Competition between rice and weeds is often accompanied by allelopathy, particularly prominent during the seedling stage of plant growth, with profound implications for plant competition and establishment (Li et al., 2021). To investigate the interaction between rice (Oryza sativa L.) and Leptochloa chinensis, we generated a system consist of a cylindrical barrier composed of 0.45 μm nylon mesh positioned at the center of each pot ( Figures 1A, B ). The mesh was used: 1) to prevent the penetration of roots but to allow chemical and bacterial interactions; 2) to function as a barrier obstructing mycorrhizal linkages (Kong et al., 2018). Through co-culturing rice Nipponbare and L. chinensis and comparing them with their respective mono-cultures, we observed an obvious reduction in root growth of rice (31%) and L. chinensis (28%) ( Figures 1A, C ).

To determine the presence of root-mediated chemical signal communication that results in mutual root growth inhibition between rice and L. chinensis, we performed a widely used Liquid Chromatography Tandem Mass Spectrometry (LC-MS/MS) based metabolomics. Principal component analysis (PCA) of the metabolomic profiles indicated that the metabolites presented distinct variations at different conditions and high reproducibility among replicates ( Figures 2A, B ). A comprehensive analysis identified a total of 131 DEMs in rice and 143 DEMs in L. chinensis ( Table S1 ). Subsequently, a correlation analysis was conducted to investigate the relationships among the identified DEMs ( Figures 2C, D ). A heatmap with hierarchical clustering analysis of proportional content for all DEMs is shown in Figures 2E, F . Volcano plots show all differential metabolites for mono-cultured rice v.s. co-cultured rice (50 upregulated and 81 downregulated) and mono-cultured L. chinensis v.s. co-cultured L. chinensis (45 upregulated and 98 downregulated) ( Figures 3A, B ; Table S1 ). The accumulation of the majority of differential metabolites was lower in monoculture conditions than in co-culture environments for rice and L. chinensis. Compared to their respective monoculture conditions, obvious changes in the metabolic profiles of both rice and L. chinensis following their co-culture were detected, indicating a robust metabolic modification.

Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the interaction between rice and L. chinensis influences multiple biological pathways ( Figures 3C, D ), such as the biosynthesis of isoquinoline alkaloid, phenylpropanoid and flavonoid. These identified pathways are intricately associated with allelopathy (Whittaker and Feeny, 1971), and the enrichment of these pathways in both rice and L. chinensis implies the adoption of highly similar coping strategies by both species in response to such interactions. The metabolites released by L. chinensis in response to allelopathy exhibited a higher degree of specific enrichment compared to rice in metabolite category phenylalanine metabolism. These findings suggest a convergence of molecular mechanisms underlying their respective responses, which provides a potential explanation for the observed inhibition of root length in co-cultures of rice and L. chinensis.

In order to validate the findings from the metabolomic analysis, we conducted transcriptome analysis to investigate the underlying genetic basis of the interaction between rice and L. chinensis. Root samples from four distinct treatments [rice mono-culture, co-culture samples (rice and L. chinensis) and L. chinensis mono-culture] were subjected to RNA-sequencing. Through a comparative analysis of gene expression between mono-cultured rice and co-cultured rice, we identified a total of 1,948 differentially expressed genes (DEGs, 1,026 upregulated and 922 downregulated) in rice ( Figure 4A ). We observed 2,598 differentially expressed genes in L. chinensis, with 1,734 genes showing significant up-regulation and 864 genes demonstrating significant down-regulation after co-cultured with rice ( Figure 5A ; Table S2 ). These results suggest that the interaction between rice and L. chinensis make substantial changes in the expression patterns of genes.

Gene Ontology (GO) enrichment analysis showed that the DEGs in rice were primarily associated with biological processes such as oxidation-reduction process, phenylpropanoid biosynthesis, and secondary metabolic process. These genes were also enriched in molecular functions, such as iron ion binding, antioxidant activity, and oxidoreductase activity and enriched in cell components such extracellular region and apoplast ( Figure 4B ). Similarly, the DEGs in L. chinensis were associated with biological processes such as oxidation-reduction process, toxin metabolic process and secondary metabolic process. These DEGs were also enriched in molecular functions, such as heme binding, antioxidant activity, and oxidoreductase activity and enriched in cell components such as extracellular region and membrane ( Figure 5B ). Additional, KEGG analysis showed that interaction between rice and L. chinensis affect pathways in rice, including phenylpropanoid biosynthesis, flavonoid biosynthesis, and glutathione metabolism ( Figure 4C ). In L. chinensis, the pathways affected by this interaction also exhibit striking similarities to those observed in rice, including phenylpropanoid biosynthesis, flavonoid biosynthesis, and cysteine and methionine metabolism ( Figure 5C ). This parallelism suggests a conserved response mechanism across these two species. Results of the metabolomic and transcriptomic analyses together indicated that the interaction between rice and L. chinensis is likely attributed to allelopathy. The observed similarities in the affected pathways between L. chinensis and rice align with the parallel patterns identified in their metabolomic profiles. This congruence further supports the notion that L. chinensis and rice share common metabolic responses and employ similar strategies in response to their interaction.

