Host growth and defense pathways drive microbiome-mediated maize heterosis

Host growth and defense pathways drive microbiome-mediated maize heterosis | 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 Host growth and defense pathways drive microbiome-mediated maize heterosis Danning Wang, Xiaoming He, Kiran Suresh, Lukas Schreiber, Frank Hochholdinger, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6347267/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Hybrid vigor, commonly harnessed in maize breeding to boost productivity and stress resistance, is largely attributed to genetic factors. However, recent studies suggest that environmental influences, particularly the plant microbiome, may play a pivotal role in mediating heterosis expression. This study investigates the impact of the rhizosphere microbiome on maize heterosis by exploring interkingdom interactions between plant transcriptomes and microbial communities. We identify a key link between microbial taxa and plant traits associated with heterosis, with a particular focus on root length, growth vigor and rhizoshealth. Through a combination of microbiome profiling, gene expression analysis, and functional assays, we reveal that hybrid plants may harbor a more beneficial and diverse microbiome, which could enhance traits like root development and stress tolerance. Our findings suggest that the plant microbiome, particularly through specific taxa, plays a correlative role in the manifestation of heterosis, offering new opportunities for optimizing maize breeding strategies. The study underscores the importance of the microbiome in hybrid vigor and suggests that future research into microbiome-assisted breeding could lead to more sustainable and productive maize cultivation, particularly in marginal or stressed environments. Heterosis maize microbiome rhizosphere root transcriptome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Heterosis, also known as hybrid vigor, refers to the phenomenon where hybrid plants exhibit superior growth and enhanced stress resistance compared to their parental lines 1 . This effect is most prominently observed in maize ( Zea mays L.) when two genetically distinct inbred lines are crossed. Hybrid varieties typically show significant improvements in both productivity and stress resilience 2 . Heterosis is especially evident during seedling development, where root heterosis plays a critical role for superior plant performance 3 . Early root development is a key determinant of a plant’s ability to establish itself in the soil, access nutrition, and water, and ultimately influencing its overall yield potential 4 . This early vigor sets the stage for faster establishment and configuration of root architecture and distribution during the crucial early stages of growth. It is well known that root heterosis depends on the genetic diversity of the parental donors 5 and transcriptional regulation of specific genes 6 , 7 in maize. The rhizosphere, the region of soil directly influenced by plant roots, enhance the plant’s interaction with its surrounding environment, contributing to better nutrient acquisition and microbial functions 8 . The rhizosphere-associated microbiome, composed of bacteria, fungi and other microorganisms, plays a pivotal role in shaping these mutual interactions with host plants. It has been shown that the microbial communities in the rhizosphere can directly or indirectly impact plant traits such as stress tolerance, growth rate, and nutrient utilization, creating a sophisticated feedback loop between plant and microbial communities 9 . Recently, it was observed that the microbial communities surrounding the roots of maize hybrids were distinct from, and not a mix of, the inbred parental lines 10 . Soil sterilization and inoculation experiments further highlight that the microbiome plays a divergent effect on the performance of inbred lines and hybrids, leading to changes of heterosis upon environmental changes 11 . Early studies have shown that hybrid plants tend to harbor a higher microbial diversity and a higher proportion of beneficial microbes in maize 12 – 14 . This difference in microbial composition is likely driven by genetic factors, as hybrid plants release different root exudates - organic compounds that can attract specific microbes - compared to inbred plants 15 . Thus, the interactions between plants and their microbial communities in the rhizosphere might be an underappreciated but significant factor in the expression of heterosis 16 , 17 . There has been increasing interest in the relationship between the microbiome and heterosis, but it remains largely unknown how gene expression in plants regulates the microbiome underlying hybrid vigor 18 . Moreover, it remains unclear how and to which degree the genetics and gene regulation of the host plant affects the microbiome that influences the manifestation of heterosis. In this context, understanding how plant genes regulate the interaction between the rhizosphere microbiome and heterosis-driven root traits could open new avenues for enhancing crop performance and resilience. In our study, we conducted a comprehensive investigation into the mechanistic regulation of the root transcriptome and rhizosphere microbiome at the maize seedling stage. This involved the use of cortex tissue-specific root RNA sequencing (RNA-seq), and bacterial 16S rRNA and fungal ITS gene sequencing. Our detailed analysis of root traits - encompassing morphology, anatomy, and physiology -revealed that root suberization might play a critical role in exudation-driven microbiome assembly. This suggests a complex interplay between plant genes and the rhizosphere microbiome, influencing root-microbial interactions. Moreover, the identification of several root cortex-specific maize genes underscores the potential niche for microbiome-driven regulation of heterosis expression. Overall, our findings highlight the synergistic relationship between plant genetics and microbiome dynamics as a key factor in driving hybrid vigor in the rhizosphere. These novel insights offer promising prospects for crop breeding, where optimizing plant-microbe interactions could prove as essential as genetic selection in the development of high-performing hybrid varieties. Results Heterosis patterns in root phenotypic traits mirror genetic relationships To investigate how heterosis patterns in root development vary across different kingdoms along a single primary root in maize, we examined various root and rhizosphere compartments in the primary root (Fig. 1 a), including the cortex tissues of the root differentiation zone (Fig. 1 b), along with the closely attached rhizospheres from the primary roots. We selected seven genetically distinct genotypes covering the major heterosis groups including stiff-stalk (SS) varieties such as B73 and H84, and non-stiff stalk (NSS) varieties such Mo17, A554, W64A, Oh43 and H99 6 . The maternal and paternal inbred lines were crossed to generate F1 hybrid offspring (Fig. 1 c). This study was conducted with four biological replicates under three different nutrient conditions: control soil (with sufficient nutrients), low nitrogen (LN) soil, and low phosphorus (LP) soil 19 . We determined phenotypic traits such as primary root length and weight, anatomical traits, and suberin content. For each phenotypic trait, we calculated the mid-parent heterosis (MPH) and better parent heterosis (BPH) percentages between the hybrid and its parents. Primary root length was measured and heterosis patterns were assessed across all three soil conditions. Notably, 60% of the hybrids showed either MPH or BPH across all conditions (Figure S1 ). The heterosis percentages were consistently positive, indicating that hybrids always displayed longer primary roots than the parents (average BPH percentage: CK: 0.16, LN: 0.14, LP: 0.13) (Table S1 ). Heterosis patterns for the Mo17xA554, A554xH84, B73xOh43, and B73xH99 hybrids were conserved across the three soil conditions, while no unique heterosis pattern emerged for each individual soil condition (Table S1 ). Interestingly, the Mo17xW64A, B73xH84, and W64AxH99 hybrids did not show any heterosis across the conditions. W64A exhibited the longest primary roots among all parents (Figure S2 ), which could explain why hybrids involving this genotype either showed MPH or no heterosis. The absence of heterosis for B73xH84 likely arose from the fact that both parents belong to the same lineage (i.e. SS), resulting in no heterosis across any soil condition. In contrast, the B73xH99 hybrid consistently displayed the longest primary roots across all treatments, suggesting that greater genetic distance between the parent lines (B73 and H99) led to higher heterosis. Effect of microbiome on heterosis is manifested in early root development To investigate whether the soil microbiome is linked to the manifestation of heterosis, we sterilized the soil by autoclaving and assessed both biomass production and mid-parent heterosis under conditions with and without microbes. We selected a diverse range of maize inbred lines and their F1-hybrids, evaluating them at 3, 7, and 21 days after transfer (DAT) to the soil. In general, we observed that F1-hybrids were more sensitive to the soil microbiome in terms of primary root length (Fig. 2 a), primary root weight (Fig. 2 b), and normalized rhizosheath weight (Fig. 2 c) compared to the parental inbred lines, even at the very early seedling stage (3 DAT). Importantly, we observed a significant increase in mid-parent heterosis for maize rhizosheath when the soil microbiome was removed (Fig. 2 d), though no similar effects were seen for other traits, such as primary root length or primary root weight. Importantly, this modified heterotic effect may be independent of secondary factors related to soil sterilization, such as changes in soil nutrition, structure, and organic matter (Fig. 2 e). Consequently, the enhanced rhizosheath could improve the plant's interaction with its surrounding environment, potentially contributing to the manifestation of heterosis during early root development. Maize heterosis is more evident in the transcriptome than in microbial community composition To explore the potential interactions of host expressed genes and rhizosphere microbiome, we performed transcriptome analyses through RNA sequencing for the root cortex tissue, bacterial microbiome analysis via 16S (V4) rRNA gene sequencing, and fungal analysis via ITS1 sequencing for the rhizosphere compartments (Fig. 3 ). First, we performed a Principal Component Analysis (PCA) on the root transcriptome across all genotypes and treatments. The samples were primarily clustered by genotype, followed by soil treatment (Fig. 3 a). This clustering pattern aligns with the results from the PERMANOVA statistical test, where the genotype factor explained 34% of the variance in the transcriptome, while the treatment factor accounted for only 8% of the variance (Genotype: R² = 0.34, P = 5e-4; Treatment: R² = 0.084, P = 5e-4). All hybrids clustered between their parental inbred lines, reflecting the combined effects of the gene expression from both parents. Interestingly, B73 appeared distant from other inbred lines but was closer to H84, which corresponds to their phylogenetic relationship of the "stiff stalk" and "non-stiff stalk" group. Next, to investigate whether hybrids differed from the inbred lines in terms of microbiome composition, we examined both bacterial (Fig. 3 b) and fungal (Fig. 3 c) alpha diversity via Shannon index and the overall community composition separately. Given that the soil treatment explained a larger proportion of variance in bacterial and fungal alpha diversity and community composition (bacteria: alpha diversity R² = 0.18, P = 1.46e-13; beta diversity R² = 0.17, P = 5e-4; fungi: alpha diversity R² = 0.12, P = 2.42e-09; beta diversity R² = 0.25, P = 5e-4) compared to genotype (bacteria: alpha diversity R² = 0.049, P = 0.66; beta diversity R² = 0.049, P = 0.97; fungi: alpha diversity R² = 0.053, P = 0.65; beta diversity R² = 0.058, P = 0.0035), we compared the microbial differences between hybrids and inbred lines under each soil condition. For alpha diversity, no significant differences were observed between hybrids and inbred lines for both bacteria and fungi (Figure S3). However, the PERMANOVA test showed that hybrids differed from inbred lines across all three soil conditions (bacteria: R² = 0.0064, P = 