Long-read nanopore sequencing reveals genotype-dependent microbiome shifts and host-microbe interactions in the barley rhizosphere

Long-read nanopore sequencing reveals genotype-dependent microbiome shifts and host-microbe interactions in the barley rhizosphere | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-read nanopore sequencing reveals genotype-dependent microbiome shifts and host-microbe interactions in the barley rhizosphere Bennet Rohan Fernando Devasahayam, Thomas McNeil, Tesfaye Wubet, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8336676/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Barley ( Hordeum vulgare L.) provides a suitable model for studying domestication-driven plant-microbiome interactions. Although wild, landrace, and modern genotypes host distinct rhizosphere communities, the extent to which roots and microbes reciprocally influence each other remains unclear. Here, we applied an integrated multi-omics approach combining long-read metagenomics, root transcriptomics, and plant genomics to understand genotype-specific host-microbiome coordination. Results Oxford Nanopore whole metagenome sequencing (WMS) revealed clear genotype-dependent shifts in rhizosphere communities across seasons. Functional profiling showed a conserved metabolic backbone including amino acid metabolism, energy production, and secondary metabolite biosynthesis accompanied by genotype-specific differences in carbohydrate metabolism and transport-associated pathways. Genome-resolved analysis through metagenome-assembled genomes (MAGs) further detailed the taxonomic and functional architecture of key rhizosphere lineages. Root transcriptomics identified extensive differential expression linked to microbial perception, signaling, defense, and metabolic reprogramming. Integrating host and microbiome data revealed coordinated molecular responses, indicating that barley genotypes not only shape microbial assembly but also program their transcriptional activity in response to microbial cues. Conclusions These findings demonstrate that domestication has shaped a bidirectional interaction network in which barley genotypes and their rhizosphere microbiomes jointly modulate microbial community structure and host transcriptional regulation. The coordinated exchange provides new insights into the evolutionary tuning of plant-microbiome relationships and highlights opportunities for microbiome-informed crop improvement. Rhizosphere microbiome long-read nanopore sequencing metagenomics transcriptomics host-microbiome interactions Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Barley ( Hordeum vulgare L.) represents one of the earliest domesticated cereal crops, with archaeological evidence tracing its cultivation back more than 10,000 years in the Fertile Crescent [ 1 , 2 ]. Its domestication from the wild progenitor Hordeum spontaneum led to the establishment of landraces adapted to diverse agroecological environments, and later to modern cultivars shaped by intensive breeding [ 3 , 4 ]. Importantly, barley is not only a major cereal for food, feed, and brewing, but also serves as a model for crop genomics due to its diploid genome [ 5 ]. The Hi-C guided Morex assembly produced the first chromosome-scale reference for barley (~ 5 Gb), providing a robust scaffold for read mapping, variant discovery, and gene localization, and establishing a common coordinate system for downstream analyses [ 6 ]. Moving beyond a single reference, the first barley pan-genome, which included 20 varieties spanning wild accessions, landraces, and elite cultivars, revealed extensive presence or absence variation and large inversion polymorphisms in elite germplasm that could not be detected from a single genome [ 7 ]. A recent expanded pan-genome with 76 long-read assemblies and short-read data for 1,315 genotypes now charts structural variants and copy-number rich loci across domestication space, substantially improving the catalog of allelic diversity for breeding [ 8 ]. At gene-space resolution, exome capture platforms and studies on diverse panels including 267 geo-referenced landraces/wilds, ~ 371 domesticated lines and other regional panels uncovered adaptive variants tied to environment and agronomic performance, providing cost-effective variant discovery for large cohorts [ 3 ]. Wild barley de novo assemblies complement these resources by exposing structural and gene-content differences relative to Morex, refining domestication inferences and improving read-mapping for wild introgressions [ 9 ]. Contemporary reviews summarize this shift from single-reference to pangenomic thinking and outline how structural variation (SV) drives phenotypic diversification under domestication and improvement [ 5 ]. Methodologically, these assemblies and panels translate into concrete bioinformatics practice such as aligning reads to chromosome-scale pseudomolecules, discovery of structural variants with long-reads/graph genomes, and exome data enable high-density single nucleotide polymorphism (SNP) matrices for genome-wide association study (GWAS) and genomic prediction [ 10 ]. Together, these genomic resources enable precise dissection of transitions from wild to landraces to elite cultivars, including domestication footprints at candidate loci affecting adaptation, quality, and stress response [ 7 ]. As barley genomics converges on graph- and pan-reference frameworks, downstream analyses such as variant calling, GWAS and expression quantitative trait locus (eQTL) will increasingly account for non-reference haplotypes which is an important consideration when relating host genotype to rhizosphere microbiome and root transcriptional phenotypes in wild, landrace, and modern genotypes. Beyond the genetic and agronomic importance of barley, recent studies suggest that domestication-driven selection also extends belowground, influencing how plant roots interact with the rhizosphere microbiome [ 11 , 12 ]. This diverse community contributes to nutrient acquisition, stress tolerance, and pathogen resistance, thereby playing a critical role in overall plant fitness [ 13 , 14 ]. The rhizosphere directly influenced by root exudates, represents one of the most dynamic interfaces between plants and their associated microbiota [ 15 ]. Functional members of the rhizosphere microbiome can fix atmospheric nitrogen, solubilize phosphate, produce siderophores, and modulate plant hormone signaling, collectively acting as a “second genome” of the plant [ 16 ]. In cereals such as rice, wheat and maize, rhizosphere microbial composition has been shown to vary with genotype, developmental stage, and environmental conditions [ 17 – 19 ]. Although barley is a model crop with extensive genetic resources, our understanding of its rhizosphere microbiome lags behind other cereals, with recent studies only beginning to uncover the diversity and host-specificity of its microbial communities [ 20 ]. Given the central role of the rhizosphere in mediating plant - microbe interactions, understanding how microbiome composition varies across barley types offers a unique opportunity to link host genetic background, microbial diversity, and plant performance. Domestication and modern breeding have not only reshaped the genetic architecture of crops but also influenced their interactions with soil microbial communities. Comparative studies across cereals indicate that both wild relatives and domesticated cultivars assemble distinct and functionally diverse rhizosphere microbiomes, with patterns largely determined by host genotype and domestication status [ 21 , 22 ]. Importantly, wild barley genotypes have been reported to enrich for microbial taxa associated with nutrient mobilization and stress resilience, whereas elite cultivars tend to assemble more streamlined microbial communities [ 23 ]. These findings highlight that domestication-driven shifts in microbial diversity and functionality may have contributed to changes in plant adaptability, raising the question of how variation in barley types continues to influence host-microbiome associations under field conditions. In barley, the host genotype accounts for a smaller proportion of rhizosphere variation than the local soil microhabitat, yet it still exerts a consistent influence on microbial community composition [ 11 ]. The differences between wild and modern barley genotypes have been linked to specific host loci, most notably the QRMC-3HS locus on chromosome 3H, which influences the recruitment of diverse bacterial taxa and explains up to 20% of their variation. Bacteria preferentially recruited by the wild allele at this locus include Variovorax , Holophaga , Sorangium , Tahibacter , and Rhodanobacter , representing functionally diverse groups involved in complex carbon turnover [ 20 ]. By contrast, elite cultivars often show stronger enrichment of Actinobacteria, a phylum associated with antimicrobial production and stress-adapted lifestyles, consistent with a more filtered microbiome [ 24 , 25 ]. Metagenome-assembled genomes (MAGs) from barley rhizospheres further reveal that the partitioning of key microbial functions, including nitrogen metabolism and transporter repertoires, is strongly genotype-dependent, indicating that domestication has not only influenced the microbial community structure but also their functional potential [ 26 ]. Plant-soil feedback experiments reinforce this view, showing that a phylogenetically diverse microbial consortium supports optimal growth of elite barley usnder nitrogen limitation, emphasizing that functional diversity remains advantageous even for modern genotypes [ 23 ]. Mechanistically, immune-related host factors are likely central in this process. For instance, QRMC-3HS harbors a candidate nucleotide-binding leucine-rich repeat (NLR) gene BaRT2v18chr3HG123500 with structural variation across the barley pan-genome, highlighting immune-microbiome crosstalk as a key domestication-sensitive trait [ 20 ]. Earlier studies also identified root-enriched families such as Comamonadaceae , Flavobacteriaceae , and Rhizobiaceae , and found positive selection on microbial protein families involved in pathogenesis, secretion, phage interactions, and nutrient mobilization which are signatures of long-term host-microbe co-adaptation [ 11 ]. Differences among barley cultivars also take place at the strain and functional levels. For example, inoculation with matched rhizosphere extracts or Pseudomonas synthetic communities (SynComs) produces cultivar-specific growth responses, suggesting that elite genotypes respond to narrower sets of microbial partners compared to wild or landrace backgrounds [ 27 ]. Collectively, cross-crop studies highlight that domestication has reshaped the composition and functional potential of rhizosphere microbiomes, consistently influencing the abundance of taxa comprising key microbial functional guilds [ 24 ]. Altogether, these observations support a model where wild barley maintains broader microbial functional repertoires, while elite cultivars exhibit selective filtering that favors a leaner set of partners, with trade-offs for resilience vs input-dependent performance. Building on the domestication-driven patterns in the rhizosphere microbiome, it is essential to understand how these microbial communities induce transcriptional changes within barley roots. Transcriptome studies on barley roots inoculated with species of bacteria show that colonization can rapidly rewire the barley transcriptome activating defense-associated pathways, transport processes, and signaling modules [ 28 ]. Complementary work demonstrates that barley differentiates its transcriptional response to distinct beneficial taxa including Ensifer , Pantoea , and Pseudomonas , indicating that host signaling discriminates among commensals and promotes tailored root programs rather than a generic beneficial microbe response [ 29 ]. In the context of symbiosis, arbuscular mycorrhizal fungi (AMF) modulate nutrient-uptake circuits in barley roots. For instance, AMF inoculation increased grain Zn and alters root ZIP transporter expression in a modern barley cultivar, a clear instance where microbial association reshapes host ion-homeostasis genes [ 30 ]. In addition, genetic gating of these interactions is evident from the MLO ( Mildew Resistance Locus O ), which is classically known for conferring broad-spectrum, stable resistance to powdery mildew in barley [ 31 ]. Beyond this canonical function, MLO has recently been shown to regulate colonization by arbuscular mycorrhizal fungi and root endophytes [ 32 ], indicating that immune-related loci can influence both rhizosphere assembly and host transcriptional responses. Cultivar-specific differences in barley also influence microbiota-host signaling, as seen in the recruitment and activity of Pseudomonas populations accompanied by microbial transcriptome shifts, indicating that host genotype determines distinct transcriptional trajectories during colonization [ 27 ]. Although most barley transcriptome studies address abiotic stress, they consistently reveal root modules such as hormone, transport, redox, and cell-wall pathways that are also engaged during microbe-plant interactions, with parallel evidence from AMF studies in wheat and meta-analyses linking biotic stress and hormone networks [ 33 , 34 ]. Recent studies also highlight defense priming and nutrition-linked transcriptional shifts in barley exposed to rhizobacteria, connecting root gene expression to whole-plant outcomes such as reduced pest burdens, emphasizing that microbial effects are routed through host transcriptional control layers [ 35 ]. Methodologically, cell-type-resolved and spatial transcriptomics are emerging to dissect which root tissues execute these programs during beneficial colonization, promising sharper resolution of barley’s microbe-responsive gene networks [ 36 ]. Together, these findings support a model in which microbial association states including commensal, or mutualist are read out as distinct barley root transcriptional fingerprints spanning immune receptors, secondary signaling, phytohormone circuitry, transporters and secretory machinery. Importantly, they also align with the domestication signal which indicates that host genotype shapes not just which microbes arrive, but how root gene networks respond once they do, implying that wild, landrace, and modern cultivars may differ in the magnitude and architecture of microbially induced transcriptional states. In order to address genotype-dependent microbiome shifts and host-microbe interactions, we established a multi-omics approach integrating whole metagenome sequencing (WMS) of the rhizosphere microbiome, root transcriptome profiling and plant genomics across different barley genotypes. Long-read metagenomics revealed genotype- and season-dependent restructuring of the rhizosphere microbiome, capturing dominant bacterial and fungal members and their functional shifts. Root transcriptome profiling showed that genotypes activate a characteristic set of defense- and signaling-related responses to its associated microbiome. Complementary plant genomic analysis further identified structural variation at a defense-related locus, suggesting possible links between genomic architecture and elevated transcriptional activity. Together, these approaches demonstrate that variation in microbial community composition corresponds with distinct host transcriptional programs, indicating a coordinated interaction between barley roots and their microbiota. Overall, our findings show that domestication has shaped not only the structure and function of the rhizophore microbiome, but also the host’s molecular response, revealing an integrated, genotype-dependent interaction network. Materials and Methods Collection of soil and plant material Rhizosphere soil was collected from barley plants grown for eight weeks at the experimental field station of Martin Luther University (MLU, Kühnfeld, Halle, Germany). At harvest, the rhizosphere soil was defined as the fraction adhering to roots after gentle shaking and was collected with sterile spatulas. Soil samples were transferred into sterile bags, transported on dry ice, and stored at -80°C until DNA extraction. As a non-treated control, soil was collected from two separate field locations. For transcriptome analyses, root material was harvested from the same plants at the same developmental stage. Roots were cut with sterile scissors, washed several times with sterile water to remove loosely attached soil, blotted dry, and stored at -80°C until RNA isolation. In addition, field soil was collected and sterilized by autoclaving and used for plant cultivation under greenhouse conditions. As a control for RNA-Seq, Gretchen plants were grown in sterilized soil, and roots were harvested for RNA isolation at 8 weeks. Each experimental condition was represented by two independent biological replicates with three technical replicates each. For genomic DNA (gDNA) sequencing, barley plants were cultivated in the greenhouse at the Institute of Agricultural and Nutritional Sciences (MLU, Halle (Saale), Germany). Leaves were harvested after four weeks and approximately 1g of leaf tissue was collected and stored at -80°C until DNA isolation. Soil DNA isolation and 16S rRNA amplicon sequencing Soil microbial gDNA extraction for amplicon sequencing was performed using the DNeasy PowerSoil Pro Isolation Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The concentration of gDNA was quantified using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Montchanin, DE, United States), and the extracts were adjusted to 20 ng/µL template concentration followed by amplification of the bacterial 16S rRNA gene's V4 region using the primer pair 515F (GTGYCAGCMGCCGCGGTAA) and 806R (GGACTACNVGGGTWTCTAAT) [ 37 ] with Illumina adapter overhangs. The amplified products were purified with Agencourt AMPure XP beads (Beckmann Coulter, Krefeld, Germany). The amplified fragments were indexed using Illumina Nextera XT indices at both ends through indexing PCR. The indexed products were subsequently purified with AMPure beads, quantified by Quant-iT dsDNA high-sensitivity assay kit using a TECAN INFINITE PLEX plate reader (TECAN, Germany) and sample-based amplicon libraries were equimolarly pooled to achieve a final pool concentration of 4 nM. Finally, paired-end 2x300 bp sequencing was performed with a MiSeq Reagent kit v2 on an Illumina MiSeq platform (Illumina Inc., San Diego, CA, United States). Soil DNA isolation for whole metagenome sequencing Soil gDNA for whole metagenome sequencing (WMS) was extracted from rhizosphere soil using a combined mechanical and chemical lysis approach adapted from different soil DNA extraction protocols [ 38 , 39 ]. Approximately 250 mgs of soil was placed in a 2 mL tube containing ~ 200 mg of 0.5 mm glass beads (Roth, Karlsruhe, Germany), followed by the addition of 1 mL of preheated CTAB extraction buffer (2% CTAB, 1.4 M NaCl, 100 mM Tris-HCl pH 8.0, 20 mM EDTA, 0.2 M mannitol, and 2.6% PVP). Samples were homogenized twice at 25 Hz for 2.5 min in a TissueLyser (Qiagen, Haan, Germany), reorienting the adapter between runs, and incubated at 65°C for 30 min with periodic inversion. Cell debris was pelleted by centrifugation at 14,000 × g for 10 min at 4°C, and the supernatant was subjected to phenol: chloroform: isoamyl alcohol (25:24:1) extraction, followed by a second purification with chloroform:isoamyl alcohol (24:1). DNA was precipitated with 0.6 volumes of cold isopropanol and 0.1 volume of 3 M sodium acetate, incubated overnight at -20°C, and recovered by centrifugation. Pellets were washed with 70% ethanol, air-dried, and resuspended in 25 µL of nuclease-free water. To remove residual inhibitors, DNA was further purified using the Zymo DNA Clean & Concentrator kit (Zymo Research, Freiburg, Germany). DNA yield was measured with a Qubit fluorometer (Thermo Fisher Scientific, Singapore), and the sample was adjusted to 500ng for sequencing purposes. Isolation of plant genomic DNA for whole genome sequencing Plant gDNA for whole genome sequencing (WGS) was isolated from 4-week-old barley plants using the NucleoBond HMW DNA Kit (Machery-Nagel, Düren, Germany) according to the manufacturer’s instructions. Frozen leaf samples were ground to a fine powder and transferred into a 2mL tube containing two sterile steel beads (Ø 3.2 mm) (Hecht Kugellager GmbH & CoKG, Winnenden, Germany). Samples were homogenized in a TissueLyser at 25 Hz for 30 sec and DNA was extracted using the kit protocol. DNA concentration was measured with a Qubit fluorometer, and the sample was adjusted to 400ng for sequencing. Isolation of RNA from barley roots Total RNA was isolated from barley root tissue using the Nucleospin RNA Plant and Fungi kit (Macherey-Nagel, Düren, Germany) following the manufacturer’s instructions with minor modifications. Frozen root samples stored at -80°C were ground to a fine powder in a pre-chilled mortar with liquid nitrogen. Approximately 300 mg of powdered root tissue was transferred into a 2 mL RNase-free tube containing two sterile steel beads (Ø 3.2 mm). Samples were homogenized in a TissueLyser at 30 Hz for 1 min, the adapter was reoriented and homogenization was repeated to ensure complete disruption. RNA was then extracted using the kit protocol, yield was measured with a Qubit fluorometer and the concentration was adjusted to 500ng total RNA for sequencing purposes. Oxford long-read nanopore library preparation and sequencing The gDNA from rhizosphere samples and barley leaves was prepared for nanopore sequencing using the SQK-NBD114.96 and SQK-NBD114.24 native barcoding kit (Oxford Nanopore Technologies (ONT), Oxford, England), respectively, following the manufacturer’s instructions with minor adjustments. A high molecular weight (HMW) DNA standard (Zymo Research, California, USA) was also included as a performance control for WMS to evaluate long-read sequencing efficiency and data quality. DNA repair, end preparation, native barcode and adapter ligation were carried out according to the kit protocol. Clean up steps were performed using AMPure XP beads and adapter ligation was performed with long fragment buffer (LFB) to retain longer DNA fragments for WMS. For transcriptome sequencing, libraries were prepared using the cDNA-PCR Barcoding Kit SQK-PCB114.24 (ONT, Oxford, England) according to the manufacturer’s instructions. A total of 750 ng of high-quality RNA was reverse transcribed into cDNA, barcoded, and amplified by PCR for 16 cycles, followed by exonuclease treatment and cleaned up as described in the kit protocol. All libraries were sequenced on R10.1 flow cells on a PromethION platform for three consecutive days, with fresh libraries reloaded on the second- and third-days following flow cell washing using the Flow Cell Wash Kit EXP-WSH004 (ONT, Oxford, England). Sequencing was managed using MinKNOW software (ONT), and basecalling was performed with the Dorado super accurate model using a minimum Q-score of 10 for genomic and metagenomic, and 7 for transcriptomic libraries. Processing and analysis of Illumina data for 16S study The sequence data analysis was performed using QIIME2 (v2023.9) [ 40 ]. Briefly, forward and reverse primers from demultiplexed reads were trimmed using q2-cutapdapt [ 41 ] followed by denoising with DADA2 (v1.26) [ 42 ], chimera removal and clustering into Amplicon Sequence Variants (ASVs). Taxonomy was assigned to ASVs using the q2-feature‐classifier [ 43 ] sklearn-naïve Bayes taxonomy against the silva-138-99-515-806-nb-classifier reference database [ 44 ]. The bacterial phylogenetic tree was inferred using the q2-phylogeny plugin which employs MAFFT (v7.520) [ 45 ] for sequence alignment and FastTree (v2.2.0) for Maximum Likelihood tree construction [ 46 ]. The tree was midpoint-rooted to ensure a robust and unbiased phylogenetic framework. The ASV matrices, taxonomic tables, phylogenetic tree, and representative sequences were merged with the sample metadata file using the phyloseq package (v1.52.0) [ 47 ] in R (v4.5.1) [ 48 ]. The bacterial ASV matrices were rarefied to ensure uniform sequencing depth of 30375 reads per sample. The role of the barley domestication status on the rhizosphere bacterial community compositions (based on Bray-Curtis distance dissimilarity) was analyzed through PERMANOVA using the adonis2 function from the vegan package (v2.7.1) [ 49 ], followed by a pairwise adonis test using the pairwise.adonis2 package (v0.4) [ 50 ] with Benjamini‐Hochberg false discovery rate (FDR) correction [ 51 ]. The ordinations were visualized using Principal Coordinates analysis (PCoA) as implemented in the plot ordination function of phyloseq using Bray-Curtis distances. Processing and analysis of ONT data for metagenomics Raw reads generated from Oxford Nanopore sequencing were uploaded to the Galaxy Europe platform ( https://usegalaxy.eu/ ) for downstream analysis. Read quality was assessed with fastplong (v0.4.1) [ 52 ], low-quality reads trimmed with cutadapt (v5.1), and microbial reads were assigned taxonomically with Kraken2 (v2.1.3) [ 53 ] using the prebuilt bacterial and fungal genome databases with a confidence threshold of 0.1. Kraken2 read counts were summarized at phylum, genus, and species levels and imported into R for analysis. Biological replicates were merged, relative abundances were calculated, and the ten most abundant taxa at each rank were retained. Data handling and visualization were performed with tidyverse package (v2.2.0) [ 54 ] and ggplot2 (v4.0.0) [ 55 ]. Differential abundance was analyzed with DESeq2 (v1.46.0) [ 56 ], using the control as reference and applying apeglm shrinkage (v1.28.0) [ 57 ]. Genera with adjusted p < 0.05 were visualized using ComplexHeatmap package (v2.22.0) [ 58 ]. Shared and unique bacterial genera among control, wild accession, landrace and modern groups were visualized with VennDiagram package (v1.7.3) [ 59 ]. Alpha diversity metrices including species richness, Shannon and Simpson index were calculated with vegan [ 49 ] and visualized in ggplot2. Statistical differences were tested using the non-parametric Kruskal-Wallis test [ 60 ]. Beta diversity was assessed using Bray-Curtis and Jaccard dissimilarities, followed by principal coordinate analysis (PCoA) in ape (v5.8.1) [ 61 ], and visualized in ggplot2. Functional profiling of rhizosphere microbiome Nanopore long-read sequencing metagenomic data were assembled and functionally annotated to understand the functional potential of the rhizosphere microbiome. Briefly, the biological replicates for each sample were merged to improve coverage, and assemblies were generated with metaFlye (v2.9.6) [ 62 ]. Open reading frames (ORFs) were predicted with Prodigal (v2.6.3) [ 63 ], and protein sequences were annotated using eggNOG-mapper (v2.1.12) [ 64 ] with the DIAMOND algorithm and a threshold of 0.001. Annotations included Clusters of Orthologous Genes (COGs), KEGG Orthologs (KOs), pathways and modules. Functional summaries were generated using pandas (v2.2.2) [ 65 ], and KEGG pathways were visualized in R using the tidyverse and ggplot2. Predicted proteins further screened for Pfam domains ( http://pfam.xfam.org/ ) [ 66 ] through eggNOG-mapper, and Pfam frequencies were calculated for each sample. Only domains with at least 5 occurrences were retained for comparison. The top 20 Pfam families were visualized in ggplot2 and ggalluvial (v0.12.5) [ 67 ]. Pfam accessions were cross-referenced with their corresponding functional descriptions based on the Pfam-A database (v35.0) for biological interpretation. Metagenome-assembled genomes (MAGs) reconstruction and quality assessment Metagenomic reads from 2024 and 2025 were co-assembled to generate a single composite assembly representing the pangenome of the microbial community. The concatenated dataset was assembled with metaFlye in nano-hq mode. The resulting contigs were used as a reference for read mapping with minimap2 (v2.28) [ 68 ] to estimate per-sample contig coverage, and coverage profiles were summarized as a depth matrix for binning with MetaBAT2 (v2.17) [ 69 ]. MAG quality was evaluated with checkM (v1.2.4) [ 70 ] by estimating completeness and contamination based on lineage-specific marker genes. Taxonomic assignment was performed using GTDB-Tk (Genome Taxonomy Database Toolkit, v2.5.2) [ 71 ], yielding standardized lineage classifications for all reconstructed bins. In addition, 16S rRNA genes were identified using Barrnap (v0.9) [ 72 ], aligned with MAFFT and used for phylogenetic reconstruction with FastTree. Data visualization and graphical summaries were performed in R using the ggtree package (v3.10.0) [ 73 ]. Processing of transcriptome data Raw ONT reads were assessed for quality using fastplong within the galaxy platform. Adapter sequences and low-quality bases were removed with cutadapt and poly A/T stretches of at least 10 bases were trimmed to prevent alignment artifacts and a Phred quality cutoff of 20 was applied to remove low-quality bases. The trimmed reads were aligned to the Hordeum vulgare reference genome ( https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/fasta/hordeum_vulgare/dna/ ; accessed on October 3, 2025) using Minimap2 in spliced alignment mode optimized for ONT reads (map-ont) with splice junction support from the corresponding gene annotation file ( https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/gff3/hordeum_vulgare/ ). Gene level read counts were obtained with featureCounts (v2.1.1) [ 74 ], and differential expression analysis was performed with DESeq2 (v2.11.40.8). Genes with an adjusted p 2 or < 0.5 were considered significant for increased or decreased transcript abundance, respectively. Principal component analysis (PCA) and sample-to-sample correlation matrices were generated in DESeq2 and the numbers of unique and shared differentially expressed genes (DEGs) were visualized with the VennDiagram package in R. Functional annotation and enrichment analysis of transcriptome Functional categorization of DEGs was performed with the Mercator annotation platform ( https://www.plabipd.de/mercator_main.html ) [ 75 ], which assigns genes to hierarchical functional bins based on conserved domains and sequence homology. The H. vulgare protein reference file ( https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/fasta/hordeum_vulgare/pep/ ; assessed on October 7, 2025) was used for annotation. DEGs obtained from the RNA-Seq analysis were mapped to Mercator annotations and merged DEG-annotation tables in R using tidyverse. Genes were grouped by functional category and the ten most represented bins were identified. Gene Ontology enrichment analysis was carried out on significantly expressed genes ( p- adj < 0.05) with gprofiler2 [ 76 ], using H. vulgare (Morex v3) genome as reference. Enrichment was calculated against the background of all expressed genes using Fischer exact testing with Benjamini-Hochberg correlation (FDR < 0.05). GO was performed on the Biological Process (BP) category, and the most significant terms were visualized as bubble plots in ggplot2. To investigate transcriptomic responses of barley roots to microbial interactions, DEGs were screened for functional classes associated with categories of defense, signaling, and secondary metabolism using the Mercator / MapMan annotations [ 77 ]. Category-based DEG counts were summarized and visualized in ggplot2. Processing and analysis of ONT data for plant genomics Raw ONT WGS reads from all barley genotypes were analyzed using the GeneToCN tool [ 78 ], which performs alignment-free copy number estimation based on k -mer frequencies. Reference genome sequences of Morex BPGv2 ( https://panbarlex.ipk-gatersleben.de/ ) were used to extract the target gene and its flanking regions, which were processed with the GeneToKmer script to generate 25-bp k -mers unqiue to reference locus. These gene-specific and locus-wide k -mers were queried against genotype-specific ONT reads using GenomeTester4 [ 79 ]. For each genotype, k -mer frequencies were counted and the median gene-specific k -mer depth was calculated as a proxy for genomic dosage. Locus-wide and gene-specific k -mer counts were then summarized to assess sequence similarity and divergence relative to the Morex reference. Pairwise k -mer intersections between genotypes were used to compute a similarity matrix, which was visualized as a heatmap and hierarchical clustering dendrogram. Spatial variation in k -mer abundance were examined by mapping k -mer counts back to genomic coordinates, binning, and smoothing to generate coverage profiles using ggplot2. For the analysis of read-based variant calling to identify SNPs and insertions and deletions (INDELS), the ONT reads were aligned to the Morex reference genome using minimap2. Subsequently, we screened for genetic diversity using clair3 [ 80 ] for SNPs as well as sniffles2 [ 81 ] for INDELS. Per-base SNPs within the coding region were visualized using gpplot2. All downstream data handling, and figure generation were performed in R. Results Rhizosphere microbial community assembly Illumina MiSeq-based short-read profiling of rhizosphere soil microbial communities from 21 barley genotypes (seven genotypes per domestication group) and two controls revealed that barley domestication status significantly influenced microbiome assembly (PERMANOVA: R² = 0.2273, p < 0.001). Pairwise comparisons further showed that all three barley status groups differed significantly from the controls, with the largest dissimilarity observed for wild (31.5%), followed by landrace (25%) and modern (10.8%) barley genotypes. Although modern genotypes differed significantly from both wild and landrace groups, no significant difference was detected between wild and landrace genotypes (Supporting Table S1 ). Consistent with these results, PCoA ordination demonstrated clear separation among the genotype groups, with the first two axes explaining 25.5% of the variation in rhizosphere bacterial community composition (Supporting Figure. 1). Together, these findings indicate that domestication status is a major determinant of rhizosphere microbial community structure across the barley genotypes. To enable a deeper and genome-resolved analysis of the microbial communities associated with barley domestication, we next selected a representative subset of genotypes for whole metagenome sequencing (WMS). Based on the ordination patterns and the observed community divergence across domestication groups, two genotypes each from the wild (HID0144 and HID0380) and landrace (HID1029 and HID1104) groups, and one genotype from the modern group (Gretchen) were chosen for high-resolution long read Oxford Nanopore (ONT) sequencing. This targeted selection allowed us to capture the wealth of microbiome variation revealed by short-read profiling while enabling an in-depth understanding of the microbial communities that shape barley genotypes. Oxford Nanopore long-read metagenome sequencing of rhizosphere microbiome The use of long-read sequencing offers a distinct advantage for complex soil microbiomes, as it improves assembly continuity, gene-level annotation, and recovery of complete biosynthetic gene clusters [ 82 ]. In our study, ONT sequencing of barley rhizosphere microbiome samples across 2024 and 2025 produced high-quality long-read data suitable for downstream metagenomic analysis. In 2024, the mean read length ranged between approx. 2.4 to 4.9 kb, with average Phred quality scores of about 15 (Fig. 1A). Similarly, in 2025, mean read length varied between 2.3 and 3.5 kb, and mean quality scores remained above 15 across all samples (Fig. 1B). The sequencing depths expressed as the total number of reads per sample, ranged between 0.5 to 1.3 million reads in 2024, and 0.3 to 1 million reads in 2025. Total sequencing yield, represented by the sum of bases generated, varied from 2.6 to 4.5 Gb in 2024 and 0.7 to 3.8 Gb in 2025 (Figs. 1C and D). The summary statistics of WMS runs from 2024 and 2025 are provided in Supporting Tables S2 and S3. We further assessed the proportion of classified and unclassified reads across all samples using Kraken2 in Galaxy. The fraction of classified to unclassified reads differed largely between the bacterial and fungal datasets. For bacteria, the prebuilt standard Kraken2 database successfully classified approx. 30% of the reads in 2024 and 2025. In contrast, fungal reads showed lower classification rates. Testing several fungal reference databases modestly improved assignment, but variability among databases prompted us to use the standard fungi reference genome database available in Galaxy for final taxonomic classification. The remaining unclassified reads likely reflect the limited representation of fungal genomes in current reference libraries. Of the classified reads, we also compared the proportions of bacterial and fungal sequences. Across all samples, bacterial reads predominated, accounting for approx. 59–73% and 63–68% of total classified reads in 2024 and 2025, respectively (Figs. 1E and F). These findings indicate that ONT metagenomic sequencing revealed higher bacterial richness than fungal diversity in the rhizosphere, consistent with previous studies reporting that fungal sequences are underrepresented in metagenomic databases, limiting taxonomic accuracy [ 83 ]. Taxonomic composition of the barley rhizosphere microbiomes reveals genotype-dependent community shifts The rhizosphere microbiome plays an important role in plant growth, nutrient turnover, and stress resilience [ 15 , 84 ]. The composition and structure of root-associated microbial communities are key determinants of plant fitness [ 85 ] and have strongly been influenced by processes of domestication and breeding. To investigate genotype-specific microbial community structures and their diversity across seasons, rhizosphere samples from wild accessions HID0144 and HID0380, landraces HID1029 and HID1104, and the modern cultivar Gretchen were analyzed at different taxonomic ranks (Fig. 2). At the phylum level, the rhizosphere bacterial communities of different barley genotypes were dominated by Pseudomonadota and Actinomycetota, which together accounted for the majority of reads across both field seasons. These two phyla formed a stable microbiome shared among wild, landrace, and modern barley genotypes. Temporal variation was evident, with Actinomycetota increasing in relative abundance from 12.1% in 2024 to 27% in 2025 (Figs. 2A and B). At the genus level, taxa including Nocardioides , Bradyrhizobium , and Streptomyces were consistently dominant across all barley types. While these genera were consistently present across all samples, their relative abundance varied markedly among genotypes and between years. Interestingly, between years, the relative abundance of Streptomyces increased in Gretchen, from 3.5% in 2024 to 14.1% in 2025, suggesting temporal shifts in this actinobacterial genus (Figs. 2C and D). In contrast, Bradyrhizobium declined by approximately 10% over the same period, possibly reflecting environmental influences on nitrogen-fixing microbial populations. These trends highlight shifts in rhizosphere bacterial composition influenced by both host genotype and environmental changed between years. Such genotype-dependent patterns suggest that host signaling may actively shape microbial recruitment, consistent with previous findings in cereals where host genetics governs microbial assembly [ 11 , 21 ]. In genus-level profiles, fungal reads represented only a minor fraction of total microbial reads, consistent with lower taxonomic richness in fungi than in bacteria. Correspondingly, the number of fungal genera in each sample was substantially lower than that of the bacterial genera. Among fungi, only Fusarium , Aspergillus , Cryptococcus and Kluyveromyces were consistently observed. Of these, Fusarium dominated, contributing on average of 92.4% and 79.6% of fungal reads in 2024 and 2025, respectively (Figs. 2E and F). As shotgun metagenomics quantifies DNA, the disproportionately low fungal read counts and genus richness support the interpretation that fungal propagules are relatively less prevalent than bacteria in rhizosphere samples [ 86 , 87 ]. To compare compositional shifts at the species level, we performed two complementary WMS analyses that identified genera with high internal diversity and strong changes in abundance. This approach revealed the taxa most enriched or depleted relative to the control. Genera such as Pseudomonas and Streptomyces consistently exhibited high species richness across all barley types, reflecting their widespread association in the rhizosphere. However, the number of species within these genera varied between years. For instance, an average of 23 Streptomyces species were detected in 2024, increasing to 71 in 2025, and Pseudomonas species increased from 32 to 46 during the same period (Figs. 2G and H). Differential abundance analysis revealed clear contrasts among host genotypes and between years. In both years, less diverse genera exhibited stronger enrichment relative to control soil, highlighting the functional contribution of rare taxa to community composition (Figs. 2I and J). Interestingly, plant growth-promoting bacteria such as Devosia , Neorhizobium , and Aeromicrobium were detected predominantly in the rhizosphere of 2024 (Fig. 2I), whereas Kosakonia and Enterobacter were more abundant in 2025. The modern cultivar Gretchen displayed higher levels of Erwinia , Enterobacter , and Tardiphaga in 2025 (Fig. 2J). These dramatic shifts in non-dominant, but functionally active taxa suggest intense recruitment patterns shaped by host genotype and environmental variation. In order to compare the shared and unique bacterial genera among barley groups, genus-level data were categorized into four groups including control, wild accessions, landraces, and elite cultivar. A genus was considered present within a category if detected in at least one sample from that group, and only these presence-based genera were used to compute shared and unique sets for Venn analysis. In both years, a substantial number of taxa was shared across all barley types and the control soil, comprising 104 and 96 genera in 2024 and 2025, respectively (Figs. 2K and L; Supporting Tables S4 and S5). Despite this common core, distinct sets of genera were unique to each category, indicating genotype- and year-specific associations. Wild accessions from 2024 and landraces from 2025 comprised the highest number of unique genera accounting to 26 each, whereas the elite cultivar Gretchen contained only eight across both years. This pattern suggests that wild accessions recruit a broader but more variable microbiome, while domestication has favored a narrower and more stable assemblage of bacterial lineages. Taken together, these results indicate that distinct barley genotypes shape and support characteristic rhizosphere microbiomes. While the overall community structure remained conserved across genotypes, consistent with a shared core microbiome, distinct abundance patterns highlight that host genotype exerts a measurable influence on rhizosphere composition, driving differential recruitment and adaptation of specific bacterial lineages in the rhizosphere. Rhizosphere microbial alpha- and beta-diversity Alpha and beta diversity analyses were performed on WMS-derived bacterial profiles to assess within (alpha) and between-sample (beta) variation across barley genotypes in 2024 and 2025. Alpha diversity indices, including species richness (Supporting Figs. 2A and D), Shannon (Supporting Figs. 2B and E), and Simpson (Supporting Figs. 2C and F), showed no significant differences among groups (Kruskal-Wallis, p > 0.05; Richness: χ² = 9.59, p = 0.0877 in 2024 and χ² = 10.50, p = 0.0623 in 2025), indicating comparable overall community diversity. On average, 100–150 bacterial genera were detected in 2024, whereas 2025 samples showed higher richness (220–450 genera) (Supporting Figs. 2A and D), consistent with a more complex rhizosphere community. Wild accessions tended to display slightly higher richness than landraces and the modern cultivar, suggesting modest but non-significant trends consistent with early observations that domestication can narrow rhizosphere diversity [ 11 ]. Beta diversity analyses revealed clear genotype- and year-dependent community differences. Principal Coordinate Analysis (PCoA) based on Bray-Curtis and Jaccard distances showed distinct clustering by genotype and year, indicating strong compositional differences, similar to patterns observed in maize and rice [ 17 , 88 ]. PERMANOVA confirmed significant variation among groups (Bray-Curtis: R² = 0.79, F = 4.64, p = 0.001 in 2024, Supporting Fig. 2G; R² = 0.93, F = 16.91, p = 0.001 in 2025, Supporting Fig. 2I). In addition, Jaccard-based comparisons supported these findings ( R² = 0.60, F = 1.84, p = 0.001 in 2024, Supporting Fig. 2H; R² = 0.56, F = 1.54, p = 0.001 in 2025, Supporting Fig. 2J), and homogeneity of dispersion tests confirmed that these dissimilarities were not driven by unequal variance among groups. Together, the diversity metrics confirm genotype-dependent community composition, with stronger divergence in 2025 suggesting additional genotype-by-environment effect on microbial recruitment. Functional profiling of the microbiome reveals conserved yet genotype-specific enrichment of microbial pathways across barley genotypes To understand whether genotype-dependent community shifts correspond to functional differences, metagenomic assemblies were annotated with eggNOG-mapper to obtain COG and KEGG classifications. COG profiles showed highly consistent functional compositions across genotypes and years, indicating a stable core metabolic structure of the rhizosphere microbiome (Supporting Fig. 3) [ 89 ]. The dominant categories across all samples included amino acid transport and metabolism, energy production and conversion, and cell wall-related biogenesis, with ~ 20% of proteins annotated as hypothetical, reflecting the prevalence of uncharacterized microbial functions. Although the total number of predicted proteins varied among genotypes and years, the proportional representation of COG categories remained comparable, suggesting that functional capacities were largely conserved despite taxonomic differences (Supporting Table S6). To further understand the specific biochemical pathways involved in the rhizosphere microbial activity, KEGG pathway enrichment analysis was performed on the annotated protein datasets. Across both years, microbial communities from all genotypes exhibited broad enrichment of core metabolic processes, including amino acid metabolism, energy metabolism and secondary metabolite (SM) biosynthesis (Figs. 3A and B). In 2024 and 2025, an average of 531 and 487, 602 and 348, 555 and 518, and 473 and 379 pathways were enriched in control soil, wild accessions, landraces and the modern cultivar