A Combination of Metabolic Biomarkers for Wheat Light Smut Screened Based on Multi-omics Research and Its Application

Abstract

This invention discloses a combination of metabolic biomarkers for wheat light brown smut screened based on multi-omics research and their applications. By detecting changes in the content of these biomarkers, this invention can distinguish between the latent infection stage and the symptomatic infection stage: the combination of cis-11,14,17-eicosatrienoic acid, rosin acid, and heptadecanal is more effective as a defense and early warning indicator for latent infection; the combination of vitamin K1 oxide and cis-11,14-eicosatrienoic acid, or the combination of ergosterol, vitamin K1 oxide, and cis-11,14,17-eicosatrienoic acid, can serve as an important indicator of interaction outbreaks, suggesting symptomatic infection. This invention solves the technical problems of existing technologies that cannot distinguish infection stages and assess disease severity, and has the advantages of high sensitivity, strong specificity, and short detection cycle, providing a scientific basis for early latent warning and graded control of wheat light brown smut.

Description

Technical Field

[0001] This invention belongs to the field of plant pathology and molecular detection technology, specifically relating to a combination of metabolic biomarkers for wheat light brown smut based on multi-omics research and their application. Background Technology

[0002] Wheat smut is a fungal disease caused by *Ustilago maydis*, which occurs in all wheat-growing regions worldwide and seriously threatens wheat yield and quality. The pathogen is mainly transmitted through seeds and soil, infecting wheat seedlings and then spreading systematically, eventually forming black powdery spore masses on the wheat ears, leading to reduced yield or even crop failure. Because its infection is latent and difficult to detect in its early stages, late-stage control measures are limited. Therefore, early monitoring and accurate warning of this disease are crucial for guiding field control and ensuring food security.

[0003] Currently, the detection of wheat smut mainly relies on morphological observation and molecular biology techniques, but both have significant limitations. Traditional morphological detection depends on visual or microscopic examination of wheat plants or seeds in the later stages of disease to look for typical smut spores or diseased ear characteristics. However, this method has several drawbacks: firstly, low sensitivity, as the pathogen biomass is extremely low during the incubation period and there are no visible symptoms, making early warning impossible; secondly, strong subjectivity, easily influenced by the observer's experience and environmental factors, making it difficult to quantitatively assess the severity of the disease. Molecular detection techniques, such as polymerase chain reaction (PCR), perform qualitative analysis by specifically amplifying the pathogen's DNA, significantly improving detection sensitivity. However, existing PCR technologies still face key technical bottlenecks when applied to wheat smut monitoring: firstly, long detection time and cumbersome process; secondly, susceptibility to interference, leading to false positive results and affecting detection accuracy.

[0004] In summary, existing technologies suffer from the contradiction of being sensitive but susceptible to interference, and visible but highly subjective. There is an urgent need in this field to develop a new monitoring technology capable of achieving latent early warning and precise identification. Summary of the Invention

[0005] Objective of the Invention: This invention aims to address the technical challenge of lacking precise identification and assessment methods for different infection stages (especially the latent period) of *Ustilago maydis* in wheat. To this end, this invention first provides a set of metabolic biomarkers for monitoring the infection status of *Ustilago maydis* in wheat.

[0006] Another objective of this invention is to provide a screening method for the combination of metabolic biomarkers, which identifies key biomarkers through a multi-omics joint analysis strategy.

[0007] Another objective of this invention is to provide a method for monitoring the infection status of wheat smut using the aforementioned combination of metabolic markers, thereby enabling early warning and staged diagnosis of the disease.

[0008] Another objective of this invention is to provide the application of the method in the latent early warning, accurate identification, and quantitative assessment of the severity of wheat smut.

[0009] The final objective of this invention is to provide a detection kit that, based on a multi-metabolic biomarker discrimination matrix, forms a combined screening of transcription and metabolism, and integrates a multi-indicator matrix for discrimination and precise monitoring and control, overcoming the shortcomings of existing technologies such as low sensitivity, inability to identify latent infections, and inability to assess disease progression.

[0010] Technical solution: To solve the above technical problems, the present invention provides a combination of metabolic biomarkers for wheat smut based on multi-omics research screening. The biomarker combination includes several of the following: cis-11,14,17-eicosatetrienoic acid, rosin acid, heptadecanal, ergosterol, 2,4-dihydroxy-2H-1,4-benzoxazine-3(4h)-one (DIBOA), cis-11,14-eicosatetrienoic acid, and vitamin K1 oxide.

[0011] The biomarker combination includes cis-11,14,17-eicosatetrienoic acid, rosin acid, and heptadecanal; or the biomarker combination includes vitamin K1 oxide and cis-11,14-eicosatedienoic acid; or the biomarker combination includes ergosterol, vitamin K1 oxide, and cis-11,14-eicosatedienoic acid.

[0012] The present invention also provides a method for screening the aforementioned combination of metabolic biomarkers, comprising the following steps:

[0013] (1) Normal healthy wheat, wheat with asymptomatic smut fungus and wheat with typical smut fungus infection symptoms were divided into a healthy control group, an asymptomatic infection group and a symptomatic infection group, respectively.

[0014] (2) Transcriptome sequencing was performed on the three groups of samples to screen differentially expressed genes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed;

[0015] (3) Perform non-targeted metabolomics detection on samples from the same batch to screen for differentially expressed metabolites;

[0016] (4) Combine the analysis of key pathways enriched by the transcriptome with differential metabolites of the metabolome to identify metabolites that simultaneously meet the dual conditions of key pathway node function and significant difference in content, and obtain the combination of metabolic markers.

