany) and was originally developed in Poland. SUN is a CMS-based hybrid derived from an Argentinian landrace (Geiger and Schnell, 1970) and a European source (Miedaner et al., 2017).

### 2.2. Study sites and experimental design

Ryes were grown in two experimental fields located ca 100 km apart (linear distance) in the Italian SW-Alps. The Bruzolo field (45.14376 lat 7.19665 long, province of Torino; 450 m a.s.l.) was used in the previous

years to grow either rye or wheat, while the Entracque field (44.24960 lat 7.38432 long, province of Cuneo; 900 m a.s.l.) was a freshly ploughed permanent grassland. Sites differed with respect to local climate and soil characteristics (Table 1). The Bruzolo field is sited in the Susa Valley, the same area of origin of EXI, while the Entracque field is sited in the SW-Alps, the area where MAR and ORM originated.

At each site, in October 2019, each rye type was sown in three (5 × 2 m) separate plots at a seed density of 350 seeds/m² (see Sardella et al., 2023 for further details). Genotypes were assigned to experimental plots using a randomized block design, with three replicates. Neither fertilizers nor pesticides were applied before or during the growing season. Plants were sampled in July 2020 at the flowering stage, which occurred ca 2 weeks earlier at Bruzolo compared to Entracque. We assumed the plants were at flowering stage when 50 % of anthers were mature, corresponding to stage 65 of cereals growth stages (Meier, 2018).

At each site and for each rye type, the root system of a single plant was excavated in each of the three plots (i.e. three plants per accession and per site), placed in a 50 ml plastic tube filled with PBS buffer (Phosphate-Buffered Saline) at 4 °C and brought back to the laboratory where it was processed within less than 24 h. 250 g of bulk soil were also sampled in each plot and sieved (2 mm mesh size) to remove roots and large debris. The three sieved soil samples collected under the same rye accession were pooled (i.e. five bulk soil samples per site, representing each of the five rye accessions).

Roots and rhizospheric soil were separated according to Bulgarelli et al. (2012). In the laboratory, roots and adhering rhizospheric soil were separated by agitation in fresh PBS and the resulting PBS soil suspension was centrifuged to recover a rhizospheric soil pellet. Roots were then thoroughly cleaned from remaining adhering soil particles by sonication in fresh PBS for 10 min (3 s-long pulses). Regarding root sampling, we collected root fragments in different points of the entire root system to minimize its spatial heterogeneity that may result in the production of different biologically-active exudates at different points of the system (Cordovez et al., 2019; Legay et al., 2014). Bulk soil, rhizospheric soil and root samples were stored, for less than one month, at -20 °C until DNA extraction.

### 2.3. Metabarcoding

Root DNA was extracted from 200 mg fresh samples using the DNeasy Plant Pro and Plant Kits (Qiagen Hilden, Germany). Bulk and rhizospheric soil DNA were extracted from 250 mg samples using the DNeasy PowerSoil Pro Kit (Qiagen), following the manufacturer's protocol. DNA extracts were used as templates to amplify the V5–V7 region of the bacterial 16S rRNA gene. We used primers 799f and 1193r (Beckers et al., 2016; Bodenhausen et al., 2013). The ITS2 region of the fungal nuclear rDNA repeat was amplified with primers ITS9 and ITS4 (Blaalid et al., 2013; Ihrmark et al., 2012; Schoch et al., 2012). All primers included a common 3'–"Illumina" tail for indexing. Each PCR mix was prepared in 25 µl using 15 µl H₂O, 5 µl *Pfu* PCR Buffer with dNTPs, 2 µl forward primer (5 µM), 2 µl reverse primer (5 µM), 1 µl DNA (ca 30 ng), and 0.5 µl *Pfu* polymerase (GeneSpin, Italy). Amplifications were performed in a Biometra TProfessional Basic Gradient thermocycler. Cycling parameters were as followed; 95 °C for 5 min; 7 cycles of 95 °C for 45 s, 65 °C for 1 min (decreasing this latter temperature of 2 °C every cycle), and 72 °C for 90 s, followed by 30 cycles of 95 °C for 45 s, 50 °C for 30 s, and 72 °C for 90 s. Amplification was followed by a final 10 min-long extension at 72 °C. Three independent amplifications were carried out on each DNA extract. Amplification products (5 µl) were controlled by electrophoresis on 1 % agarose gels. The remaining 3x20µl of PCR products were pooled. In the case of the prokaryotic 16S rRNA gene amplified from root DNA, agarose gel electrophoresis highlighted the presence of two distinct amplified fragments (Fig. S1). The longest fragment was attributable to the plant ribosomal 16S gene. The shortest fragment of microbial origin was extracted from the gel using the NucleoSpin Gel and PCR Clean-Up by Macherey-Nagel (Dueren,
<caption>**Table 1**</caption>

