A biomarker for moldy green roughage and a screening method thereof

Abstract

The present invention provides an efficient and reliable biomarker for moldy green roughage and a screening method thereof. The method comprises the following steps: S1. preparing moldy green roughage samples and conducting dynamic testing; S2. establishing a green roughage mold degree evaluation model to identify fresh and early-stage slightly moldy green roughage; S3. obtaining differential metabolites through metabolomics testing and analysis; S4. developing mold biomarkers; and S5. validating the mold biomarkers. This method is efficient and reliable and suitable for the early identification of moldy green roughage and the screening of green roughage mold markers.

Description

Technical Field

[0001] The present invention relates to a biomarker and a screening method thereof, in particular to a green roughage mildew biomarker and a screening method thereof, belonging to the technical field of biological detection. Background Art

[0002] According to the Food and Agriculture Organization of the United Nations, approximately 25% of global food crops are contaminated with mycotoxins, posing a serious threat to food quality and safety. Moldy feed not only poses a safety risk to livestock products but also seriously harms the health of livestock and poultry. In addition to aflatoxins, feed often contains zearalenone, vomitoxin, fumonisins, and ochratoxins. In mild cases, these can affect livestock and poultry feed intake and production performance, while in severe cases, they can cause miscarriage in females, stunted growth in young animals, immunosuppression, and even death. Green roughage, such as dregs and crop straw, accounts for over 30% of all roughage used by cattle and sheep farms. Due to their high moisture content (≥70%), improper storage and feeding often lead to oxidation, deterioration, and mold. Reports of cattle and sheep poisoning from feeding moldy green roughage are common.

[0003] Aspergillus flavus, Fusarium spp., Alternaria alternata, and Penicillium spp. are the primary coexisting microorganisms in green roughage, along with a variety of bacteria and yeasts. These fungi and bacteria are mostly sourced from pre-harvest field infections. During storage, green roughage undergoes degradation of nutrients due to cellular respiration and oxidation, as well as bacterial and yeast activity, producing metabolites such as alcohols, acids, and amines, generating heat. Oxidative deterioration and elevated temperatures accelerate mold proliferation, leading to mold and spoilage, and even the production of mycotoxins. Numerous studies have shown that an increase in the total number of mold species in moldy green roughage is associated with significant losses of organic matter, fermentable carbohydrates, and protein. The mold growth process in green roughage is influenced by environmental conditions, storage conditions, and feed characteristics. The distribution of mold species and numbers within the silo is uneven, and the presence of other bacteria, yeasts, and insects contributes to a complex degree of mold. The development of biomarkers for green roughage mold that are forward-looking, sensitive, specific, stable, and easy to detect would facilitate early detection of moldy green roughage and contribute to the development of freshness-preserving and mold-preventing technologies and products, with promising applications. As far as the applicant knows, there is currently a severe lack of efficient and reliable green roughage mold biomarkers and screening methods therefor. Summary of the Invention

[0004] Based on the above background, the object of the present invention is to provide an efficient and reliable green roughage mold biomarker and a screening method thereof.

[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions:

[0006] A method for screening biomarkers of moldy green roughage, comprising the following steps:

[0007] S1. Preparation of moldy green roughage samples and dynamic detection: Preparation and collection of green roughage samples with different degrees of mold stored on different dates, determination of nutritional components, microbial indicators and mycotoxins, and other indicators data, statistical analysis of dynamic moldy sample indicator data;

[0008] S2. Establish a green roughage mold degree evaluation model to identify fresh and early-stage slightly molded green roughage: Indicators with large variations, single, non-overlapping change directions were selected as evaluation indicators. A green roughage mold degree evaluation model was established using the grey correlation analysis method. Based on the mold degree scores of the green roughage samples, fresh and early-stage slightly molded green roughage were identified.

[0009] S3. Identification of differential metabolites through metabolomics detection and analysis: Liquid chromatography-tandem mass spectrometry (LC-MS) was used to perform metabolomics analysis on fresh and slightly moldy green roughage samples. Differential metabolites were identified through statistical analysis and mass spectrometry interpretation.

[0010] S4. Identify mold biomarkers: Develop receiver operating characteristic (ROC) curves based on sample metabolite data. Further screen differential metabolites based on the area under the curve (AUC) values ​​to identify preselected biomarkers. Correlation analysis is performed between the relative abundance of preselected biomarkers and mold scores, combined with metabolic pathway analysis, to identify mold biomarkers.