[0017] The present invention also provides a method for monitoring the infection status of wheat smut using the aforementioned combination of metabolic markers, comprising the following steps:

[0018] (1) Sample pretreatment: Collect wheat grain samples to be tested, grind them and extract metabolites to obtain metabolite extract;

[0019] (2) Metabolite detection: The content of each component of the metabolic marker combination described above in the metabolite extract was detected by liquid chromatography-tandem mass spectrometry (LC-MS / MS);

[0020] (3) Infection status determination: The infection status of the sample is determined based on the changes in the content of the metabolic markers in the sample to be tested.

[0021] The criteria for determining the infection status in step (3) are as follows: if the content of cis-11,14,17-eicosatetrienoic acid and / or rosin acid and / or heptadecanal in the sample to be tested is significantly increased compared with the baseline value of the healthy control sample, it is determined to be the latent infection period; if the content of ergosterol and / or cis-11,14-eicosatetrienoic acid and / or vitamin K1 oxide in the sample to be tested is significantly higher than that in the healthy control sample, it is determined to be the symptomatic infection period.

[0022] In step (2), the liquid chromatography-tandem mass spectrometry detection is performed using multiple reaction monitoring mode for quantitative analysis.

[0023] The present invention also provides a detection kit for monitoring the infection status of wheat smut fungus, the kit containing reagents required for detecting the combination of metabolic markers.

[0024] The reagents include standards for cis-11,14,17-eicosatotrienoic acid, rosin acid, heptadecanal, ergosterol, cis-11,14-eicosatotrienoic acid, vitamin K1 oxide, metabolite extraction reagents, and normal healthy wheat samples.

[0025] The kit also contains reagents necessary for the detection.

[0026] The application of the metabolic biomarker combination, the screening method, and the detection kit described in this invention in the latent early warning, and / or accurate identification and / or quantitative assessment of the degree of infection of wheat light brown smut.

[0027] The principle of this invention is to screen combinations of metabolic biomarkers based on combined transcriptomic and metabolomic analysis, use LC-MS / MS to target and quantitatively detect changes in biomarker content, construct a multidimensional discriminant matrix, and achieve accurate staged diagnosis of the latent and symptomatic stages of wheat light brown smut.

[0028] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: Based on a combined transcriptomic and metabolomic analysis strategy, this invention has screened a specific biomarker combination that can be used for defense and early warning of wheat smut. This biomarker combination can accurately distinguish between the latent period and the symptomatic period, solving the bottleneck of existing technologies that can only qualitatively assess the severity of the disease but cannot stage it. This provides a scientific basis for formulating graded control strategies. By detecting changes in the content of the above biomarkers, this invention can distinguish between the latent infection period and the symptomatic infection period: the combination of cis-11,14,17-eicosatetrienoic acid, rosin acid, and heptadecanal can more effectively serve as a defense and early warning indicator to indicate latent infection. The combination of vitamin K1 oxide and cis-11,14-eicosatetrienoic acid, or the combination of ergosterol, vitamin K1 oxide, and cis-11,14,17-eicosatetrienoic acid, can serve as an important indicator of interaction outbreaks to indicate symptomatic infection. This invention solves the technical problems of existing technologies being unable to distinguish infection stages and assess disease severity. It has the advantages of high sensitivity, strong specificity, and short detection cycle, and can provide a scientific basis for early latent warning and graded control of wheat smut. Attached Figure Description

[0029] Figure 1 Phenotypic characteristics of the samples and morphological identification of *Ustilago maydis* spores; A, Phenotypic characteristics of the three groups of samples; B, Black powder exposed after the infected wheat grains were broken; C, Microscopic image of *Ustilago maydis* teliospores; D, Scanning electron micrograph of *Ustilago maydis* teliospores.

[0030] Figure 2 The following are the results of transcriptomics analysis. A and B are principal component analysis (PCA); C and D are volcano plots showing differentially expressed genes in different groups; E and F are hierarchical clustering heatmaps.

[0031] Figure 3 The following are the results of metabolomics analysis. A, Composition and proportion of identified metabolites; B, Principal component analysis (PCA) in negative ion mode; C, Principal component analysis (PCA) in positive ion mode; D, Hierarchical clustering analysis in negative ion mode; E, Hierarchical clustering heatmap in positive ion mode.

[0032] Figure 4 ROC curve analysis of the biomarkers rosin acid, cis-11,14,17-eicosatotrienoic acid, and the combined diagnostics of the two in the AG vs CG groups;

[0033] Figure 5 ROC curve analysis of rosin acid, heptadetrienal, and their combined diagnostic markers in the AG vs CG groups;

[0034] Figure 6ROC curve analysis of the biomarkers cis-11,14,17-eicostrienoic acid, heptadecanal, and the combined diagnosis of the two in the AG vs CG groups;

[0035] Figure 7 ROC curve analysis of the biomarkers rosin acid, cis-11,14,17-eicostrienoic acid, heptadecanal, and the combination of the three in the AG vs CG groups;

[0036] Figure 8 ROC curve analysis of vitamin K1 oxide, cis-11,14,17-eicosatotrienoic acid (CTA), and the combined diagnostic value of the two in the IG vs AG groups;

[0037] Figure 9 ROC curve analysis of DIBOA, ergosterol, vitamin K1 oxide and the combined diagnosis of the three markers in the IG vs AG groups;

[0038] Figure 10 ROC curve analysis of DIBOA, cis-11,14,17-eicosatotrienoic acid, ergosterol, and the combined diagnosis of the three markers in the IG vs AG groups;

[0039] Figure 11 ROC curve analysis of the biomarkers vitamin K1 oxide, cis-11,14,17-eicosatotrienoic acid, ergosterol, and the combined diagnosis of the three in the IG vs AG groups;