Sampling design: Sampling design included two sites, briefly described in the “site feature” column and five rye landraces/varieties. To each of them, in each site, we collected three replicates (corresponding to three plants) of roots and rhizosphere and a single bulk soil sample.

<table><thead><tr><th>Site</th><th>Accession</th><th>Compartment</th><th>Sample No.</th><th>Variety features</th><th>Site features</th></tr></thead><tbody><tr><td>Bruzolo</td><td>ANT</td><td>root</td><td>3</td><td>Antoninskie, commercial modern variety</td><td></td></tr><tr><td>Bruzolo</td><td>ANT</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Bruzolo</td><td>ANT</td><td>bulk soil</td><td>1</td><td></td><td></td></tr><tr><td>Bruzolo</td><td>EXI</td><td>root</td><td>3</td><td>Exilles landrace from Susa valley, Torino, Piemonte</td><td></td></tr><tr><td>Bruzolo</td><td>EXI</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Bruzolo</td><td>EXI</td><td>bulk soil</td><td>1</td><td></td><td></td></tr><tr><td>Bruzolo</td><td>MAR</td><td>root</td><td>3</td><td>Marmora landrace from Maira valley, Cuneo, Piemonte</td><td>Bruzolo (TO) municipality; at 450 m asl. Private field usually cultivated with rye and wheat. SSE exposition to sun, 1564 mm mean annual precipitation, 6 °C mean annual temperature. Substrate is a Luvisol with 605 dg/kg soil organic Carbon; 429 g/kg Nitrogen (601 g/kg) and pH 7.</td></tr><tr><td>Bruzolo</td><td>MAR</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Bruzolo</td><td>MAR</td><td>bulk soil</td><td>1</td><td></td><td></td></tr><tr><td>Bruzolo</td><td>ORM</td><td>root</td><td>3</td><td>Ormea landrace from Tanaro valley, Cuneo, Piemonte</td><td></td></tr><tr><td>Bruzolo</td><td>ORM</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Bruzolo</td><td>ORM</td><td>bulk soil</td><td>1</td><td></td><td></td></tr><tr><td>Bruzolo</td><td>SUN</td><td>root</td><td>3</td><td>Su Nasri, modern hybrid variety</td><td></td></tr><tr><td>Bruzolo</td><td>SUN</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Bruzolo</td><td>SUN</td><td>bulk soil</td><td>1</td><td></td><td></td></tr><tr><td>Entracque</td><td>ANT</td><td>root</td><td>3</td><td>Antoninskie, commercial modern variety</td><td></td></tr><tr><td>Entracque</td><td>ANT</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Entracque</td><td>ANT</td><td>bulk soil</td><td>1</td><td></td><td></td></tr><tr><td>Entracque</td><td>EXI</td><td>root</td><td>3</td><td>Exilles landrace from Susa valley, Torino, Piemonte</td><td></td></tr><tr><td>Entracque</td><td>EXI</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Entracque</td><td>EXI</td><td>bulk soil</td><td>1</td><td></td><td></td></tr><tr><td>Entracque</td><td>MAR</td><td>root</td><td>3</td><td>Marmora landrace from Maira valley, Cuneo, Piemonte</td><td>Entracque (CN) municipality: at 900 m asl. Ploughed grassland. NNE exposition to sun, 1194 mm mean annual precipitation, 4.9 °C mean annual temperature. Substrate is a Cambisol rich in soil organic Carbon (1136 dg/kg) and Nitrogen (601 g/kg) and 6.5pH.</td></tr><tr><td>Entracque</td><td>MAR</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Entracque</td><td>MAR</td><td>bulk soil</td><td>1</td><td></td><td></td></tr><tr><td>Entracque</td><td>ORM</td><td>root</td><td>3</td><td>Ormea landrace from Tanaro valley, Cuneo, Piemonte</td><td></td></tr><tr><td>Entracque</td><td>ORM</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Entracque</td><td>ORM</td><td>bulk soil</td><td>1</td><td></td><td></td></tr><tr><td>Entracque</td><td>SUN</td><td>root</td><td>3</td><td>Su Nasri, modern hybrid variety largely used in whole Europe</td><td></td></tr><tr><td>Entracque</td><td>SUN</td><td>rhizospere</td><td>3</td><td></td><td></td></tr><tr><td>Entracque</td><td>SUN</td><td>bulk soil</td><td>1</td><td></td><td></td></tr></tbody></table>