[0011] S5. Verification of mold biomarkers: The optimized liquid chromatography detection method was used to quantitatively detect the content of the proposed mold biomarker in green roughage samples with different mold degrees, and a Pearson correlation analysis was performed with the mold degree score results of the green roughage samples. If R 2 A value above 0.70 was considered to be effective for the proposed mold biomarker.

[0012] Preferably, in step S1, the step of preparing and collecting green roughage samples stacked on different dates and having different degrees of mildew comprises:

[0013] At least 6 groups of green roughage from different sources were collected and piled in summer. Green roughage that had been piled for 0 days, 1 day, 3 days, and 6 days were collected to obtain green roughage samples with different degrees of moldiness.

[0014] Preferably, in step S2, establishing a green roughage mildew degree evaluation model using a grey correlation analysis method comprises:

[0015] The degree of moldiness of green roughage is expressed by calculating the correlation coefficient between all test series and reference series. The smaller the correlation coefficient, the higher the degree of moldiness of green roughage sample. The calculation formula of the correlation coefficient is:

[0016] ξ i(k)={min△i(k)+P max△i(k)} / {△i(k)+P max△i(k)};

[0017] Where, △i(k)=|X'0(k)-X' i (k), X' i =X i (k) / X0(k), X0(k) is the reference sequence, X i (k) is the reference series, P is the resolution coefficient, and its value is 0.5.

[0018] Preferably, in step S3, the criteria for screening differential metabolites by statistical analysis are that the P value and Q value of the T test method are both less than 0.05, and the variable projection importance VIP value calculated by the OPLS-DA model is greater than 1.

[0019] Preferably, in step S4, ROC curves are drawn for the metabolite data of green roughage samples stored for 0 to 3 days and AUC values ​​are calculated, and the biomarker analysis function module of MetaboAnalyst 5.0 platform is used to screen differential metabolites with AUC values ​​greater than 0.95.

[0020] Preferably, in step S5, the optimized liquid chromatography detection method includes:

[0021] The green roughage samples were treated by ultrasonic extraction and separated and determined on an Aglilent ZORBX SB-C18 column. The mobile phase was methanol-water isocratic elution with a flow rate of 1 mL / min, a detection wavelength of 260 nm, and a column temperature of 30°C.

[0022] A green roughage mildew biomarker is guanosine.

[0023] Compared with the prior art, the present invention has the following advantages:

[0024] The present invention provides a method for screening biomarkers of green roughage mildew, which applies grey correlation analysis to establish a green roughage mildew degree evaluation model, combines metabolomics analysis to screen mildew biomarkers, and uses precise quantitative detection for auxiliary verification. The method is efficient and reliable, and is suitable for screening green roughage mildew markers. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0026] Figure 1 This is a schematic flow chart of a method for screening biomarkers of moldy green roughage according to the present invention;

[0027] Figure 2 These are correlation diagrams of the screening and formulation process of tofu dregs moldy biomarkers in the embodiments of the present invention, wherein a is a screenshot of differential metabolites obtained by statistical analysis, b is a screenshot of metabolites with an AUC value >0.95 calculated by the ROC curve, c is a correlation analysis diagram of guanosine and moldy score, and d is a screenshot of the metabolic pathway involved in guanosine. DETAILED DESCRIPTION

[0028] The technical solution of the present invention will be further described in detail below through specific embodiments and in conjunction with the accompanying drawings. It should be understood that the implementation of the present invention is not limited to the following embodiments, and any form of modification and / or change made to the present invention will fall within the scope of protection of the present invention.

[0029] In the present invention, unless otherwise specified, all parts and percentages are by weight. The equipment and raw materials used are commercially available or commonly used in the art. The methods in the following embodiments, unless otherwise specified, are conventional methods in the art. The components or equipment in the following embodiments, unless otherwise specified, are all universal standard parts or components known to those skilled in the art. Their structures and principles are known to those skilled in the art through technical manuals or routine experimental methods.

[0030] refer to Figure 1 The embodiment of the present invention discloses a method for screening biomarkers of moldy green roughage, which comprises the following steps:

[0031] S1. Prepare and dynamically test moldy green roughage samples. Prepare and collect green roughage samples with different degrees of moldiness from different stacking dates, measure their nutritional content, microbial indicators, and mycotoxins, and perform statistical analysis on the dynamic moldy sample indicator data.