[0040] Figure 12 ROC curve analysis of DIBOA, vitamin K1 oxide, cis-11,14,17-eicosatotrienoic acid, and the combined diagnosis of the three markers in the IG vs AG groups;

[0041] Figure 13 ROC curve analysis of DIBOA, vitamin K1 oxide, cis-11,14,17-eicosatotrienoic acid, ergosterol, and the combined diagnosis of the four markers in the IG vs AG groups. Detailed Implementation

[0042] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0043] Example 1 Transcriptomics Analysis

[0044] 1. Experimental Materials and Grouping

[0045] The wheat variety tested in this invention is "Ningmai 13". The tested inoculum was teliospores of *Ustilago maydis*, collected from infected wheat ears in the field in Fenghuang Town, Zhangjiagang City, Jiangsu Province. Morphological and molecular biological identification confirmed it to be *Ustilago maydis*. Morphological identification results are shown below. Figure 1C and Figure 1 D. The experiment consisted of three groups, with six biological replicates in each group: CG group (healthy control group): normal wheat plants were used. At harvest, grains from 20 wheat ears were collected, mixed thoroughly, and then six samples (CG1-CG6) were randomly selected, with approximately 50 wheat grains per sample group; AG group (asymptomatic infection group): *Ustilago maydis* teliospores germinating to the secondary basidiospore stage were injected into the roots of wheat seedlings for artificial inoculation. The injection volume was 1 mL per plant, and the teliospore concentration was 10. 6 At wheat harvest, 20 asymptomatic wheat plants, confirmed to be infected by PCR, were collected and mixed thoroughly. Six groups of samples (AG1-AG6) were randomly selected, with approximately 50 wheat grains per group. The IG group (successfully infected group) was artificially inoculated by injecting teliospores of *Ustilago maydis* germinating to the secondary basidiospore stage into the roots of wheat seedlings. The injection volume was 1 mL per plant, and the teliospore concentration was 10⁻⁶. 6 10 wheat grains / mL were collected from 20 wheat plants exhibiting typical infection symptoms at harvest time. After thorough mixing, 6 groups of samples (IG1~IG6 groups) were randomly selected, with approximately 50 wheat grains in each group. The specific phenotypic characteristics of each group of samples are as follows: Figure 1 As shown in A in the diagram.

[0046] 2. Transcriptome sequencing analysis

[0047] 1) RNA extraction and transcriptome sequencing: Total RNA was extracted from wheat grains in the above experimental groups. RNA integrity and concentration were assessed using the Agilent RNA 6000 Nano assay kit on a Bioanalyzer 2100 system (Agilent Technologies, USA).

[0048] 2) Dual RNA Sequencing and Alignment Analysis: Cluster analysis of the indexed samples was performed on the cBot ClusterGeneration System using the TruSeq PE Cluster Kit v3-cBot-HS (Illumia, USA), strictly following the manufacturer's instructions. Sequencing was performed on the Illumina Novaseq platform, generating 150 bp paired-end reads. The clean reads were aligned to the wheat reference genome (downloaded from the genome database: http: / / plants.ensembl.org / Triticum_aestivum / Info / Index) and the *Triticum aestivum* reference genome (obtained by our team through whole-genome sequencing; the *Triticum aestivum* reference genome has been uploaded to the National Center for Biotechnology Information, see https: / / ngdc.cncb.ac.cn / gsa, accession number: CRA030493).

[0049] The quality control metrics for the sequencing data are summarized in Tables 1 and 2. In the infected group (IG), the overall alignment rate and unique alignment rate to the wheat genome were extremely low, below 0.08% and 0.07%, respectively; conversely, the overall alignment rate and unique alignment rate to the *Ustilago maydis* genome in the healthy control group (CG) were both below 0.07%. This result reveals the effectiveness of dual RNA sequencing: the IG sequencing data mainly originated from the pathogen, while the CG data mainly consisted of host data. Therefore, the differential expression analysis focused on comparisons between AG and CG, and between IG and AG. Overall, the quality control data indicate high sequencing read quality and excellent library construction, suitable for subsequent alignment and differential expression analysis.

[0050] Table 1. Sequencing data quality (aligned with wheat reference gene sequence)

[0051]

[0052] Note: Raw Data (G): Total number of bases in the raw data. Clean Data (G): Total number of bases in the filtered high-quality data. Total Alignment: Total reads mapped to the genome (count and percentage). Unique Alignment: Reads uniquely mapped to the reference genome (count and percentage). Q20 (%): Percentage of bases with a quality score ≥ Q20 after filtering. Q30 (%): Percentage of bases with a quality score ≥ Q30 after filtering. GC (%): GC content after filtering. CG1-CG6: Control samples. AG1-AG6: Asymptomatic samples; IG1-IG6: Infected samples.

[0053] Table 2 Sequencing data quality (aligned with reference gene sequence of *Ustilago maydis*).

[0054]

[0055] Note: Raw Data (G): Total number of bases in the raw data. Clean Data (G): Total number of bases in the filtered high-quality data. Total Alignment: Total reads mapped to the genome (count and percentage). Unique Alignment: Reads uniquely mapped to the reference genome (count and percentage). Q20 (%): Percentage of bases with a quality score ≥ Q20 after filtering. Q30 (%): Percentage of bases with a quality score ≥ Q30 after filtering. GC (%): GC content after filtering. CG1-CG6: Control samples. AG1-AG6: Asymptomatic samples; IG1-IG6: Infected samples.