Germany). Purified PCR products were quantified using the Qubit dsDNA BR Assay kit and Qubit Fluorimeter 2.0 (Thermo Fisher Scientific, Waltham, Massachusetts, U.S.A.). Control blank samples (H₂O) were extracted, amplified, and sequenced to identify laboratory contaminations. PCR products were sequenced by Biofidal (Vaulx-en-Velin, France) using an Illumina MiSeq platform (2x300bp paired-end).

## 2.4. Bioinformatic analysis

Raw reads were quality-checked using FastQC v0.11.9 (Andrews, 2010). Libraries were imported into QIIME 2 (Quantitative Insights Into Microbial Ecology) v2021.8 (Bolyen et al., 2019) for denoising and Amplicon Sequence Variants (ASVs) detection. Demultiplexed reads were first denoised using DADA2 plugin (Callahan et al., 2016). For 16S rDNA reads, 13 nucleotides at the 5' ends were trimmed ('-p-trim-left-f/ r' parameters), while for ITS2 reads no trimming was applied as recommended by the QIIME2 user manual. Due to a decrease in quality, reads were truncated at their 3' ends after 285 nt (forward) and 225 nt (reverse) for the 16S sequence fragment. In the case of the fungal ITS2 fragment, reads were truncated at their 3' ends after 280 nt (forward) and 220 nt (reverse). ASVs were taxonomically annotated using the preformatted SILVA v138 database for QIIME (Bokulich et al., 2020; Quast et al., 2013) at 99 % level identity for Prokaryotes and the QIIME release of the UNITE + INSDC v8 database without singletons in dynamic mode (Abarenkov et al., 2010) for Fungi. Taxonomy was assigned using the q2-feature-classifier command (Bokulich et al., 2018). Raw sequencing data were submitted to Sequence Read Archive (NCBI-SRA) under Bio-Project accession number PRJNA765145; BioSamples SAMN27016493-SAMN27016634.

## 2.5. Statistical analyses

All analyses were performed in R v4.0.4 environment (R Core Team, 2022) with the RStudio workbench v1.4.1717 (RStudio Team, 2020).

Analyses were performed on a Dell OptiPlex-7080 system (16 cores, 32GB RAM) running Ubuntu 20.04.4 LTS. Alpha and beta diversity analyses were performed using the 'vegan' v2.5-6 R package (Oksanen et al., 2022). Differential abundance analysis was performed using DESeq2 (Love et al., 2014). Graphical elaborations were performed using ggplot2 v3.3.2 (Wickham, 2016).

Prokaryotic (16S) and fungal (ITS2) datasets were analyzed separately but following the same workflow. Sequences from blank controls were considered laboratory contaminants and were removed from environmental datasets using the R package microDecon v1.0.2 (McKnight et al., 2019). Samples were then ranked by sequencing depth, and those with insufficient read counts were excluded using an outlier-based visual method. The remaining datasets were normalized by rarefaction to the sample with the lowest read number. Community structure was assessed through beta diversity using Chao distance (Chao et al., 2005), and differences among environmental factors were tested with PerMANOVA followed by variance partitioning. To visualize community structure, non-metric multidimensional scaling (NMDS) was performed (k = 4 for fungi, k = 3 for bacteria). Alpha diversity was evaluated with three indices: observed species richness, Shannon diversity (Shannon, 1948), and Simpson's evenness (Simpson, 1949). The effect of environmental factors on richness was further tested using a General Linear Model (GLM) implemented in the R package MASS v7.3-55 (Venables and Ripley, 2002).