Gretchen, respectively (Supporting Table S6). Although most pathways were shared among genotypes, the relative enrichment varied systematically among wild, landrace, and modern types, indicating that host genotype not only shapes microbial composition but also selects for distinct metabolic functions within the rhizosphere. In 2024, the wild accession HID0380 displayed strong enrichment in amino acid and sugar metabolism, whereas its closest related counterpart HID0144 showed higher enrichment in glyoxylate and dicarboxylate metabolism (Fig. 3A). The landraces HID1029 and HID1104 exhibited moderate but balanced enrichment across most pathways. Interestingly, HID1029 showed a moderate enrichment in glycolysis or gluconeogenesis, while HID1104 was enriched in ATP-binding cassette (ABC) transporters and amino acid metabolism of glycine, serine and threonine. The modern cultivar Gretchen displayed a strong enrichment in quorum sensing as well as amino acid biosynthesis, suggesting that microbial communities in its rhizosphere may be more specialized in signaling and metabolic exchange [ 90 ]. The dataset from 2025 revealed distinct differences in functional enrichment patterns (Fig. 3B), with an overall higher diversity of enriched pathways compared with 2024. The wild accession HID0144 showed pronounced enrichment in carbon fixation pathways, while HID0380 was enriched in alanine, aspartate and glutamate metabolism, indicating an increased emphasis on nitrogen assimilation and amino acid turnover. The landraces maintained moderate yet broad enrichment across all major pathways, displaying a similar metabolic profile between HID1029 and HID1104. Interestingly, Gretchen exhibited an altered enrichment pattern relative to its previous year, with elevated activity across aminoacyl-tRNA biosynthesis, ABC transporters and pyrimidine metabolism, implying enhanced translational and transport process in its microbiome. Across both years, pathways with antibiotic biosynthesis and SM production were consistently enriched in different genotypes. This pattern points to a metabolically competitive and chemically dynamic rhizosphere environment, where microbial taxa likely engage in both antagonistic and cooperative interactions that shape community structure and potentially contribute to plant protection. Collectively, the functional profiling demonstrates that while core metabolic capacities are conserved across barley genotypes, differential pathway enrichment reveals host-specific microbial adaptation, underscoring the genotype’s influence on microbiome function. Metagenome-assembled genomes (MAGs) reveal the taxonomic structure and recovery quality of the barley rhizosphere microbiome To extend beyond community-level inference and understand genome-resolved diversity, we reconstructed metagenome-assembled genomes (MAGs) from the long-read metagenomic datasets obtained in 2024 and 2025. A total of 445 MAG bins were recovered across all samples, representing genome fragments derived from diverse bacterial lineages (Fig. 4A). The number of reconstructed MAGs varied between samples and years, ranging from 200 and 153 in Control, 153 and 98 in HID0380, 201 and 190 in HID0144, 98 and 233 in HID1029, 212 and 191 in HID1104, and 190 and 143 in Gretchen over 2024 and 2025, respectively. Quality assessment based on CheckM revealed that the majority of MAGs exhibited low completeness (< 50%), with only about 20 bins exceeding this threshold (Fig. 4B). Such fragmentation is characteristic of soil metagenomes, where high species richness, uneven sequencing depth, the intrinsic error profile of nanopore reads, and strain-level complexity limit the recovery of near-complete genomes [ 91 , 92 ]. Taxonomic classification using GTDB-Tk enabled assignment of MAGs at different ranks, with the number of classified bins increasing from domain to genus level (Fig. 4C). Approximately 15, 23, 27, and 36 distinct phyla, orders, families, and genera were recovered, while only 2 species-level MAGs were identified, reflecting the limited number of high-quality assemblies. A subset of MAGs remained unclassified at lower taxonomic levels, likely due to incomplete marker sets or absence of closely related reference genomes. The most represented bacterial phyla were Pseudomonadota, Actinomycetota, Bacteroidota, and Acidobacteriota, together encompassing the majority of recovered MAGs (Fig. 4D). Among these, Pseudomonadota and Actinomycetota dominated, accounting for 25 and 22 of all bins, respectively. Members of these groups are frequently associated with plant-associated microbiomes and include metabolically versatile taxa capable of stress adaptation, SM production, and organic matter turnover [ 13 , 93 , 94 ]. To further resolve evolutionary relationships, a phylogenetic cladogram was constructed using the 16S rRNA genes identified within 93 MAGs (Fig. 4E). The 16S-based tree revealed well-defined clusters corresponding to the four dominant phyla, confirming taxonomic assignments and illustrating the genomic diversity within each lineage. Genera such as Bradyrhizobium , Chitinophaga , Flavobacterium , Nocardioides , and Streptomyces were among the most represented. The close phylogenetic grouping of these taxa underscores their ecological coherence and prevalence within the barley rhizosphere. Collectively, these results demonstrate that genome-resolved metagenomics enabled recovery and taxonomic placement of bacterial genomes from complex rhizosphere communities, highlighting the predominance of Actinomycetota, Pseudomonadota, and Bacteroidota lineages and revealing fine-scale phylogenetic structure through 16S-based analysis. Transcriptional response of barley roots to distinct microbiomes In order to investigate the genotype-specific transcriptional response of H. vulgare roots to distinct microbiome compositions, total mRNA was isolated from root tissues of five barley genotypes in 2025, including wild accessions HID0144 and HID0380, landraces HID1029 and HID1104 and modern cultivar Gretchen. As a control, the high yielding cultivar Gretchen was grown under greenhouse conditions in pots containing sterilized field soil to exclude microbial influence. Two independent biological replicates were analyzed for each genotype, yielding 12 libraries. The ONT sequencing generated approx. 12 million raw reads, of which 10.8 million high-quality reads were retained after trimming. On average, 90% of these clean reads were successfully mapped to the reference genome of H. vulgare Morex v3, corresponding to a total of 35,106 genes. Differentially expressed genes (DEGs) were identified using a significance threshold of p 2 for increased and < 0.5 for decreased transcript abundances. Principal Component analyses (PCA) showed tight clustering of biological replicates within each genotype, indicating uniformity and high reproducibility of transcriptome profiles, whereas clear separation among genotypes reflected distinct transcriptional signatures (Fig. 5A). This pattern was further supported by a heatmap of the correlation matrices, with samples clustering based on normalized transcript counts (Fig. 5B). The number of DEGs varied among genotypes with 1,266, 1,844, 1,941, 1,657 and 1,384 DEGs identified in HID0144, HID0380, HID1029, HID1104 and Gretchen, respectively. Of the 1,266 and 1,844 DEGs of the wild accessions HID0144 and HID0380, 808 and 1757 genes showed increased and 458 and 87 exhibited decreased transcript abundances, respectively. Interestingly, HID0380 had more than twice the number of transcriptionally induced up-regulated genes compared with HID0144. The landraces HID1029 and HID1104 showed 1,188 and 893 genes with increased expression and 753 and 764 genes with reduced expression, respectively. In the modern cultivar Gretchen, 1,067 genes were up- and 317 were down- regulated (Fig. 5C). The in-detail summary of the DEGs in all samples are provided in the Supporting Tables S7 - S11. DEGs were functionally categorized using Mercator, revealing strong representation of enzyme-related functions, followed by categories linked to chromatin organization and RNA biosynthesis. Several DEGs were also assigned to functional groups involved in translational processed, including protein homeostasis, biosynthesis and modification (Fig. 5D). Intriguingly, the Venn plot illustrated genotype-specific transcriptional signatures, identifying 11, 463, 189, 61 and 44 unique up-regulated genes and 166, 13, 295, 256 and 56 unique down-regulated genes in HID0144, ID0380, HID1029, HID1104 and Gretchen, respectively (Figs. 5E and F; Supporting Tables S12 and S13). Notably, the number of shared up-regulated genes across genotypes were approx. tenfold higher than shared down-regulated genes. These results indicate that barley genotypes activate distinct transcriptional responses towards different microbial consortia, highlighting the ability of individual genotypes to respond distinctively to varying microbiome compositions. This correspondence between microbial variation and host transcriptional response suggests a feedback mechanism whereby genotype-specific microbiomes elicit tailored molecular programs in the host, reinforcing the genotype-microbiome linkage. To assess functional patterns among differentially expressed genes, gene ontology (GO) enrichment analysis was performed using the biological process (GO:BP) category. The top enriched terms revealed both shared and genotype-specific responses of barley roots to distinct microbiomes (Supporting Fig. 4; Supporting Table S14). Core processes such as response to stress, small-molecule metabolism, regulation of respiration, and translation-associated functions were consistently enriched across all genotypes. Genotype-specific differences were also evident as HID0144 showed enrichment in nucleoside phosphate metabolism, HID0380 and Gretchen were enriched in catabolic processes, and the landraces displayed similar but quantitatively variable enrichment profiles. The elite cultivar Gretchen additionally showed enrichment related to nucleosome organization, suggesting chromatin-associated regulation. Overall, while major biological processes were conserved, variation in pathway enrichment reflects subtle yet distinct transcriptional programs shaped by genotype and associated microbiomes. Host transcriptional activation coordinates with microbial signaling in the barley rhizosphere In order to investigate the transcriptional response of barley roots to distinct microbial communities, DEGs were classified into eight biological categories based on Mercator annotation. These categories were selected for their relevance to plant-microbe interactions, and included transporters, secondary metabolism, redox reactions, receptor-like protein kinases (RLKs), hormone signaling, pattern recognition receptors (PRRs) and nucleotide-binding leucine-rich repeat receptors (NLRs), defense-related regulatory proteins, and cell wall organization (Fig. 6A; Supporting Table S15). Overall, all genotypes exhibited a higher proportion of upregulated genes across most categories, indicating an active transcriptional reprogramming in response to the microbiome. Among the wild accessions, HID1104 and HID0380 showed 171 and 445 DEGs with increased, and 60 and 9 DEGs with decreased transcript abundance, respectively. Notably, HID0380 displayed the strongest transcriptional activation, with 172 and 91 upregulated genes belonging to defense-related regulatory proteins and transporters, respectively. In contrast, only 5 and no downregulated genes were detected in the same category. This highlights the presence of dramatic transcriptional activation in HID0380 taking place in response to the associated microbiota. The landraces HID1029 and HID1104 displayed comparable enrichment patterns, with 250 and 199 upregulated DEGs, respectively. Both genotypes showed a substantial number of regulatory proteins associated with defense, with 82 and 58 DEGs with increased, and 50 and 56 with decreased transcript abundance. The modern cultivar Gretchen showed a total of 253 and 57 up- and down-regulated DEGs, including approx. 110 genes with increased activity belonging to the defense-related regulatory proteins category. Across all genotypes, other plant immunity-related groups including PRRs / NLRs, RLKs and secondary metabolism also contained a substantial number of DEGs, though their representation varied among genotypes. As the different plant gene categories associated with microbial interactions and signaling showed pronounced transcriptional activation, we decided to look into the protein families within the rhizosphere microbiome from 2025 to understand how microbial communities may respond to host-derived cues. The Pfam-based analysis revealed a conserved set of protein families broadly represented across all samples, suggesting a shared molecular repertoire underlying microbial functionality in the barley rhizosphere. Pfam-based domain annotation identified a total of 537, 682, 62, 4008, 543 and 212 functional domains across control, HID0144, HID0380, HID1029, HID1104 and Gretchen, respectively. Each Pfam count represents the number of predicted proteins containing a specific conserved domain within a sample (Supporting Table S16). The most abundant domains included response regulators, histidine kinases, ABC transporter, and major facilitator superfamily proteins, which are key components of bacterial signal transduction and substrate transport (Fig. 6B) [ 95 , 96 ]. Their consistent enrichment across all barley genotypes suggests that rhizosphere microbes actively sense and respond to plant-derived chemical cues. Interestingly, domains related to redox regulation, including PAS (Per-Arnt-Sim) domain and radical SAM (S-adenosyl-L-methionine) superfamily members, as well as energy-driven transport processes such as ATPases and enoyl-acyl carrier protein reductases, were also highly represented, reflecting strong microbial integration with plant roots. In addition, the prevalence of signal transduction and transporter-associated Pfam families across samples indicates active microbial communication aligned with host signaling. This reciprocal regulation underscores a sophisticated molecular dialogue between barley and its microbiome [ 97 ], forming a dynamic communication network that supports rhizosphere resilience and metabolic balance. The connection between host defense and microbial signaling pathways implies that barley genotypes not only shape but also dynamically co-regulate their microbiomes, reflecting a co-adaptive relationship that likely contributes to genotype-specific performance under natural conditions. Genomic variation at a pathogen defense-related locus reveals structural divergence among barley genotypes Given the strong transcriptional activation of defense-related regulatory proteins, we further examined whether variation in the underlying genomic loci could help explain these responses. Among the eight functional categories associated with plant-microbe interactions, defense-related regulatory proteins contained the highest number of upregulated genes across all genotypes. UniProt annotation of 21 commonly upregulated genes revealed consistent activation of protease inhibitors, ubiquitin-proteasome components, and molecular chaperones such as BAG-domain proteins and calreticulin, all of which contribute to proteostasis during stress. Notably, two of the most highly expressed genes, HORVU.MOREX.PROJ.1HG00003980.1 (BPGv2) and HORVU.MOREX.PROJ.1HG00011330.1 (BPGv2), encode subtilisin/chymotrypsin inhibitor 2A proteins, typically induced during pathogen challenge and central to protease-mediated immunity [ 98 ]. Because these genes showed strong transcriptional response across all genotypes, we investigated whether genomic structural variation at these loci may contribute to the observed differences in expression or microbial response. Examination of the barley pangenome (BPGv2), together with ONT genome sequencing of our five genotypes, revealed that the two inhibitor genes are paralogs and vary in copy number. PanBarlex ( https://panbarlex.ipk-gatersleben.de/ ) [ 8 ] analysis showed that Morex carried two paralogous copies, while the wild accession HID0380 contains 16 homologous gene copies within a ~ 600 Kbp cluster, indicating substantial expansion of this defense-associated locus. To quantify structural divergence in more detail, we applied alignment-free k -mer profiling using GeneToCN [ 78 ]. The statistical summary of ONT genomic sequencing is provided in Supporting Table S17. Of the two target genes, only HORVU.MOREX.PROJ.1HG00003980.1 provided sufficient informative k-mers for reliable analysis, and we therefore focused on this locus. Median gene-region k -mer depth varied strongly among genotypes, with HID0380 showing the highest depth, consistent with multiple Morex-like copies, whereas HID0144 showed near-zero depth, suggesting absence or extreme divergence of the Morex allele (Fig. 7A). Other genotypes displayed low to moderate similarity to the reference. To distinguish divergence within the gene from that of the surrounding locus, we separated 25-mers matching the Morex coding sequence (gene-specific) from those derived from the flanking region (flank-specific). All genotypes except HID0144 retained all 30 gene-specific 25-mers, indicating that the coding region itself is structurally conserved. In contrast, flank-specific k -mers showed substantial variation with HID1029 retaining 1202 k -mers, indicating highest similarity, whereas HID0380, HID1104 and Gretchen retaining only 12–26 such k -mers (Fig. 7B). Pairwise comparisons (Fig. 7C) of locus-wide k -mer sets and hierarchical clustering (Fig. 7D) further resolved these relationships. HID0380 and HID1029 formed the closest pair, HID1104 and Gretchen displayed intermediate similarity, while HID0144 was clearly separated from other genotypes. Spatial visualization of k -mer abundance reflected the depth of Morex-derived 25-mers at each genomic position across the 30 gene-specific k -mers, thereby indicating the relative dosage of Morex-like sequence. This visualization revealed a sharp coverage peak at the gene region for HID0380, reaching a maximum depth of 29, whereas HID1029, HID1104 and Gretchen showed progressively lower depths of 4, 9 and 3, respectively (Fig. 7E). To determine whether this structural variation was accompanied by nucleotide-level divergence, we performed read-based SNP calling across HORVU.MOREX.PROJ.1HG00003980.1 (Supporting Table S18). SNP frequencies varied among genotypes, with HID0380 showing the highest number (6 SNPs), and forming at least two distinct haplotype groups, suggesting potential sub-functionalization among its duplicated copies. The landrace HID1104 displayed 4 SNPs, followed by the genotypes HID0144, HID1029 and Gretchen each showing 3 SNPs (Fig. 7F). To assess the positional distribution of these variants, genotype-specific consensus sequences were aligned to Morex and visualized as a per-base match / SNP profile across the 224 bp region of the gene. This revealed that SNPs were mostly uniformly distributed except for the wild accession HID0380, which showed a slightly variable pattern with base substitutions (Fig. 7G). Together, these results indicate that, although the subtilisin-chymotrypsin inhibitor gene HORVU.MOREX.PROJ.1HG00003980.1 is structurally conserved across barley genotypes, both k -mer patterns and nucleotide-level polymorphisms reveal genotype-specific copy-number and sequence variation that likely contribute to the observed transcriptional variation. Collectively, our multi-omics approach using long-read nanopore sequencing shows that distinct barley genotypes actively shape both the structure and function of their rhizosphere microbiomes. Genotype dependent shifts in community composition were accompanied by conserved metabolic functions with changes in associated pathways indicating genotype-specific functional changes within the microbiome. In addition, host transcriptional activation of defense, signaling, and metabolic processes paralleled microbial enrichment in transporter and signaling domains, revealing a genotype-dependent communication network that sustains rhizosphere stability. Together with the strong structural divergence observed at a pathogen defense-related locus, these findings link plant genomic variation to transcriptional deregulation and microbiome composition, highlighting barley genotypes as a driver shaping the ecological structure and functionality of the rhizosphere microbiome. Discussion The rhizosphere represents one of the most complex microbial habitats, where diverse bacteria, fungi, archaea, and protists coexist and interact with plant roots [ 15 ]. These dynamic communities are crucial for nutrient turnover, stress resilience, and disease suppression, collectively contributing to plant fitness [ 100 ]. Their composition is strongly shaped by root exudates, soil type, and host genotype, creating a highly structured yet variable ecosystems [ 101 , 102 ]. Despite extensive study, the rhizosphere remains difficult to fully characterize due to its immense diversity, uneven microbial distributions, and frequent horizontal gene transfer, which hinder accurate taxonomic assignment and genome assembly [ 94 , 103 ]. In our study, long-read nanopore sequencing provided insights into how host genotype shapes both the taxonomical and functional landscape of the rhizosphere microbiome. The platform’s ability to generate extended reads enabled improved assembly continuity [ 104 ], capturing dominant lineages such as Streptomyces , Bradyrhizobium , and Nocardioides , taxa known for their contributions to nutrient mobilization and stress tolerance [ 93 ]. However, a substantial proportion of reads remained unclassified, emphasizing the still-limited representation of soil genomes in reference databases and the challenges inherent to analyzing complex microbial communities [ 105 , 106 ]. Despite these limitations, the Nanopore-based assemblies revealed distinct community structures across genotypes, illustrating how differences in plant genotypes are reflected in microbial composition and genomic complexity. Genotype-dependent shifts of the barley rhizosphere microbiome represents one of the most important patterns revealed in our study. Across barley genotypes, clear differences in bacterial composition reflect distinct recruitment strategies shaped by domestication and breeding. Similar patterns have been reported in cereals, where wild relatives assemble more diverse and functionally flexible microbiomes than elite cultivars [ 12 ]. In barley, host genotype influences community composition through genotype-specific patterns and immune cues [ 11 ]. In our study, wild accessions and landrace genotypes harbored broader microbial repertoires, whereas the modern cultivar supported a streamlined but stable community, suggesting a trade-off between resilience and specialization under agricultural selection [ 23 ]. In addition, variation in dominant bacterial groups such as Actinomycetota and Pseudomonadota implies genotype-linked modulation of nutrient cycling and stress-associated functions [ 107 ]. Preferential enrichment of Streptomyces in the modern cultivar and Bradyrhizobium in wild genotypes across the years indicates that specific guilds are maintained according to host nutritional and defensive priorities [ 14 ]. Together, these observations reveal that barley genotypes do not passively host their microbiota