0.001; fungi: R² = 0.0064, P = 5e-4). Additionally, we performed a Principal Coordinate Analysis (PCoA) separately for bacteria and fungi under each soil condition, after removing batch effects. Under each soil condition, bacterial samples were mainly separated by inbred lines and hybrids along the first principal component (Fig. 3 b, S4), which contrasts with the transcriptome data where each hybrid was positioned between its maternal and paternal inbred lines. A similar pattern was observed for the fungal community composition (Fig. 3 c, S5). Overall, these findings suggest that while the relationships between hybrids and their parents are more apparent in the host transcriptome, these differences are small but significant in the corresponding microbial community composition. Improved rhizosheath is associated with root development and cortex suberin components To further explore the relationships between the size of rhizosheath and root anatomical and physiological traits in association with heterosis, we determined the root anatomy and root suberin content for all 22 inbred lines and hybrids under controlled soil conditions. Root anatomical traits were assessed in development zones 1 (25%), 2 (50%), and 3 (75%) of the primary root, with only a few hybrids showing heterosis in each zone (Table S2 ). Zone 1, the newest development zone, exhibited the lowest number of heterotic traits, while most of differentiation zone 3 displayed the highest number of heterotic traits. Specifically, the hybrid Mo17xH84 consistently showed heterosis across all zones, with the heterosis percentage decreasing from zone 3 to zone 1. For suberin traits, only a few hybrids exhibited heterosis for each suberin component (Table S3). Again, Mo17xH84 demonstrated a heterosis pattern across all suberin traits, except for aromatic suberin. Notably, suberin traits exhibited significantly negative association with manifestation of heterosis. For all phenotypic traits examined, the majority of heterosis observed was better parent heterosis (BPH) (Figure S6). While most suberin traits displayed negative heterosis percentages, heterosis for other traits, such as primary root length and anatomical traits, was predominantly positive (Fig. 4 ). This suggests that hybrids generally outperformed their parents in terms of root development and growth under controlled soil conditions, and that the genetic distance between parental lines plays a significant role in determining the magnitude and direction of heterosis. Thus, an enhanced rhizosheath, driven by improvements in root anatomy and cortex suberin content, might offer a significant potential for improving plant resilience, nutrient uptake, and stress tolerance. Growth and defense pathways drive microbiome-mediated heterosis To identify bacteria and genes associated with the heterosis of phenotypic traits, we calculated the mid-parent heterosis (MPH) for both bacteria and genes and correlated them with the MPH of phenotypic traits. Bacteria or genes with a P -value < 0.05 were related to phenotypic heterosis. To minimize false positives, we only retained bacteria and genes that were heterotic in at least 6 hybrids ( P < 0.05) and excluded phenotypic traits that were highly correlated with each other. We then calculated the correlation between heterosis-related bacterial abundance and gene expression to construct multi-omics networks linking heterosis across these domains. Under control soil conditions, a significant portion of phenotypic heterosis-related bacteria belonged to the Order Vicinamibacterales, while the top hubscore genes were primarily involved in cell cycle regulation or defense response (Fig. 6 a). Notably, the cortex area Z2 showed the highest hubscore among all phenotypic traits, and the gene Zm00001eb199470 was strongly correlated with the MPH of both cortex area Z2 and w_OH. This gene is also linked with most of the other phenotypic heterosis-related ASVs and is involved in mitotic cell cycle phase transition (Table S11). For cortex area Z2, only one heterosis-related bacterium, bASV162 (family: Vicinamibacteraceae), showed a correlation with genes involved in auxin metabolism and lignin metabolism. Interestingly, bASV162 was also identified as a w_OH heterosis-related bacterium. For w_OH, five heterosis-related bacterial ASVs were identified, with associated gene functions in cell growth and auxin polar transport. Additionally, four bacterial ASVs were correlated with the MPH of primary root length, with the associated genes involved in alkaloid metabolic processes. We also performed the same correlation analysis for fungi and found that two fungal ASVs, fASV5 ( Sarocladium zeae , a known endophyte against Fusarium ) and fASV637 (Phylum Ascomycota), were related to the heterosis of primary root length and diacid traits, respectively ( P < 0.05). However, no significant correlations were observed between the phenotypic heterosis-related bacteria and fungi. The fungi-related genes also had roles in cell cycle transition and auxin transport. Ultimately, we constructed a comprehensive network showing all phenotypic heterosis-related genes, bacteria, and fungi (Fig. 5 a). Under low nitrogen soil conditions, we found no significant heterosis-related fungi for primary root length and few genes and bacteria associated with primary root length heterosis (Fig. 6 b). The bacterium Haliangium was particularly important, linking phenotypic heterosis with host gene expression, such as Zm00001eb412510 (involved in immune response), Zm00001eb417450 (involved in lignin biosynthesis), and Zm00001eb406770 (involved in defense response) (Table S11). Under low phosphorus conditions, no significant correlations were found between fungi and the heterosis of primary root length, nor between bacteria and gene expression. As a result, the network under low phosphorus soil was simpler compared to the other two soil conditions (Fig. 6 c). The three primary root length heterosis-related bacteria were all from the genus Nocardioides and the family Vicinamibacteraceae. The genes associated with these bacteria had similar functions to those identified under other soil conditions, such as Zm00001eb223800 (involved in mitotic cell cycle) and Zm00001eb406770 (involved in defense response) (Table S11). Together, the network associations identified candidate genes involved in development and immunity-related pathways that are likely linked to microbial taxa influencing heterosis expression. Discussion Hybrid vigor has been extensively utilized in agricultural practices, especially in the cereal maize, to enhance productivity and stress resistance. However, genetic factors alone do not fully explain the magnitude of heterosis observed. Other factors, such as environmental influences and the role of microbiome inhabiting in plant niches, have recently emerged as a significant factor influencing plant performance and heterosis 17 . Indeed, the microbiome not only helps in plant growth and stress tolerance but also plays a role in mediating interactions between the plant and its environment, ultimately influencing the plant's fitness and productivity 21 , 22 . In particular, the rhizosphere microbiome plays an indispensable role in nutrient cycling and acquisition 23 , and disease resistance and suppression 24 . Recent studies highlight the importance of the microbiome in contributing to heterosis by enhancing early seed germination 25 , growth vigor 11 and disease resistance 26 . In general, hybrid plants could potentially have a more diverse or more beneficial microbiome 12 , 14 , 27 , 28 , which might explain the robustness and resilience. Different inbred lines have distinct composition of the rhizosphere microbiome, and hybridization could result in an optimal combination of microbial partners, which in turn can affect traits associated with heterosis 16 . Nevertheless, it remains unclear whether specific microbial taxa influence the expression of heterosis and, if so, which plant traits they might affect. By leveraging microbiome-based strategies, we developed diverse combinations of maize inbred lines and crossing triplets, and subjected to interkingdom interactions analyses by investigating root transcriptomes and rhizosphere microbiomes. For the first time, we constructed a plant gene expression network linked to a diverse array of microbial taxa, in relation to heterosis expression in maize (Fig. 3 ). Specifically, the functions of the candidate genes identified are associated with key pathways in cell development and defense responses. Notably, these genes exhibit a tissue-specific expression pattern, predominantly in the root cortex (Fig. 4 ), where localized immune responses are activated to safeguard the plant's internal tissues 29 , 30 . Furthermore, the differentiation of the endodermis begins with the synthesis and deposition of a lignified Casparian strip, which forms a central ring around endodermal cells, playing a key role in mediating interactions with both pathogenic and beneficial microbes 31 . Our findings underscore the potential function and role of endodermal cells in regulating heterosis expression, particularly in relation to interactions with the soil microbiome (Figs. 5 and 6 ). However, further studies are needed to investigate the genetic differentiation of root cell-type-specific effects on heterosis expression in relation to the rhizosphere microbiome 32 . Additionally, it is crucial to understand how specific microbial taxa mediate the manifestation and extent of heterosis-driven root traits through single-cell and spatial transcriptomics approaches 33 . Microbial community composition is influenced by the composition of root exudates 34 . This process, however, can be disrupted by the diffusion barriers created by endodermal cells, which result in distinct patterns of root exudation 35 . While the specific compounds involved and their roles in recruiting particular bacterial taxa remain unclear, future research should focus on determining whether alterations in endodermal barriers affect microbial community assembly. Additionally, it will be essential to understand how changes in endodermal differentiation and exudate composition influence the colonization of beneficial or pathogenic microbes, particularly in the context of heterosis. Surprisingly, we identified a novel bacterial taxon, Nocardioides , associated with mitotic cell cycle and defense response genes, while also linking it to root length heterosis in maize (Fig. 6 ). As a plant growth-promoting rhizobacterium (PGPR), Nocardioides can form beneficial symbiotic or commensal relationships with plant roots. Through these interactions, Nocardioides may influence root morphology, enhancing the root’s ability to explore the soil for nutrients and water. This is achieved by the production of growth-promoting substances, such as indole acetic acid (IAA), an auxin that modulates root development. This finding highlights the significant potential of exploring the genetic underpinnings of the beneficial microbiome, which drives enhanced growth and productivity through the concept of heterosis. The discovery of a link between heterosis and the microbiome opens new avenues for maize breeding based on the ability to foster beneficial microbial communities and root traits (Fig. 7 ). Moreover, specific microbial taxa or consortia could be applied in marginal or stressed environments to enhance hybrid performance and stress tolerance. While the association between maize heterosis and the microbiome is promising, several challenges remain to be resolved. The rhizosphere microbiome is highly dynamic and context-dependent, making it difficult to apply specific microbial taxa responsible for heterosis. Considering the environmental heterogeneity (e.g. soil type, climate), future studies should explore the functional conservation of specific taxa across different locations. Furthermore, accurate profiling of the microbiome and understanding the genetic mechanisms behind these interactions require high-resolution and functional genomics to identify keystone microbial taxa in maximizing heterosis in crops. Overall, while genetic factors undoubtedly contribute to hybrid vigor, the microbiome also plays a pivotal role in influencing maize performance. Our study underscores the importance of gaining a deeper understanding of these microbial communities, as harnessing their potential could lead to innovative breeding strategies aimed at