Differential abundance analyses were conducted on non-rarefied data with DESeq2, applying the 'ashr' prior shrinkage estimator (Stephens, 2017) to obtain moderated log2 fold changes. For each rye accession, we identified ASVs significantly enriched in root samples compared to the rhizosphere and contrasted them against all other accessions. Communities defined by differentially abundant ASVs were compared using PerMANOVA and variance partitioning based on Bray-Curtis distances. Functional predictions for enriched bacterial ASVs were generated with Tax4FunR v1.1.5 (Wemheuer et al., 2020). To focus on relevant features, we excluded constitutive functions and rare
categories, retaining only functional profiles above the mean abundance but within the 98th percentile. Functional profiles were compared among rye varieties with a Kruskal–Wallis test. Finally, we assessed the relationship between endophytic community dissimilarities and host genetic distances (Adamo et al., 2021) using Mantel's tests and Pearson correlation matrices.

## 3. Results

### 3.1. Rye microbiome assembly is strongly influenced by growing site and root compartment

Sequencing produced a total of 8,623,779 (16S) and 3,319,095 (fungal ITS2) initial Illumina paired-end reads (2 × 300 bp). After removal of unmatched and low-quality reads and of sequences identified in control blank samples, we delineated 9803 prokaryotic 16S ASVs and

5611 fungal ITS2 ones in 71 bulk soil, rhizospheric soil and root samples. One ITS2 (fEE29) and one 16S (bBE1) samples had far lower numbers of reads compared to the other samples and were discarded (Fig. S2). When necessary, individual sequence datasets were rarefied to 18,515 (16S) and 5921 (ITS2) reads. Rarefaction did not significantly affect Good's coverage of individual samples that was always above 0.9.

Regarding alpha-diversity, all three computed indices (species richness, Shannon index and Evenness), were collinear (Pearson's | r | > 0.70) and we therefore only used the Shannon index to compare the alpha-diversity response to environmental variables by means of a GLM. Only two highly significant (*P* < 0.001) differences were identified. Globally, fungal communities, but not the bacterial ones, were richer in the Entracque site and both fungal and bacterial communities were less diverse in the root endosphere compared to the soil ones. Rye genotype had no significant impact on either fungal or bacterial community alpha diversity (Fig. 1, Table S1).

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Impact of environmental and plant variables on fungal and bacterial species richness. A GLM compared the alpha-diversity (Shannon index) response to different experimental factors (site, sample type (matrix), landrace) of bacterial and fungal communities. For fungi, site and matrix results significant and have an impact on species richness (*p* < 0.001); for bacteria only, matrix results significant (*p* < 0.001). Su Nasri and Bruzolo were used as baselines for plant genotype and site respectively.</figcaption></figure>
PerMANOVA on ASV tables (Table S2) highlighted, for both bacterial and fungal communities, significant differences between the two study sites (Entracque vs. Bruzolo, *P* < 0.001), sample types (bulk soil vs. rhizospheric soil vs. root endosphere, *P* < 0.001), but not between rye genotypes (*P* > 0.1). While sites explained 34 % and 17 % of the observed variance for Bacteria and Fungi respectively, sample type contributed to 24 % (Bacteria) and 15 % (Fungi) of the explained variance (Fig. 2b). The impact of plant genotype was negligible; estimated to 1 % of the explained variance in the case of fungal communities, and even less than 0.1 % in the case of bacterial communities. In the case of Fungi (ITS2 ASVs) we however observed a significant site x landrace interaction effect (*P* = 0.030; Table S2), suggesting difference between landraces among sites.

NMDSs ordination of the samples (Prokaryotes (16S) NMDS: k = 3, stress = 0.049; Fungi (ITS2) NMDS: k = 4, stress = 0.065; Fig. 2a) gave excellent representation with very low possibility of misinterpretation (Dexter et al., 2018) and perfectly reflected the PerMANOVA results. For example, in the case of fungi, axis 1 clearly separated samples from the two sites, while axis 2 separated samples according to sample type (bulk soil vs. rhizospheric soil vs. root endosphere).