but actively filter and modulate their composition, establishing distinct microbial consortia that align with host functional needs and form the basis for genotype-specific plant-microbe interactions. In natural and cultivated systems alike, changes in microbial communities are tightly coupled to functional specialization, as root exudation and immune signaling select for organisms with compatible nutrient and signaling profiles [ 108 ]. In our study, across genotypes, metagenomes retained a conserved metabolic backbone dominated by amino acid metabolism, energy generation, and SM production, which are central to survival plant-microbe communication [ 109 ]. Notably, genotype-specific differences appeared in carbohydrate utilization and transport processes, reflecting selective pressures imposed by host-derived carbon inputs. Such variation in metabolic pathways is consistent with reports that plants shape the biochemical landscape of their rhizospheres through differential secretion of sugars, amino acids, and phenolics [ 102 , 110 ]. These exudates, in turn, guide microbial recruitment and activity, forming feedback loops that sustain nutrient exchange and immune balance [ 111 ]. Long-read data further enabled recovery of metagenome-assembled genomes, which highlighted the dominance of Actinomycetota, Pseudomonadota, and Bacteroidota. These taxa are widely recognized inhabitants of plant-associated niches and include metabolically versatile lineages capable of stress adaptation, SM synthesis, and organic matter turnover [ 112 , 113 ]. Further, genera such as Chitinophaga , Flavobacterium , Streptomyces , and Sphingomonas were among the most represented, mirroring patterns commonly observed in rhizosphere communities of cereals and other crops. These taxa likely occupy complementary niches that reinforce rhizosphere stability and enhance plant protection through antibiotic and siderophore biosynthesis [ 73 – 76 ]. Together, these functional and genome-resolved patterns depict a microbiome tuned to host identity, forming a metabolic consortium that co-evolves with barley roots and sets the molecular initiation for coordinated plant-microbe signaling. The coordination between host roots and the microbiome is rather defined by reciprocal signaling than unilateral responses. In barley roots, the pronounced increase in the transcriptional activation of defense-related genes indicates the active perception of microbial cues by the host and thereby responding to maintain homeostasis [ 27 ]. These gene families are central to the recognition of microbe-associated molecular patterns (MAMPs) and to the establishment of compatibility with beneficial partners [ 118 ]. In support of this argument, the rhizosphere microbiome also revealed presence of abundant histidine kinases, response regulators and transporter-related Pfam domains, which are canonical components of bacterial two-component systems (TCS), thereby suggesting that microbes also sense, process, and react to plant-derived signals [ 95 ]. The mirrored activation of plant perception pathways and microbial signaling systems supports a mutual partnership and has been observed in multiple host-microbe interactions [ 78 – 80 ]. For instance, in maize, the exudation of sugars, amino acids, and SMs such as benzoxazinoids has been shown to influence rhizosphere assembly and microbial gene expression [ 111 , 122 ]. In our study, the activation of transporter and SM genes suggests that barley roots modulate chemical exchange, while microbial enrichment in ABC transporters and redox-active domains implies adaptive response to plant-derived chemicals. This reciprocal exchange highlights a coordinated signaling system that maintains metabolic balance and ecological stability at the root-soil interface [ 123 ]. The genotype-dependent variation in these interactions further underscores that domestication has shaped the complexity of plant-microbe communication in the rhizosphere. Wild and landrace genotypes exhibited broader transcriptional activation of redox and signaling genes, aligning with their richer and more flexible microbiomes, whereas the modern cultivar Gretchen favored selective programs that emphasize metabolic regulation and moderated defense. Similar patterns of genotype-dependent microbiome shifts were observed in wheat and maize, where ancestral accessions recruited taxonomically richer microbial communities that stimulated stronger immune and signaling responses [ 12 , 88 ]. These support the hypothesis that breeding has streamlined microbial diversity while selecting associations that preserve core functions with greater stability in cultivated environments [ 124 ]. At the transcriptional level, enrichment of defense and hormone signaling related DEGs supports an integrated regulation of immunity and metabolism in response to microbial presence. In addition, the increased expression of genes belonging to redox and cell-wall related processes reflects the idea of microbe colonization and biofilm formation along the root surface [ 125 ]. On the microbial side, enrichment of radical SAM and PAS domains indicates broad redox and sensory signaling capacities that enable microbes to perceive chemical fluctuations in the rhizosphere, potentially shaping association with plant roots. For instance, the PAS-domain chemoreceptor PcpI in Pseudomonas senses plant hormones such as salicyclic and indole-3-acetic acid (IAA) and drives chemotactic response to these cues [ 126 ]. Together, these host and microbial related processes point towards a chemical dialogue driven by multiple signaling processes. This coordination likely extends also to the regulation of SMs, which functions as chemical messengers in plant-microbe interactions. The observed transcriptional activation of SM genes in barley coincided with microbial genomic signatures for non-ribosomal peptide synthetases and polyketide biosynthesis pathways frequently linked to antimicrobial and siderophore production [ 127 , 128 ]. This is also supported by the enrichment of SM and antibiotic pathways in KEGG from our study. Such co-regulation of chemistries reinforces the concept of “defensive mutualism”, in which microbial metabolites contribute to plant immunity while host provide the substrates and microenvironments required for microbial SM biosynthesis [ 129 ]. For instance, Pseudomonas fluorescens producing the polyketide 2,4-diacetylphloroglucinol (2,4-DAPG) was shown to induce systemic resistance (ISR) in Arabidopsis thaliana , with mutants deficient in this metabolite losing their ISR-eliciting ability [ 130 ]. Similarly, volatiles such as 2,3-butanediol emitted by Bacillus subtilis and related rhizobacteria have been demonstrated to trigger systemic immune responses and enhance pathogen resistance in Arabidopsis [ 131 ]. This parallel enrichment of secondary metabolism genes in both host and microbiome thus reflects a co-adaptive chemical exchange shaping the functional ecology of the rhizosphere. Interestingly, our transcriptional data also highlighted the active participation of transporters and receptor kinases, which may represent molecular gateways through which barley roots respond to microbial signals. In plants, members of the ABC subfamily B (ABCB) have been shown to be essential for arbuscular mycorrhizal symbiosis, facilitating lipid and SM transfer across the peri-arbuscular membrane, thereby sustaining mutualistic nutrient exchange [ 132 , 133 ]. Similarly, ABCG and ABCC transporters are known to translocate phytohormones, antimicrobial compounds, and signaling molecules, linking transport activity with systemic defense and rhizosphere communication. In case of bacteria, ABC transporters often function as high-affinity importers for amino acids, oligopeptides, and siderophores, serving as key mediators of nutrient acquisition and environmental sensing [ 134 , 135 ]. Thus, these findings indicate coordinated, two-directional trafficking of small molecules that enables nutrient sharing and signal flow across the root-soil boundary. Against this background of transcriptional and microbial community variation, plant genomic diversity provides an additional mechanistic layer for understanding genotype-specific microbiome responses. For instance, studies in Arabidopsis and maize have shown that natural variation in host loci is associated with shifts in the abundance of key microbial taxa, leading to heritable differences in rhizosphere community structure [ 88 , 136 ]. Furthermore, SNPs in host immune genes can act as major determinants of microbiome composition, supporting a causal link between host defense and microbial assembly [ 137 ]. Consistent with this, a GWAS analysis across 200 sorghum genotypes demonstrated that plant genetic variation significantly shapes rhizosphere communities, with distinct loci predicting the abundance of microbial lineages, indicating direct genetic control over microbiome assembly [ 138 ]. In our study, the structural variation we observed at a subtilisin-chymotrypsin inhibitor locus, together with its strong transcriptional activation, suggests that defense-related genomic regions may contribute to genotype-specific modulation of root-associated microbiomes. Taken together, the integration of rhizosphere metagenomics, barley root transcriptomics, and plant genomics supports a model in which plant and microbial partners operate as a synchronized biological network. The concurrent enrichment of signaling, transport, and secondary-metabolic functions across domains shows that the microbiome is an actively signaling entity shaping host physiology, while barley reciprocally remodels the chemical and nutritional landscape through transcriptional programming. This dynamic interaction, evident in both wild and domesticated genotypes, represents a fine-tuned evolutionary partnership supporting rhizosphere resilience, nutrient exchange, and adaptive stress tolerance, offering actionable entry points for breeding and microbiome-informed crop management. Conclusions Domestication has reshaped not only the genetic architecture of barley but also its association with rhizosphere microorganisms. Using a multi-omics framework based on Oxford Nanopore long-read sequencing, this study revealed that the barley rhizosphere microbiome is both taxonomically conserved and functionally enriched across wild, landrace, and modern genotypes. The bacterial community, dominated by the phyla Pseudomonadota and Actinomycetota and by genera such as Streptomyces , Bradyrhizobium , and Nocardioides , formed a stable microbiome whose relative abundances varied between genotypes and across growing seasons. Functional annotation of metagenomic assemblies identified a conserved metabolic backbone encompassing amino acid metabolism, energy production, and SM biosynthesis, alongside genotype-dependent variation in carbohydrate metabolism and transport pathways, reflecting host-specific microbial adaptation. Reconstruction of MAGs provided genomic-scale resolution of dominant bacterial lineages, with Chitinophaga , Flavobacterium , Streptomyces , and Sphingomonas emerging as key contributors to carbon turnover, SM production, and root-associated processes. At the host level, transcriptome profiling showed that barley roots exhibit strong transcriptional reprogramming in response to distinct microbiomes, particularly in defense, signaling, and transport processes. These host responses were mirrored by the microbial enrichment of Pfam domains associated with histidine kinases, response regulators, and transporters, indicating a functional reciprocity between host signaling and microbial sensing. Together, these findings demonstrate that barley genotype governs both the structural and function organization of the rhizosphere microbiome and that host and microbial communities operate in a coordinated molecular manner shaped by domestication. This research established a multi-omics framework linking crop genomic, metagenomic and transcriptional responses to ecological outcomes, offering a foundation for leveraging future efforts to harness host-microbiome interactions in barley improvement and sustainable crop management. Abbreviations ABC ATP-binding cassette BPGv2 Barley Pangenome version2 COG Cluster of Orthologous Genes DEGs Differentially Expressed Genes GO Gene Ontology GO:BP Gene Ontology Biological Process GWAS Genome-wide Association Study HMW DNA High Molecular Weight DNA KEGG Kyoto Encyclopedia of Genes and Genomes KO KEGG Orthology MAGs Metagenome-assembled Genomes MAMP Microbe-associated Molecular pattern NLR Nucleotide-binding leucine-rich repeat receptor ONT Oxford Nanopore Technologies PAS Per-Arnt-Sim PCA Principal Component Analysis PCR Polymerase Chain Reaction PCoA Principal Coordinate Analysis PRR Pattern Recognition Receptors rRNA ribosomal RNA RLK Receptor-like protein Kinases SAM S-adenosyl-L-methionine SNP Single Nucleotide Polymorphism WGS Whole Genome Sequencing WMS Whole Metagenome Sequencing Declarations Acknowledgments We would like to express our gratitude to Ben Kohnert for support of the field trial, Marvin Behnke and Stefan Kranz for support in initial data pre-processing. We are also indebted to Jana Müglitz and Johanna Nordmeier for their valuable assistance with nanopore sequencing. Furthermore, we thank Dr. Yvonne Pöschl for her assistance in data submission to ENA. Authors’ contributions B . R.F .D: Conceptualization, methodology, formal analysis, writing - original draft, writing - review and editing, data curation; T.M: Methodology, formal analysis, writing - review and editing, data curation; T.W: Funding acquisition, methodology, formal analysis, writing - review and editing, data curation; T.S: conceptualization, funding acquisition, supervision, writing - review and editing, data curation. Funding This work was supported by the BMFTR (grant number 031B1443). Data availability All the sequence data collected in this study have been deposited at the European Nucleotide Archive (ENA) under BioProjects PRJEBNNNNN (WMS microbiome ONT), PRJEBNNNNN (16S amplicon Illumina), and PRJEBNNNN (Plant genomics). 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16:54:23","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":738709,"visible":true,"origin":"","legend":"","description":"","filename":"BMCBiologyBarleyMicrobiomeSupportingTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8336676/v1/23226aab9aad7b35f64b6d67.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-read nanopore sequencing reveals genotype-dependent microbiome shifts and host-microbe interactions in the barley rhizosphere","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBarley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) represents one of the earliest domesticated cereal crops, with archaeological evidence tracing its cultivation back more than 10,000 years in the Fertile Crescent [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Its domestication from the wild progenitor \u003cem\u003eHordeum spontaneum\u003c/em\u003e led to the establishment of landraces adapted to diverse agroecological environments, and later to modern cultivars shaped by intensive breeding [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Importantly, barley is not only a major cereal for food, feed, and brewing, but also serves as a model for crop genomics due to its diploid genome [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The Hi-C guided Morex assembly produced the first chromosome-scale reference for barley (~\u0026thinsp;5 Gb), providing a robust scaffold for read mapping, variant discovery, and gene localization, and establishing a common coordinate system for downstream analyses [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Moving beyond a single reference, the first barley pan-genome, which included 20 varieties spanning wild accessions, landraces, and elite cultivars, revealed extensive presence or absence variation and large inversion polymorphisms in elite germplasm that could not be detected from a single genome [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A recent expanded pan-genome with 76 long-read assemblies and short-read data for 1,315 genotypes now charts structural variants and copy-number rich loci across domestication space, substantially improving the catalog of allelic diversity for breeding [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. At gene-space resolution, exome capture platforms and studies on diverse panels including 267 geo-referenced landraces/wilds, ~\u0026thinsp;371 domesticated lines and other regional panels uncovered adaptive variants tied to environment and agronomic performance, providing cost-effective variant discovery for large cohorts [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Wild barley de novo assemblies complement these resources by exposing structural and gene-content differences relative to Morex, refining domestication inferences and improving read-mapping for wild introgressions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Contemporary reviews summarize this shift from single-reference to pangenomic thinking and outline how structural variation (SV) drives phenotypic diversification under domestication and improvement [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Methodologically, these assemblies and panels translate into concrete bioinformatics practice such as aligning reads to chromosome-scale pseudomolecules, discovery of structural variants with long-reads/graph genomes, and exome data enable high-density single nucleotide polymorphism (SNP) matrices for genome-wide association study (GWAS) and genomic prediction [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Together, these genomic resources enable precise dissection of transitions from wild to landraces to elite cultivars, including domestication footprints at candidate loci affecting adaptation, quality, and stress response [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. As barley genomics converges on graph- and pan-reference frameworks, downstream analyses such as variant calling, GWAS and expression quantitative trait locus (eQTL) will increasingly account for non-reference haplotypes which is an important consideration when relating host genotype to rhizosphere microbiome and root transcriptional phenotypes in wild, landrace, and modern genotypes.\u003c/p\u003e \u003cp\u003eBeyond the genetic and agronomic importance of barley, recent studies suggest that domestication-driven selection also extends belowground, influencing how plant roots interact with the rhizosphere microbiome [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This diverse community contributes to nutrient acquisition, stress tolerance, and pathogen resistance, thereby playing a critical role in overall plant fitness [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The rhizosphere directly influenced by root exudates, represents one of the most dynamic interfaces between plants and their associated microbiota [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Functional members of the rhizosphere microbiome can fix atmospheric nitrogen, solubilize phosphate, produce siderophores, and modulate plant hormone signaling, collectively acting as a \u0026ldquo;second genome\u0026rdquo; of the plant [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In cereals such as rice, wheat and maize, rhizosphere microbial composition has been shown to vary with genotype, developmental stage, and environmental conditions [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Although barley is a model crop with extensive genetic resources, our understanding of its rhizosphere microbiome lags behind other cereals, with recent studies only beginning to uncover the diversity and host-specificity of its microbial communities [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Given the central role of the rhizosphere in mediating plant - microbe interactions, understanding how microbiome composition varies across barley types offers a unique opportunity to link host genetic background, microbial diversity, and plant performance.\u003c/p\u003e \u003cp\u003eDomestication and modern breeding have not only reshaped the genetic architecture of crops but also influenced their interactions with soil microbial communities. Comparative studies across cereals indicate that both wild relatives and domesticated cultivars assemble distinct and functionally diverse rhizosphere microbiomes, with patterns largely determined by host genotype and domestication status [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Importantly, wild barley genotypes have been reported to enrich for microbial taxa associated with nutrient mobilization and stress resilience, whereas elite cultivars tend to assemble more streamlined microbial communities [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These findings highlight that domestication-driven shifts in microbial diversity and functionality may have contributed to changes in plant adaptability, raising the question of how variation in barley types continues to influence host-microbiome associations under field conditions. In barley, the host genotype accounts for a smaller proportion of rhizosphere variation than the local soil microhabitat, yet it still exerts a consistent influence on microbial community composition [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The differences between wild and modern barley genotypes have been linked to specific host loci, most notably the QRMC-3HS locus on chromosome 3H, which influences the recruitment of diverse bacterial taxa and explains up to 20% of their variation. Bacteria preferentially recruited by the wild allele at this locus include \u003cem\u003eVariovorax\u003c/em\u003e, \u003cem\u003eHolophaga\u003c/em\u003e, \u003cem\u003eSorangium\u003c/em\u003e, \u003cem\u003eTahibacter\u003c/em\u003e, and \u003cem\u003eRhodanobacter\u003c/em\u003e, representing functionally diverse groups involved in complex carbon turnover [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. By contrast, elite cultivars often show stronger enrichment of Actinobacteria, a phylum associated with antimicrobial production and stress-adapted lifestyles, consistent with a more filtered microbiome [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Metagenome-assembled genomes (MAGs) from barley rhizospheres further reveal that the partitioning of key microbial functions, including nitrogen metabolism and transporter repertoires, is strongly genotype-dependent, indicating that domestication has not only influenced the microbial community structure but also their functional potential [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Plant-soil feedback experiments reinforce this view, showing that a phylogenetically diverse microbial consortium supports optimal growth of elite barley usnder nitrogen limitation, emphasizing that functional diversity remains advantageous even for modern genotypes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Mechanistically, immune-related host factors are likely central in this process. For instance, QRMC-3HS harbors a candidate nucleotide-binding leucine-rich repeat (NLR) gene BaRT2v18chr3HG123500 with structural variation across the barley pan-genome, highlighting immune-microbiome crosstalk as a key domestication-sensitive trait [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Earlier studies also identified root-enriched families such as \u003cem\u003eComamonadaceae\u003c/em\u003e, \u003cem\u003eFlavobacteriaceae\u003c/em\u003e, and \u003cem\u003eRhizobiaceae\u003c/em\u003e, and found positive selection on microbial protein families involved in pathogenesis, secretion, phage interactions, and nutrient mobilization which are signatures of long-term host-microbe co-adaptation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Differences among barley cultivars also take place at the strain and functional levels. For example, inoculation with matched rhizosphere extracts or \u003cem\u003ePseudomonas\u003c/em\u003e synthetic communities (SynComs) produces cultivar-specific growth responses, suggesting that elite genotypes respond to narrower sets of microbial partners compared to wild or landrace backgrounds [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Collectively, cross-crop studies highlight that domestication has reshaped the composition and functional potential of rhizosphere microbiomes, consistently influencing the abundance of taxa comprising key microbial functional guilds [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Altogether, these observations support a model where wild barley maintains broader microbial functional repertoires, while elite cultivars exhibit selective filtering that favors a leaner set of partners, with trade-offs for resilience vs input-dependent performance.