improving maize yield, stress resilience, and disease resistance. As our knowledge of plant-microbe interactions expands, we anticipate the emergence of more sustainable and productive maize cultivation practices, with microbiome-assisted breeding becoming a key component of future agricultural advancements. Conclusion This study highlights the crucial role of the microbiome in influencing the expression of heterosis in maize, with implications for both root development and plant growth. In particular, the identification of specific microbial taxa such as Nocardioides linked to root length heterosis opens exciting new possibilities for enhancing hybrid performance through microbiome-based strategies. The interplay between plant genetics and the microbiome, particularly in the rhizosphere, might offer novel avenues for optimizing maize breeding, improving yields, and increasing stress tolerance. Materials and Methods Maize Material, Soil Preparation, and Experimental Design This study utilized the genetically distinct maize inbred lines - A554, B73, H84, H99, Mo17, Oh43, and W64A - along with their F1-hybrids 6 . The soil used was sourced from a long-term (over 100 years) fertilizer field experiment located in Dikopshof (50˚48′21′′N, 6˚59′9′′E). Three distinct soil types were collected: the control soil (CK) with full nutrient fertilization, low nitrogen soil (LN) lacking nitrogen, and low phosphorus soil (LP) lacking phosphorus. Detailed soil nutrient information is provided by 19 . The freshly dug soil was air-dried and sieved through a 1 mm mesh before use. The pot experiment was conducted in a complete randomized design, using pots measuring 7 cm × 7 cm × 19 cm. The experimental setup included seven maize genotypes and three different nutrient treatments, with at least four biological replicates per treatment. Additionally, empty pots filled with soil were used as "bulk soil" controls. All pots within each tray were randomized using a true random generator (Excel function “RAND”), and the trays were reshuffled weekly in the growth chamber without regard to pot labels. Soil water content was carefully maintained by weighing the pots every two days to account for water loss. To simulate early root development, plants were grown for one week, focusing on primary root formation with minimal lateral root growth. During this period, only sterilized water was applied to prevent potential contamination. Sample collection and cortex tissue separation The root and rhizosphere samples were harvested from 1-week-old maize plants of all genotypes grown under different nutrient treatment conditions. Specifically, the entire root system was carefully taken out of each pot and gently shaken to remove any loosely attached soil. To ensure precise comparison among genotypes, we focused on the primary root, which is present in all genotypes. Large soil particles were carefully removed from the primary roots to avoid contamination. Only the root segments with tightly attached soil were placed into 15 ml Falcon tubes (Sarstedt), immediately frozen in liquid nitrogen, and stored at -80°C prior to rhizosphere soil extraction. The rhizosphere samples were defined and extracted into PowerBead tubes (MP Biomedical) as described by 36 . Root samples were collected from separate plants, and the attached soil was first washed with tap water, then rinsed three times with sterilized water. The roots were dried before being placed into PowerBead tubes. Additionally, the stele and cortex tissues from the differentiation zone of the primary root were carefully peeled by hand, as outlined by 37 . Bulk soil samples were collected from unplanted pots and used as controls. Phenotypic Measurement of Seedling Root Traits To assess root phenotypic traits, the length of the primary root was measured manually. After gently washing off any loosely attached soil, the weight of the primary root was recorded. The size of the rhizosheath was determined by subtracting the total root weight from the weight of the root with its attached rhizosphere, and the resulting value was normalized based on the primary root length. Determination of Seedling Root Anatomy The attached soil on the primary root was gently washed off, and root segments from three distinct developmental zones were selected for anatomical trait measurement. The primary roots were divided into three zones based on the development of apoplastic barriers, such as Casparian bands and suberin lamellae. The zones were defined as follows: (1) zone 1: 2 cm from the root tip; (2) zone 3: 2 cm from the shoot-borne roots; and (3) zone 2: located at 50% of the total primary root length. Root fragments (5 mm) were then embedded in 8% agarose containing 0.5% gelatin, and transverse sections (200 µm) were prepared using a vibratome (Leica VT1200S, Nussloch, Germany). The sections were mounted in distilled water and immediately examined under bright-field illumination using an Axio-Imager epifluorescence microscope (Carl Zeiss, Germany). The number of metaxylem vessels was counted, and the diameter and total area of the cortex, stele, and endodermis were measured using ImageJ software (version 1.40, NIH, Bethesda, MD, USA). Determination of Root Suberization in the Primary Root Cortex To explore the relationship between root physiology and root heterosis, we specifically measured root suberization in the primary root cortex of all maize inbred lines and hybrids. The cortex tissue samples were preserved in 70% ethanol. For data normalization, the length, diameter, and anatomical characteristics of the dissected root cortex were recorded. Each biological replicate consisted of three independent primary roots from three separate seedlings. The root cortex samples were enzymatically digested and depolymerized, following the protocol described by Baales et al. 2021 38 . After depolymerization with BF 3 /MeOH, the samples were extracted with chloroform and spiked with dotriacontane (20 µg in 100 µl solution; Fulka, Sigma-Aldrich, USA) as an internal standard. Suberin contents were quantified using Gas Chromatography with Flame Ionization Detection (GC-FID, HP 5890 Series H, Agilent), while individual suberin monomers were identified using Gas Chromatography-Mass Spectrometry (GC-MS, HP 5971 quadrupole mass selective detector, Agilent) using an in-house-created library. A 1 µl aliquot of each sample was analyzed on 30 m DB-1 GC columns (Agilent, USA). RNA Sequencing and Bacterial 16S rRNA, Fungal ITS1 Gene Sequencing Total RNA was extracted from the primary root cortex tissue samples using the RNeasy Plus Universal Mini Kit (Qiagen). Complementary DNA (cDNA) libraries for RNA sequencing (RNA-seq) were prepared using the MGIEasy RNA Library Construction Kit. For DNA extraction, rhizosphere samples were collected and processed immediately following the FastDNA™ SPIN Kit for Soil protocol (MP Biomedical). The quality and quantity of both RNA and DNA were assessed using Agilent RNA or DNA Chips (Agilent Technologies). For bacterial 16S rRNA gene sequencing, the V4 region was amplified using universal primers F515 (5′ GTGCCAGCMGCCGCGGTAA 3′) and R806 (5′ GGACTACHVGGGTWTCTAAT 3′). For fungal amplicon sequencing, the fungal ITS1 gene was amplified using the primer pair F (5′ CTTGGTCATTTAGAGGAAGTAA 3′) and R (5′ GCTGCGTTCTTCATCGATGC 3′). PCR amplification was performed using Phusion High-Fidelity PCR Master Mix (New England Biolabs) following the manufacturer's guidelines. Only PCR products with the brightest bands in the 400–450 bp range were selected for library preparation. PCR products were mixed in equal amounts and purified using the Qiagen Gel Extraction Kit. Sequencing libraries were prepared using the NEBNext Ultra DNA Library Prep Kit for Illumina, incorporating sequence indices as per the manufacturer's protocol. Library quality was evaluated using a Qubit 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. Finally, the qualified libraries were sequenced on an Illumina MiSeq platform using 250-bp paired-end reads. Transcriptome Data Processing and Statistical Analysis The processing and trimming of raw RNA-seq reads were performed as previously described 39 . Briefly, low-quality sequences and low complexity polyA tails were removed. The reference genome index was constructed, and sequences were mapped to the maize reference genome (v.5; available at: Zea_mays.Zm-B73-REFERENCE-NAM-5.0) using HISAT2 (v2.1.0) 40 . Default parameters and all standard commands were applied in HISAT2. Downstream analyses were performed in R (v4.2.2) 41 . BAM files generated by HISAT2 were input into the ‘featureCounts’ function 42 from the Rsubread (v2.12.3) package to generate a gene expression table, using the maize reference annotation (v.5; available at: Zea_mays.Zm-B73-REFERENCE-NAM-5.0.53.chr.gtf). Chimeric reads and reads mapped to multiple positions were discarded. Only genes with a minimum of five mapped reads in at least four samples were considered expressed and included in subsequent analyses. Prior to statistical analysis, data were normalized by library size using the DESeq2 (v1.38.3) package 43 in R. Principal component analysis (PCA) was conducted using the ‘prcomp’ function in base R (v4.2.2). To evaluate the marginal effects of nutrient treatment conditions and genotype on the transcriptome, a permutation-based PERMANOVA test was applied to the Euclidean distance matrix between transcriptomic samples using the ‘adonis2’ function in the vegan (v2.6.4) R package 44 . All plots were generated using the ggplot2 (v3.4.2) R package 45 . Bacterial 16S rRNA and Fungal ITS1 Gene Sequence Processing and Data Analysis Raw sequencing reads were processed using a stepwise pipeline. Briefly, paired-end 16S rRNA amplicon sequencing reads were assigned to samples based on their unique barcodes and truncated by removing the barcode and primer sequences. Paired-end reads were merged using FLASH (v1.2.7) 46 , and the spliced sequences were designated as raw tags. Sequence analysis was performed with QIIME 2 (v2020.6) 47 . The raw sequence data were demultiplexed and quality-filtered using the q2-demux plugin, followed by denoising with DADA2 48 (via q2-dada2). Sequences were truncated at position 250, and each unique sequence was assigned to a distinct Amplicon Sequence Variant (ASV). Taxonomy was assigned to ASVs using the q2-feature-classifier and the classify-sklearn naive Bayes taxonomy classifier, referencing the SSU rRNA SILVA 99% amplicon sequence variant reference sequences (v138) 49 at each taxonomic rank (kingdom, phylum, class, order, family, genus, species). Mitochondrial- and chloroplast-assigned ASVs were removed. ITS1 amplicon data were processed similarly to the 16S amplicon data, except that the UNITE 99% ASV reference sequences (v10.05.2021) 50 were used for taxonomy annotation. All downstream statistical analyses were conducted in R (v4.2.2) (R Core Team, 2022). For α-diversity analysis, the Shannon index was calculated using rarefied ASV tables with 10,000 reads. Abundant ASVs that represented ≥ 0.05% relative abundance in ≥ 5% of samples were retained for downstream analysis, and no samples were excluded. Bray–Curtis distances between samples were calculated using ASV tables normalized with the varianceStabilizingTransformation function from the DESeq2 (v1.38.3) package 43 in R. If not otherwise specified, all data analyses were performed using the normalized ASV table. Principal Coordinate Analysis (PCoA) was conducted using the 'ordinate' function in the phyloseq (v1.42.0) R package 51 . To examine the marginal effects of nutrient treatment conditions and genotype on microbial community composition, a permutation-based PERMANOVA test was performed using the Bray–Curtis distance matrix with the 'adonis2' function in the vegan (v2.6.4) R package 44 . All plots were generated using the ggplot2 (v3.4.2) R package 45 . Differential Gene Expression and Functional Characterization Differential gene expression between hybrids and inbred lines was analyzed using the ‘DESeq’ function in the DESeq2 R package (v1.38.3). Differentially expressed genes (DEGs) for each comparison were identified by controlling the False Discovery Rate (FDR)-adjusted p-values of Wald tests at 0.05 and setting a fold change threshold of > 2. To functionally characterize the DEGs, we classified expression patterns according to Gene Ontology (GO) terms using g:Profiler 52 . The GO annotation system categorizes gene products based on four