[0056] 3) Principal component analysis and identification of differentially expressed genes: Principal component analysis results ( Figure 2 A in Figure 2 Figure B shows that there are significant differences in transcriptional profiles between the IG and AG groups, while the CG and AG groups are difficult to distinguish. The results of differentially expressed gene identification are shown in the volcano plot. Figure 2 C in Figure 2 (D in the original text). Statistical analysis revealed 1,486 differentially expressed genes between the AG and CG groups, with 348 upregulated and 1,138 downregulated; and 1,943 differentially expressed genes between the IG and AG groups, with 1,576 upregulated and 367 downregulated. Heatmap analysis ( Figure 2 E in Figure 2 The F) further visualized the changes in gene expression profiles: the comparison between the AG group and the CG group revealed transcriptional changes related to active defense against *Ustilago maydis* in wheat; while the comparison between the IG group and the AG group distinguished the expression characteristics of symptomatic infection and latent infection, revealing the molecular dynamics mechanism of host-pathogen interaction.

[0057] 4) KEGG pathway enrichment analysis: KEGG enrichment analysis based on the wheat genome revealed that differentially expressed genes in the AG and CG groups constitute a multi-layered defense network, comprising four core dimensions and five strategic modules: barrier construction (phenylpropanoid metabolism, N-glycan biosynthesis, cytoskeletal proteins), chemical counterattack (diterpenoid and alkaloid biosynthesis), signal transduction (oxidoreductases, glutathione metabolism), regulatory instructions (basal transcription factors, ubiquitin hydrolases), and controlled sacrifice (cysteine ​​peptidases). Conversely, comparative analysis of the IG and AG groups based on the pathogen genome highlighted three functional clusters: carbohydrate and energy metabolism (carbon metabolism, pyruvate metabolism, tricarboxylic acid cycle, and glyoxylate metabolism); cellular component biosynthesis (terpene skeleton, steroids, fatty acids, and purine metabolism); and virulence and signal transduction (MAPK signaling, ABC transporters, phagosomes, and endoplasmic reticulum protein processing).

[0058] Example 2: Non-targeted metabolomics analysis

[0059] 1. Experimental Materials and Grouping

[0060] Same experimental materials and groups as in Example 1.

[0061] 2. Non-targeted metabolomics analysis

[0062] 1) Metabolite Extraction: Samples from each experimental group were flash-frozen in liquid nitrogen and then ground into powder. 0.5 g of each powder was weighed and added to 1 mL of pre-cooled 80% methanol solution, vortexed, and resuspended. The mixture was incubated on ice for 5 minutes, then centrifuged at 15,000 g for 20 minutes at 4°C. An appropriate amount of the supernatant was taken and diluted with LC-MS grade water to a final methanol concentration of 53%. The sample was then transferred to a new centrifuge tube and centrifuged again at 15,000 g for 20 minutes at 4°C. The supernatant was used for UHPLC-MS / MS analysis. Quality control (QC) samples were prepared by mixing equal volumes of all experimental samples and were used to adjust the UHPLC-MS / MS system status and monitor instrument stability throughout the experiment.

[0063] 2) UHPLC-MS / MS Screening Analysis Conditions: The instrument platform used was a Vanquish UHPLC system (ThermoFisher) tandem Orbitrap series mass spectrometer (Q Exactive™ HF, HF-X, or Exploris™ 120). Chromatographic separation was performed using Hypersil Gold (100 × 2.1 mm, 1.9 μm), with a flow rate set at 0.2 mL / min and a total run time of 12 minutes. The mobile phase consisted of 0.1% formic acid aqueous solution (phase A) and methanol (phase B). The gradient elution program was as follows: 0–1.5 min, phase B maintained at 2%; 1.5–3 min, phase B increased to 85%; 3–10 min, phase B increased to 100%; 10–10.1 min, phase B decreased to 2%, followed by column equilibration. The sample injection volume was 10 μL. The column temperature was 40℃. The mass spectrometry acquisition mode was positive / negative ion switching scan. Main parameter settings: spray voltage 3.5 kV, capillary temperature 320°C, sheath flow rate 35 psi, auxiliary flow rate 10 L / min, auxiliary gas heater temperature 350°C.

[0064] 3) Comprehensive classification and analysis of metabolites: Peak alignment, peak selection, and quantitative analysis were performed using XCMS software. Metabolites were identified by comparison with the NovoMetDB database (mass deviation <10 ppm). A total of 2,209 metabolites were identified and classified into 16 categories (Table 3). See Table 3 and... Figure 3 As shown in Figure A, lipids and lipid-like molecules accounted for the highest proportion, reaching 35.04% (774 types); followed by organic heterocyclic compounds (354 types, 16.03%) and organic acids and their derivatives (340 types, 15.39%). In addition, benzene compounds (9.33%) and phenylpropanes and polyketides (9.14%) also had relatively high proportions. Organic oxygen-containing compounds and alkaloids and their derivatives accounted for 8.42% and 2.44%, respectively. This classification distribution reflects the complex characteristics of the sample's metabolic profile, which is dominated by lipid metabolism and secondary metabolism.

[0065] Table 3. List of Metabolite Types

[0066]

[0067] 4) Principal component analysis and cluster heatmap analysis of metabolomics data: such as Figure 3 B and Figure 4 As shown in Figure C, principal component analysis (PCA) revealed that under both ion modes, the metabolic profiles of the CG and AG groups showed no significant separation, while the IG group formed independent clusters, indicating a fundamental change in its metabolic state. The tight clustering of replicate samples within each group validated the reliability of the experimental results. Cluster heatmap analysis results ( Figure 3 D in Figure 3 The results (E) were consistent with PCA, further confirming the metabolic similarity between the CG and AG groups and the metabolic specificity of the IG group.