\u003c/p\u003e \u003cp\u003eBuilding on the domestication-driven patterns in the rhizosphere microbiome, it is essential to understand how these microbial communities induce transcriptional changes within barley roots. Transcriptome studies on barley roots inoculated with species of bacteria show that colonization can rapidly rewire the barley transcriptome activating defense-associated pathways, transport processes, and signaling modules [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Complementary work demonstrates that barley differentiates its transcriptional response to distinct beneficial taxa including \u003cem\u003eEnsifer\u003c/em\u003e, \u003cem\u003ePantoea\u003c/em\u003e, and \u003cem\u003ePseudomonas\u003c/em\u003e, indicating that host signaling discriminates among commensals and promotes tailored root programs rather than a generic beneficial microbe response [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In the context of symbiosis, arbuscular mycorrhizal fungi (AMF) modulate nutrient-uptake circuits in barley roots. For instance, AMF inoculation increased grain Zn and alters root ZIP transporter expression in a modern barley cultivar, a clear instance where microbial association reshapes host ion-homeostasis genes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, genetic gating of these interactions is evident from the \u003cem\u003eMLO\u003c/em\u003e (\u003cem\u003eMildew Resistance Locus O\u003c/em\u003e), which is classically known for conferring broad-spectrum, stable resistance to powdery mildew in barley [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Beyond this canonical function, \u003cem\u003eMLO\u003c/em\u003e has recently been shown to regulate colonization by arbuscular mycorrhizal fungi and root endophytes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], indicating that immune-related loci can influence both rhizosphere assembly and host transcriptional responses. Cultivar-specific differences in barley also influence microbiota-host signaling, as seen in the recruitment and activity of \u003cem\u003ePseudomonas\u003c/em\u003e populations accompanied by microbial transcriptome shifts, indicating that host genotype determines distinct transcriptional trajectories during colonization [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although most barley transcriptome studies address abiotic stress, they consistently reveal root modules such as hormone, transport, redox, and cell-wall pathways that are also engaged during microbe-plant interactions, with parallel evidence from AMF studies in wheat and meta-analyses linking biotic stress and hormone networks [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Recent studies also highlight defense priming and nutrition-linked transcriptional shifts in barley exposed to rhizobacteria, connecting root gene expression to whole-plant outcomes such as reduced pest burdens, emphasizing that microbial effects are routed through host transcriptional control layers [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Methodologically, cell-type-resolved and spatial transcriptomics are emerging to dissect which root tissues execute these programs during beneficial colonization, promising sharper resolution of barley\u0026rsquo;s microbe-responsive gene networks [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Together, these findings support a model in which microbial association states including commensal, or mutualist are read out as distinct barley root transcriptional fingerprints spanning immune receptors, secondary signaling, phytohormone circuitry, transporters and secretory machinery. Importantly, they also align with the domestication signal which indicates that host genotype shapes not just which microbes arrive, but how root gene networks respond once they do, implying that wild, landrace, and modern cultivars may differ in the magnitude and architecture of microbially induced transcriptional states.\u003c/p\u003e \u003cp\u003eIn order to address genotype-dependent microbiome shifts and host-microbe interactions, we established a multi-omics approach integrating whole metagenome sequencing (WMS) of the rhizosphere microbiome, root transcriptome profiling and plant genomics across different barley genotypes. Long-read metagenomics revealed genotype- and season-dependent restructuring of the rhizosphere microbiome, capturing dominant bacterial and fungal members and their functional shifts. Root transcriptome profiling showed that genotypes activate a characteristic set of defense- and signaling-related responses to its associated microbiome. Complementary plant genomic analysis further identified structural variation at a defense-related locus, suggesting possible links between genomic architecture and elevated transcriptional activity. Together, these approaches demonstrate that variation in microbial community composition corresponds with distinct host transcriptional programs, indicating a coordinated interaction between barley roots and their microbiota. Overall, our findings show that domestication has shaped not only the structure and function of the rhizophore microbiome, but also the host\u0026rsquo;s molecular response, revealing an integrated, genotype-dependent interaction network.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of soil and plant material\u003c/h2\u003e \u003cp\u003eRhizosphere soil was collected from barley plants grown for eight weeks at the experimental field station of Martin Luther University (MLU, K\u0026uuml;hnfeld, Halle, Germany). At harvest, the rhizosphere soil was defined as the fraction adhering to roots after gentle shaking and was collected with sterile spatulas. Soil samples were transferred into sterile bags, transported on dry ice, and stored at -80\u0026deg;C until DNA extraction. As a non-treated control, soil was collected from two separate field locations. For transcriptome analyses, root material was harvested from the same plants at the same developmental stage. Roots were cut with sterile scissors, washed several times with sterile water to remove loosely attached soil, blotted dry, and stored at -80\u0026deg;C until RNA isolation. In addition, field soil was collected and sterilized by autoclaving and used for plant cultivation under greenhouse conditions. As a control for RNA-Seq, Gretchen plants were grown in sterilized soil, and roots were harvested for RNA isolation at 8 weeks. Each experimental condition was represented by two independent biological replicates with three technical replicates each.\u003c/p\u003e \u003cp\u003eFor genomic DNA (gDNA) sequencing, barley plants were cultivated in the greenhouse at the Institute of Agricultural and Nutritional Sciences (MLU, Halle (Saale), Germany). Leaves were harvested after four weeks and approximately 1g of leaf tissue was collected and stored at -80\u0026deg;C until DNA isolation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSoil DNA isolation and 16S rRNA amplicon sequencing\u003c/h3\u003e\n\u003cp\u003eSoil microbial gDNA extraction for amplicon sequencing was performed using the DNeasy PowerSoil Pro Isolation Kit (Qiagen, Hilden, Germany) according to the manufacturer\u0026rsquo;s instructions. The concentration of gDNA was quantified using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Montchanin, DE, United States), and the extracts were adjusted to 20 ng/\u0026micro;L template concentration followed by amplification of the bacterial 16S rRNA gene's V4 region using the primer pair 515F (GTGYCAGCMGCCGCGGTAA) and 806R (GGACTACNVGGGTWTCTAAT) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] with Illumina adapter overhangs. The amplified products were purified with Agencourt AMPure XP beads (Beckmann Coulter, Krefeld, Germany). The amplified fragments were indexed using Illumina Nextera XT indices at both ends through indexing PCR. The indexed products were subsequently purified with AMPure beads, quantified by Quant-iT dsDNA high-sensitivity assay kit using a TECAN INFINITE PLEX plate reader (TECAN, Germany) and sample-based amplicon libraries were equimolarly pooled to achieve a final pool concentration of 4 nM. Finally, paired-end 2x300 bp sequencing was performed with a MiSeq Reagent kit v2 on an Illumina MiSeq platform (Illumina Inc., San Diego, CA, United States).\u003c/p\u003e\n\u003ch3\u003eSoil DNA isolation for whole metagenome sequencing\u003c/h3\u003e\n\u003cp\u003eSoil gDNA for whole metagenome sequencing (WMS) was extracted from rhizosphere soil using a combined mechanical and chemical lysis approach adapted from different soil DNA extraction protocols [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Approximately 250 mgs of soil was placed in a 2 mL tube containing\u0026thinsp;~\u0026thinsp;200 mg of 0.5 mm glass beads (Roth, Karlsruhe, Germany), followed by the addition of 1 mL of preheated CTAB extraction buffer (2% CTAB, 1.4 M NaCl, 100 mM Tris-HCl pH 8.0, 20 mM EDTA, 0.2 M mannitol, and 2.6% PVP). Samples were homogenized twice at 25 Hz for 2.5 min in a TissueLyser (Qiagen, Haan, Germany), reorienting the adapter between runs, and incubated at 65\u0026deg;C for 30 min with periodic inversion. Cell debris was pelleted by centrifugation at 14,000 \u0026times; g for 10 min at 4\u0026deg;C, and the supernatant was subjected to phenol: chloroform: isoamyl alcohol (25:24:1) extraction, followed by a second purification with chloroform:isoamyl alcohol (24:1). DNA was precipitated with 0.6 volumes of cold isopropanol and 0.1 volume of 3 M sodium acetate, incubated overnight at -20\u0026deg;C, and recovered by centrifugation. Pellets were washed with 70% ethanol, air-dried, and resuspended in 25 \u0026micro;L of nuclease-free water. To remove residual inhibitors, DNA was further purified using the Zymo DNA Clean \u0026amp; Concentrator kit (Zymo Research, Freiburg, Germany). DNA yield was measured with a Qubit fluorometer (Thermo Fisher Scientific, Singapore), and the sample was adjusted to 500ng for sequencing purposes.\u003c/p\u003e\n\u003ch3\u003eIsolation of plant genomic DNA for whole genome sequencing\u003c/h3\u003e\n\u003cp\u003e Plant gDNA for whole genome sequencing (WGS) was isolated from 4-week-old barley plants using the NucleoBond HMW DNA Kit (Machery-Nagel, D\u0026uuml;ren, Germany) according to the manufacturer\u0026rsquo;s instructions. Frozen leaf samples were ground to a fine powder and transferred into a 2mL tube containing two sterile steel beads (\u0026Oslash; 3.2 mm) (Hecht Kugellager GmbH \u0026amp; CoKG, Winnenden, Germany). Samples were homogenized in a TissueLyser at 25 Hz for 30 sec and DNA was extracted using the kit protocol. DNA concentration was measured with a Qubit fluorometer, and the sample was adjusted to 400ng for sequencing.\u003c/p\u003e\n\u003ch3\u003eIsolation of RNA from barley roots\u003c/h3\u003e\n\u003cp\u003eTotal RNA was isolated from barley root tissue using the Nucleospin RNA Plant and Fungi kit (Macherey-Nagel, D\u0026uuml;ren, Germany) following the manufacturer\u0026rsquo;s instructions with minor modifications. Frozen root samples stored at -80\u0026deg;C were ground to a fine powder in a pre-chilled mortar with liquid nitrogen. Approximately 300 mg of powdered root tissue was transferred into a 2 mL RNase-free tube containing two sterile steel beads (\u0026Oslash; 3.2 mm). Samples were homogenized in a TissueLyser at 30 Hz for 1 min, the adapter was reoriented and homogenization was repeated to ensure complete disruption. RNA was then extracted using the kit protocol, yield was measured with a Qubit fluorometer and the concentration was adjusted to 500ng total RNA for sequencing purposes.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOxford long-read nanopore library preparation and sequencing\u003c/h2\u003e \u003cp\u003eThe gDNA from rhizosphere samples and barley leaves was prepared for nanopore sequencing using the SQK-NBD114.96 and SQK-NBD114.24 native barcoding kit (Oxford Nanopore Technologies (ONT), Oxford, England), respectively, following the manufacturer\u0026rsquo;s instructions with minor adjustments. A high molecular weight (HMW) DNA standard (Zymo Research, California, USA) was also included as a performance control for WMS to evaluate long-read sequencing efficiency and data quality. DNA repair, end preparation, native barcode and adapter ligation were carried out according to the kit protocol. Clean up steps were performed using AMPure XP beads and adapter ligation was performed with long fragment buffer (LFB) to retain longer DNA fragments for WMS.\u003c/p\u003e \u003cp\u003eFor transcriptome sequencing, libraries were prepared using the cDNA-PCR Barcoding Kit SQK-PCB114.24 (ONT, Oxford, England) according to the manufacturer\u0026rsquo;s instructions. A total of 750 ng of high-quality RNA was reverse transcribed into cDNA, barcoded, and amplified by PCR for 16 cycles, followed by exonuclease treatment and cleaned up as described in the kit protocol.\u003c/p\u003e \u003cp\u003eAll libraries were sequenced on R10.1 flow cells on a PromethION platform for three consecutive days, with fresh libraries reloaded on the second- and third-days following flow cell washing using the Flow Cell Wash Kit EXP-WSH004 (ONT, Oxford, England). Sequencing was managed using MinKNOW software (ONT), and basecalling was performed with the Dorado super accurate model using a minimum Q-score of 10 for genomic and metagenomic, and 7 for transcriptomic libraries.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcessing and analysis of Illumina data for 16S study\u003c/h3\u003e\n\u003cp\u003eThe sequence data analysis was performed using QIIME2 (v2023.9) [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Briefly, forward and reverse primers from demultiplexed reads were trimmed using q2-cutapdapt [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] followed by denoising with DADA2 (v1.26) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], chimera removal and clustering into Amplicon Sequence Variants (ASVs). Taxonomy was assigned to ASVs using the q2-feature‐classifier [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] sklearn-na\u0026iuml;ve Bayes taxonomy against the silva-138-99-515-806-nb-classifier reference database [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The bacterial phylogenetic tree was inferred using the q2-phylogeny plugin which employs MAFFT (v7.520) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] for sequence alignment and FastTree (v2.2.0) for Maximum Likelihood tree construction [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The tree was midpoint-rooted to ensure a robust and unbiased phylogenetic framework. The ASV matrices, taxonomic tables, phylogenetic tree, and representative sequences were merged with the sample metadata file using the phyloseq package (v1.52.0) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] in R (v4.5.1) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The bacterial ASV matrices were rarefied to ensure uniform sequencing depth of 30375 reads per sample. The role of the barley domestication status on the rhizosphere bacterial community compositions (based on Bray-Curtis distance dissimilarity) was analyzed through PERMANOVA using the adonis2 function from the vegan package (v2.7.1) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], followed by a pairwise adonis test using the pairwise.adonis2 package (v0.4) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] with Benjamini‐Hochberg false discovery rate (FDR) correction [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The ordinations were visualized using Principal Coordinates analysis (PCoA) as implemented in the plot ordination function of phyloseq using Bray-Curtis distances.\u003c/p\u003e\n\u003ch3\u003eProcessing and analysis of ONT data for metagenomics\u003c/h3\u003e\n\u003cp\u003eRaw reads generated from Oxford Nanopore sequencing were uploaded to the Galaxy Europe platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://usegalaxy.eu/\u003c/span\u003e\u003cspan address=\"https://usegalaxy.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for downstream analysis. Read quality was assessed with fastplong (v0.4.1) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], low-quality reads trimmed with cutadapt (v5.1), and microbial reads were assigned taxonomically with Kraken2 (v2.1.3) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] using the prebuilt bacterial and fungal genome databases with a confidence threshold of 0.1. Kraken2 read counts were summarized at phylum, genus, and species levels and imported into R for analysis. Biological replicates were merged, relative abundances were calculated, and the ten most abundant taxa at each rank were retained. Data handling and visualization were performed with tidyverse package (v2.2.0) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e] and ggplot2 (v4.0.0) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Differential abundance was analyzed with DESeq2 (v1.46.0) [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], using the control as reference and applying apeglm shrinkage (v1.28.0) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Genera with adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were visualized using ComplexHeatmap package (v2.22.0) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Shared and unique bacterial genera among control, wild accession, landrace and modern groups were visualized with VennDiagram package (v1.7.3) [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Alpha diversity metrices including species richness, Shannon and Simpson index were calculated with vegan [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and visualized in ggplot2. Statistical differences were tested using the non-parametric Kruskal-Wallis test [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Beta diversity was assessed using Bray-Curtis and Jaccard dissimilarities, followed by principal coordinate analysis (PCoA) in ape (v5.8.1) [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e], and visualized in ggplot2.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctional profiling of rhizosphere microbiome\u003c/h2\u003e \u003cp\u003eNanopore long-read sequencing metagenomic data were assembled and functionally annotated to understand the functional potential of the rhizosphere microbiome. Briefly, the biological replicates for each sample were merged to improve coverage, and assemblies were generated with metaFlye (v2.9.6) [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Open reading frames (ORFs) were predicted with Prodigal (v2.6.3) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e], and protein sequences were annotated using eggNOG-mapper (v2.1.12) [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] with the DIAMOND algorithm and a threshold of 0.001. Annotations included Clusters of Orthologous Genes (COGs), KEGG Orthologs (KOs), pathways and modules. Functional summaries were generated using pandas (v2.2.2) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and KEGG pathways were visualized in R using the tidyverse and ggplot2. Predicted proteins further screened for Pfam domains (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://pfam.xfam.org/\u003c/span\u003e\u003cspan address=\"http://pfam.xfam.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] through eggNOG-mapper, and Pfam frequencies were calculated for each sample. Only domains with at least 5 occurrences were retained for comparison. The top 20 Pfam families were visualized in ggplot2 and ggalluvial (v0.12.5) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Pfam accessions were cross-referenced with their corresponding functional descriptions based on the Pfam-A database (v35.0) for biological interpretation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMetagenome-assembled genomes (MAGs) reconstruction and quality assessment\u003c/h2\u003e \u003cp\u003eMetagenomic reads from 2024 and 2025 were co-assembled to generate a single composite assembly representing the pangenome of the microbial community. The concatenated dataset was assembled with metaFlye in nano-hq mode. The resulting contigs were used as a reference for read mapping with minimap2 (v2.28) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] to estimate per-sample contig coverage, and coverage profiles were summarized as a depth matrix for binning with MetaBAT2 (v2.17) [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. MAG quality was evaluated with checkM (v1.2.4) [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e] by estimating completeness and contamination based on lineage-specific marker genes. Taxonomic assignment was performed using GTDB-Tk (Genome Taxonomy Database Toolkit, v2.5.2) [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], yielding standardized lineage classifications for all reconstructed bins. In addition, 16S rRNA genes were identified using Barrnap (v0.9) [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], aligned with MAFFT and used for phylogenetic reconstruction with FastTree. Data visualization and graphical summaries were performed in R using the ggtree package (v3.10.0) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eProcessing of transcriptome data\u003c/h2\u003e \u003cp\u003eRaw ONT reads were assessed for quality using fastplong within the galaxy platform. Adapter sequences and low-quality bases were removed with cutadapt and poly A/T stretches of at least 10 bases were trimmed to prevent alignment artifacts and a Phred quality cutoff of 20 was applied to remove low-quality bases. The trimmed reads were aligned to the \u003cem\u003eHordeum vulgare\u003c/em\u003e reference genome (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/fasta/hordeum_vulgare/dna/\u003c/span\u003e\u003cspan address=\"https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/fasta/hordeum_vulgare/dna/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; accessed on October 3, 2025) using Minimap2 in spliced alignment mode optimized for ONT reads (map-ont) with splice junction support from the corresponding gene annotation file (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/gff3/hordeum_vulgare/\u003c/span\u003e\u003cspan address=\"https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/gff3/hordeum_vulgare/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene level read counts were obtained with featureCounts (v2.1.1) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e], and differential expression analysis was performed with DESeq2 (v2.11.40.8). Genes with an adjusted \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and a fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;2 or \u0026lt;\u0026thinsp;0.5 were considered significant for increased or decreased transcript abundance, respectively. Principal component analysis (PCA) and sample-to-sample correlation matrices were generated in DESeq2 and the numbers of unique and shared differentially expressed genes (DEGs) were visualized with the VennDiagram package in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation and enrichment analysis of transcriptome\u003c/h2\u003e \u003cp\u003eFunctional categorization of DEGs was performed with the Mercator annotation platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.plabipd.de/mercator_main.html\u003c/span\u003e\u003cspan address=\"https://www.plabipd.de/mercator_main.