The study site is located in the Demonstration Park of Modern Agricultural Science and Technology of Qingdao Agricultural University, Qingdao, Shandong Province (36°26 ‘22”N, 120°04’ 43 “E, elevation 1 m). This area has a warm temperate monsoon climate, with an average annual temperature of 12.3°C and an average annual precipitation of about 680.5 mm, and an average annual air pressure of 1008.7 Mpa. The annual frost-free period is 251 days, and the annual sunshine duration is 2,708 h. The soil at the test site is loam.

Alfalfa root soil was collected at the test site and immediately brought to the laboratory to isolate the fungi. Fungal separation methods were based on the experimental instructions and separation method of Pinellia (Zhang et al., 2014). After grinding the collected rhizosphere soil, it was passed through a 60-mesh sieve for use. Samples (2 g) and 18 ml of sterile water were placed in a 50-ml conical bottle, and the samples were fully shaken to disperse evenly in the water. Then, 0.1-ml dispersion was absorbed on the PDA medium, according to the colony morphology on the plate, a different color was observed, and a single colony was selected for continuous purification. The purified strains were inoculated on a Petri dish, spread evenly, and cultured in an incubator at 30°C at a constant temperature. Colonies with growth rates and morphologies similar to the morph were screened, and DNA was cleaved from the lysate using the Cymbidium Direct Expansion Kit. ITS1F (5’-TTCCGTAGGTGAACCTGCGG-3’) and ITS4R (5’-CATATCAATAAGCGGAGGAA-3’) were used to amplify the 18SrDNA gene of the fungal genome. PCR amplification was performed in a 50-μl system, including 1 × T3 Mix 46 μl upper and downstream primers (10 μmol/L) (1 μl each) and DNA template (2 μl). The amplification procedure for the PCR reaction was as follows: pre-denaturation at 98°C for 3 min; 35 cycles of denaturation at 98°C for 10 s, annealed at 55°C for 15 s, extended at 72°C for 20 s; extension for 2 min at 72°C; and storage at 4°C. The PCR products were sequenced after they were gelled and recovered.

For the pot experiment, we collected soil from alfalfa fields in Jiaozhou City, Shandong Province, China. Before planting, 1 kg of sterilized air-dried soil was filled in a pot, the inoculation method of fungal Mortierella was referred to in a previous study (Li et al., 2018). Mortierella was expanded and cultured for 7 days at 28°C/180 revolutions in the dark. The amplified strains were filtered through sterile gauze and washed several times with sterile distilled water. Finally, pure strains were suspended in sterile deionized water for soil inoculation. There were two experimental groups: treatment and control. The treatment group was inoculated with 200 ml of suspension (~5 g of fresh mycelia) of the morycetes strains, and 200 ml of sterile deionized water was added to the control group, with four replicates for each treatment. The seeds were transplanted after germination, and the corresponding bacterial suspensions and sterile water were applied twice within a week of transplantation. Sterile water was applied regularly. All pots were cultured in the same climate chamber with 16 h of light (28 ± 2°C) and 8 h of darkness (20 ± 2°C). Relative humidity was maintained at 60–80%.

The chlorophyll content (SPAD) of the second and third true leaves was determined using a portable chlorophyll analyzer, plant height was determined with a ruler, and fresh weight was measured using a balance. The stems and leaves of Digitaria sanguinalis were immediately frozen in liquid nitrogen and stored at −80°C for the determination of enzyme activity and plant hormones. Enzyme activity was determined using an enzyme activity kit provided by Jiangsu Coming Biological Company.

Fresh plant samples were harvested, immediately frozen in liquid nitrogen, ground into powder (30 Hz, 1 min), and stored at −80°C until needed. Fifty milligrams of plant sample were weighed into a 2-ml plastic microtube, frozen in liquid nitrogen, and dissolved in 1 ml of methanol/water/formic acid (15:4:1, V/V/V). Ten microliters of mixed internal standard solution (100 ng/ml) were added to the extract as an internal standard (IS) for quantification. The mixture was vortexed for 10 min, centrifuged for 5 min (12,000 r/min, and 4°C), transferred to clean plastic microtubes, evaporated to dryness, dissolved in 100 μl 80% methanol (v/v), and filtered through a 0.22-μm membrane filter for further LC–MS/MS analysis (Cai et al., 2014; Floková et al., 2014; Li Y. et al., 2016).