[0032] S2. Establish a green roughage mold degree evaluation model to identify fresh and early-stage slightly moldy green roughage. Select indicators with large variations, single and non-overlapping change directions as evaluation indicators, and use the grey correlation analysis method to establish a green roughage mold degree evaluation model. Based on the mold degree scores of green roughage samples, identify fresh and early-stage slightly moldy green roughage.

[0033] S3. Identify differential metabolites through metabolomics detection and analysis. Liquid chromatography-tandem mass spectrometry (LC-MS) was used to perform metabolomics analysis on fresh and slightly moldy green roughage samples. Differential metabolites were identified through statistical analysis and mass spectrometry analysis.

[0034] S4. Identify mold biomarkers. Develop receiver operating characteristic (ROC) curves based on sample metabolite data. Further screen differential metabolites based on the area under the curve (AUC) values ​​to identify preselected biomarkers. Correlation analysis is performed between the relative abundance of preselected biomarkers and mold scores. Combined with metabolic pathway analysis, guanosine, a mold biomarker, is identified.

[0035] S5. Verify the mold biomarker. The optimized liquid chromatography detection method was used to quantitatively detect the content of guanosine in green roughage samples with different mold degrees, and Pearson correlation analysis was performed with the mold degree score results of the green roughage samples. If R 2 A value above 0.70 was considered to be effective for the proposed mold biomarker.

[0036] In step S1, the preparation of green roughage samples with different degrees of mildew after being stacked on different dates includes:

[0037] At least 6 groups of green roughage from different sources were collected and piled in summer. Green roughage that had been piled for 0 days, 1 day, 3 days, and 6 days were collected to obtain green roughage samples with different degrees of moldiness.

[0038] In step S2, the establishment of a green roughage mildew degree evaluation model using the grey correlation analysis method includes:

[0039] The degree of moldiness of green roughage is expressed by calculating the correlation coefficient between all test series and reference series. The smaller the correlation coefficient, the higher the degree of moldiness of green roughage sample. The calculation formula of the correlation coefficient is:

[0040] ξ i (k)={min△i(k)+P max△i(k)} / {△i(k)+P max△i(k)};

[0041] Where, △i(k)=|X'0(k)-X' i (k), X' i =X i (k) / X0(k), X0(k) is the reference sequence, X i (k) is the reference series, P is the resolution coefficient, and its value is 0.5.

[0042] In step S3, the criteria for screening differential metabolites by statistical analysis are that the P value and Q value of the T test method are both less than 0.05, and the variable projection importance VIP value calculated by the OPLS-DA model is greater than 1.

[0043] In step S4, the ROC curve is drawn for the metabolite data of the green roughage samples with a stacking date of 0 to 3 days and the AUC value is calculated. The biomarker analysis function module of the MetaboAnalyst 5.0 platform is used to screen differential metabolites with an AUC value greater than 0.95.

[0044] In step S5, the optimized liquid chromatography detection method includes:

[0045] The green roughage samples were treated by ultrasonic extraction and separated and determined on an Aglilent ZORBX SB-C18 column. The mobile phase was methanol-water isocratic elution with a flow rate of 1 mL / min, a detection wavelength of 260 nm, and a column temperature of 30°C.

[0046] This method for screening biomarkers of green roughage mold is based on a multi-indicator quantitative assessment of the degree of green roughage mold, and uses metabolomics and differential metabolite association analysis, functional analysis, etc. for preliminary screening. It is supplemented by precise quantitative detection of feed samples with different degrees of mold, forming a closed loop from feed mold, marker screening to marker verification.

[0047] The embodiment of the present invention further discloses a biomarker for moldy green roughage, wherein the biomarker for moldy green roughage is guanosine.

[0048] The following is a detailed description of an embodiment of the present invention using bean curd dregs as a green roughage feed. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiment of the present invention.

[0049] Fresh tofu dregs were collected from more than 6 ranches in summer and naturally piled in the laboratory to prepare tofu dregs samples with different degrees of moldiness. Tofu dregs samples with different degrees of moldiness were collected after being piled for 0, 1, 3, and 6 days. The physicochemical and microbiological indices of the tofu dregs samples with different piled-up days were tested. The effects of piled-up days on the physicochemical and microbiological indices of the tofu dregs samples are shown in Table 1.