To gain a more comprehensive understanding of the distribution of differential metabolites and differential genes within the biosynthesis pathways of phenylpropanoid and flavonoid, we performed an integrated analysis by combining transcriptomic and metabolomic data. This approach allowed us to map the expression patterns of these two pathways and identify key regulatory nodes that contribute to the allelopathic interaction between rice and L. chinensis.

Concerning phenylpropanoid biosynthesis, the accumulation of p-coumaric acid and coniferyl-aldehyde was higher in co-cultured rice than in mono-cultured rice, while the accumulation of metabolite tyrosine lower. The DEGs associated with the phenylpropanoid metabolism pathway in rice was predominantly up-regulated in the co-culture. In L. chinensis, major metabolites such as p-coumaric acid, tyrosine, and, phenylalanine were remarkably elevated in the co-culture. Similar to rice, DEGs associated with the phenylpropanoid metabolism pathway in L. chinensis was mainly up-regulated in the co-culture ( Figure 6A ). However, at the step from coniferyl-alcohol to guaiacyl lignin in this pathway, gene expression demonstrated a down-regulation trend in L. chinensis, which differed from the up-regulation observed in rice. These differences in gene expression patterns suggest that rice and L. chinensis respond differently to the allelopathic signals.

Concerning flavonoid biosynthesis, the observation of up-regulation of metabolite naringenin in both rice and L. chinensis in co-cultures compared to mono-cultures suggests a common response to the allelopathic interaction between these two plant species. This shared up-regulation of naringenin indicates that it may play a crucial role in mediating the allelopathic effects during their co-culture. Furthermore, the finding of highly similar differential gene expression patterns related to flavonoid metabolism in both rice and L. chinensis reinforces the notion of a conserved response to the allelopathic signals ( Figure 6B ). These results suggested that the flavonoid metabolism pathway is conserved in the two species and may be a critical component of their allelopathic response.

Rice Nipponbare and L. chinensis seeds were immersed in 0.3% gibberellin solution for 30 min. After rinsing thoroughly with distilled water, they were germinated in Petri dishes for 72 h. Germinating seedlings were grown under long-day conditions at 26-28°C.

The experiment for rice–neighbor interactions were carried out in plastic pots (11 cm diameter × 12 cm height) that contained a central cylinder (7.5 cm diameter, 12 cm height) where a barrier covered with 0.45 μm nylon mesh (prevented penetration of both roots and common mycorrhizal hyphae but allowed chemical and bacterial interactions) (Kong et al., 2018). Rice and L. chinensis at 8:8 proportions were sown simultaneously in the pots. Monocultures of rice or L. chinensis served as the controls. The experiments were conducted in a completely randomized design with three replicates for each treatment or control. All pots from the experiments described above were placed in a greenhouse with 20–30°C night and daytime temperatures and 65–90% relative humidity, watered daily and their positions randomized once a week. Seedlings were harvested after 3 weeks for subsequent metabolome and transcriptome analyses. To be better compare the metabolite and gene expression, we have employed a specific approach. In our co-culture experiment, we compare the outer rice plants exclusively to the outer rice plants in the mono-culture experiment. Similarly, the inner L. chinensis plants in the co-culture experiment are compared solely to the inner L. chinensis plants in the mono-culture L. chinensis experiments. A set of experimental pots was prepared using 800 g of soil sourced from the top layer (0-10 cm) of a rice field. This soil collection method ensured representation of the surface soil characteristics and facilitated the subsequent analyses conducted in the study.

Metabolite extraction and profiling was performed as previously described (De Vos et al., 2007; Chen et al., 2013). The freeze-dried samples of root tissue were crushed with a mixer mill (Retsch, Germany) for 1 min at 60 Hz. 100 mg powder of each sample was transferred to 5 mL eppendorf tube, and extracted with 3000 μL methanol/water mixture (v:v=3:1). The samples were rotated for 30s, added with 2 small steel balls (MISUMI, China), ground at 35 Hz for 4 min, and ultrasonic in ice bath for 15 min. Then the samples were followed by overnighting shaking at 4°C. All the samples were centrifuged at 12000 rpm for 15 min at 4°C. 1800 μL of supernatant was transferred to a fresh 5 mL eppendorf tube and nitrogen blow dride. Here, the dried samples were reconstituted in 900 μL of 50% methanol for 15 min in ice-water bath. Then, all the samples were centrifuged at 12000 rpm for 15 min at 4°C. The resulting supernatants were through the 0.22 μm filter membrane. The resulting supernatants were diluted 10 times with methanol/water mixture (v:v=3:1) and vortexed for 30 s and transferred to 2 mL glass vials. The quality control (QC) sample was prepared by mixing of an equal aliquot of the supernatants from all of the samples. Then stored at -80°C until the UHPLC-MS/MS analysis.