The sample extracts were analyzed using UPLC-ESI-MS/MS (Mortierellacn/). The analytical conditions were as follows: LC column, Waters ACQUITY UPLC HSS T3 C18 (100 mm × 2.1 mm i.d., 1.8 μm); solvent system, water with 0.04% acetic acid (A), acetonitrile with 0.04% acetic acid (B); gradient program, started at 5% B (0–1 min), increased to 95% B (1–8 min), 95% B (8–9 min), finally ramped back to 5% B (9.1–12 min); flow rate, 0.35 ml/min; temperature, 40°C; injection volume: 2 μl (Niu et al., 2014; Cui et al., 2015; Xiao et al., 2018).

Linear ion trap (LIT) and triple quadrupole (QQQ) scans were acquired on a triple quadrupole-linear ion trap mass spectrometer (QTRAP), QTRAP® 6,500+ LC–MS/MS System, equipped with an ESI Turbo Ion-Spray interface, operating in both positive and negative ion mode and controlled by Analyst 1.6.3 software (Sciex). The ESI source operation parameters were as follows: ion source, ESI+/−; source temperature, 550°C; ion spray voltage (ISV), 5,500 V (positive), −4,500 V (negative); and curtain gas (CUR), 35 psi. Phytohormones were analyzed using scheduled multiple reaction monitoring (MRM). Data acquisition was performed using Analyst 1.6.3 software (Sciex). Multiquant 3.0.3 software (Sciex) was used to quantify all the metabolites. Mass spectrometry parameters, including the declustering potentials (DP) and collision energies (CE) for individual MRM transitions, were performed with further DP and CE optimization. A specific set of MRM transitions was monitored for each period according to the metabolites eluted within this period (Cui et al., 2015; Šimura et al., 2018).

Identified metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) compound database,¹ and annotated metabolites were then mapped to the KEGG pathway database.² Pathways with significantly regulated metabolites mapped were then fed into metabolite set enrichment analysis (MSEA), and hypergeometric test p-values determined their significance.

To identify genes associated with plant hormones, RNA concentration was measured using the Qubit® RNA Assay Kit in a Qubit®2.0 Fluorometer (Life Technologies, CA, USA). RNA degradation and contamination were monitored on 1% agarose gels, and RNA purity was checked using a NanoPhotometer® spectrophotometer (Implen, CA, USA). RNA integrity was assessed using the RNA Nano 6,000 Assay Kit on a Bioanalyzer 2,100 system (Agilent Technologies, CA, USA). Sequencing libraries were generated using the NEBNext® UltraTM RNALibrary Prep Kit for Illumina® (NEB, USA) following the manufacturer’s recommendations, and index codes were added to attribute sequences to each sample. Then, RNA sequencing (RNA-Seq) and data processing were performed using the Illumina HiSeq platform at Metware Biotechnology Co., Ltd. (Wuhan, China), according to Wang Y. et al. (2018).

Fastp was used to filter the original data, primarily to remove the reads with adapters. When the N content in any sequencing reads exceeded 10% of the base number of the reads, the paired reads were removed. When the number of low-quality (Q ≦ 20) bases contained in reads exceeded 50% of the bases of the reads, these paired reads were removed (Chen et al., 2018). All subsequent analyses were performed using clean reads. Transcriptome assembly was performed using Trinity software (Ning et al., 2017). For the study samples, DESeq2 was used to analyze the differential expression between the two groups (Love et al., 2014; Varet et al., 2016). The p-value was corrected using the Benjamini & Hochberg method. The corrected p-value and |log2foldchange| are used as the threshold for significant difference expression, and the pathway significant enrichment analysis was performed using the KEGG database.³

The differential hormones and differential genes in this experiment were simultaneously injected into the KEGG pathway map to better understand the relationship between genes and plant hormones. At the same time, Pearson correlation analysis was also performed on microorganisms with significant abundance changes and soil indicators to understand the relationship between microorganisms and soil indicators. Plant hormones and plant growth indicators were also jointly analyzed.