[0068] 5) Enrichment analysis of differentially metabolites identified under positive and negative ion modes: KEGG pathway enrichment analysis was performed on differentially metabolites identified under positive and negative ion modes. Results showed that the ABC transporter pathway was most significantly enriched, characterized by the accumulation of trehalose, taurine, L-ornithine, and D-ribose, suggesting key changes in osmotic regulation and stress signaling. Secondary metabolic and defense pathways were significantly affected: enrichment of ubiquinone and other terpenoid quinone biosynthesis and tryptophan metabolism indicated enhanced membrane protection and activation of hormone signaling (such as melatonin, IAA, and serotonin); enrichment of phenylpropanoid biosynthesis reflected phenolic compound-mediated chemical defense mechanisms. Primary metabolic reprogramming was manifested by the enrichment of the pentose phosphate pathway and amino acid biosynthesis, suggesting a shift in metabolic flux towards NADPH generation and stress response substrates (such as proline). Furthermore, changes in plant hormone signaling and unsaturated fatty acid biosynthesis highlighted the roles of immune regulation and membrane lipid modification. In summary, metabolic reprogramming involves defense systems, energy metabolism, and membrane stability.

[0069] Example 3: Screening of metabolic biomarkers using combined metabolomics and transcriptomics analysis

[0070] Based on the transcriptomics and metabolomics data obtained in Examples 1 and 2, this invention employs a multidimensional joint analysis strategy to deeply analyze the differential expression characteristics of wheat smut during the latent infection period (AG group) and the healthy control group (CG group), and screens metabolic biomarkers with early diagnostic value. The specific screening process is as follows:

[0071] 1. Setting screening criteria

[0072] To ensure the statistical significance and biological relevance of the screened biomarkers, the following screening thresholds are set:

[0073] (1) Difference factor: |Log2 FC| > 0.5;

[0074] (2) Statistical significance: P value < 0.05 (Student's t-test);

[0075] (3) Significance of pathway enrichment: P-value for KEGG pathway enrichment < 0.05;

[0076] (4) Consistency of expression trends: The expression trends of differential metabolites and key enzyme genes in their pathways are positively correlated.

[0077] 2. Screening and confirmation of metabolic biomarkers

[0078] (1) Screening of cis-11,14,17-eicostrienoic acid

[0079] Metabolomics data mining revealed a highly significant upregulation trend of cis-11,14,17-eicosatotrienoic acid in the AG vs. CG group, with a fold change as high as Log2 FC = 1.86 and a low P-value of 0.001. This metabolite showed the highest fold change and statistical significance among all differentially expressed metabolites, demonstrating optimal analytical performance as a core quantitative biomarker. Further pathway enrichment analysis indicated that this metabolite mainly belongs to the unsaturated fatty acid biosynthesis pathway. Conjoint analysis revealed that the accumulation of this metabolite is not an isolated event, but rather a holistic reprogramming of host cell membrane lipid metabolism. Its high-level expression as a precursor to lipid signaling molecules reflects the early lipid fingerprint characteristics of changes in cell membrane fluidity and stress adaptation during the latent infection period in wheat. Based on its outstanding statistical significance and biological significance, it was selected as a core early warning biomarker.

[0080] (2) Screening of rosin acid

[0081] In metabolomics screening, abietic acid was significantly upregulated in the AG group (Log2 FC = 0.52, P = 0.03). Unlike other metabolites with random fluctuations, combined transcriptomic and metabolomic analysis showed that this metabolite was enriched in the diterpenoid biosynthetic pathway (P = 0.036), and its upstream key enzyme genes (such as ent-cobabyl pyrophosphate synthase) were simultaneously and significantly upregulated in the transcriptomic data. This dual upregulation of gene and metabolite confirms that the accumulation of abietic acid is a result of transcriptional regulation actively initiated by wheat during the latent period. Therefore, it was selected as a key biomarker reflecting the host's active defense status.

[0082] (3) Screening of heptadetrienal

[0083] Metabolomics analysis revealed a significant accumulation of heptadecanal in the AG group (Log2 FC = 0.75, P = 0.01), and it was significantly enriched in the α-linolenic acid metabolic pathway. Combined analysis indicated a significant increase in the expression levels of key enzyme genes catalyzing this reaction step (such as phospholipase A1 / LCAT3). α-Linolenic acid metabolism is a core pathway in the biosynthesis of jasmonic acid (JA) in plants, and the increased content of heptadecanal, a key intermediate in this pathway, directly reflects the activation of the jasmonic acid signaling pathway. During the latent infection period, the pre-activation state of the JA signaling pathway is the molecular basis for the host to establish an immune system warning system. Therefore, heptadecanal was chosen as a mechanistic biomarker to reveal the pre-activation state of the host's immune signaling.

[0084] Example 4: Screening of metabolic biomarkers using combined metabolomics and transcriptomics analysis

[0085] Based on combined transcriptomics and metabolomics analysis, this invention employs a multidimensional joint screening strategy to identify a set of core metabolic biomarkers for determining the symptomatic infection status of wheat light brown smut, comparing the symptomatic infection group (IG) and the asymptomatic infection group (AG). The specific screening process is as follows:

[0086] 1. Setting screening criteria

[0087] To ensure the specificity and diagnostic efficacy of the biomarkers, the following screening thresholds are set:

[0088] (1) Variable fold: |Log2 FC| > 3.0, to ensure that metabolites change drastically during the symptomatic period;

[0089] (2) Statistical significance: P value < 0.01;

[0090] (3) Biological relevance: Differential metabolites need to be enriched in the KEGG pathway, which is closely related to pathogen proliferation, host defense or energy metabolism, and accompanied by synergistic expression changes of key enzyme genes.