structured vocabularies: biological processes, cellular components, molecular functions, and KEGG pathways, offering a species-independent overview. We then performed Gene Set Enrichment Analysis (GSEA) to identify significantly overrepresented functional categories within the DEG data. Integration of Host Transcriptome, Microbial Community, and Phenotypic Traits Network-based analysis is a powerful and biologically interpretable tool for exploring associations among variables, such as the relationships between microbial communities, gene expression, and phenotypic traits 53 . Weighted Correlation Network Analysis (WGCNA) is a data-driven method that clusters genes into modules based on weighted correlations between gene transcripts 54 . To identify keystone microbes that significantly correlate with heterosis level of phenotypic traits and the host transcriptome, we performed WGCNA on both microbial ASVs and host root-expressed genes using R. The analysis involved the following steps: (1) Clustering gene/ASV co-expression modules across different genotypes under each of the three soil nutrient conditions. (2) Associating module eigengenes with heterosis level of phenotypic traits and selecting gene modules that show high expression in hybrids but low expression in inbred lines. (3) Correlating selected gene modules with ASV modules to identify microbial ASVs associated with specific gene expression patterns. (4) Selecting genes/ASVs with high module membership values that significantly correlate with heterosis level of phenotypic traits. The detailed construction of integrative networks is described in 39 . Soil Microbiome Effect on Maize Heterosis To understand if soil microbiome will affect the manifestation of heterosis, maize plants were grown in pots filled with sterilized soil with either sterilized water or soil suspension solution or filtered (0.2 µm pore size) soil suspension under controlled environmental conditions. The root phenotypes including primary root length, primary root weight and standardized rhizosheath were determined after 7-day cultivation. Declarations Data availability All raw plant RNA-seq data and bacterial 16S sequencing data reported in this study have been deposited in the Sequence Read Archive (SRA) under accession number PRJNA1243758 (http://www.ncbi.nlm.nih.gov/sra). RNA-seq reads were mapped to the maize reference genome version 5 (Zea mays Zm-B73-REFERENCE-NAM-5.0) available at ftp://ftp.ensemblgenomes.org/pub/plants/current/fasta/zea_mays/dna/Zea_mays.Zm-B73-REFERENCE-NAM-5.0.dna.toplevel.fa.gz, and gene annotations were derived from reference gene models version 5 at ftp://ftp.ensemblgenomes.org/pub/plants/current/gtf/zea_mays/Zea_mays.Zm-B73-REFERENCE-NAM-5.0.53.chr.gtf.gz. Gene Ontology (GO) term annotations were performed using g:Profiler (https://biit.cs.ut.ee/gprofiler/gost). Acknowledgements We thank Selina Siemens and Alexa Brox for soil and root DNA extractions at the University of Bonn, Germany. Funding This research was supported by the Deutsche Forschungsgemeinschaft (DFG) through grants YU272/4-1 (514003603) and the Emmy Noether Programme 444755415 to P.Y., as well as the German Excellence Strategy – EXC 2070 – grant 390732324 to P.Y., the DFG Priority Program (SPP2089) ‘Rhizosphere Spatiotemporal Organisation – A Key to Rhizosphere Functions’ grant 403671039 to P.Y and F.H. and the DFG grant HO 2249/18-1. Contributions P.Y. conceived and designed research; X.H. and P.Y. performed the root tissue separation and rhizosphere sampling; D.W. performed all RNA-seq and microbiome data analysis; K.S. and L.S. performed root suberin analysis; D.W. and P.Y. wrote the manuscript, with input from X.C. and F.H. and all other authors. All authors read and approved the final manuscript. Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. 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FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics. 2011;27:2957–63. Bokulich NA, et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 2018;6:1–17. Callahan BJ, et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3. Quast C, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6. Abarenkov K et al. UNITE QIIME release for Fungi. (2020). McMurdie PJ, Holmes S, Phyloseq. An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE 8, (2013). Raudvere U, et al. G:Profiler: A web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 2019;47:W191–8. Wang Q, et al. Host and microbiome multi-omics integration: applications and methodologies. Biophys Rev. 2019;11:55–65. Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 9, (2008). Additional Declarations No competing interests reported. Supplementary Files SupplementaryfileWangetal.docx SupplementarytableWangetal.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6347267","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451264744,"identity":"3848e17d-28c1-4021-8a8d-40dbccde6b48","order_by":0,"name":"Danning Wang","email":"","orcid":"","institution":"University of Bonn","correspondingAuthor":false,"prefix":"","firstName":"Danning","middleName":"","lastName":"Wang","suffix":""},{"id":451264745,"identity":"fb43c5d6-0b1a-409c-8c42-2eb1f98ea74f","order_by":1,"name":"Xiaoming He","email":"","orcid":"","institution":"University of Bonn","correspondingAuthor":false,"prefix":"","firstName":"Xiaoming","middleName":"","lastName":"He","suffix":""},{"id":451264746,"identity":"a651e995-ecda-4297-a154-396d1c5d5ef5","order_by":2,"name":"Kiran Suresh","email":"","orcid":"","institution":"University of Bonn","correspondingAuthor":false,"prefix":"","firstName":"Kiran","middleName":"","lastName":"Suresh","suffix":""},{"id":451264747,"identity":"c82f346f-9a81-4bb3-8b60-dd5e37d07b4c","order_by":3,"name":"Lukas Schreiber","email":"","orcid":"","institution":"University of Bonn","correspondingAuthor":false,"prefix":"","firstName":"Lukas","middleName":"","lastName":"Schreiber","suffix":""},{"id":451264748,"identity":"25582050-f5f1-46e7-a674-a64ca546b690","order_by":4,"name":"Frank Hochholdinger","email":"","orcid":"","institution":"University of 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Bonn","correspondingAuthor":true,"prefix":"","firstName":"Peng","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-03-31 18:38:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6347267/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6347267/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81973005,"identity":"a16d7fe6-3654-4c1e-8fc7-2e14a0355622","added_by":"auto","created_at":"2025-05-05 13:05:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":439306,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeterotic group design and sampling scheme.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003ePhylogenetic relationships of the seven maize inbred lines included in this study, with a representative image of root hairs provided to highlight genotypic differences. \u003cstrong\u003eB.\u003c/strong\u003e Representative longitudinal view of the maize seedling primary root, with labeled regions: MZ (meristematic zone), EZ (elongation zone), and DZ (differentiation zone). \u003cstrong\u003eC.\u003c/strong\u003e Overview of cross combinations involving common maternal genotypes (B73, A554, W64A, and Mo17), resulting in diverse F1-hybrid progenies.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/b496dedf27481ef9fb195306.png"},{"id":81973007,"identity":"93142ded-a836-436a-a492-692bb217117d","added_by":"auto","created_at":"2025-05-05 13:05:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":324501,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSoil microbiome influences the expression of seedling root growth heterosis.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Primary root length, primary root weight (\u003cstrong\u003eB\u003c/strong\u003e) and standardized rhizosheath (\u003cstrong\u003eC\u003c/strong\u003e) of inbred lines and F1 hybrids, with and without soil microbiome (via sterilization). \u003cstrong\u003eD.\u003c/strong\u003e Impact of soil sterilization on the expression of mid-parent heterosis for various root phenotypic traits. \u003cstrong\u003eE.\u003c/strong\u003e Mid-parent heterosis of root traits in sterilized water and sterilized, filtered (0.2 µm pore size) soil suspension.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/4456ca1c8b5cd738141adb15.png"},{"id":81973008,"identity":"b940812e-4722-44bb-a804-577092810a6a","added_by":"auto","created_at":"2025-05-05 13:05:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1979255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInterkingdom multi-omics reveals causal plant–microbe interactions between inbred lines and hybrids in the rhizosphere. \u003c/strong\u003ePCA showing the transcriptomic differences (\u003cstrong\u003eA\u003c/strong\u003e) in the cortex tissue and dissimilarity of bacterial (\u003cstrong\u003eB\u003c/strong\u003e) and fungal (\u003cstrong\u003eC\u003c/strong\u003e) diversity between inbred lines and hybrids in the rhizosphere. Triangles represent the inbred lines, whereas dots denote the hybrid variants.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/1ab1a4d6edecc2db0ad87557.png"},{"id":81973010,"identity":"d46fc9f6-90a9-41c8-b9dd-f7a6c39b18b1","added_by":"auto","created_at":"2025-05-05 13:05:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":394598,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA Redundancy Analysis (RDA) plot summarizing the variation in gene expression and microbiome across sample properties in relation to root traits. \u003c/strong\u003eIn the plot, yellow dots represent samples from hybrids, while blue dots denote samples from inbred lines. PRL refers to primary root length. The plot visualizes the associations between gene expression, microbial composition, and root trait variation across different genotypes.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/20d6ecf407c997537b14cb79.png"},{"id":81974179,"identity":"5f56f65a-03e7-44bb-ae7a-c9d73449d4b3","added_by":"auto","created_at":"2025-05-05 13:21:13","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":381654,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially abundant microbial genera between maize inbred lines and hybrids. \u003c/strong\u003eThe size of the circles indicates the abundance of microbes. Microbial taxa with fold changes greater than 2 or less than -2 and an FDR of less than 0.05 were considered as upregulated or downregulated. ns, not significant.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/48768b78290d128236c72d6b.png"},{"id":81973013,"identity":"42f769fb-ec0b-4297-ab3a-9183199a890a","added_by":"auto","created_at":"2025-05-05 13:05:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1582465,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMulti-omics network analysis linking phenotypic heterosis-related transcriptome, bacteria, and fungi under control soil (A), low nitrogen soil (B), and low phosphorus soil (C). \u003c/strong\u003eThe genes with annotated functions are highlighted with red dots.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/b940ccad3d7021545f7f5148.png"},{"id":81973012,"identity":"5290bad6-3503-4d31-8d09-51257d99962c","added_by":"auto","created_at":"2025-05-05 13:05:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":313422,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic model illustrating\u003c/strong\u003e \u003cstrong\u003ethe hypothesis that heterosis is mediated through complex, multi-layered interactions between the plant’s root development, root suberization, gene expression and the rhizosphere microbial community. \u003c/strong\u003eThis integrated model offers a holistic view of how heterosis influences root development and physiology through the interplay of genetic, morphological, physiological, and microbial factors. It suggests that heterosis in roots is not simply a function of genetic traits but involves complex interactions between the plant's root system and its associated rhizosphere environment. This model opens avenues for future breeding strategies and microbial management to further enhance heterosis in crops, particularly in hybrid systems. The colored shadow represents the rhizosphere influenced by the host root, while the colored triangles indicate the changes - either an increase or decrease - in various root traits and activities from inbred lines to hybrid.