Student’s t-test was performed using SPSS software (version 19.0) to assess the effects of Mortierella inoculation on soil and plant basic properties, and the diversity of rhizosphere bacterial and fungal communities. Pearson correlation analysis was performed using SPSS software (version 19.0) to evaluate the relationship between the abundance of rhizosphere microbial modules, the relationship between key elements and soil properties, and the relationship between plant hormones and genes. Significance at the 0.001, 0.01, and 0.05 levels is represented by ***, ****, and *, respectively. Origin2021 was used to visualize the results of the Pearson correlation analysis.

In the GY medium, the Mortierella strain showed growth of white cotton flocculent mycelia accompanied by garlic flavor (Figure 1). BLAST was used to search for the homologous gene of MXBP304, and a phylogenetic tree was constructed in MEGA11.0, using the neighbor-joining method. The results showed that the isolated strain (MXBP304) had the closest phylogenetic relationship with Mortierella and could be identified as Mortiella (Figure 2). However, it is not closely related to other Mortierella strains and may represent a new Mortierella. According to systematic nomenclature, the isolated strain BP-04 of Mortierella was named MXBP304, and the strain was stored in the typical Chinese culture preservation center with the storage number CCTCC NO:M20211660.

Inoculation treatment significantly reduced the height of Digitaria sanguinalis, aboveground biomass, and chlorophyll content of Digitaria sanguinalis weeds, but had no significant effect on underground biomass (Figure 3A). At the same time, Mortierella had no significant effects on the biomass or plant height of the target forage alfalfa (Figure 3B).

Compared with the control group, after inoculation with Mortierella sp. MXBP304, in stems and leaves, the content of superoxide anion (O²⁻) increased significantly, the content of hydrogen peroxide (H₂O₂) decreased significantly, and the content of malondialdehyde (MDA) did not change significantly. The significant increase in superoxide anion content causes an imbalance in free radicals in plants and has a negative impact on their growth and development. At the same time, the activity of the key protective enzymes in the leaves was measured, and it was found that inoculation significantly decreased the activity of ascorbate peroxidase (APX), glutathione reductase (GR), peroxidase (POD), and superoxide dismutase (SOD) in the leaves. There was no significant effect on catalase (CAT) or glutathione peroxidase (GSH-PX) enzyme activities (Figure 4).

First, the content data of plant hormones were processed using unit variance scaling (UV), and the heat map was drawn using the R software ComplexHeatmap package. Hierarchical cluster analysis (HCA) was performed to determine the accumulation patterns of metabolites in the different samples (Figure 5). Then, different hormones with fold change ≥2 and fold change ≤0.5 were selected by means of fold change analysis. The results showed that the expression of 15 plant hormones in the stems and leaves of Digitaria sanguinalis was significantly upregulated or downregulated after inoculation (Figure 6). We then used the KEGG database to annotate the metabolites, and the annotation results of the metabolite KEGG with significant differences were classified according to the KEGG pathway types, as shown in the following figure. The results showed that these 15 hormones were enriched in 24 KEGG pathways, mainly in metabolic processes, genetic information processing processes, and environmental information processing processes, among which the most enriched pathways were in metabolic processes (Supplementary Figure S1).

RNA-Seq data showed that there were 8,365 differentially expressed genes (DEGs) between the inoculation and control groups, among which the expression of 3,919 genes was upregulated and that of 4,446 genes was downregulated (Figure 7). After the genes were annotated in the KEGG database, a KEGG enrichment analysis of the DEGs was performed. We selected 50 KEGG pathways with the lowest q value in the enrichment analysis results and drew a histogram of the enrichment items. As shown in the figure below, the results of KEGG’s first classification level showed that differential genes were mainly enriched in five categories: metabolic process, genetic information processing process, environmental information processing process, cellular process, and organic system (Supplementary Figure S2). Next, we selected the nine channels with the lowest q value among the 50 channels and drew the enrichment analysis chord diagram. The results showed that the nine pathways with the lowest q values were derived from metabolic processes (Figure 8). In other words, after inoculation with Mortierella sp. MXBP304, the differential genes had the greatest influence on plant metabolic processes. The results have been uploaded to the NCBI database, accession number: PRJNA1082474.