[0091] 2. Screening and validation of core metabolic biomarkers

[0092] (1) Screening of ergosterol

[0093] Metabolomics data showed that ergosterol exhibited explosive accumulation in the IG group, with a fold change as high as Log2FC = 5.62 (P < 0.001), making it the most significantly upregulated metabolite. Conjoint analysis revealed that ergosterol specifically enriched in the steroid biosynthesis pathway. Unlike phytosterols, ergosterol is a major component of the fungal cell membrane. Transcriptomic data further confirmed that although the host plant's own sterol synthesis genes were not significantly upregulated, the expression levels of fungal-derived sterol synthesis pathway genes in the samples increased dramatically. This result indicates that the surge in ergosterol did not originate from host metabolic regulation but directly reflected the excessive proliferation of the pathogen within wheat grains. Its content level is positively correlated with pathogen biomass, making it the most specific pathogenic biomarker for distinguishing between symptomatic and latent infections.

[0094] (2) Screening of DIBOA

[0095] DIBOA was significantly upregulated in the IG group (Log2 FC = 4.52, P < 0.001), enriched in secondary metabolite biosynthesis pathways. Combined transcriptome analysis showed significant upregulation of key enzyme genes in its upstream synthetic pathway (such as 4CL, Log2 FC = 3.13). As an antifungal toxin specific to grasses, the sharp increase in DIBOA levels indicates that the host-pathogen interaction has entered an outbreak phase. During the symptomatic infection stage, the pathogen has breached the latent defense barrier, forcing the host to activate its ultimate chemical defense mechanism. Therefore, high DIBOA expression directly reflects the intensity of the host-pathogen interaction and is a key indicator for determining the outbreak phase of a disease.

[0096] (3) Screening of cis-11,14-eicosadienoic acid

[0097] Metabolomics screening revealed a significant accumulation of cis-11,14-eicosadienoic acid (cis-11,14-eicosadienoic acid) in the IG group (Log2 FC = 3.14, P < 0.01), belonging to the unsaturated fatty acid biosynthesis pathway. Conjoint analysis showed that the upregulation of this metabolite was accompanied by increased transcriptional levels of key enzyme genes (such as HADH and ACADM) in fatty acid degradation and metabolic pathways. This phenomenon reveals dramatic dynamic changes in the cell membrane system during the symptomatic infection period: pathogen invasion leads to damage to the host cell membrane, thereby inducing the host to initiate membrane lipid remodeling mechanisms to repair the damage or produce signaling molecules. This indicator reflects the substantial physical damage caused by the disease to the host at the cellular structural level and is an important marker of physiological dysfunction during the symptomatic period.

[0098] (4) Screening of vitamin K1 oxide

[0099] Vitamin K1 oxides were significantly enriched in the IG group (Log2 FC = 4.08, P < 0.001), mainly involved in the biosynthetic pathways of ubiquinone and other terpenoid quinones. Transcriptome data showed that oxidoreductase genes involved in quinone metabolism (such as wrbA, Log2 FC = 2.61) were significantly upregulated. As a cofactor in the electron transport chain, the accumulation of oxidized forms of vitamin K1 directly reflects the disruption of intracellular redox balance. During the symptomatic infection phase, pathogen infection triggers a surge in reactive oxygen species (ROS) in the host, leading to excessive oxidation of vitamin K1. High expression of this metabolite signifies the dysregulation of the host's energy metabolism system and the activation of antioxidant stress responses, serving as a physiological and biochemical indicator for assessing disease severity.

[0100] Example 5: LC-MS / MS determination method for seven metabolic biomarkers

[0101] 1. Experimental Materials and Reagents

[0102] (1) Standards: cis-11,14,17-eicosadienoic acid, rosin acid, heptadecanal, ergosterol, DIBOA, cis-11,14-eicosadienoic acid, and vitamin K1 oxide standards were purchased from Shanghai Anpu Experimental Technology Co., Ltd., and the purity of all standards was ≥98%.

[0103] (2) Quantitative method: The external standard method was used for quantitative analysis.

[0104] (3) Reagents: mass spectrometry grade acetonitrile, methanol, analytical grade formic acid.

[0105] (4) Preparation of mobile phase: Mobile phase A: 0.1% formic acid aqueous solution (measure 1 mL of formic acid and dissolve it in 1 L of ultrapure water); Mobile phase B: acetonitrile: methanol = 9:1 (v / v).

[0106] 2. Sample pretreatment

[0107] Samples were prepared for three groups: a healthy control group, an asymptomatic infection group, and a significantly infected group. 0.2 g of ground wheat grain powder was weighed into each group and placed in a 2 mL centrifuge tube. 1 mL of pre-chilled extraction solvent (acetonitrile:methanol = 80:20, v / v) was added. The mixture was vortexed for 1 min, ultrasonically extracted in an ice bath for 10 min, and then allowed to stand at -20 °C for 10 min to precipitate the protein. Subsequently, the mixture was centrifuged at 15,000 ×g for 10 min at 4 °C. The supernatant was filtered through a 0.22 μm filter membrane, and the filtrate was used for LC-MS / MS analysis.

[0108] 3. Liquid Chromatography Conditions

[0109] Chromatographic column: C18 reversed-phase column (2.1 × 100 mm, 1.7 μm); column temperature: 40 °C; injection volume: 5 μL; flow rate: 0.3 mL / min; mobile phase gradient elution program as follows: 0–1.0 min, B ratio 30%; 1.0–8.0 min, B ratio linearly increases from 30% to 95%; 8.0–12.0 min, B ratio maintains 95%; 12.0–13.0 min, B ratio decreases from 95% to 30%.

[0110] 4. Mass spectrometry conditions

[0111] Ion source: Electrospray ionization (ESI); Scanning mode: Multiple reaction monitoring (MRM); Ionization voltage: 3500 V (positive ion mode) / 3200 V (negative ion mode); Ion source temperature: 320 °C; Sheath flow rate: 35 Arb; Auxiliary flow rate: 10 Arb.

[0112] The optimized MRM parameter settings for the seven target metabolic biomarkers are shown in Table 4.