[0050] Table 1 Effects of stacking days on the physical, chemical and microbiological indicators of tofu dregs samples

[0051]

[0052]

[0053] Based on the statistical results and dispersion of the indicator data, we screened indicators for evaluating mold severity. Initially, we selected indicators such as vomitoxin, zearalenone, water-soluble carbohydrates, dry matter, starch, yeast, and mold. Given the large correlation coefficients between vomitoxin and zearalenone, water-soluble carbohydrates and starch, and yeast and mold, we selected only one indicator per group. Furthermore, considering the ease of data acquisition, we ultimately selected dry matter, water-soluble carbohydrates, total mold count, and vomitoxin as indicators for evaluating mold severity, with water-soluble carbohydrates, total mold count, and vomitoxin serving as inverse indicators.

[0054] Grey correlation analysis was used to establish a green roughage mildew degree evaluation model, and a reference series was defined, in which the reverse index took the minimum value of the variable and the positive index took the maximum value of the variable. The other sample index data sets were used as reference series. For the dimensionless treatment of the reverse index, the reference series variable value was divided by the observed value of the variable. For all positive indicators, each variable value was divided by the reference series variable value. After dimensionless treatment, each indicator in the reference series was assigned a value of 1, and the coefficient of variation of each variable in the reference series was basically consistent with the coefficient of variation of the original data, which well maintained the overall consistency of the original data and the consistency of the correlation coefficient. Grey correlation analysis was performed on the reference series, and the grey correlation degree and score of each reference series and the reference series are shown in Table 2.

[0055] Table 2 Grey correlation degree and score of each test series and reference series

[0056]

[0057]

[0058] Note: 0d-1 represents the first group of tofu dregs samples with the storage day of 0, and so on.

[0059] According to the scoring results of tofu dregs samples with different degrees of mold, it can be seen that the tofu dregs samples at 0 and 1 day are relatively fresh, slightly moldy at 3 days, and severely moldy at 6 days. Non-target metabolomics detection was performed on fresh and early moldy tofu dregs samples using LC-MS, and metabolites were analyzed by mass spectrometry. The metabolites were subjected to T-test to examine their significance and calculate their inter-group differences (FC values). Combined with the variable projection importance (VIP) in OPLS-DA analysis, 74 differential metabolites with calculated P values ​​and Q values ​​less than 0.05, FC>2 or <0.5, and VIP>1 were screened. Screenshots of some metabolites are shown in Figure 2 .a.

[0060] The metabolite data of the two groups of samples were imported into the MetaboAnalyst 5.0 platform. Using the biomarker analysis function module, the ROC curve was drawn for each metabolite and the AUC value was calculated. For some metabolite screenshots, see Figure 2 b. Four differential metabolites with AUC>0.95 were screened as pre-selected biomarkers. Pearson correlation analysis was performed between the relative abundance of pre-selected biomarkers and the mold score results. The results of the correlation analysis between guanosine and mold score are detailed in Figure 2 .c; Combined with KEGG pathway analysis, it was found that guanosine was involved in the metabolism of various microorganisms in a significantly enriched pathway. Screenshots of some metabolic pathways are shown in Figure 2 .d. Finally, guanosine was proposed as a biomarker for mold.

[0061] A liquid chromatography method for biomarker detection was established and optimized. Samples were treated with ultrasonic extraction and separated and determined on an Aglilent ZORBX SB-C18 column (5 μm, 4.6 × 250 mm). The mobile phase consisted of methanol-water isocratic elution at a flow rate of 1 mL / min, a detection wavelength of 260 nm, and a column temperature of 30°C. The results showed that the method had a good linear correlation with the peak area of ​​guanosine detected (R 2 =0.9998), the average recovery was 93.75-96.59% and RSD≤2.34%.

[0062] An optimized HPLC method was used to quantitatively measure guanosine concentrations in different tofu dregs samples. The results are shown in Table 3. Guanosine concentrations were difficult to detect in samples aged 0 and 1 day, while guanosine concentrations increased rapidly in samples aged 3 days, while the growth rate slowed significantly in samples aged 6 days. Combined with the moldy quality score, the first-day storage scored higher, indicating no moldy conditions and, therefore, no biomarkers were detected. However, around the third day, the main molds and bacteria involved in moldy conditions began to multiply rapidly, leading to a rapid increase in the biomarker concentrations in the dregs.