The UHPLC separation utilized a Waters ACQUITY UPLC HSS T3 column (100 × 2.1 mm, 1.8 μm), following a previously described method (Yan et al., 2022). Mobile phase A consisted of 0.1% formic acid in water, while mobile phase B was acetonitrile. The gradient elution followed this protocol: 0–0.5 min, 98% A, 2% B; 0.5–10 min, 50% A, 50% B; 10–11 min, 5% A, 95% B; 11–13 min, 5% A, 95% B; 13–15 min, 98% A, 2% B. The column temperature was maintained at 40°C. The auto-sampler was set to 4°C, and injections of 2 μL were made.

An AB Sciex QTOF mass spectrometer was selected for its capability to perform MS/MS spectra acquisition using information-dependent acquisition (IDA) during LC/MS experiments. In this mode, the acquisition software (Analyst) continuously assessed full scan survey MS data and triggered MS/MS spectra acquisition based on predefined criteria. In each cycle, 5 precursor ions with intensities exceeding 100 were selected for fragmentation using collision energy. The acquired mass ranges were divided into 100–300, 300–450, 450–600, 600–750, and 750–1200 with 5 injections. ESI source conditions were set as follows: ion spray voltage at +5500/−4500 V, gas curtain at 35 psi, temperature at 600°C, Gases 1 and 2 at 60 psi for the ion source, and DP at ±100 V (Luo et al., 2016; Zha et al., 2018).

For assay development, an AB Sciex QTrap 6500 mass spectrometer was employed. The ion source parameters included ion spray voltage of +5000/−4500 V, curtain gas at 35 psi, temperature at 400°C, Gases 1 and 2 at 60 psi for the ion source, and DP at ±100 V.

The high resolution MS data were converted to the mzXML format using ProteoWizard, and processed by MAPS software (version 1.0) (A Mass Spectrometry Analysis and Processing Software developed by Biotree Biotech Co., Ltd. Shanghai, China). The preprocessing results generated a data matrix that consisted of the retention time (RT), mass-to-charge ratio (m/z) values, and peak intensity. In-house MS2 database was applied in metabolites identification. And the MRM data were processed with Skyline software.

For rice and L. chinensis, RNA-sequencing (RNA-seq) was performed using root tissues. The total RNA was extracted with the TransGen RNA extraction kit (TranGen, Beijing, China). RNA degradation and contamination were monitored on 1 % agarose gels. RNA purity was checked using the NanoPhotometer® spectrum photometer(IMPLEN, CA, USA), and RNA concentration was determined using the Qubit® RNA Assay Kit (Qubit^®2.0Flurometer, Life Technologies, CA, USA). RNA integrity was assessed using the RNA Nano 6000 Assay Kit (Agilent Bioanalyzer 2100 system, Agilent Technologies, USA). The best quality RNA samples were chosen for cDNA library preparation.

We used 3 µg of RNA per sample as input for RNA sample preparations. Sequencing libraries were created using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s instructions. Index-coded sample clustering was performed on a cBot Cluster Generation System using the TruSeq PE Cluster Kit v3-cBot-HS (Illumina). Following cluster generation, the Illumina NovaSeq 6000 was used for sequencing, producing 150 bp paired-end reads. Raw data containing over 10% poly-N and more than 50% low-quality reads (Q ≤ 20) were removed using Trimmomatic v0.33 (Bolger et al., 2014). The quality metrics Q20 and Q30, GC content, and sequence duplication level were calculated for the clean data. All subsequent analyses were based on this clean, high-quality data. Clean reads were then aligned against the reference genome using Hisat2 (Kim et al., 2015).

In order to quantify the gene expression, the count based method, FeatureCounts (Liao et al., 2014) was used. Next, the transcript counts were used for pairwise differential gene expression analysis using the edgeR package. A cut-off value of |log2 FC| > 1 and P-value < 0.05 were used to filter out the significant transcripts in each case. Gene Ontology (GO, http://www.geneontology.org/) enrichment analysis of the DEGs was implemented using gOseq (v. 1.22) (Young et al., 2010) using Wallenius’noncentral hypergeometric distribution, which can adjust for gene length bias in DEGs. Affected pathways were determined using Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www.kegg.jp) (Kanehisa et al., 2008).

The statistical significance of the populations was calculated with Tukey’s test, and significance was indicated with different letters above the error bars. The heatmap was drawn by the tBtools software. Figures were drawn by Origin 8.0 (OriginLab Corp., Northampton, MA, USA).