[0113] Table 4. MRM mass spectrometry parameters for seven metabolic biomarkers

[0114]

[0115] Note: * indicates quantitative ion pairs.

[0116] 5. Results Analysis and Judgment

[0117] (1) Qualitative analysis: Under the same experimental conditions, the retention time of the target compound in the sample should be within ±0.1 min of the retention time of the standard solution, and the relative abundance of the qualitative ion pair of the target compound in the sample should be in accordance with the relevant provisions of the EU EC 657 / 2002 directive.

[0118] (2) Quantitative analysis: Plot a standard curve with the concentration of standard as the abscissa and the area of ​​quantitative ion peak as the ordinate. The correlation coefficient (R²) is ≥0.99.

[0119] 6. Method Validation

[0120] Methodological validation showed that the limits of detection (LOD) for the seven metabolic biomarkers ranged from 0.05 to 0.5 ng / mL, and the limits of quantitation (LOQ) ranged from 0.2 to 1.5 ng / mL. At low, medium, and high spiking levels, the average recoveries ranged from 85.2% to 112.4%, with relative standard deviations (RSDs) all less than 10%, meeting the requirements for trace metabolite quantitative analysis.

[0121] Example 6: ROC Curve Analysis and Diagnostic Efficacy Assessment

[0122] To verify the diagnostic efficacy of the seven selected metabolic biomarkers and their combinations at different infection stages of *Ustilago maydis*, this invention quantitatively evaluated an independent validation sample set using the operating characteristic (ROC) curves of the test samples. This embodiment focuses on comparing the discriminative power of single biomarkers versus combinations of multiple biomarkers, seeking the optimal biomarker combination that can overcome the limitations of single detection methods.

[0123] Three different types of samples were collected: normal wheat (CG group), asymptomatic infected wheat (AG group), and significantly infected wheat (IG group). The number of samples and the grouping of samples were the same as in Example 1, but the samples selected in Example 1 did not overlap to ensure the authenticity and validity of the verification conclusions.

[0124] 1. Evaluation of the early warning effectiveness of the AG and CG groups during the latent infection period

[0125] The independent validation set samples were analyzed according to the LC-MS / MS method established in Example 5. Specifically: 0.2 g of ground wheat grain powder was accurately weighed and extracted using a pre-cooled extraction solvent (acetonitrile:methanol = 80:20, v / v); separation was performed using a C18 reversed-phase column with gradient elution using 0.1% formic acid aqueous solution and acetonitrile:methanol (9:1) as the mobile phase; quantitative analysis was performed using electrospray ionization (ESI) and multiple reaction monitoring (MRM) mode.

[0126] Using the obtained quantitative data, a logistic regression model was employed to construct a combined diagnostic panel for multiple biomarkers. The diagnostic efficacy was evaluated by calculating the area under the curve (AUC), sensitivity, and specificity of each individual biomarker and different combinations. First, the diagnostic ability of three biomarkers selected during the incubation period (cis-11,14,17-eicosatotrienoic acid, rosin acid, and heptadecanal) was assessed as individual biomarkers. Then, all possible combinations were analyzed. The results are shown in Table 5. Figures 4-7 As shown.

[0127] From Table 5 and Figures 4-7 It is known that the AUC value of any single biomarker is around 0.70-0.80, with a sensitivity generally between 60% and 75% and a specificity generally between 90% and 95%. This confirms that in the early stages of latent infection, due to low bacterial abundance and high background noise in the samples, fluctuations in a single metabolite cannot serve as a reliable diagnostic basis, resulting in a high risk of missed diagnoses and misdiagnoses. Introducing combined biomarkers did not fundamentally change the diagnostic performance. However, the triple diagnostic panel, constructed by combining all three biomarkers, achieved an AUC value of 0.912, with a sensitivity of 100% and a specificity of 95%, far exceeding the diagnostic effects of single indicators and pairwise combinations.

[0128] Table 5. Comparison of diagnostic efficacy of single biomarkers and different combination biomarkers during the latent infection period (AG vs CG).

[0129]

[0130] 2. Evaluation of the accuracy of differential diagnosis between the IG and AG groups during the symptomatic infection period.

[0131] The independent validation set samples were analyzed according to the LC-MS / MS method established in Example 5. Specifically: 0.2 g of ground wheat grain powder was accurately weighed and extracted using a pre-cooled extraction solvent (acetonitrile:methanol = 80:20, v / v); separation was performed using a C18 reversed-phase column with gradient elution using 0.1% formic acid aqueous solution and acetonitrile:methanol (9:1) as the mobile phase; quantitative analysis was performed using electrospray ionization (ESI) and multiple reaction monitoring (MRM) mode.

[0132] Using the obtained quantitative data, a logistic regression model was employed to construct a combined diagnostic panel for multiple biomarkers. The diagnostic efficacy was evaluated by calculating the area under the curve (AUC), sensitivity, and specificity of each individual biomarker and different combinations. For four biomarkers in the symptomatic phase (ergosterol, DIBOA, cis-11,14-eicosadienoic acid, and vitamin K1 oxide), a comparative evaluation of single-indicator and multiple-indicator combinations was also conducted. Specific results are shown in Table 6 and... Figures 8-13 As shown.

[0133] (1) Diagnostic efficacy of single biomarkers: The results show that ergosterol has a relatively good distinguishing ability among the four biomarkers, with an AUC of 0.707, corresponding to a sensitivity of 65.0% and a specificity of 76.2%. Vitamin K1 oxide has an AUC of 0.682, with a sensitivity of 47.5% and a specificity of 85.7%; however, the diagnostic sensitivity of a single biomarker is low and it cannot achieve a good diagnosis.