## 3.2. Landrace roots select specific microbial communities

In a further analysis, we tested the hypothesis that rye could recruit a set of genotype-specific endospheric microorganisms, through their roots. We used DESeq2 to identify fungal and bacterial ASVs that were significantly enriched in the root endosphere compared to the (bulk and

rhizospheric) soil. We tested the link between landrace adaptation and composition of root-enriched microbial community using a Bray-Curtis distance-based hierarchical clustering (Fig. 3). Both these root-enriched fungal and bacterial communities clustered in two main groups discriminating between landraces and modern varieties in 27 out of 29 cases. The two mismatches were from the ORM landrace, when cultivated in Entracque. Considering these root-enriched microbial community, variability in their composition is mainly explained by rye genotype; the variance explained by the genotype is 17 % for fungi and 11 % for bacteria, while sites of growth do not influence the community composition (Fig. S4). NMDS ordination of the endospheric-enriched microbial communities, based on Bray-Curtis distance (Fig. S3), also, highlighted differences between rye genotypes. Such differences were not visible in the analysis performed on the whole microbial communities (Fig. 2).

Differences between accessions resulted not only from different proportions of shared microbial taxa, but also from the exclusive presence or absence of specific enriched taxa. For example, the highly divergent root communities associated to EXI landrace were characterized by the exclusive presence of Ascomycetes belonging to the genera *Tetracladium*, *Slopeiomyces* and *Ophiosphaerella* and of bacterial genera *Umezawaes* and *Pseudomonas*. On the contrary, this landrace lacked *Rubrobacter* and *Microlunatus* enriched in the root endosphere of all other accessions (Fig. 4). Similarly, SUN root bacterial community that differed from all others (pairwise-PerMANOVA test *P* < 0.01; see Table S3) presented a specific enrichment in taxa affiliated to *Bradyrhizobium*.

<figure><img src="image_3.png" /><figcaption>**Fig. 2.** Site and sampled compartments have a strong impact in shaping fungal and bacterial communities. a. Nonmetric multidimensional scaling (NMDS) ordination based on Chao distances of fungal and bacteria communities. NMDS ordination highlighted clear differences among sites (Entracque and Bruzolo) and compartments (root, rhizosphere, and soil): (Bacteria NMDS: k = 3, stress = 0.049; Fungi NMDS: k = 4, stress = 0.065). b. Contribution of the different studied variables to the overall variance between samples. For both bacterial and fungal communities site and compartments contribute most to the observed diversity (respectively 34 % and 24 % for bacteria and 17 % and 15 % for Fungi). Landraces effect accounted for less than 1 % of variance for both fungi and bacteria.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 3.** Hierarchical clustering of differentially abundant root microbial communities separate rye landraces from modern varieties. Hierarchical clustering, based on Bray-Curtis indices, was performed separately for bacterial and fungal communities that are significantly enriched in the root endosphere compared to the surrounding soil. Communities from local landraces (in bold) group together irrespective of the growing site (ent, Entracque; brz, Bruzolo) with the exception of two samples from the ORM landrace cultivated in Entracque.</figcaption></figure>

By means of the Tax4Fun R package we predicted the putative metabolic profiles of bacterial communities specifically enriched in the roots of the different rye accessions. Predominance of each predicted metabolic pathway in each of the microbial community was estimated using reads abundance of the taxa from which this pathway was predicted. We identified 25 different predicted functional categories that varied significantly between rye genotypes (One-Way ANOVA test, *P* < 0.001) and rye associated bacterial community differed from each other with respect to their predicted metabolic profiles.

Compared to the other bacterial communities, those associated to EXI landrace had the most divergent metabolic profiles characterized by an underrepresentation of pathways related to benzoate, amino-benzoate, caprolactam, limonene and geraniol degradation, and an overrepresentation of amino sugar and nucleotide sugar metabolism and biofilm formation (Fig. 5). Individual sample-associated communities clustered according to rye landrace/variety indicating that plant genotype still shaped their functional profiles (Fig. 5).