First, we conducted a pearson correlation analysis between these 15 hormones and the growth indicators of Digitaria sanguinalis. The results showed that there was no significant correlation between these 15 hormones and the chlorophyll and underground biomass of Digitaria sanguinalis. Among the 15 plant hormones, IAA-Phe and 2-Coumarate were negatively correlated with plant height and aboveground biomass, while the other 13 plant hormones were positively correlated with plant height and aboveground biomass (Figure 9). In other words, these 15 plant hormones were related to the plant height and biomass of Digitaria sanguinalis.

We then conducted a joint analysis of differential hormones and genes. First, we annotated Digitaria sanguinalis differential hormones and genes into the KEGG pathway at the same time and annotated 24 pathways. Among them, the top three pathways with the most annotated differential substances and differential genes were the metabolic, secondary metabolite synthesis, and plant hormone signal transduction pathways (Supplementary Figure S3). We conducted a comparative analysis of these 24 pathway maps and found that among these pathways, only genes directly related to the synthesis and metabolism of phenylalanine, tryptophan, and abscisic acid were differentially expressed after inoculation. Although DEGs and hormones were also noted in other pathways, they did not directly correspond. The synthesis and metabolic pathways of phenylalanine, tryptophan, and abscisic acid were reorganized and simplified (Figure 10).

According to the pathway map, the upregulation and downregulation in the expression of GOT1 and PTAL directly affected phenylalanine synthesis and metabolism after inoculation. The downregulation in the expression of trpB and DDC/TDC genes in the tryptophan synthesis pathway significantly affects the synthesis and metabolism of indole and tryptophan. Phenylalanine and tryptophan are synthetic precursors of auxin. The decrease in these two substances further affects auxin synthesis. In the carotenoid biosynthesis pathway, the expression of the genes, AAO3, and CYP707A, which are directly related to the synthesis and metabolism of abscisic acid, is upregulated. Moreover, the NCED gene is also a key gene in the synthesis of abscisic acid. In this pathway, the downregulation in the expression of NCED significantly reduced the synthesis of abscisic acid.

In recent years, Mortierella species found in agricultural soils have come from different rhizosphere soils in different regions, such as wheat soil (Smit et al., 1999), and potato soil (Hýsek and Brožová, 2001). In maize (Li et al., 2018), ginseng (Bian et al., 2020), and sorghum (Silva et al., 2017) rhizosphere soils. To the best of our knowledge, Mortierella has not been isolated from the rhizosphere soil of alfalfa, a newly discovered Mortierella species. In addition, many studies have found that the application of Sporoides has positive effects on soil activity and plant growth parameters (including crop species). For example, Sporoides in salt-invaded coastal soils in China can increase plant roots and aboveground dry weight. Furthermore, the activities of urease, neutral phosphatase, alkaline phosphatase, and catalase in the soil increased (Zhang et al., 2011). Mortierella isolated from maize rhizosphere soil can also increase the dry weight of plants, the concentrations of IAA and ABA in roots, and the concentrations of available nitrogen and phosphorus in soil to varying degrees (Li et al., 2020). Sporoides in the soil of saffron planted in India not only increased plant biomass but also chlorophyll and carotenoid content (Wani et al., 2017), but high-throughput sequencing results showed that Mortierella is the dominant pathogen of soybean varieties HN66 and HN48 (Guo et al., 2021). Therefore, the growth-promoting effects of Mortierella may be plant-specific. Our results showed that the newly discovered strain Mortierella sp. MXBP304 had a significant inhibitory effect on weed Digitaria sanguinalis biomass, plant height, and plant hormones ABA, IAA, and CK, which differed from the promoting effect of Mortierella reported in previous studies. Based on previous studies, we speculated that the reason for this inhibitory effect may be the difference between the species of Mortierella and the host plant.