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e], which assigns genes to hierarchical functional bins based on conserved domains and sequence homology. The \u003cem\u003eH. vulgare\u003c/em\u003e protein reference file (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/fasta/hordeum_vulgare/pep/\u003c/span\u003e\u003cspan address=\"https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/fasta/hordeum_vulgare/pep/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; assessed on October 7, 2025) was used for annotation. DEGs obtained from the RNA-Seq analysis were mapped to Mercator annotations and merged DEG-annotation tables in R using tidyverse. Genes were grouped by functional category and the ten most represented bins were identified. Gene Ontology enrichment analysis was carried out on significantly expressed genes (\u003cem\u003ep-\u003c/em\u003eadj\u0026thinsp;\u0026lt;\u0026thinsp;0.05) with gprofiler2 [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], using \u003cem\u003eH. vulgare\u003c/em\u003e (Morex v3) genome as reference. Enrichment was calculated against the background of all expressed genes using Fischer exact testing with Benjamini-Hochberg correlation (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). GO was performed on the Biological Process (BP) category, and the most significant terms were visualized as bubble plots in ggplot2. To investigate transcriptomic responses of barley roots to microbial interactions, DEGs were screened for functional classes associated with categories of defense, signaling, and secondary metabolism using the Mercator / MapMan annotations [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Category-based DEG counts were summarized and visualized in ggplot2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eProcessing and analysis of ONT data for plant genomics\u003c/h2\u003e \u003cp\u003eRaw ONT WGS reads from all barley genotypes were analyzed using the GeneToCN tool [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], which performs alignment-free copy number estimation based on \u003cem\u003ek\u003c/em\u003e-mer frequencies. Reference genome sequences of Morex BPGv2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://panbarlex.ipk-gatersleben.de/\u003c/span\u003e\u003cspan address=\"https://panbarlex.ipk-gatersleben.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to extract the target gene and its flanking regions, which were processed with the GeneToKmer script to generate 25-bp \u003cem\u003ek\u003c/em\u003e-mers unqiue to reference locus. These gene-specific and locus-wide \u003cem\u003ek\u003c/em\u003e-mers were queried against genotype-specific ONT reads using GenomeTester4 [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. For each genotype, \u003cem\u003ek\u003c/em\u003e-mer frequencies were counted and the median gene-specific \u003cem\u003ek\u003c/em\u003e-mer depth was calculated as a proxy for genomic dosage. Locus-wide and gene-specific \u003cem\u003ek\u003c/em\u003e-mer counts were then summarized to assess sequence similarity and divergence relative to the Morex reference. Pairwise \u003cem\u003ek\u003c/em\u003e-mer intersections between genotypes were used to compute a similarity matrix, which was visualized as a heatmap and hierarchical clustering dendrogram. Spatial variation in \u003cem\u003ek\u003c/em\u003e-mer abundance were examined by mapping \u003cem\u003ek\u003c/em\u003e-mer counts back to genomic coordinates, binning, and smoothing to generate coverage profiles using ggplot2. For the analysis of read-based variant calling to identify SNPs and insertions and deletions (INDELS), the ONT reads were aligned to the Morex reference genome using minimap2. Subsequently, we screened for genetic diversity using clair3 [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] for SNPs as well as sniffles2 [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e] for INDELS. Per-base SNPs within the coding region were visualized using gpplot2. All downstream data handling, and figure generation were performed in R.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRhizosphere microbial community assembly\u003c/h2\u003e \u003cp\u003eIllumina MiSeq-based short-read profiling of rhizosphere soil microbial communities from 21 barley genotypes (seven genotypes per domestication group) and two controls revealed that barley domestication status significantly influenced microbiome assembly (PERMANOVA: R\u0026sup2; = 0.2273, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Pairwise comparisons further showed that all three barley status groups differed significantly from the controls, with the largest dissimilarity observed for wild (31.5%), followed by landrace (25%) and modern (10.8%) barley genotypes. Although modern genotypes differed significantly from both wild and landrace groups, no significant difference was detected between wild and landrace genotypes (Supporting Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Consistent with these results, PCoA ordination demonstrated clear separation among the genotype groups, with the first two axes explaining 25.5% of the variation in rhizosphere bacterial community composition (Supporting Figure. 1). Together, these findings indicate that domestication status is a major determinant of rhizosphere microbial community structure across the barley genotypes.\u003c/p\u003e \u003cp\u003eTo enable a deeper and genome-resolved analysis of the microbial communities associated with barley domestication, we next selected a representative subset of genotypes for whole metagenome sequencing (WMS). Based on the ordination patterns and the observed community divergence across domestication groups, two genotypes each from the wild (HID0144 and HID0380) and landrace (HID1029 and HID1104) groups, and one genotype from the modern group (Gretchen) were chosen for high-resolution long read Oxford Nanopore (ONT) sequencing. This targeted selection allowed us to capture the wealth of microbiome variation revealed by short-read profiling while enabling an in-depth understanding of the microbial communities that shape barley genotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eOxford Nanopore long-read metagenome sequencing of rhizosphere microbiome\u003c/h2\u003e \u003cp\u003eThe use of long-read sequencing offers a distinct advantage for complex soil microbiomes, as it improves assembly continuity, gene-level annotation, and recovery of complete biosynthetic gene clusters [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. In our study, ONT sequencing of barley rhizosphere microbiome samples across 2024 and 2025 produced high-quality long-read data suitable for downstream metagenomic analysis. In 2024, the mean read length ranged between approx. 2.4 to 4.9 kb, with average Phred quality scores of about 15 (Fig.\u0026nbsp;1A). Similarly, in 2025, mean read length varied between 2.3 and 3.5 kb, and mean quality scores remained above 15 across all samples (Fig.\u0026nbsp;1B). The sequencing depths expressed as the total number of reads per sample, ranged between 0.5 to 1.3\u0026nbsp;million reads in 2024, and 0.3 to 1\u0026nbsp;million reads in 2025. Total sequencing yield, represented by the sum of bases generated, varied from 2.6 to 4.5 Gb in 2024 and 0.7 to 3.8 Gb in 2025 (Figs.\u0026nbsp;1C and D). The summary statistics of WMS runs from 2024 and 2025 are provided in Supporting Tables S2 and S3.\u003c/p\u003e \u003cp\u003eWe further assessed the proportion of classified and unclassified reads across all samples using Kraken2 in Galaxy. The fraction of classified to unclassified reads differed largely between the bacterial and fungal datasets. For bacteria, the prebuilt standard Kraken2 database successfully classified approx. 30% of the reads in 2024 and 2025. In contrast, fungal reads showed lower classification rates. Testing several fungal reference databases modestly improved assignment, but variability among databases prompted us to use the standard fungi reference genome database available in Galaxy for final taxonomic classification. The remaining unclassified reads likely reflect the limited representation of fungal genomes in current reference libraries. Of the classified reads, we also compared the proportions of bacterial and fungal sequences. Across all samples, bacterial reads predominated, accounting for approx. 59\u0026ndash;73% and 63\u0026ndash;68% of total classified reads in 2024 and 2025, respectively (Figs.\u0026nbsp;1E and F). These findings indicate that ONT metagenomic sequencing revealed higher bacterial richness than fungal diversity in the rhizosphere, consistent with previous studies reporting that fungal sequences are underrepresented in metagenomic databases, limiting taxonomic accuracy [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomic composition of the barley rhizosphere microbiomes reveals genotype-dependent community shifts\u003c/h2\u003e \u003cp\u003eThe rhizosphere microbiome plays an important role in plant growth, nutrient turnover, and stress resilience [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. The composition and structure of root-associated microbial communities are key determinants of plant fitness [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] and have strongly been influenced by processes of domestication and breeding. To investigate genotype-specific microbial community structures and their diversity across seasons, rhizosphere samples from wild accessions HID0144 and HID0380, landraces HID1029 and HID1104, and the modern cultivar Gretchen were analyzed at different taxonomic ranks (Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eAt the phylum level, the rhizosphere bacterial communities of different barley genotypes were dominated by Pseudomonadota and Actinomycetota, which together accounted for the majority of reads across both field seasons. These two phyla formed a stable microbiome shared among wild, landrace, and modern barley genotypes. Temporal variation was evident, with Actinomycetota increasing in relative abundance from 12.1% in 2024 to 27% in 2025 (Figs.\u0026nbsp;2A and B). At the genus level, taxa including \u003cem\u003eNocardioides\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, and \u003cem\u003eStreptomyces\u003c/em\u003e were consistently dominant across all barley types. While these genera were consistently present across all samples, their relative abundance varied markedly among genotypes and between years. Interestingly, between years, the relative abundance of \u003cem\u003eStreptomyces\u003c/em\u003e increased in Gretchen, from 3.5% in 2024 to 14.1% in 2025, suggesting temporal shifts in this actinobacterial genus (Figs.\u0026nbsp;2C and D). In contrast, \u003cem\u003eBradyrhizobium\u003c/em\u003e declined by approximately 10% over the same period, possibly reflecting environmental influences on nitrogen-fixing microbial populations. These trends highlight shifts in rhizosphere bacterial composition influenced by both host genotype and environmental changed between years. Such genotype-dependent patterns suggest that host signaling may actively shape microbial recruitment, consistent with previous findings in cereals where host genetics governs microbial assembly [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn genus-level profiles, fungal reads represented only a minor fraction of total microbial reads, consistent with lower taxonomic richness in fungi than in bacteria. Correspondingly, the number of fungal genera in each sample was substantially lower than that of the bacterial genera. Among fungi, only \u003cem\u003eFusarium\u003c/em\u003e, \u003cem\u003eAspergillus\u003c/em\u003e, \u003cem\u003eCryptococcus\u003c/em\u003e and \u003cem\u003eKluyveromyces\u003c/em\u003e were consistently observed. Of these, \u003cem\u003eFusarium\u003c/em\u003e dominated, contributing on average of 92.4% and 79.6% of fungal reads in 2024 and 2025, respectively (Figs.\u0026nbsp;2E and F). As shotgun metagenomics quantifies DNA, the disproportionately low fungal read counts and genus richness support the interpretation that fungal propagules are relatively less prevalent than bacteria in rhizosphere samples [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo compare compositional shifts at the species level, we performed two complementary WMS analyses that identified genera with high internal diversity and strong changes in abundance. This approach revealed the taxa most enriched or depleted relative to the control. Genera such as \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003eStreptomyces\u003c/em\u003e consistently exhibited high species richness across all barley types, reflecting their widespread association in the rhizosphere. However, the number of species within these genera varied between years. For instance, an average of 23 \u003cem\u003eStreptomyces\u003c/em\u003e species were detected in 2024, increasing to 71 in 2025, and \u003cem\u003ePseudomonas\u003c/em\u003e species increased from 32 to 46 during the same period (Figs.\u0026nbsp;2G and H). Differential abundance analysis revealed clear contrasts among host genotypes and between years. In both years, less diverse genera exhibited stronger enrichment relative to control soil, highlighting the functional contribution of rare taxa to community composition (Figs.\u0026nbsp;2I and J). Interestingly, plant growth-promoting bacteria such as \u003cem\u003eDevosia\u003c/em\u003e, \u003cem\u003eNeorhizobium\u003c/em\u003e, and \u003cem\u003eAeromicrobium\u003c/em\u003e were detected predominantly in the rhizosphere of 2024 (Fig.\u0026nbsp;2I), whereas \u003cem\u003eKosakonia\u003c/em\u003e and \u003cem\u003eEnterobacter\u003c/em\u003e were more abundant in 2025. The modern cultivar Gretchen displayed higher levels of \u003cem\u003eErwinia\u003c/em\u003e, \u003cem\u003eEnterobacter\u003c/em\u003e, and \u003cem\u003eTardiphaga\u003c/em\u003e in 2025 (Fig.\u0026nbsp;2J). These dramatic shifts in non-dominant, but functionally active taxa suggest intense recruitment patterns shaped by host genotype and environmental variation.\u003c/p\u003e \u003cp\u003eIn order to compare the shared and unique bacterial genera among barley groups, genus-level data were categorized into four groups including control, wild accessions, landraces, and elite cultivar. A genus was considered present within a category if detected in at least one sample from that group, and only these presence-based genera were used to compute shared and unique sets for Venn analysis. In both years, a substantial number of taxa was shared across all barley types and the control soil, comprising 104 and 96 genera in 2024 and 2025, respectively (Figs.\u0026nbsp;2K and L; Supporting Tables S4 and S5). Despite this common core, distinct sets of genera were unique to each category, indicating genotype- and year-specific associations. Wild accessions from 2024 and landraces from 2025 comprised the highest number of unique genera accounting to 26 each, whereas the elite cultivar Gretchen contained only eight across both years. This pattern suggests that wild accessions recruit a broader but more variable microbiome, while domestication has favored a narrower and more stable assemblage of bacterial lineages.\u003c/p\u003e \u003cp\u003eTaken together, these results indicate that distinct barley genotypes shape and support characteristic rhizosphere microbiomes. While the overall community structure remained conserved across genotypes, consistent with a shared core microbiome, distinct abundance patterns highlight that host genotype exerts a measurable influence on rhizosphere composition, driving differential recruitment and adaptation of specific bacterial lineages in the rhizosphere.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eRhizosphere microbial alpha- and beta-diversity\u003c/h2\u003e \u003cp\u003eAlpha and beta diversity analyses were performed on WMS-derived bacterial profiles to assess within (alpha) and between-sample (beta) variation across barley genotypes in 2024 and 2025. Alpha diversity indices, including species richness (Supporting Figs.\u0026nbsp;2A and D), Shannon (Supporting Figs.\u0026nbsp;2B and E), and Simpson (Supporting Figs.\u0026nbsp;2C and F), showed no significant differences among groups (Kruskal-Wallis, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Richness: χ\u0026sup2; = 9.59, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0877 in 2024 and χ\u0026sup2; = 10.50, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0623 in 2025), indicating comparable overall community diversity. On average, 100\u0026ndash;150 bacterial genera were detected in 2024, whereas 2025 samples showed higher richness (220\u0026ndash;450 genera) (Supporting Figs.\u0026nbsp;2A and D), consistent with a more complex rhizosphere community. Wild accessions tended to display slightly higher richness than landraces and the modern cultivar, suggesting modest but non-significant trends consistent with early observations that domestication can narrow rhizosphere diversity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Beta diversity analyses revealed clear genotype- and year-dependent community differences. Principal Coordinate Analysis (PCoA) based on Bray-Curtis and Jaccard distances showed distinct clustering by genotype and year, indicating strong compositional differences, similar to patterns observed in maize and rice [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. PERMANOVA confirmed significant variation among groups (Bray-Curtis: \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.79, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.64, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 in 2024, Supporting Fig.\u0026nbsp;2G; \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.93, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16.91, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 in 2025, Supporting Fig.\u0026nbsp;2I). In addition, Jaccard-based comparisons supported these findings (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.60, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 in 2024, Supporting Fig.\u0026nbsp;2H; \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.56, \u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001 in 2025, Supporting Fig.\u0026nbsp;2J), and homogeneity of dispersion tests confirmed that these dissimilarities were not driven by unequal variance among groups. Together, the diversity metrics confirm genotype-dependent community composition, with stronger divergence in 2025 suggesting additional genotype-by-environment effect on microbial recruitment.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional profiling of the microbiome reveals conserved yet genotype-specific enrichment of microbial pathways across barley genotypes\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo understand whether genotype-dependent community shifts correspond to functional differences, metagenomic assemblies were annotated with eggNOG-mapper to obtain COG and KEGG classifications. COG profiles showed highly consistent functional compositions across genotypes and years, indicating a stable core metabolic structure of the rhizosphere microbiome (Supporting Fig.\u0026nbsp;3) [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. The dominant categories across all samples included amino acid transport and metabolism, energy production and conversion, and cell wall-related biogenesis, with ~\u0026thinsp;20% of proteins annotated as hypothetical, reflecting the prevalence of uncharacterized microbial functions. Although the total number of predicted proteins varied among genotypes and years, the proportional representation of COG categories remained comparable, suggesting that functional capacities were largely conserved despite taxonomic differences (Supporting Table S6).\u003c/p\u003e \u003cp\u003eTo further understand the specific biochemical pathways involved in the rhizosphere microbial activity, KEGG pathway enrichment analysis was performed on the annotated protein datasets. Across both years, microbial communities from all genotypes exhibited broad enrichment of core metabolic processes, including amino acid metabolism, energy metabolism and secondary metabolite (SM) biosynthesis (Figs.\u0026nbsp;3A and B). In 2024 and 2025, an average of 531 and 487, 602 and 348, 555 and 518, and 473 and 379 pathways were enriched in control soil, wild accessions, landraces and the modern cultivar Gretchen, respectively (Supporting Table S6). Although most pathways were shared among genotypes, the relative enrichment varied systematically among wild, landrace, and modern types, indicating that host genotype not only shapes microbial composition but also selects for distinct metabolic functions within the rhizosphere. In 2024, the wild accession HID0380 displayed strong enrichment in amino acid and sugar metabolism, whereas its closest related counterpart HID0144 showed higher enrichment in glyoxylate and dicarboxylate metabolism (Fig.\u0026nbsp;3A). The landraces HID1029 and HID1104 exhibited moderate but balanced enrichment across most pathways. Interestingly, HID1029 showed a moderate enrichment in glycolysis or gluconeogenesis, while HID1104 was enriched in ATP-binding cassette (ABC) transporters and amino acid metabolism of glycine, serine and threonine. The modern cultivar Gretchen displayed a strong enrichment in quorum sensing as well as amino acid biosynthesis, suggesting that microbial communities in its rhizosphere may be more specialized in signaling and metabolic exchange [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. The dataset from 2025 revealed distinct differences in functional enrichment patterns (Fig.