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/63a76ed0628a7e53c8049f20.png"},{"id":96963648,"identity":"138c7aa4-9dd4-4524-9e0c-0a5cd5db8531","added_by":"auto","created_at":"2025-11-28 05:53:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6725943,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/b533e520-fa0a-49c3-92d6-dbd6831606f0.pdf"},{"id":81973925,"identity":"18a17e89-7b17-4f47-9d06-e2b5eb6efc48","added_by":"auto","created_at":"2025-05-05 13:13:12","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1949859,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryfileWangetal.docx","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/0b4df6f5d1a53bab9e70adef.docx"},{"id":81973034,"identity":"78edf739-17d1-4861-bfbb-95b3dda5e393","added_by":"auto","created_at":"2025-05-05 13:05:14","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18193678,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableWangetal.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6347267/v1/c4fbe09a202ad68c9e93529e.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Host growth and defense pathways drive microbiome-mediated maize heterosis","fulltext":[{"header":"Background","content":"\u003cp\u003eHeterosis, also known as hybrid vigor, refers to the phenomenon where hybrid plants exhibit superior growth and enhanced stress resistance compared to their parental lines \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This effect is most prominently observed in maize (\u003cem\u003eZea mays\u003c/em\u003e L.) when two genetically distinct inbred lines are crossed. Hybrid varieties typically show significant improvements in both productivity and stress resilience \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Heterosis is especially evident during seedling development, where root heterosis plays a critical role for superior plant performance \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Early root development is a key determinant of a plant\u0026rsquo;s ability to establish itself in the soil, access nutrition, and water, and ultimately influencing its overall yield potential \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This early vigor sets the stage for faster establishment and configuration of root architecture and distribution during the crucial early stages of growth. It is well known that root heterosis depends on the genetic diversity of the parental donors \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and transcriptional regulation of specific genes \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e in maize.\u003c/p\u003e \u003cp\u003eThe rhizosphere, the region of soil directly influenced by plant roots, enhance the plant\u0026rsquo;s interaction with its surrounding environment, contributing to better nutrient acquisition and microbial functions \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The rhizosphere-associated microbiome, composed of bacteria, fungi and other microorganisms, plays a pivotal role in shaping these mutual interactions with host plants. It has been shown that the microbial communities in the rhizosphere can directly or indirectly impact plant traits such as stress tolerance, growth rate, and nutrient utilization, creating a sophisticated feedback loop between plant and microbial communities \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Recently, it was observed that the microbial communities surrounding the roots of maize hybrids were distinct from, and not a mix of, the inbred parental lines \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Soil sterilization and inoculation experiments further highlight that the microbiome plays a divergent effect on the performance of inbred lines and hybrids, leading to changes of heterosis upon environmental changes \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Early studies have shown that hybrid plants tend to harbor a higher microbial diversity and a higher proportion of beneficial microbes in maize \u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This difference in microbial composition is likely driven by genetic factors, as hybrid plants release different root exudates - organic compounds that can attract specific microbes - compared to inbred plants \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Thus, the interactions between plants and their microbial communities in the rhizosphere might be an underappreciated but significant factor in the expression of heterosis \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. There has been increasing interest in the relationship between the microbiome and heterosis, but it remains largely unknown how gene expression in plants regulates the microbiome underlying hybrid vigor \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Moreover, it remains unclear how and to which degree the genetics and gene regulation of the host plant affects the microbiome that influences the manifestation of heterosis.\u003c/p\u003e \u003cp\u003eIn this context, understanding how plant genes regulate the interaction between the rhizosphere microbiome and heterosis-driven root traits could open new avenues for enhancing crop performance and resilience. In our study, we conducted a comprehensive investigation into the mechanistic regulation of the root transcriptome and rhizosphere microbiome at the maize seedling stage. This involved the use of cortex tissue-specific root RNA sequencing (RNA-seq), and bacterial 16S rRNA and fungal ITS gene sequencing. Our detailed analysis of root traits - encompassing morphology, anatomy, and physiology -revealed that root suberization might play a critical role in exudation-driven microbiome assembly. This suggests a complex interplay between plant genes and the rhizosphere microbiome, influencing root-microbial interactions. Moreover, the identification of several root cortex-specific maize genes underscores the potential niche for microbiome-driven regulation of heterosis expression. Overall, our findings highlight the synergistic relationship between plant genetics and microbiome dynamics as a key factor in driving hybrid vigor in the rhizosphere. These novel insights offer promising prospects for crop breeding, where optimizing plant-microbe interactions could prove as essential as genetic selection in the development of high-performing hybrid varieties.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHeterosis patterns in root phenotypic traits mirror genetic relationships\u003c/h2\u003e \u003cp\u003eTo investigate how heterosis patterns in root development vary across different kingdoms along a single primary root in maize, we examined various root and rhizosphere compartments in the primary root (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), including the cortex tissues of the root differentiation zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), along with the closely attached rhizospheres from the primary roots. We selected seven genetically distinct genotypes covering the major heterosis groups including stiff-stalk (SS) varieties such as B73 and H84, and non-stiff stalk (NSS) varieties such Mo17, A554, W64A, Oh43 and H99 \u003csup\u003e6\u003c/sup\u003e. The maternal and paternal inbred lines were crossed to generate F1 hybrid offspring (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). This study was conducted with four biological replicates under three different nutrient conditions: control soil (with sufficient nutrients), low nitrogen (LN) soil, and low phosphorus (LP) soil \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. We determined phenotypic traits such as primary root length and weight, anatomical traits, and suberin content. For each phenotypic trait, we calculated the mid-parent heterosis (MPH) and better parent heterosis (BPH) percentages between the hybrid and its parents. Primary root length was measured and heterosis patterns were assessed across all three soil conditions. Notably, 60% of the hybrids showed either MPH or BPH across all conditions (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The heterosis percentages were consistently positive, indicating that hybrids always displayed longer primary roots than the parents (average BPH percentage: CK: 0.16, LN: 0.14, LP: 0.13) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Heterosis patterns for the Mo17xA554, A554xH84, B73xOh43, and B73xH99 hybrids were conserved across the three soil conditions, while no unique heterosis pattern emerged for each individual soil condition (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Interestingly, the Mo17xW64A, B73xH84, and W64AxH99 hybrids did not show any heterosis across the conditions. W64A exhibited the longest primary roots among all parents (Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), which could explain why hybrids involving this genotype either showed MPH or no heterosis. The absence of heterosis for B73xH84 likely arose from the fact that both parents belong to the same lineage (i.e. SS), resulting in no heterosis across any soil condition. In contrast, the B73xH99 hybrid consistently displayed the longest primary roots across all treatments, suggesting that greater genetic distance between the parent lines (B73 and H99) led to higher heterosis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffect of microbiome on heterosis is manifested in early root development\u003c/h3\u003e\n\u003cp\u003eTo investigate whether the soil microbiome is linked to the manifestation of heterosis, we sterilized the soil by autoclaving and assessed both biomass production and mid-parent heterosis under conditions with and without microbes. We selected a diverse range of maize inbred lines and their F1-hybrids, evaluating them at 3, 7, and 21 days after transfer (DAT) to the soil. In general, we observed that F1-hybrids were more sensitive to the soil microbiome in terms of primary root length (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), primary root weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), and normalized rhizosheath weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) compared to the parental inbred lines, even at the very early seedling stage (3 DAT). Importantly, we observed a significant increase in mid-parent heterosis for maize rhizosheath when the soil microbiome was removed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), though no similar effects were seen for other traits, such as primary root length or primary root weight. Importantly, this modified heterotic effect may be independent of secondary factors related to soil sterilization, such as changes in soil nutrition, structure, and organic matter (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). Consequently, the enhanced rhizosheath could improve the plant's interaction with its surrounding environment, potentially contributing to the manifestation of heterosis during early root development.\u003c/p\u003e\n\u003ch3\u003eMaize heterosis is more evident in the transcriptome than in microbial community composition\u003c/h3\u003e\n\u003cp\u003eTo explore the potential interactions of host expressed genes and rhizosphere microbiome, we performed transcriptome analyses through RNA sequencing for the root cortex tissue, bacterial microbiome analysis via 16S (V4) rRNA gene sequencing, and fungal analysis via ITS1 sequencing for the rhizosphere compartments (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). First, we performed a Principal Component Analysis (PCA) on the root transcriptome across all genotypes and treatments. The samples were primarily clustered by genotype, followed by soil treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This clustering pattern aligns with the results from the PERMANOVA statistical test, where the genotype factor explained 34% of the variance in the transcriptome, while the treatment factor accounted for only 8% of the variance (Genotype: R\u0026sup2; = 0.34, P\u0026thinsp;=\u0026thinsp;5e-4; Treatment: R\u0026sup2; = 0.084, P\u0026thinsp;=\u0026thinsp;5e-4). All hybrids clustered between their parental inbred lines, reflecting the combined effects of the gene expression from both parents. Interestingly, B73 appeared distant from other inbred lines but was closer to H84, which corresponds to their phylogenetic relationship of the \"stiff stalk\" and \"non-stiff stalk\" group. Next, to investigate whether hybrids differed from the inbred lines in terms of microbiome composition, we examined both bacterial (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) and fungal (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) alpha diversity via Shannon index and the overall community composition separately. Given that the soil treatment explained a larger proportion of variance in bacterial and fungal alpha diversity and community composition (bacteria: alpha diversity R\u0026sup2; = 0.18, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.46e-13; beta diversity R\u0026sup2; = 0.