During plant growth, plant hormone signaling pathways are activated when the environment stimulates plants, thus playing an important role in responding to various biological and abiotic stresses (Egamberdieva, 2013). In addition, transcription factors play an important role in plant resistance to various environmental stresses (Wang et al., 2006). The application of exogenous microbial inoculants can cause changes in the plant environment, which is equivalent to biological stress. As an important plant hormone, ABA plays an important role in plant responses to biological and abiotic stressors (Li and Liu, 2019). Some studies have reported that the association between fungi and plants regulates the expression of ABA-related genes (Porcel et al., 2012). NCED is a key enzyme in ABA synthesis (Lin et al., 2007). Some studies have shown that ABA biosynthesis genes ZEP and NCED increase ABA levels in seeds (Tuan et al., 2018). However, the ABA catabolic CYP707A gene family reduces ABA levels (Frey et al., 2012). In this study, the NCED and CYP707A gene families were detected in Digitaria sanguinalis plants after inoculation with the exogenous microbial inoculant strain MXBP304. Among them, the expression of CYP707A is upregulated, expression of NCED is downregulated, and ABA level decreased, which is consistent with the results of previous studies (Kushiro et al., 2004).

The phenylpropane metabolic pathway is an important metabolic pathway in plant growth, development, and metabolism, including the phenylalanine, coumaric acid, and flavonoid pathways (Muro-Villanueva et al., 2019). The phenylalanine pathway is the core pathway of phenylpropane metabolism, which starts with phenylalanine, passes through phenyl pyruvic acid, coumaric acid, and other intermediate products, and finally synthesizes a variety of bioactive secondary metabolites, such as flavonoids, tannins, anthocyanins, and vanillic acid (Craven-Bartle et al., 2013). In this study, the GOT1 and PTAL genes, which directly regulate the synthesis and metabolism of phenylalanine and L-phenylalanine in the phenylpropane metabolic pathway, underwent significant changes. Studies have shown that PTAL can catalyze the deamination of phenylalanine and tyrosine simultaneously in the phenylpropane pathway and participate in the synthesis of downstream secondary metabolites, such as lignin and flavonoids (Barros et al., 2016). GOT plays an important role in regulating nitrogen metabolism and has a significant impact on plant quality and yield (Reiner et al., 2010).

Tryptophan is an important amino acid in plants, a physiological prerequisite for plant growth hormone (IAA) synthesis, and the metabolic precursor of many secondary metabolites (Samani et al., 2019). Tryptophan synthase (LetrpB) is the key rate-limiting enzyme in the final step of tryptophan synthesis. Overexpression of tryptophan synthase can also increase the number of Arabidopsis side roots. Simultaneously, biomass increased to a certain extent (Sanjaya et al., 2008). In this study, the expression of trpB was downregulated, and the level of tryptophan was reduced, which is consistent with previous studies.

Tillering, an important component of plant type in the grass family, plays an important role in crop yield and quality. The regulatory effect of ABA on lateral branches is related to the promotion of lateral bud dormancy and the inhibition of lateral bud germination and growth. It has been reported that an increase in ABA levels during tillering of rice and wheat inhibits tillering and its subsequent growth and development (Liu et al., 2011; Dong et al., 2023). In other words, the increase in ABA levels inhibits plant growth, and the decrease in ABA levels in this experiment suggests that ABA is not the key hormone that inhibits the growth of Digitaria sanguinalis. It has been reported that phenylalanine can significantly improve the carbon and nitrogen content and photosynthetic production capacity of wheat seedlings (Nguyen et al., 2016). L-phenylalanine is a precursor of phenylpropanoid metabolism in plants and an aromatic amino acid, which has an impact on many physiological processes, including plant development and growth, signal transmission, metabolizing energy production, and plant resistance to stress (Hildebrandt et al., 2015; Teixeira et al., 2017). Some studies have also found that spraying L-phenylalanine on a sage leaf surface can increase the fresh and dry weight of sage and promote its growth (Samani et al., 2019). L-phenylalanine can also significantly improve the activity of nitrate reductase and catalase in maize and promote the absorption of nitrogen, phosphorus, potassium and zinc by maize (Chen et al., 2005). It can also significantly enhance the activity of superoxide dismutase, peroxidase, and phenylalanine ammoniase in thyme adventite buds (Li X. D. et al., 2016). Studies have shown that tryptophan can promote the growth of wheat, improve its antioxidant activity and photosynthetic characteristics (Qiang, 2019). Thus, phenylalanine and tryptophan have important effects on plant growth and development. In this study, regulating the differential gene expression of phenylalanine and tryptophan reduced the content of these two amino acids, so it is inferred that the decrease in the biomass of Digitaria sanguinalis is related to phenylalanine and tryptophan to a certain extent.