\u0026nbsp;3B), with an overall higher diversity of enriched pathways compared with 2024. The wild accession HID0144 showed pronounced enrichment in carbon fixation pathways, while HID0380 was enriched in alanine, aspartate and glutamate metabolism, indicating an increased emphasis on nitrogen assimilation and amino acid turnover. The landraces maintained moderate yet broad enrichment across all major pathways, displaying a similar metabolic profile between HID1029 and HID1104. Interestingly, Gretchen exhibited an altered enrichment pattern relative to its previous year, with elevated activity across aminoacyl-tRNA biosynthesis, ABC transporters and pyrimidine metabolism, implying enhanced translational and transport process in its microbiome. Across both years, pathways with antibiotic biosynthesis and SM production were consistently enriched in different genotypes. This pattern points to a metabolically competitive and chemically dynamic rhizosphere environment, where microbial taxa likely engage in both antagonistic and cooperative interactions that shape community structure and potentially contribute to plant protection. Collectively, the functional profiling demonstrates that while core metabolic capacities are conserved across barley genotypes, differential pathway enrichment reveals host-specific microbial adaptation, underscoring the genotype\u0026rsquo;s influence on microbiome function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eMetagenome-assembled genomes (MAGs) reveal the taxonomic structure and recovery quality of the barley rhizosphere microbiome\u003c/h2\u003e \u003cp\u003eTo extend beyond community-level inference and understand genome-resolved diversity, we reconstructed metagenome-assembled genomes (MAGs) from the long-read metagenomic datasets obtained in 2024 and 2025. A total of 445 MAG bins were recovered across all samples, representing genome fragments derived from diverse bacterial lineages (Fig.\u0026nbsp;4A). The number of reconstructed MAGs varied between samples and years, ranging from 200 and 153 in Control, 153 and 98 in HID0380, 201 and 190 in HID0144, 98 and 233 in HID1029, 212 and 191 in HID1104, and 190 and 143 in Gretchen over 2024 and 2025, respectively. Quality assessment based on CheckM revealed that the majority of MAGs exhibited low completeness (\u0026lt;\u0026thinsp;50%), with only about 20 bins exceeding this threshold (Fig.\u0026nbsp;4B). Such fragmentation is characteristic of soil metagenomes, where high species richness, uneven sequencing depth, the intrinsic error profile of nanopore reads, and strain-level complexity limit the recovery of near-complete genomes [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. Taxonomic classification using GTDB-Tk enabled assignment of MAGs at different ranks, with the number of classified bins increasing from domain to genus level (Fig.\u0026nbsp;4C). Approximately 15, 23, 27, and 36 distinct phyla, orders, families, and genera were recovered, while only 2 species-level MAGs were identified, reflecting the limited number of high-quality assemblies. A subset of MAGs remained unclassified at lower taxonomic levels, likely due to incomplete marker sets or absence of closely related reference genomes. The most represented bacterial phyla were Pseudomonadota, Actinomycetota, Bacteroidota, and Acidobacteriota, together encompassing the majority of recovered MAGs (Fig.\u0026nbsp;4D). Among these, Pseudomonadota and Actinomycetota dominated, accounting for 25 and 22 of all bins, respectively. Members of these groups are frequently associated with plant-associated microbiomes and include metabolically versatile taxa capable of stress adaptation, SM production, and organic matter turnover [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. To further resolve evolutionary relationships, a phylogenetic cladogram was constructed using the 16S rRNA genes identified within 93 MAGs (Fig.\u0026nbsp;4E). The 16S-based tree revealed well-defined clusters corresponding to the four dominant phyla, confirming taxonomic assignments and illustrating the genomic diversity within each lineage. Genera such as \u003cem\u003eBradyrhizobium\u003c/em\u003e, \u003cem\u003eChitinophaga\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eNocardioides\u003c/em\u003e, and \u003cem\u003eStreptomyces\u003c/em\u003e were among the most represented. The close phylogenetic grouping of these taxa underscores their ecological coherence and prevalence within the barley rhizosphere. Collectively, these results demonstrate that genome-resolved metagenomics enabled recovery and taxonomic placement of bacterial genomes from complex rhizosphere communities, highlighting the predominance of Actinomycetota, Pseudomonadota, and Bacteroidota lineages and revealing fine-scale phylogenetic structure through 16S-based analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptional response of barley roots to distinct microbiomes\u003c/h2\u003e \u003cp\u003eIn order to investigate the genotype-specific transcriptional response of \u003cem\u003eH. vulgare\u003c/em\u003e roots to distinct microbiome compositions, total mRNA was isolated from root tissues of five barley genotypes in 2025, including wild accessions HID0144 and HID0380, landraces HID1029 and HID1104 and modern cultivar Gretchen. As a control, the high yielding cultivar Gretchen was grown under greenhouse conditions in pots containing sterilized field soil to exclude microbial influence. Two independent biological replicates were analyzed for each genotype, yielding 12 libraries. The ONT sequencing generated approx. 12\u0026nbsp;million raw reads, of which 10.8\u0026nbsp;million high-quality reads were retained after trimming. On average, 90% of these clean reads were successfully mapped to the reference genome of \u003cem\u003eH. vulgare\u003c/em\u003e Morex v3, corresponding to a total of 35,106 genes. Differentially expressed genes (DEGs) were identified using a significance threshold of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and a fold change (FC) cutoff of \u0026gt;\u0026thinsp;2 for increased and \u0026lt;\u0026thinsp;0.5 for decreased transcript abundances.\u003c/p\u003e \u003cp\u003ePrincipal Component analyses (PCA) showed tight clustering of biological replicates within each genotype, indicating uniformity and high reproducibility of transcriptome profiles, whereas clear separation among genotypes reflected distinct transcriptional signatures (Fig.\u0026nbsp;5A). This pattern was further supported by a heatmap of the correlation matrices, with samples clustering based on normalized transcript counts (Fig.\u0026nbsp;5B). The number of DEGs varied among genotypes with 1,266, 1,844, 1,941, 1,657 and 1,384 DEGs identified in HID0144, HID0380, HID1029, HID1104 and Gretchen, respectively. Of the 1,266 and 1,844 DEGs of the wild accessions HID0144 and HID0380, 808 and 1757 genes showed increased and 458 and 87 exhibited decreased transcript abundances, respectively. Interestingly, HID0380 had more than twice the number of transcriptionally induced up-regulated genes compared with HID0144. The landraces HID1029 and HID1104 showed 1,188 and 893 genes with increased expression and 753 and 764 genes with reduced expression, respectively. In the modern cultivar Gretchen, 1,067 genes were up- and 317 were down- regulated (Fig.\u0026nbsp;5C). The in-detail summary of the DEGs in all samples are provided in the Supporting Tables S7 - S11. DEGs were functionally categorized using Mercator, revealing strong representation of enzyme-related functions, followed by categories linked to chromatin organization and RNA biosynthesis. Several DEGs were also assigned to functional groups involved in translational processed, including protein homeostasis, biosynthesis and modification (Fig.\u0026nbsp;5D). Intriguingly, the Venn plot illustrated genotype-specific transcriptional signatures, identifying 11, 463, 189, 61 and 44 unique up-regulated genes and 166, 13, 295, 256 and 56 unique down-regulated genes in HID0144, ID0380, HID1029, HID1104 and Gretchen, respectively (Figs.\u0026nbsp;5E and F; Supporting Tables S12 and S13). Notably, the number of shared up-regulated genes across genotypes were approx. tenfold higher than shared down-regulated genes. These results indicate that barley genotypes activate distinct transcriptional responses towards different microbial consortia, highlighting the ability of individual genotypes to respond distinctively to varying microbiome compositions. This correspondence between microbial variation and host transcriptional response suggests a feedback mechanism whereby genotype-specific microbiomes elicit tailored molecular programs in the host, reinforcing the genotype-microbiome linkage.\u003c/p\u003e \u003cp\u003eTo assess functional patterns among differentially expressed genes, gene ontology (GO) enrichment analysis was performed using the biological process (GO:BP) category. The top enriched terms revealed both shared and genotype-specific responses of barley roots to distinct microbiomes (Supporting Fig.\u0026nbsp;4; Supporting Table S14). Core processes such as response to stress, small-molecule metabolism, regulation of respiration, and translation-associated functions were consistently enriched across all genotypes. Genotype-specific differences were also evident as HID0144 showed enrichment in nucleoside phosphate metabolism, HID0380 and Gretchen were enriched in catabolic processes, and the landraces displayed similar but quantitatively variable enrichment profiles. The elite cultivar Gretchen additionally showed enrichment related to nucleosome organization, suggesting chromatin-associated regulation. Overall, while major biological processes were conserved, variation in pathway enrichment reflects subtle yet distinct transcriptional programs shaped by genotype and associated microbiomes.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eHost transcriptional activation coordinates with microbial signaling in the barley rhizosphere\u003c/h2\u003e \u003cp\u003eIn order to investigate the transcriptional response of barley roots to distinct microbial communities, DEGs were classified into eight biological categories based on Mercator annotation. These categories were selected for their relevance to plant-microbe interactions, and included transporters, secondary metabolism, redox reactions, receptor-like protein kinases (RLKs), hormone signaling, pattern recognition receptors (PRRs) and nucleotide-binding leucine-rich repeat receptors (NLRs), defense-related regulatory proteins, and cell wall organization (Fig.\u0026nbsp;6A; Supporting Table S15). Overall, all genotypes exhibited a higher proportion of upregulated genes across most categories, indicating an active transcriptional reprogramming in response to the microbiome. Among the wild accessions, HID1104 and HID0380 showed 171 and 445 DEGs with increased, and 60 and 9 DEGs with decreased transcript abundance, respectively. Notably, HID0380 displayed the strongest transcriptional activation, with 172 and 91 upregulated genes belonging to defense-related regulatory proteins and transporters, respectively. In contrast, only 5 and no downregulated genes were detected in the same category. This highlights the presence of dramatic transcriptional activation in HID0380 taking place in response to the associated microbiota. The landraces HID1029 and HID1104 displayed comparable enrichment patterns, with 250 and 199 upregulated DEGs, respectively. Both genotypes showed a substantial number of regulatory proteins associated with defense, with 82 and 58 DEGs with increased, and 50 and 56 with decreased transcript abundance. The modern cultivar Gretchen showed a total of 253 and 57 up- and down-regulated DEGs, including approx. 110 genes with increased activity belonging to the defense-related regulatory proteins category. Across all genotypes, other plant immunity-related groups including PRRs / NLRs, RLKs and secondary metabolism also contained a substantial number of DEGs, though their representation varied among genotypes.\u003c/p\u003e \u003cp\u003eAs the different plant gene categories associated with microbial interactions and signaling showed pronounced transcriptional activation, we decided to look into the protein families within the rhizosphere microbiome from 2025 to understand how microbial communities may respond to host-derived cues. The Pfam-based analysis revealed a conserved set of protein families broadly represented across all samples, suggesting a shared molecular repertoire underlying microbial functionality in the barley rhizosphere. Pfam-based domain annotation identified a total of 537, 682, 62, 4008, 543 and 212 functional domains across control, HID0144, HID0380, HID1029, HID1104 and Gretchen, respectively. Each Pfam count represents the number of predicted proteins containing a specific conserved domain within a sample (Supporting Table S16). The most abundant domains included response regulators, histidine kinases, ABC transporter, and major facilitator superfamily proteins, which are key components of bacterial signal transduction and substrate transport (Fig.\u0026nbsp;6B) [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. Their consistent enrichment across all barley genotypes suggests that rhizosphere microbes actively sense and respond to plant-derived chemical cues. Interestingly, domains related to redox regulation, including PAS (Per-Arnt-Sim) domain and radical SAM (S-adenosyl-L-methionine) superfamily members, as well as energy-driven transport processes such as ATPases and enoyl-acyl carrier protein reductases, were also highly represented, reflecting strong microbial integration with plant roots. In addition, the prevalence of signal transduction and transporter-associated Pfam families across samples indicates active microbial communication aligned with host signaling. This reciprocal regulation underscores a sophisticated molecular dialogue between barley and its microbiome [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], forming a dynamic communication network that supports rhizosphere resilience and metabolic balance. The connection between host defense and microbial signaling pathways implies that barley genotypes not only shape but also dynamically co-regulate their microbiomes, reflecting a co-adaptive relationship that likely contributes to genotype-specific performance under natural conditions.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGenomic variation at a pathogen defense-related locus reveals structural divergence among barley genotypes\u003c/h2\u003e \u003cp\u003eGiven the strong transcriptional activation of defense-related regulatory proteins, we further examined whether variation in the underlying genomic loci could help explain these responses. Among the eight functional categories associated with plant-microbe interactions, defense-related regulatory proteins contained the highest number of upregulated genes across all genotypes. UniProt annotation of 21 commonly upregulated genes revealed consistent activation of protease inhibitors, ubiquitin-proteasome components, and molecular chaperones such as BAG-domain proteins and calreticulin, all of which contribute to proteostasis during stress. Notably, two of the most highly expressed genes, HORVU.MOREX.PROJ.1HG00003980.1 (BPGv2) and HORVU.MOREX.PROJ.1HG00011330.1 (BPGv2), encode subtilisin/chymotrypsin inhibitor 2A proteins, typically induced during pathogen challenge and central to protease-mediated immunity [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. Because these genes showed strong transcriptional response across all genotypes, we investigated whether genomic structural variation at these loci may contribute to the observed differences in expression or microbial response. Examination of the barley pangenome (BPGv2), together with ONT genome sequencing of our five genotypes, revealed that the two inhibitor genes are paralogs and vary in copy number. PanBarlex (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://panbarlex.ipk-gatersleben.de/\u003c/span\u003e\u003cspan address=\"https://panbarlex.ipk-gatersleben.de/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] analysis showed that Morex carried two paralogous copies, while the wild accession HID0380 contains 16 homologous gene copies within a\u0026thinsp;~\u0026thinsp;600 Kbp cluster, indicating substantial expansion of this defense-associated locus.\u003c/p\u003e \u003cp\u003eTo quantify structural divergence in more detail, we applied alignment-free \u003cem\u003ek\u003c/em\u003e-mer profiling using GeneToCN [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The statistical summary of ONT genomic sequencing is provided in Supporting Table S17. Of the two target genes, only HORVU.MOREX.PROJ.1HG00003980.1 provided sufficient informative k-mers for reliable analysis, and we therefore focused on this locus. Median gene-region \u003cem\u003ek\u003c/em\u003e-mer depth varied strongly among genotypes, with HID0380 showing the highest depth, consistent with multiple Morex-like copies, whereas HID0144 showed near-zero depth, suggesting absence or extreme divergence of the Morex allele (Fig.\u0026nbsp;7A). Other genotypes displayed low to moderate similarity to the reference. To distinguish divergence within the gene from that of the surrounding locus, we separated 25-mers matching the Morex coding sequence (gene-specific) from those derived from the flanking region (flank-specific). All genotypes except HID0144 retained all 30 gene-specific 25-mers, indicating that the coding region itself is structurally conserved. In contrast, flank-specific \u003cem\u003ek\u003c/em\u003e-mers showed substantial variation with HID1029 retaining 1202 \u003cem\u003ek\u003c/em\u003e-mers, indicating highest similarity, whereas HID0380, HID1104 and Gretchen retaining only 12\u0026ndash;26 such \u003cem\u003ek\u003c/em\u003e-mers (Fig.\u0026nbsp;7B). Pairwise comparisons (Fig.\u0026nbsp;7C) of locus-wide \u003cem\u003ek\u003c/em\u003e-mer sets and hierarchical clustering (Fig.\u0026nbsp;7D) further resolved these relationships. HID0380 and HID1029 formed the closest pair, HID1104 and Gretchen displayed intermediate similarity, while HID0144 was clearly separated from other genotypes. Spatial visualization of \u003cem\u003ek\u003c/em\u003e-mer abundance reflected the depth of Morex-derived 25-mers at each genomic position across the 30 gene-specific \u003cem\u003ek\u003c/em\u003e-mers, thereby indicating the relative dosage of Morex-like sequence. This visualization revealed a sharp coverage peak at the gene region for HID0380, reaching a maximum depth of 29, whereas HID1029, HID1104 and Gretchen showed progressively lower depths of 4, 9 and 3, respectively (Fig.\u0026nbsp;7E). To determine whether this structural variation was accompanied by nucleotide-level divergence, we performed read-based SNP calling across HORVU.MOREX.PROJ.1HG00003980.1 (Supporting Table S18). SNP frequencies varied among genotypes, with HID0380 showing the highest number (6 SNPs), and forming at least two distinct haplotype groups, suggesting potential sub-functionalization among its duplicated copies. The landrace HID1104 displayed 4 SNPs, followed by the genotypes HID0144, HID1029 and Gretchen each showing 3 SNPs (Fig.\u0026nbsp;7F). To assess the positional distribution of these variants, genotype-specific consensus sequences were aligned to Morex and visualized as a per-base match / SNP profile across the 224 bp region of the gene. This revealed that SNPs were mostly uniformly distributed except for the wild accession HID0380, which showed a slightly variable pattern with base substitutions (Fig.\u0026nbsp;7G). Together, these results indicate that, although the subtilisin-chymotrypsin inhibitor gene HORVU.MOREX.PROJ.1HG00003980.1 is structurally conserved across barley genotypes, both \u003cem\u003ek\u003c/em\u003e-mer patterns and nucleotide-level polymorphisms reveal genotype-specific copy-number and sequence variation that likely contribute to the observed transcriptional variation.\u003c/p\u003e \u003cp\u003eCollectively, our multi-omics approach using long-read nanopore sequencing shows that distinct barley genotypes actively shape both the structure and function of their rhizosphere microbiomes. Genotype dependent shifts in community composition were accompanied by conserved metabolic functions with changes in associated pathways indicating genotype-specific functional changes within the microbiome. In addition, host transcriptional activation of defense, signaling, and metabolic processes paralleled microbial enrichment in transporter and signaling domains, revealing a genotype-dependent communication network that sustains rhizosphere stability. Together with the strong structural divergence observed at a pathogen defense-related locus, these findings link plant genomic variation to transcriptional deregulation and microbiome composition, highlighting barley genotypes as a driver shaping the ecological structure and functionality of the rhizosphere microbiome.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe rhizosphere represents one of the most complex microbial habitats, where diverse bacteria, fungi, archaea, and protists coexist and interact with plant roots [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. These dynamic communities are crucial for nutrient turnover, stress resilience, and disease suppression, collectively contributing to plant fitness [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Their composition is strongly shaped by root exudates, soil type, and host genotype, creating a highly structured yet variable ecosystems [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. Despite extensive study, the rhizosphere remains difficult to fully characterize due to its immense diversity, uneven microbial distributions, and frequent horizontal gene transfer, which hinder accurate taxonomic assignment and genome assembly [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. In our study, long-read nanopore sequencing provided insights into how host genotype shapes both the taxonomical and functional landscape of the rhizosphere microbiome. The platform\u0026rsquo;s ability to generate extended reads enabled improved assembly continuity [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], capturing dominant lineages such as \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, and \u003cem\u003eNocardioides\u003c/em\u003e, taxa known for their contributions to nutrient mobilization and stress tolerance [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. However, a substantial proportion of reads remained unclassified, emphasizing the still-limited representation of soil genomes in reference databases and the challenges inherent to analyzing complex microbial communities [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. Despite these limitations, the Nanopore-based assemblies revealed distinct community structures across genotypes, illustrating how differences in plant genotypes are reflected in microbial composition and genomic complexity.