## 4. Discussion

We observed that root-associated (rhizospheric and endospheric) microbial communities recruited by rye were mainly affected by the

growing site and the root compartment (rhizosphere versus endosphere) but not by the plant genotype. A clear legacy/dependence on plant genotype was however highlighted when we focused our analyses on microbial taxa (bacteria or fungi) that were enriched in the root endosphere by comparison to those belonging to the surrounding rhizosphere. In the two fungal and bacterial sub-communities, we recorded not only a dependence of composition on plant genotype but also a local landrace versus modern variety recruitment effect.

### 4.1. Rye microbiome varies according to the study site and to the sample type

Plants largely recruit microbial communities horizontally from the pool of microorganisms inhabiting the soil where they grow, and where, ultimately, they are affected by a plethora of local environmental variables (i.e., physical and chemical soil properties, fertility, land use) (Xiong et al., 2021; Madegwa and Uchida, 2021; Romdhane et al., 2022). The impact of these factors on the plant microbiome is strong. However, plant-specific features as the ability to excrete bioactive molecules in the soil together with other signal molecules, can also shape the microbial community composition (Ercole et al., 2022; Zhang et al., 2023). In our study, microbiota recruited by rye differed
<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Taxonomic profiles (genus level) of differentially abundant root microbial communities. Profiles are illustrated for each rye accession for Fungi (a) and Bacteria (b). Several genera are present in only one of the different accessions. Identical letters on top of the stacked bars indicate statistically similar taxonomic profiles (pairwise-PerMANOVA test, *P* < 0.05).</figcaption></figure>

<figure><img src="image_6.png" /></figure>

significantly with the site of growth (Entracque and Bruzolo), demonstrating the obvious fact that the complexity of aspects linked to topography, pedology and climatic variations encountered in a specific geographic site, give a substantial contribution to the structure and functioning of the microbiome (Adamo et al., 2020).

Besides the site effect, we also observed a strong sample type (rhizosphere, endosphere and bulk soil) effect accounting for 15 % and 24 % of the explained variance, for Fungi and Bacteria, respectively (Fig. 2b). Plants can indeed be considered as a landscape with different ecological niches for microorganisms. Distinct microbial communities, which nevertheless share many different species, have been reported not only in belowground plant organs (rhizosphere, rhizoplane and root endosphere) but also in leaves (the phyllosphere) (Bashir et al., 2022). Each of these plant organs presents unique physico-chemical characteristics that favor the growth of specific microorganisms at the expense of others (Edwards et al., 2015; Cregger et al., 2018; Xiong et al., 2021a).

In our NMDS analysis, root endosphere communities were taxonomically well separated from those living in bulk soils and rhizospheric soils, which were closer to each other. These two last communities are probably more similar to each other because they thrive in the same matrix influenced by either low (bulk soil) or high (rhizospheric soil) concentrations of root exudates (Badri and Vivanco, 2009; Sasse et al., 2018).

## 4.2. Root endospheric communities differ according to plant genotype

When considering all molecular taxa together, we did not detect a significant effect of plant genotype on the composition of root-associated microbial communities. However, focusing specifically on microorganisms differentially abundant in the root endosphere compared to the rhizosphere revealed a clear plant genotype effect (PerMANOVA, *P* < 0.01; Fig. 4). This effect was even stronger than the influence of growing site. This result indicates that rye may host genotype-specific microbial

communities, consistent with patterns previously reported in other cereals such as maize, barley, and rice, as well as in more distantly related plants including *Arabidopsis thaliana*, *Spiranthes* species, and *Olea europaea* (Bulgarelli et al., 2012; Calevo et al., 2021; Chialva et al., 2021). Importantly, microorganisms identified as root-enriched accounted for about 60 % of all reads in root samples (58 % for bacteria and 61 % for fungi), showing that these recruited taxa are not rare, but instead represent a substantial portion of the community.