[0063] Pearson correlation analysis was performed on guanosine concentration and mildew score, and the results showed that the two were significantly negatively correlated (R 2 =0.712, P<0.01), indicating that the mold biomarker is reliable and effective.

[0064] Table 3 Guanosine concentration and moldy scores of different tofu dregs samples

[0065]

[0066] In summary, quantitative detection and correlation analysis showed that guanosine can be used as a biomarker for moldy green roughage, and also showed that the screening method is scientific and effective.

[0067] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. It should be noted that those skilled in the art can make various improvements and modifications to the present invention without departing from the principles of the present invention, and such improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for screening biomarkers of moldy green roughage, characterized by: The screening method comprises the following steps: S1. Preparation of moldy green roughage samples and dynamic testing: Preparation and collection of green roughage samples stacked on different dates with different degrees of mold, determination of nutritional components, microbial indicators and mycotoxin index data, statistical analysis of dynamic moldy sample index data; S2. Establish a green roughage mold degree evaluation model to identify fresh and early-stage slightly molded green roughage: Indicators with large variability, single, and non-overlapping change directions were selected as evaluation indicators. A green roughage mold degree evaluation model was established using gray correlation analysis. Based on the mold degree scores of green roughage samples, fresh and early-stage slightly molded green roughage were identified. S3. Identification of differential metabolites through metabolomics analysis: Liquid chromatography-tandem mass spectrometry (LC-MS) was used to analyze the metabolome of fresh and slightly moldy green roughage samples. Differential metabolites were identified through statistical analysis and mass spectrometry interpretation. S4. Identify mold biomarkers: Develop receiver operating characteristic (ROC) curves based on sample metabolite data. Further screen differential metabolites based on the area under the curve (AUC) values ​​to identify preselected biomarkers. Correlation analysis is performed between the relative abundance of preselected biomarkers and mold scores. Combined with metabolic pathway analysis, biomarkers for mold are identified. S5. Verification of mold biomarkers: The optimized liquid chromatography detection method was used to quantitatively detect the content of the proposed mold biomarker in green roughage samples with different mold degrees, and Pearson correlation analysis was performed with the mold degree score results of the green roughage samples. If R 2 A value above 0.70 was considered to be effective for the proposed mold biomarker; In step S3, the criteria for screening differential metabolites by statistical analysis are that the P value and Q value of the T test method are both less than 0.05, and the variable projection importance VIP value calculated by the OPLS-DA model is greater than 1; In step S4, the ROC curve is drawn for the metabolite data of the green roughage samples with a stacking date of 0 to 3 days and the AUC value is calculated. The biomarker analysis function module of the MetaboAnalyst 5.0 platform is used to screen differential metabolites with an AUC value greater than 0.

95.

2. The method for screening biomarkers of mildew in green roughage according to claim 1, wherein: In step S1, the preparation and collection of green roughage samples stacked on different dates and having different degrees of mildew include: At least 6 groups of green roughage from different sources were collected and piled in summer. Green roughage that had been piled for 0 days, 1 day, 3 days, and 6 days were collected to obtain green roughage samples with different degrees of moldiness.

3. The method for screening biomarkers of mildew in green roughage according to claim 1, wherein: In step S2, the establishment of a green roughage mildew degree evaluation model using the grey correlation analysis method includes: The degree of moldiness of green roughage is expressed by calculating the correlation coefficient between all test series and reference series. The smaller the correlation coefficient, the higher the degree of moldiness of green roughage sample. The calculation formula of the correlation coefficient is: ξ i ( k) = { min△i( k) + P max△i( k)} / { △i( k) + P max△i( k)}; Where, △i(k) = |X'0(k) - X' i (k), X' i = X i ( k) / X0( k), X0( k) is the reference sequence, X i (k) is the reference series, P is the resolution coefficient, and its value is 0.

5.

4. The method for screening biomarkers of mildew in green roughage according to claim 1, wherein: In step S5, the optimized liquid chromatography detection method includes: The green roughage samples were treated by ultrasonic extraction and separated and determined on an Aglilent ZORBX SB-C18 column. The mobile phase was methanol-water isocratic elution at a flow rate of 1 mL / min, the detection wavelength was 260 nm, and the column temperature was 30°C.

5. The method for screening biomarkers of mildew in green roughage according to claim 1, characterized in that: The green roughage mold biomarker is guanosine.

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