[0134] (2) Diagnostic efficacy of combined biomarkers: Among the six pairwise combinations, vitamin K1 oxide + cis-11,14-eicosadienoic acid (CDA) performed best, with an AUC of 0.842, indicating that the combination of these two indicators had a strong ability to distinguish between groups; followed by ergosterol + cis-11,14-eicosadienoic acid (CDA), with an AUC of 0.774. Relatively speaking, the combined efficacy of DIBOA + vitamin K1 oxide was the lowest, but still better than the single DIBOA analysis. The diagnostic efficacy of the triple model was significantly better than that of the single indicator. Ergosterol + vitamin K1 oxide + cis-11,14-eicosadienoic acid (CDA) had an AUC of 0.835, a sensitivity of 75.0%, and a specificity of 90.5%. The AUC of the combination of the four metabolites was 0.763, with a sensitivity of 65.0% and a specificity of 81.0%. Based on this combined diagnosis, the diagnostic efficacy of the four metabolite combinations is actually lower than that of the three combinations of ergosterol, vitamin K1 oxide, and cis-11,14-eicosadienoic acid.

[0135] Table 6. Comparison of diagnostic efficacy of single biomarkers and different combination biomarkers during the symptomatic infection phase (IG vs AG).

[0136]

[0137] In summary, the ROC curve analysis of this embodiment shows that in complex plant matrices, single metabolic biomarkers lack biological robustness, resulting in low independent diagnostic efficacy and failing to meet the needs of accurate detection. The biomarker combinations screened in this invention are not simply a quantitative summation of indicators; rather, only specific combinations can produce significant nonlinear synergistic enhancements. For example, combining indicators for the latent period (a specific combination of three biomarkers) and the symptom onset period (a specific combination of two or three biomarkers) can significantly improve the sensitivity and specificity of detection. This result confirms that the specific biomarker combinations of this invention overcome the technical bottleneck of existing single-indicator detection, which is susceptible to interference and cannot accurately identify pathogens, establishing a reliable and interference-resistant diagnostic system for wheat smut.

Claims

1. A combination of metabolic biomarkers for wheat light smut disease screened based on multi-omics research, characterized in that, The combination of markers includes one or more of cis-11,14,17-eicosatotrienoic acid, rosin acid, heptadecanal, ergosterol, 2,4-dihydroxy-2H-1,4-benzoxazine-3(4h)-one, cis-11,14-eicosatotrienoic acid, and vitamin K1 oxide.

2. The combination of metabolic biomarkers for wheat light brown smut based on multi-omics research screening according to claim 1, characterized in that, The biomarker combination includes cis-11,14,17-eicosatetrienoic acid, rosin acid, and heptadecanal; or the biomarker combination includes vitamin K1 oxide and cis-11,14-eicosatedienoic acid; or the biomarker combination includes ergosterol, vitamin K1 oxide, and cis-11,14-eicosatedienoic acid.

3. The method for screening combinations of metabolic biomarkers as described in claim 1 or 2, characterized in that, Includes the following steps: (1) Normal healthy wheat, wheat with asymptomatic smut fungus and wheat with typical smut fungus infection symptoms were divided into a healthy control group, an asymptomatic infection group and a symptomatic infection group, respectively. (2) Transcriptome sequencing was performed on the three groups of samples to screen differentially expressed genes and KEGG pathway enrichment analysis was conducted. (3) Perform non-targeted metabolomics detection on samples from the same batch to screen for differentially expressed metabolites; (4) Combine the analysis of key pathways enriched by the transcriptome with differential metabolites of the metabolome to identify metabolites that simultaneously meet the dual conditions of key pathway node function and significant difference in content, and obtain the combination of metabolic markers.

4. A method for monitoring the infection status of wheat smut fungus using the combination of metabolic markers described in claim 1 or 2, characterized in that, Includes the following steps: (1) Sample pretreatment: Collect wheat grain samples to be tested, grind them and extract metabolites to obtain metabolite extract; (2) Metabolite detection: The content of each component of the metabolic marker combination described in claim 1 or 2 in the metabolite extract was detected by liquid chromatography-tandem mass spectrometry. (3) Infection status determination: The infection status of the sample is determined based on the changes in the content of the metabolic markers in the sample to be tested.

5. The method according to claim 4, characterized in that, The criteria for determining the infection status in step (3) are as follows: if the content of cis-11,14,17-eicosatetrienoic acid and / or rosin acid and / or heptadecanal in the sample to be tested is significantly increased compared with the baseline value of the healthy control sample, it is determined to be the latent infection period; if the content of ergosterol and / or cis-11,14-eicosatetrienoic acid and / or vitamin K1 oxide in the sample to be tested is significantly higher than that in the healthy control sample, it is determined to be the symptomatic infection period.

6. The method according to claim 4, characterized in that, The liquid chromatography-tandem mass spectrometry detection in step (2) uses multiple reaction monitoring mode for quantitative analysis.

7. A detection kit for monitoring the infection status of wheat smut, characterized in that, The kit contains reagents required for detecting the combination of metabolic biomarkers as described in claim 1 or 2.

8. The detection kit according to claim 7, characterized in that, The reagents include standards for cis-11,14,17-eicosatotrienoic acid, rosin acid, heptadecanal, ergosterol, cis-11,14-eicosatotrienoic acid, vitamin K1 oxide, metabolite extraction reagents, and normal healthy wheat samples.

9. The detection kit according to claim 7, characterized in that, The kit also contains reagents necessary for the test.

10. The application of the combination of metabolic markers according to claim 1 or 2, the screening method according to any one of claims 3-6, or the detection kit according to any one of claims 7-9 in the latent early warning, and / or accurate identification and / or quantitative assessment of the degree of infection of wheat light smut.

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