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5e-4; fungi: alpha diversity R\u0026sup2; = 0.12, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.42e-09; beta diversity R\u0026sup2; = 0.25, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5e-4) compared to genotype (bacteria: alpha diversity R\u0026sup2; = 0.049, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66; beta diversity R\u0026sup2; = 0.049, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97; fungi: alpha diversity R\u0026sup2; = 0.053, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65; beta diversity R\u0026sup2; = 0.058, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0035), we compared the microbial differences between hybrids and inbred lines under each soil condition.\u003c/p\u003e \u003cp\u003eFor alpha diversity, no significant differences were observed between hybrids and inbred lines for both bacteria and fungi (Figure S3). However, the PERMANOVA test showed that hybrids differed from inbred lines across all three soil conditions (bacteria: R\u0026sup2; = 0.0064, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; fungi: R\u0026sup2; = 0.0064, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5e-4). Additionally, we performed a Principal Coordinate Analysis (PCoA) separately for bacteria and fungi under each soil condition, after removing batch effects. Under each soil condition, bacterial samples were mainly separated by inbred lines and hybrids along the first principal component (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, S4), which contrasts with the transcriptome data where each hybrid was positioned between its maternal and paternal inbred lines. A similar pattern was observed for the fungal community composition (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, S5). Overall, these findings suggest that while the relationships between hybrids and their parents are more apparent in the host transcriptome, these differences are small but significant in the corresponding microbial community composition.\u003c/p\u003e\n\u003ch3\u003eImproved rhizosheath is associated with root development and cortex suberin components\u003c/h3\u003e\n\u003cp\u003eTo further explore the relationships between the size of rhizosheath and root anatomical and physiological traits in association with heterosis, we determined the root anatomy and root suberin content for all 22 inbred lines and hybrids under controlled soil conditions. Root anatomical traits were assessed in development zones 1 (25%), 2 (50%), and 3 (75%) of the primary root, with only a few hybrids showing heterosis in each zone (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Zone 1, the newest development zone, exhibited the lowest number of heterotic traits, while most of differentiation zone 3 displayed the highest number of heterotic traits. Specifically, the hybrid Mo17xH84 consistently showed heterosis across all zones, with the heterosis percentage decreasing from zone 3 to zone 1. For suberin traits, only a few hybrids exhibited heterosis for each suberin component (Table S3). Again, Mo17xH84 demonstrated a heterosis pattern across all suberin traits, except for aromatic suberin. Notably, suberin traits exhibited significantly negative association with manifestation of heterosis.\u003c/p\u003e \u003cp\u003eFor all phenotypic traits examined, the majority of heterosis observed was better parent heterosis (BPH) (Figure S6). While most suberin traits displayed negative heterosis percentages, heterosis for other traits, such as primary root length and anatomical traits, was predominantly positive (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This suggests that hybrids generally outperformed their parents in terms of root development and growth under controlled soil conditions, and that the genetic distance between parental lines plays a significant role in determining the magnitude and direction of heterosis. Thus, an enhanced rhizosheath, driven by improvements in root anatomy and cortex suberin content, might offer a significant potential for improving plant resilience, nutrient uptake, and stress tolerance.\u003c/p\u003e\n\u003ch3\u003eGrowth and defense pathways drive microbiome-mediated heterosis\u003c/h3\u003e\n\u003cp\u003eTo identify bacteria and genes associated with the heterosis of phenotypic traits, we calculated the mid-parent heterosis (MPH) for both bacteria and genes and correlated them with the MPH of phenotypic traits. Bacteria or genes with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were related to phenotypic heterosis. To minimize false positives, we only retained bacteria and genes that were heterotic in at least 6 hybrids (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and excluded phenotypic traits that were highly correlated with each other. We then calculated the correlation between heterosis-related bacterial abundance and gene expression to construct multi-omics networks linking heterosis across these domains. Under control soil conditions, a significant portion of phenotypic heterosis-related bacteria belonged to the Order Vicinamibacterales, while the top hubscore genes were primarily involved in cell cycle regulation or defense response (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). Notably, the cortex area Z2 showed the highest hubscore among all phenotypic traits, and the gene Zm00001eb199470 was strongly correlated with the MPH of both cortex area Z2 and w_OH. This gene is also linked with most of the other phenotypic heterosis-related ASVs and is involved in mitotic cell cycle phase transition (Table S11). For cortex area Z2, only one heterosis-related bacterium, bASV162 (family: Vicinamibacteraceae), showed a correlation with genes involved in auxin metabolism and lignin metabolism. Interestingly, bASV162 was also identified as a w_OH heterosis-related bacterium. For w_OH, five heterosis-related bacterial ASVs were identified, with associated gene functions in cell growth and auxin polar transport. Additionally, four bacterial ASVs were correlated with the MPH of primary root length, with the associated genes involved in alkaloid metabolic processes. We also performed the same correlation analysis for fungi and found that two fungal ASVs, fASV5 (\u003cem\u003eSarocladium zeae\u003c/em\u003e, a known endophyte against \u003cem\u003eFusarium\u003c/em\u003e) and fASV637 (Phylum Ascomycota), were related to the heterosis of primary root length and diacid traits, respectively (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, no significant correlations were observed between the phenotypic heterosis-related bacteria and fungi. The fungi-related genes also had roles in cell cycle transition and auxin transport. Ultimately, we constructed a comprehensive network showing all phenotypic heterosis-related genes, bacteria, and fungi (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eUnder low nitrogen soil conditions, we found no significant heterosis-related fungi for primary root length and few genes and bacteria associated with primary root length heterosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The bacterium \u003cem\u003eHaliangium\u003c/em\u003e was particularly important, linking phenotypic heterosis with host gene expression, such as Zm00001eb412510 (involved in immune response), Zm00001eb417450 (involved in lignin biosynthesis), and Zm00001eb406770 (involved in defense response) (Table S11). Under low phosphorus conditions, no significant correlations were found between fungi and the heterosis of primary root length, nor between bacteria and gene expression. As a result, the network under low phosphorus soil was simpler compared to the other two soil conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). The three primary root length heterosis-related bacteria were all from the genus \u003cem\u003eNocardioides\u003c/em\u003e and the family Vicinamibacteraceae. The genes associated with these bacteria had similar functions to those identified under other soil conditions, such as Zm00001eb223800 (involved in mitotic cell cycle) and Zm00001eb406770 (involved in defense response) (Table S11). Together, the network associations identified candidate genes involved in development and immunity-related pathways that are likely linked to microbial taxa influencing heterosis expression.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eHybrid vigor has been extensively utilized in agricultural practices, especially in the cereal maize, to enhance productivity and stress resistance. However, genetic factors alone do not fully explain the magnitude of heterosis observed. Other factors, such as environmental influences and the role of microbiome inhabiting in plant niches, have recently emerged as a significant factor influencing plant performance and heterosis \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Indeed, the microbiome not only helps in plant growth and stress tolerance but also plays a role in mediating interactions between the plant and its environment, ultimately influencing the plant's fitness and productivity \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In particular, the rhizosphere microbiome plays an indispensable role in nutrient cycling and acquisition \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and disease resistance and suppression \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Recent studies highlight the importance of the microbiome in contributing to heterosis by enhancing early seed germination \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, growth vigor \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and disease resistance \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In general, hybrid plants could potentially have a more diverse or more beneficial microbiome \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, which might explain the robustness and resilience. Different inbred lines have distinct composition of the rhizosphere microbiome, and hybridization could result in an optimal combination of microbial partners, which in turn can affect traits associated with heterosis \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Nevertheless, it remains unclear whether specific microbial taxa influence the expression of heterosis and, if so, which plant traits they might affect.