Hierarchical clustering of these root-enriched communities (Fig. 3) further emphasized genotype-specific patterns: local landraces consistently formed clusters distinct from modern varieties. Interestingly, this separation reflected the geographical origin of the rye rather than their genetic proximity. For example, the EXI and MAR landraces grouped together despite belonging to distinct genetic clusters according to Adamo et al. (2021). The ORM landrace occupied an intermediate position between local and modern varieties, which is consistent with evidence that ORM shares genomic similarities with the modern variety ANT, likely as a result of repeated hybridization in open fields. These patterns suggest that clustering of microbial communities in landraces may reflect long-term adaptation to local environmental conditions in the Southwestern Alps.

A complementary perspective emerged from functional predictions. Clustering based on the abundance of functions associated with root-enriched bacterial taxa also produced a clear separation among rye genotypes. Unlike the taxonomy-based clustering, however, this functional approach did not distinguish landraces from modern varieties. For instance, EXI appeared as the most divergent landrace in functional terms, while ORM and MAR were positioned closer to the modern variety ANT.

The predicted metabolic functions underlying these separations (Fig. 4) were mainly related to carbon metabolism (including monosaccharide and amino-sugar pathways, carbon fixation, and oxidative phosphorylation), nitrogen metabolism (particularly amino-acid
<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Heatmap of predicted functional profiles of differentially abundant root bacterial communities. Functional profiles were deduced from 16S data using Tax4Funr and compared among rye varieties using a Kruskall-Wallis rank sum test, in order to evidence differences between accessions. Only 25 functional categories displaying highly significant difference (*P* < 0.01) among accessions are included in the heatmap. EXI displays the most divergent profile.</figcaption></figure>

pathways), and the transformation of recalcitrant molecules often linked to secondary metabolism, such as limonene, pinene, geraniol, and other terpenoids. These results suggest that the local root chemical environment could act as a strong selective factor for the recruitment of bacterial taxa. Although we did not measure root chemical profiles directly, previous work has shown that the ORM, EXI, and MAR landraces differ significantly in grain phenolic content (Sardella et al., 2023). Such differences are consistent with studies demonstrating that root exudates play a key role in shaping rhizosphere microbial communities (Bais et al., 2006; Doornbos et al., 2012; Olanrewaju and Babalola, 2022). Moreover, genome-wide association studies in other crops have highlighted host loci involved in development, immunity, nutrient uptake, and exudate production as major contributors to plant-microbiome interactions (Zhang et al., 2023).

**5. Conclusion**

The overall composition of root-associated microbes depends mostly on where the plants grow and on the specific soil, rhizosphere, or root environment. However, focusing on microbes abundant in roots, we identified a signal separating local from modern varieties. This effect was largely independent of the growing site.

We also observed that bacterial communities differed with respect to their predicted functions, suggesting they could contribute to various plant metabolic processes. These differences can be observed even though the genetic differences between accessions are small. We hypothesize that the ability to recruit functionally distinct microbial communities may have played a role in the landraces' adaptation to their local environments.
In other words, rye landraces are a valuable source not only of plant genes, but also of microbial diversity. If this microbial diversity contributes to the performance of the rye accessions under different soil and climate conditions, the preservation of the whole plant-microbiota association (the holobiont) seems necessary from the point of view of selecting climate-resilient crops. Thus, selecting and preserving the most suitable microbial communities should be just as important as preserving the plants themselves.

## CRediT authorship contribution statement

**Martino Adamo:** Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Gianluca Grasso:** Writing – original draft, Visualization, Investigation, Formal analysis. **Valeria Bianciotto:** Writing – review & editing, Investigation. **Roland Marmeisse:** Writing – review & editing, Validation, Supervision, Funding acquisition, Conceptualization. **Marco Mucciarelli:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

The field part of the present study was supported by Fondazione CRT within the project SECALP and by Fondazione CRC within the project SECNALP. MA and MM acknowledge the support of National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.4 - Call for tender No. 3138 of 16 December 2021, rectified by Decree n.3175 of 18 December 2021 of Italian Ministry of University and Research funded by the European Union – NextGenerationEU; Award Number: Project code CN_000000033, Concession Decree No. 1034 of 17 June 2022 adopted by the Italian Ministry of University and Research, CUP D13C22001350001, Project title “National Biodiversity Future Center - NBFC”.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.apsoil.2025.106581.

## Data availability

The data presented in this study are available in NCBI-SRA under BioProject PRJNA765145.

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