\u003c/p\u003e \u003cp\u003eGenotype-dependent shifts of the barley rhizosphere microbiome represents one of the most important patterns revealed in our study. Across barley genotypes, clear differences in bacterial composition reflect distinct recruitment strategies shaped by domestication and breeding. Similar patterns have been reported in cereals, where wild relatives assemble more diverse and functionally flexible microbiomes than elite cultivars [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In barley, host genotype influences community composition through genotype-specific patterns and immune cues [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In our study, wild accessions and landrace genotypes harbored broader microbial repertoires, whereas the modern cultivar supported a streamlined but stable community, suggesting a trade-off between resilience and specialization under agricultural selection [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In addition, variation in dominant bacterial groups such as Actinomycetota and Pseudomonadota implies genotype-linked modulation of nutrient cycling and stress-associated functions [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. Preferential enrichment of \u003cem\u003eStreptomyces\u003c/em\u003e in the modern cultivar and \u003cem\u003eBradyrhizobium\u003c/em\u003e in wild genotypes across the years indicates that specific guilds are maintained according to host nutritional and defensive priorities [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Together, these observations reveal that barley genotypes do not passively host their microbiota but actively filter and modulate their composition, establishing distinct microbial consortia that align with host functional needs and form the basis for genotype-specific plant-microbe interactions.\u003c/p\u003e \u003cp\u003eIn natural and cultivated systems alike, changes in microbial communities are tightly coupled to functional specialization, as root exudation and immune signaling select for organisms with compatible nutrient and signaling profiles [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e]. In our study, across genotypes, metagenomes retained a conserved metabolic backbone dominated by amino acid metabolism, energy generation, and SM production, which are central to survival plant-microbe communication [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. Notably, genotype-specific differences appeared in carbohydrate utilization and transport processes, reflecting selective pressures imposed by host-derived carbon inputs. Such variation in metabolic pathways is consistent with reports that plants shape the biochemical landscape of their rhizospheres through differential secretion of sugars, amino acids, and phenolics [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. These exudates, in turn, guide microbial recruitment and activity, forming feedback loops that sustain nutrient exchange and immune balance [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e]. Long-read data further enabled recovery of metagenome-assembled genomes, which highlighted the dominance of Actinomycetota, Pseudomonadota, and Bacteroidota. These taxa are widely recognized inhabitants of plant-associated niches and include metabolically versatile lineages capable of stress adaptation, SM synthesis, and organic matter turnover [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. Further, genera such as \u003cem\u003eChitinophaga\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, and \u003cem\u003eSphingomonas\u003c/em\u003e were among the most represented, mirroring patterns commonly observed in rhizosphere communities of cereals and other crops. These taxa likely occupy complementary niches that reinforce rhizosphere stability and enhance plant protection through antibiotic and siderophore biosynthesis [\u003cspan additionalcitationids=\"CR74 CR75\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. Together, these functional and genome-resolved patterns depict a microbiome tuned to host identity, forming a metabolic consortium that co-evolves with barley roots and sets the molecular initiation for coordinated plant-microbe signaling.\u003c/p\u003e \u003cp\u003eThe coordination between host roots and the microbiome is rather defined by reciprocal signaling than unilateral responses. In barley roots, the pronounced increase in the transcriptional activation of defense-related genes indicates the active perception of microbial cues by the host and thereby responding to maintain homeostasis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These gene families are central to the recognition of microbe-associated molecular patterns (MAMPs) and to the establishment of compatibility with beneficial partners [\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e]. In support of this argument, the rhizosphere microbiome also revealed presence of abundant histidine kinases, response regulators and transporter-related Pfam domains, which are canonical components of bacterial two-component systems (TCS), thereby suggesting that microbes also sense, process, and react to plant-derived signals [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. The mirrored activation of plant perception pathways and microbial signaling systems supports a mutual partnership and has been observed in multiple host-microbe interactions [\u003cspan additionalcitationids=\"CR79\" citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. For instance, in maize, the exudation of sugars, amino acids, and SMs such as benzoxazinoids has been shown to influence rhizosphere assembly and microbial gene expression [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e]. In our study, the activation of transporter and SM genes suggests that barley roots modulate chemical exchange, while microbial enrichment in ABC transporters and redox-active domains implies adaptive response to plant-derived chemicals. This reciprocal exchange highlights a coordinated signaling system that maintains metabolic balance and ecological stability at the root-soil interface [\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e]. The genotype-dependent variation in these interactions further underscores that domestication has shaped the complexity of plant-microbe communication in the rhizosphere.\u003c/p\u003e \u003cp\u003eWild and landrace genotypes exhibited broader transcriptional activation of redox and signaling genes, aligning with their richer and more flexible microbiomes, whereas the modern cultivar Gretchen favored selective programs that emphasize metabolic regulation and moderated defense. Similar patterns of genotype-dependent microbiome shifts were observed in wheat and maize, where ancestral accessions recruited taxonomically richer microbial communities that stimulated stronger immune and signaling responses [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. These support the hypothesis that breeding has streamlined microbial diversity while selecting associations that preserve core functions with greater stability in cultivated environments [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the transcriptional level, enrichment of defense and hormone signaling related DEGs supports an integrated regulation of immunity and metabolism in response to microbial presence. In addition, the increased expression of genes belonging to redox and cell-wall related processes reflects the idea of microbe colonization and biofilm formation along the root surface [\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. On the microbial side, enrichment of radical SAM and PAS domains indicates broad redox and sensory signaling capacities that enable microbes to perceive chemical fluctuations in the rhizosphere, potentially shaping association with plant roots. For instance, the PAS-domain chemoreceptor PcpI in \u003cem\u003ePseudomonas\u003c/em\u003e senses plant hormones such as salicyclic and indole-3-acetic acid (IAA) and drives chemotactic response to these cues [\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e]. Together, these host and microbial related processes point towards a chemical dialogue driven by multiple signaling processes. This coordination likely extends also to the regulation of SMs, which functions as chemical messengers in plant-microbe interactions. The observed transcriptional activation of SM genes in barley coincided with microbial genomic signatures for non-ribosomal peptide synthetases and polyketide biosynthesis pathways frequently linked to antimicrobial and siderophore production [\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e]. This is also supported by the enrichment of SM and antibiotic pathways in KEGG from our study. Such co-regulation of chemistries reinforces the concept of \u0026ldquo;defensive mutualism\u0026rdquo;, in which microbial metabolites contribute to plant immunity while host provide the substrates and microenvironments required for microbial SM biosynthesis [\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e]. For instance, \u003cem\u003ePseudomonas fluorescens\u003c/em\u003e producing the polyketide 2,4-diacetylphloroglucinol (2,4-DAPG) was shown to induce systemic resistance (ISR) in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, with mutants deficient in this metabolite losing their ISR-eliciting ability [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e]. Similarly, volatiles such as 2,3-butanediol emitted by \u003cem\u003eBacillus subtilis\u003c/em\u003e and related rhizobacteria have been demonstrated to trigger systemic immune responses and enhance pathogen resistance in \u003cem\u003eArabidopsis\u003c/em\u003e [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e]. This parallel enrichment of secondary metabolism genes in both host and microbiome thus reflects a co-adaptive chemical exchange shaping the functional ecology of the rhizosphere. Interestingly, our transcriptional data also highlighted the active participation of transporters and receptor kinases, which may represent molecular gateways through which barley roots respond to microbial signals. In plants, members of the ABC subfamily B (ABCB) have been shown to be essential for arbuscular mycorrhizal symbiosis, facilitating lipid and SM transfer across the peri-arbuscular membrane, thereby sustaining mutualistic nutrient exchange [\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e]. Similarly, ABCG and ABCC transporters are known to translocate phytohormones, antimicrobial compounds, and signaling molecules, linking transport activity with systemic defense and rhizosphere communication. In case of bacteria, ABC transporters often function as high-affinity importers for amino acids, oligopeptides, and siderophores, serving as key mediators of nutrient acquisition and environmental sensing [\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e]. Thus, these findings indicate coordinated, two-directional trafficking of small molecules that enables nutrient sharing and signal flow across the root-soil boundary.\u003c/p\u003e \u003cp\u003eAgainst this background of transcriptional and microbial community variation, plant genomic diversity provides an additional mechanistic layer for understanding genotype-specific microbiome responses. For instance, studies in \u003cem\u003eArabidopsis\u003c/em\u003e and maize have shown that natural variation in host loci is associated with shifts in the abundance of key microbial taxa, leading to heritable differences in rhizosphere community structure [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e]. Furthermore, SNPs in host immune genes can act as major determinants of microbiome composition, supporting a causal link between host defense and microbial assembly [\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e]. Consistent with this, a GWAS analysis across 200 sorghum genotypes demonstrated that plant genetic variation significantly shapes rhizosphere communities, with distinct loci predicting the abundance of microbial lineages, indicating direct genetic control over microbiome assembly [\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e]. In our study, the structural variation we observed at a subtilisin-chymotrypsin inhibitor locus, together with its strong transcriptional activation, suggests that defense-related genomic regions may contribute to genotype-specific modulation of root-associated microbiomes.\u003c/p\u003e \u003cp\u003eTaken together, the integration of rhizosphere metagenomics, barley root transcriptomics, and plant genomics supports a model in which plant and microbial partners operate as a synchronized biological network. The concurrent enrichment of signaling, transport, and secondary-metabolic functions across domains shows that the microbiome is an actively signaling entity shaping host physiology, while barley reciprocally remodels the chemical and nutritional landscape through transcriptional programming. This dynamic interaction, evident in both wild and domesticated genotypes, represents a fine-tuned evolutionary partnership supporting rhizosphere resilience, nutrient exchange, and adaptive stress tolerance, offering actionable entry points for breeding and microbiome-informed crop management.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDomestication has reshaped not only the genetic architecture of barley but also its association with rhizosphere microorganisms. Using a multi-omics framework based on Oxford Nanopore long-read sequencing, this study revealed that the barley rhizosphere microbiome is both taxonomically conserved and functionally enriched across wild, landrace, and modern genotypes. The bacterial community, dominated by the phyla Pseudomonadota and Actinomycetota and by genera such as \u003cem\u003eStreptomyces\u003c/em\u003e, \u003cem\u003eBradyrhizobium\u003c/em\u003e, and \u003cem\u003eNocardioides\u003c/em\u003e, formed a stable microbiome whose relative abundances varied between genotypes and across growing seasons. Functional annotation of metagenomic assemblies identified a conserved metabolic backbone encompassing amino acid metabolism, energy production, and SM biosynthesis, alongside genotype-dependent variation in carbohydrate metabolism and transport pathways, reflecting host-specific microbial adaptation. Reconstruction of MAGs provided genomic-scale resolution of dominant bacterial lineages, with \u003cem\u003eChitinophaga\u003c/em\u003e, \u003cem\u003eFlavobacterium\u003c/em\u003e, \u003cem\u003eStreptomyces\u003c/em\u003e, and \u003cem\u003eSphingomonas\u003c/em\u003e emerging as key contributors to carbon turnover, SM production, and root-associated processes. At the host level, transcriptome profiling showed that barley roots exhibit strong transcriptional reprogramming in response to distinct microbiomes, particularly in defense, signaling, and transport processes. These host responses were mirrored by the microbial enrichment of Pfam domains associated with histidine kinases, response regulators, and transporters, indicating a functional reciprocity between host signaling and microbial sensing.\u003c/p\u003e \u003cp\u003eTogether, these findings demonstrate that barley genotype governs both the structural and function organization of the rhizosphere microbiome and that host and microbial communities operate in a coordinated molecular manner shaped by domestication. This research established a multi-omics framework linking crop genomic, metagenomic and transcriptional responses to ecological outcomes, offering a foundation for leveraging future efforts to harness host-microbiome interactions in barley improvement and sustainable crop management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eABC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;ATP-binding cassette\u003c/p\u003e\n\u003cp\u003eBPGv2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Barley Pangenome version2\u003c/p\u003e\n\u003cp\u003eCOG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Cluster of Orthologous Genes\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDEGs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Differentially Expressed Genes\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003eGO:BP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene Ontology Biological Process\u003c/p\u003e\n\u003cp\u003eGWAS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Genome-wide Association Study\u003c/p\u003e\n\u003cp\u003eHMW DNA\u0026nbsp; \u0026nbsp;\u0026nbsp;High Molecular Weight DNA\u003c/p\u003e\n\u003cp\u003eKEGG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eKO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;KEGG Orthology\u003c/p\u003e\n\u003cp\u003eMAGs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Metagenome-assembled Genomes\u003c/p\u003e\n\u003cp\u003eMAMP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Microbe-associated Molecular pattern\u003c/p\u003e\n\u003cp\u003eNLR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Nucleotide-binding leucine-rich repeat receptor\u003c/p\u003e\n\u003cp\u003eONT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Oxford Nanopore Technologies\u003c/p\u003e\n\u003cp\u003ePAS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Per-Arnt-Sim\u003c/p\u003e\n\u003cp\u003ePCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Principal Component Analysis\u003c/p\u003e\n\u003cp\u003ePCR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Polymerase Chain Reaction\u003c/p\u003e\n\u003cp\u003ePCoA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Principal Coordinate Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePRR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Pattern Recognition Receptors\u003c/p\u003e\n\u003cp\u003erRNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;ribosomal RNA\u003c/p\u003e\n\u003cp\u003eRLK\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Receptor-like protein Kinases\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSAM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;S-adenosyl-L-methionine\u003c/p\u003e\n\u003cp\u003eSNP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Single Nucleotide Polymorphism\u003c/p\u003e\n\u003cp\u003eWGS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Whole Genome Sequencing\u003c/p\u003e\n\u003cp\u003eWMS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Whole Metagenome Sequencing\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to Ben Kohnert for support of the field trial, Marvin Behnke and Stefan Kranz for support in initial data pre-processing. We are also indebted to Jana Müglitz and Johanna Nordmeier for their valuable assistance with nanopore sequencing. Furthermore, we thank Dr. Yvonne Pöschl for her assistance in data submission to ENA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003cstrong\u003eR.F\u003c/strong\u003e\u003cstrong\u003e.D:\u0026nbsp;\u003c/strong\u003eConceptualization, methodology, formal analysis, writing - original draft, writing - review and editing, data curation; \u003cstrong\u003eT.M:\u003c/strong\u003e Methodology, formal analysis, writing - review and editing, data curation; \u003cstrong\u003eT.W:\u003c/strong\u003e Funding acquisition, methodology, formal analysis, writing - review and editing, data curation; \u003cstrong\u003eT.S:\u0026nbsp;\u003c/strong\u003econceptualization, funding acquisition, supervision, writing - review and editing, data curation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the BMFTR (grant number 031B1443).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the sequence data collected in this study have been deposited at the European Nucleotide Archive (ENA) under BioProjects PRJEBNNNNN (WMS microbiome ONT), PRJEBNNNNN (16S amplicon Illumina), and PRJEBNNNN (Plant genomics).\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDomestication of plants in the Old World: the origin and spread of domesticated plants in Southwest Asia, Europe, and the Mediterranean Basin. 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ISME J. 2021;15:3181\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41396-021-00993-z\u003c/span\u003e\u003cspan address=\"10.1038/s41396-021-00993-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Biology](https://bmcbiol.biomedcentral.com/)","snPcode":"12915","submissionUrl":"https://submission.springernature.com/new-submission/12915/3","title":"BMC Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rhizosphere microbiome, long-read nanopore sequencing, metagenomics, transcriptomics, host-microbiome interactions","lastPublishedDoi":"10.21203/rs.3.rs-8336676/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8336676/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBarley (\u003cem\u003eHordeum vulgare\u003c/em\u003e L.) provides a suitable model for studying domestication-driven plant-microbiome interactions. Although wild, landrace, and modern genotypes host distinct rhizosphere communities, the extent to which roots and microbes reciprocally influence each other remains unclear. Here, we applied an integrated multi-omics approach combining long-read metagenomics, root transcriptomics, and plant genomics to understand genotype-specific host-microbiome coordination.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOxford Nanopore whole metagenome sequencing (WMS) revealed clear genotype-dependent shifts in rhizosphere communities across seasons. Functional profiling showed a conserved metabolic backbone including amino acid metabolism, energy production, and secondary metabolite biosynthesis accompanied by genotype-specific differences in carbohydrate metabolism and transport-associated pathways. Genome-resolved analysis through metagenome-assembled genomes (MAGs) further detailed the taxonomic and functional architecture of key rhizosphere lineages. Root transcriptomics identified extensive differential expression linked to microbial perception, signaling, defense, and metabolic reprogramming. Integrating host and microbiome data revealed coordinated molecular responses, indicating that barley genotypes not only shape microbial assembly but also program their transcriptional activity in response to microbial cues.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThese findings demonstrate that domestication has shaped a bidirectional interaction network in which barley genotypes and their rhizosphere microbiomes jointly modulate microbial community structure and host transcriptional regulation. The coordinated exchange provides new insights into the evolutionary tuning of plant-microbiome relationships and highlights opportunities for microbiome-informed crop improvement.\u003c/p\u003e","manuscriptTitle":"Long-read nanopore sequencing reveals genotype-dependent microbiome shifts and host-microbe interactions in the barley rhizosphere","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-23 16:53:59","doi":"10.21203/rs.3.rs-8336676/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-12T15:41:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-12T07:17:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-06T22:03:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191974963100025211283524990803592820595","date":"2025-12-22T13:39:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256537798933027124351553534647599992621","date":"2025-12-20T14:39:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-19T16:04:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-12T11:42:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-12T05:12:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Biology","date":"2025-12-11T12:08:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [BMC Biology](https://bmcbiol.biomedcentral.com/)","snPcode":"12915","submissionUrl":"https://submission.springernature.com/new-submission/12915/3","title":"BMC Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f87f284d-1533-406e-b9cb-8f0c0b8d81a4","owner":[],"postedDate":"December 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-19T15:42:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-23 16:53:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8336676","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8336676","identity":"rs-8336676","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}