\u003c/p\u003e \u003cp\u003eBy leveraging microbiome-based strategies, we developed diverse combinations of maize inbred lines and crossing triplets, and subjected to interkingdom interactions analyses by investigating root transcriptomes and rhizosphere microbiomes. For the first time, we constructed a plant gene expression network linked to a diverse array of microbial taxa, in relation to heterosis expression in maize (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, the functions of the candidate genes identified are associated with key pathways in cell development and defense responses. Notably, these genes exhibit a tissue-specific expression pattern, predominantly in the root cortex (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), where localized immune responses are activated to safeguard the plant's internal tissues \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Furthermore, the differentiation of the endodermis begins with the synthesis and deposition of a lignified Casparian strip, which forms a central ring around endodermal cells, playing a key role in mediating interactions with both pathogenic and beneficial microbes \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our findings underscore the potential function and role of endodermal cells in regulating heterosis expression, particularly in relation to interactions with the soil microbiome (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, further studies are needed to investigate the genetic differentiation of root cell-type-specific effects on heterosis expression in relation to the rhizosphere microbiome \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, it is crucial to understand how specific microbial taxa mediate the manifestation and extent of heterosis-driven root traits through single-cell and spatial transcriptomics approaches \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMicrobial community composition is influenced by the composition of root exudates \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This process, however, can be disrupted by the diffusion barriers created by endodermal cells, which result in distinct patterns of root exudation \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. While the specific compounds involved and their roles in recruiting particular bacterial taxa remain unclear, future research should focus on determining whether alterations in endodermal barriers affect microbial community assembly. Additionally, it will be essential to understand how changes in endodermal differentiation and exudate composition influence the colonization of beneficial or pathogenic microbes, particularly in the context of heterosis. Surprisingly, we identified a novel bacterial taxon, \u003cem\u003eNocardioides\u003c/em\u003e, associated with mitotic cell cycle and defense response genes, while also linking it to root length heterosis in maize (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). As a plant growth-promoting rhizobacterium (PGPR), \u003cem\u003eNocardioides\u003c/em\u003e can form beneficial symbiotic or commensal relationships with plant roots. Through these interactions, \u003cem\u003eNocardioides\u003c/em\u003e may influence root morphology, enhancing the root\u0026rsquo;s ability to explore the soil for nutrients and water. This is achieved by the production of growth-promoting substances, such as indole acetic acid (IAA), an auxin that modulates root development. This finding highlights the significant potential of exploring the genetic underpinnings of the beneficial microbiome, which drives enhanced growth and productivity through the concept of heterosis.\u003c/p\u003e \u003cp\u003eThe discovery of a link between heterosis and the microbiome opens new avenues for maize breeding based on the ability to foster beneficial microbial communities and root traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Moreover, specific microbial taxa or consortia could be applied in marginal or stressed environments to enhance hybrid performance and stress tolerance. While the association between maize heterosis and the microbiome is promising, several challenges remain to be resolved. The rhizosphere microbiome is highly dynamic and context-dependent, making it difficult to apply specific microbial taxa responsible for heterosis. Considering the environmental heterogeneity (e.g. soil type, climate), future studies should explore the functional conservation of specific taxa across different locations. Furthermore, accurate profiling of the microbiome and understanding the genetic mechanisms behind these interactions require high-resolution and functional genomics to identify keystone microbial taxa in maximizing heterosis in crops. Overall, while genetic factors undoubtedly contribute to hybrid vigor, the microbiome also plays a pivotal role in influencing maize performance. Our study underscores the importance of gaining a deeper understanding of these microbial communities, as harnessing their potential could lead to innovative breeding strategies aimed at improving maize yield, stress resilience, and disease resistance. As our knowledge of plant-microbe interactions expands, we anticipate the emergence of more sustainable and productive maize cultivation practices, with microbiome-assisted breeding becoming a key component of future agricultural advancements.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the crucial role of the microbiome in influencing the expression of heterosis in maize, with implications for both root development and plant growth. In particular, the identification of specific microbial taxa such as \u003cem\u003eNocardioides\u003c/em\u003e linked to root length heterosis opens exciting new possibilities for enhancing hybrid performance through microbiome-based strategies. The interplay between plant genetics and the microbiome, particularly in the rhizosphere, might offer novel avenues for optimizing maize breeding, improving yields, and increasing stress tolerance.\u003c/p\u003e "},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMaize Material, Soil Preparation, and Experimental Design\u003c/h2\u003e \u003cp\u003eThis study utilized the genetically distinct maize inbred lines - A554, B73, H84, H99, Mo17, Oh43, and W64A - along with their F1-hybrids \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The soil used was sourced from a long-term (over 100 years) fertilizer field experiment located in Dikopshof (50˚48\u0026prime;21\u0026prime;\u0026prime;N, 6˚59\u0026prime;9\u0026prime;\u0026prime;E). Three distinct soil types were collected: the control soil (CK) with full nutrient fertilization, low nitrogen soil (LN) lacking nitrogen, and low phosphorus soil (LP) lacking phosphorus. Detailed soil nutrient information is provided by \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The freshly dug soil was air-dried and sieved through a 1 mm mesh before use. The pot experiment was conducted in a complete randomized design, using pots measuring 7 cm \u0026times; 7 cm \u0026times; 19 cm. The experimental setup included seven maize genotypes and three different nutrient treatments, with at least four biological replicates per treatment. Additionally, empty pots filled with soil were used as \"bulk soil\" controls. All pots within each tray were randomized using a true random generator (Excel function \u0026ldquo;RAND\u0026rdquo;), and the trays were reshuffled weekly in the growth chamber without regard to pot labels. Soil water content was carefully maintained by weighing the pots every two days to account for water loss. To simulate early root development, plants were grown for one week, focusing on primary root formation with minimal lateral root growth. During this period, only sterilized water was applied to prevent potential contamination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and cortex tissue separation\u003c/h2\u003e \u003cp\u003eThe root and rhizosphere samples were harvested from 1-week-old maize plants of all genotypes grown under different nutrient treatment conditions. Specifically, the entire root system was carefully taken out of each pot and gently shaken to remove any loosely attached soil. To ensure precise comparison among genotypes, we focused on the primary root, which is present in all genotypes. Large soil particles were carefully removed from the primary roots to avoid contamination. Only the root segments with tightly attached soil were placed into 15 ml Falcon tubes (Sarstedt), immediately frozen in liquid nitrogen, and stored at -80\u0026deg;C prior to rhizosphere soil extraction. The rhizosphere samples were defined and extracted into PowerBead tubes (MP Biomedical) as described by \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Root samples were collected from separate plants, and the attached soil was first washed with tap water, then rinsed three times with sterilized water. The roots were dried before being placed into PowerBead tubes. Additionally, the stele and cortex tissues from the differentiation zone of the primary root were carefully peeled by hand, as outlined by \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Bulk soil samples were collected from unplanted pots and used as controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePhenotypic Measurement of Seedling Root Traits\u003c/h2\u003e \u003cp\u003eTo assess root phenotypic traits, the length of the primary root was measured manually. After gently washing off any loosely attached soil, the weight of the primary root was recorded. The size of the rhizosheath was determined by subtracting the total root weight from the weight of the root with its attached rhizosphere, and the resulting value was normalized based on the primary root length.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of Seedling Root Anatomy\u003c/h2\u003e \u003cp\u003eThe attached soil on the primary root was gently washed off, and root segments from three distinct developmental zones were selected for anatomical trait measurement. The primary roots were divided into three zones based on the development of apoplastic barriers, such as Casparian bands and suberin lamellae. The zones were defined as follows: (1) zone 1: 2 cm from the root tip; (2) zone 3: 2 cm from the shoot-borne roots; and (3) zone 2: located at 50% of the total primary root length. Root fragments (5 mm) were then embedded in 8% agarose containing 0.5% gelatin, and transverse sections (200 \u0026micro;m) were prepared using a vibratome (Leica VT1200S, Nussloch, Germany). The sections were mounted in distilled water and immediately examined under bright-field illumination using an Axio-Imager epifluorescence microscope (Carl Zeiss, Germany). The number of metaxylem vessels was counted, and the diameter and total area of the cortex, stele, and endodermis were measured using ImageJ software (version 1.40, NIH, Bethesda, MD, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDetermination of Root Suberization in the Primary Root Cortex\u003c/h2\u003e \u003cp\u003eTo explore the relationship between root physiology and root heterosis, we specifically measured root suberization in the primary root cortex of all maize inbred lines and hybrids. The cortex tissue samples were preserved in 70% ethanol. For data normalization, the length, diameter, and anatomical characteristics of the dissected root cortex were recorded. Each biological replicate consisted of three independent primary roots from three separate seedlings. The root cortex samples were enzymatically digested and depolymerized, following the protocol described by Baales et al. 2021\u003csup\u003e38\u003c/sup\u003e. After depolymerization with BF\u003csub\u003e3\u003c/sub\u003e/MeOH, the samples were extracted with chloroform and spiked with dotriacontane (20 \u0026micro;g in 100 \u0026micro;l solution; Fulka, Sigma-Aldrich, USA) as an internal standard. Suberin contents were quantified using Gas Chromatography with Flame Ionization Detection (GC-FID, HP 5890 Series H, Agilent), while individual suberin monomers were identified using Gas Chromatography-Mass Spectrometry (GC-MS, HP 5971 quadrupole mass selective detector, Agilent) using an in-house-created library. A 1 \u0026micro;l aliquot of each sample was analyzed on 30 m DB-1 GC columns (Agilent, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eRNA Sequencing and Bacterial 16S rRNA, Fungal ITS1 Gene Sequencing\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from the primary root cortex tissue samples using the RNeasy Plus Universal Mini Kit (Qiagen). Complementary DNA (cDNA) libraries for RNA sequencing (RNA-seq) were prepared using the MGIEasy RNA Library Construction Kit. For DNA extraction, rhizosphere samples were collected and processed immediately following the FastDNA\u0026trade; SPIN Kit for Soil protocol (MP Biomedical). The quality and quantity of both RNA and DNA were assessed using Agilent RNA or DNA Chips (Agilent Technologies). For bacterial 16S rRNA gene sequencing, the V4 region was amplified using universal primers F515 (5\u0026prime; GTGCCAGCMGCCGCGGTAA 3\u0026prime;) and R806 (5\u0026prime; GGACTACHVGGGTWTCTAAT 3\u0026prime;). For fungal amplicon sequencing, the fungal ITS1 gene was amplified using the primer pair F (5\u0026prime; CTTGGTCATTTAGAGGAAGTAA 3\u0026prime;) and R (5\u0026prime; GCTGCGTTCTTCATCGATGC 3\u0026prime;). PCR amplification was performed using Phusion High-Fidelity PCR Master Mix (New England Biolabs) following the manufacturer's guidelines. Only PCR products with the brightest bands in the 400\u0026ndash;450 bp range were selected for library preparation. PCR products were mixed in equal amounts and purified using the Qiagen Gel Extraction Kit. Sequencing libraries were prepared using the NEBNext Ultra DNA Library Prep Kit for Illumina, incorporating sequence indices as per the manufacturer's protocol. Library quality was evaluated using a Qubit 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2100 system. Finally, the qualified libraries were sequenced on an Illumina MiSeq platform using 250-bp paired-end reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome Data Processing and Statistical Analysis\u003c/h2\u003e \u003cp\u003eThe processing and trimming of raw RNA-seq reads were performed as previously described \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Briefly, low-quality sequences and low complexity polyA tails were removed. The reference genome index was constructed, and sequences were mapped to the maize reference genome (v.5; available at: Zea_mays.Zm-B73-REFERENCE-NAM-5.0) using HISAT2 (v2.1.0) \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Default parameters and all standard commands were applied in HISAT2. Downstream analyses were performed in R (v4.2.2) \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. BAM files generated by HISAT2 were input into the \u0026lsquo;featureCounts\u0026rsquo; function \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e from the Rsubread (v2.12.3) package to generate a gene expression table, using the maize reference annotation (v.5; available at: Zea_mays.Zm-B73-REFERENCE-NAM-5.0.53.chr.gtf). Chimeric reads and reads mapped to multiple positions were discarded. Only genes with a minimum of five mapped reads in at least four samples were considered expressed and included in subsequent analyses. Prior to statistical analysis, data were normalized by library size using the DESeq2 (v1.38.3) package \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e in R. Principal component analysis (PCA) was conducted using the \u0026lsquo;prcomp\u0026rsquo; function in base R (v4.2.2). To evaluate the marginal effects of nutrient treatment conditions and genotype on the transcriptome, a permutation-based PERMANOVA test was applied to the Euclidean distance matrix between transcriptomic samples using the \u0026lsquo;adonis2\u0026rsquo; function in the vegan (v2.6.4) R package \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. All plots were generated using the ggplot2 (v3.4.2) R package \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBacterial 16S rRNA and Fungal ITS1 Gene Sequence Processing and Data Analysis\u003c/h2\u003e \u003cp\u003eRaw sequencing reads were processed using a stepwise pipeline. Briefly, paired-end 16S rRNA amplicon sequencing reads were assigned to samples based on their unique barcodes and truncated by removing the barcode and primer sequences. Paired-end reads were merged using FLASH (v1.2.7) \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e, and the spliced sequences were designated as raw tags. Sequence analysis was performed with QIIME 2 (v2020.6) \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The raw sequence data were demultiplexed and quality-filtered using the q2-demux plugin, followed by denoising with DADA2 \u003csup\u003e48\u003c/sup\u003e (via q2-dada2). Sequences were truncated at position 250, and each unique sequence was assigned to a distinct Amplicon Sequence Variant (ASV). Taxonomy was assigned to ASVs using the q2-feature-classifier and the classify-sklearn naive Bayes taxonomy classifier, referencing the SSU rRNA SILVA 99% amplicon sequence variant reference sequences (v138) \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e at each taxonomic rank (kingdom, phylum, class, order, family, genus, species). Mitochondrial- and chloroplast-assigned ASVs were removed.\u003c/p\u003e \u003cp\u003eITS1 amplicon data were processed similarly to the 16S amplicon data, except that the UNITE 99% ASV reference sequences (v10.05.2021) \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e were used for taxonomy annotation. All downstream statistical analyses were conducted in R (v4.2.2) (R Core Team, 2022). For α-diversity analysis, the Shannon index was calculated using rarefied ASV tables with 10,000 reads. Abundant ASVs that represented\u0026thinsp;\u0026ge;\u0026thinsp;0.05% relative abundance in \u0026ge;\u0026thinsp;5% of samples were retained for downstream analysis, and no samples were excluded. Bray\u0026ndash;Curtis distances between samples were calculated using ASV tables normalized with the varianceStabilizingTransformation function from the DESeq2 (v1.38.3) package \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e in R. If not otherwise specified, all data analyses were performed using the normalized ASV table. Principal Coordinate Analysis (PCoA) was conducted using the 'ordinate' function in the phyloseq (v1.42.0) R package \u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. To examine the marginal effects of nutrient treatment conditions and genotype on microbial community composition, a permutation-based PERMANOVA test was performed using the Bray\u0026ndash;Curtis distance matrix with the 'adonis2' function in the vegan (v2.6.4) R package \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. All plots were generated using the ggplot2 (v3.4.2) R package \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Gene Expression and Functional Characterization\u003c/h2\u003e \u003cp\u003eDifferential gene expression between hybrids and inbred lines was analyzed using the \u0026lsquo;DESeq\u0026rsquo; function in the DESeq2 R package (v1.38.3). Differentially expressed genes (DEGs) for each comparison were identified by controlling the False Discovery Rate (FDR)-adjusted p-values of Wald tests at 0.05 and setting a fold change threshold of \u0026gt;\u0026thinsp;2.\u003c/p\u003e \u003cp\u003eTo functionally characterize the DEGs, we classified expression patterns according to Gene Ontology (GO) terms using g:Profiler \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. The GO annotation system categorizes gene products based on four structured vocabularies: biological processes, cellular components, molecular functions, and KEGG pathways, offering a species-independent overview. We then performed Gene Set Enrichment Analysis (GSEA) to identify significantly overrepresented functional categories within the DEG data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of Host Transcriptome, Microbial Community, and Phenotypic Traits\u003c/h2\u003e \u003cp\u003eNetwork-based analysis is a powerful and biologically interpretable tool for exploring associations among variables, such as the relationships between microbial communities, gene expression, and phenotypic traits \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Weighted Correlation Network Analysis (WGCNA) is a data-driven method that clusters genes into modules based on weighted correlations between gene transcripts \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. To identify keystone microbes that significantly correlate with heterosis level of phenotypic traits and the host transcriptome, we performed WGCNA on both microbial ASVs and host root-expressed genes using R. The analysis involved the following steps: (1) Clustering gene/ASV co-expression modules across different genotypes under each of the three soil nutrient conditions. (2) Associating module eigengenes with heterosis level of phenotypic traits and selecting gene modules that show high expression in hybrids but low expression in inbred lines. (3) Correlating selected gene modules with ASV modules to identify microbial ASVs associated with specific gene expression patterns. (4) Selecting genes/ASVs with high module membership values that significantly correlate with heterosis level of phenotypic traits. The detailed construction of integrative networks is described in \u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eSoil Microbiome Effect on Maize Heterosis\u003c/h2\u003e \u003cp\u003eTo understand if soil microbiome will affect the manifestation of heterosis, maize plants were grown in pots filled with sterilized soil with either sterilized water or soil suspension solution or filtered (0.2 \u0026micro;m pore size) soil suspension under controlled environmental conditions. The root phenotypes including primary root length, primary root weight and standardized rhizosheath were determined after 7-day cultivation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll raw plant RNA-seq data and bacterial 16S sequencing data reported in this study have been deposited in the Sequence Read Archive (SRA) under accession number PRJNA1243758 (http://www.ncbi.nlm.nih.gov/sra). RNA-seq reads were mapped to the maize reference genome version 5 (Zea mays Zm-B73-REFERENCE-NAM-5.0) available at ftp://ftp.ensemblgenomes.org/pub/plants/current/fasta/zea_mays/dna/Zea_mays.Zm-B73-REFERENCE-NAM-5.0.dna.toplevel.fa.gz, and gene annotations were derived from reference gene models version 5 at ftp://ftp.ensemblgenomes.org/pub/plants/current/gtf/zea_mays/Zea_mays.Zm-B73-REFERENCE-NAM-5.0.53.chr.gtf.gz. Gene Ontology (GO) term annotations were performed using g:Profiler (https://biit.cs.ut.ee/gprofiler/gost).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Selina Siemens and Alexa Brox for soil and root DNA extractions at the University of Bonn, Germany.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Deutsche Forschungsgemeinschaft (DFG) through grants YU272/4-1 (514003603) and the Emmy Noether Programme 444755415 to P.Y., as well as the German Excellence Strategy \u0026ndash; EXC 2070 \u0026ndash; grant 390732324 to P.Y., the DFG Priority Program (SPP2089) \u0026lsquo;Rhizosphere Spatiotemporal Organisation \u0026ndash; A Key to Rhizosphere Functions\u0026rsquo; grant 403671039 to P.Y and F.H. and the DFG grant HO 2249/18-1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP.Y. conceived and designed research; X.H. and P.Y. performed the root tissue separation and rhizosphere sampling; D.W. performed all RNA-seq and microbiome data analysis; K.S. and L.S. performed root suberin analysis; D.W. and P.Y. wrote the manuscript, with input from X.C. and F.H. and all other authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBirchler JA, Yao H, Chudalayandi S, Vaiman D, Veitia RA, Heterosis. Plant Cell. 2010;22:2105\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen ZJ. Genomic and epigenetic insights into the molecular bases of heterosis. Nat Rev Genet. 2013;14:471\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoecker N, Keller B, Piepho HP, Hochholdinger F. 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BMC Bioinformatics 9, (2008).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Heterosis, maize, microbiome, rhizosphere, root, transcriptome","lastPublishedDoi":"10.21203/rs.3.rs-6347267/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6347267/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHybrid vigor, commonly harnessed in maize breeding to boost productivity and stress resistance, is largely attributed to genetic factors. However, recent studies suggest that environmental influences, particularly the plant microbiome, may play a pivotal role in mediating heterosis expression. This study investigates the impact of the rhizosphere microbiome on maize heterosis by exploring interkingdom interactions between plant transcriptomes and microbial communities. We identify a key link between microbial taxa and plant traits associated with heterosis, with a particular focus on root length, growth vigor and rhizoshealth. Through a combination of microbiome profiling, gene expression analysis, and functional assays, we reveal that hybrid plants may harbor a more beneficial and diverse microbiome, which could enhance traits like root development and stress tolerance. Our findings suggest that the plant microbiome, particularly through specific taxa, plays a correlative role in the manifestation of heterosis, offering new opportunities for optimizing maize breeding strategies. The study underscores the importance of the microbiome in hybrid vigor and suggests that future research into microbiome-assisted breeding could lead to more sustainable and productive maize cultivation, particularly in marginal or stressed environments.\u003c/p\u003e","manuscriptTitle":"Host growth and defense pathways drive microbiome-mediated maize heterosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-05 13:05:08","doi":"10.21203/rs.3.rs-6347267/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96cf1c55-6d16-4577-8f9b-429d15f5f35d","owner":[],"postedDate":"May 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-28T05:53:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-05 13:05:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6347267","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6347267","identity":"rs-6347267","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}