Metabolic markers for microcystin-lr environmental exposure risk assessment and application thereof

Abstract

The application discloses a metabolic marker for microcystin-LR environmental exposure risk assessment and application thereof, and belongs to the technical field of environmental monitoring and biological detection. The MC-LR environmental exposure risk is judged by detecting the content of metabolic markers in the intestinal tissue of black soldier fly larvae in the sample. The results show that the expression amount of farnesyl pyrophosphate, deoxy cytidine, deoxy uridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine and bis-glutathione-hypusine is positively correlated with the MC-LR exposure risk; the expression amount of phosphatidylcholine, phosphatidylethanolamine and phosphatidylserine is negatively correlated with the MC-LR exposure risk. The MC-LR exposure risk assessment method and the assessment model established based on the same have high accuracy and sensitivity, and can avoid the false positive or false negative risk caused by single index detection.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and biological detection technology, and in particular to a metabolic biomarker for assessing the environmental exposure risk of microcystin-LR and its application. Background Technology

[0002] In eutrophic freshwater lakes, cyanobacteria grow rapidly, easily leading to cyanobacterial blooms. These blooms not only reduce water oxygen levels, causing aquatic organism death, but also reduce the living space of other phytoplankton, inhibit aquatic vegetation growth, and disrupt existing population structures. In particular, the most significant harm of cyanobacterial blooms lies in the cyanobacterial toxins released during the bloom and after cyanobacterial cell death, especially microcystin-LR (MC-LR), which is the most widespread and highly toxic toxin in freshwater.

[0003] MC-LR is a heptapeptide monocyclic hepatotoxin composed of L-leucine at position 2, L-arginine at position 4, and five relatively conserved amino acids. It exhibits strong water solubility but is also readily soluble in polar organic solvents such as methanol and ethanol. MC-LR's unique structure makes it resistant to acids, alkalis, and high temperatures; aqueous solutions of MC-LR maintain stable toxicity for more than half a month at room temperature. At the molecular level, MC-LR's toxic effects manifest as inhibition of protein phosphatases PP1 and PP2A activity after entering cells, disrupting the dynamic balance of protein phosphorylation and dephosphorylation, leading to dysregulation of physiological processes such as cell cycle and gene transcription, and directly damaging DNA. At the organ level, it causes multi-organ toxicity affecting the liver, gastrointestinal tract, reproductive system, and nervous system. MC-LR can accumulate in aquatic organisms and be transferred along the food chain to higher trophic level organisms, including fish, birds, mammals, and humans, posing a potential threat to ecosystems and human health. Therefore, environmental exposure risk assessment of MC-LR is of great significance.

[0004] In existing technologies, the detection and evaluation methods for microcystin-releasing hormone (MC-LR) mainly include methods for directly detecting the concentration of MC-LR in water bodies or biological samples (such as high performance liquid chromatography-mass spectrometry, enzyme-linked immunosorbent assay, etc.). However, these methods can only reflect the instantaneous exposure level and are difficult to assess the comprehensive exposure effect on organisms. For example, the published "A method for evaluating the toxicity of microcystin in real aquatic environments" (CN201910012391.1) uses human liver cancer cells for toxicity verification, and "A method for detecting microcystin-releasing hormone (MC-LR) in organisms" (CN201310120054.7) uses ultra-high performance liquid chromatography-mass spectrometry for qualitative and quantitative detection. However, these methods do not involve the assessment of endogenous metabolic responses using insects as indicator organisms. Furthermore, while the proposed "A High-Throughput Screening Method for Non-Target Biomarkers Based on Pollutant Metabolic Disturbances" (CN111505141B) suggests a screening approach for metabolomics biomarkers, it uses pollutant characteristic peaks as independent variables to establish a regression model, relying on the detection of known pollutants and failing to specify black soldier flies as an indicator organism. Therefore, there is currently a lack of a method that uses insects (especially black soldier flies) as indicator organisms to assess MC-LR environmental exposure risk by detecting changes in their gut endogenous metabolites.

[0005] Black soldier fly (BSF) is an important environmental insect, currently widely used in the harmless treatment and resource utilization of organic waste. Studies have shown that the gut of black soldier fly larvae (BSFL) harbors a rich microbial community. These gut microbes play a crucial role in assisting larval growth, improving fecal waste treatment efficiency, and reducing toxins such as antibiotics and heavy metals in the environment. Simultaneously, black soldier flies exhibit good tolerance and responsiveness to various environmental pollutants, possessing inherent potential and unique advantages as environmental pollution indicator organisms. However, there are currently no reports on technologies for assessing MC-LR environmental exposure risk using changes in endogenous metabolites in the gut of black soldier fly larvae. Therefore, developing an MC-LR environmental exposure risk assessment method based on intestinal metabolic markers of black soldier fly larvae would not only expand the application of black soldier flies in environmental monitoring but also help improve the accuracy, comprehensiveness, and ecological relevance of MC-LR ecological risk assessment, possessing significant scientific and practical value. Summary of the Invention

[0006] The purpose of this invention is to provide a metabolic biomarker for assessing the environmental exposure risk of microcystin-LR and its application, in order to overcome the shortcomings of existing MC-LR detection methods that cannot reflect the comprehensive exposure effects of organisms and lack endogenous metabolic response assessment methods using insects as indicator organisms.

[0007] To achieve the above objectives, the present invention provides the following solution: In a first aspect, the present invention provides a metabolic biomarker for assessing the environmental exposure risk of microcystin-LR, said metabolic biomarker comprising farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, diglutathione-semine, phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine.

[0008] Preferably, the expression levels of farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, and diglutathione-semine are positively correlated with the risk of microcystin-LR exposure.

[0009] Preferably, the expression levels of the phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine are negatively correlated with the risk of microcystin-LR exposure.

[0010] Secondly, the present invention also provides the application of a reagent for detecting the expression level of the said metabolic marker in the preparation of products for assessing the risk of environmental exposure to microcystin-LR.

[0011] Thirdly, the present invention also provides a product for assessing the risk of environmental exposure to microcystin-LR, including reagents for detecting the expression levels of the metabolic marker.

[0012] Fourthly, the present invention also provides a method for assessing the environmental exposure risk of microcystin-LR, comprising the following steps: Metabolites from the intestinal tissue of black soldier fly larvae were extracted from the test sample, and the content of the metabolic markers in the intestinal tissue metabolites of black soldier fly larvae was detected to assess the environmental exposure risk of microcystin-LR.

[0013] Preferably, the expression levels of farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, and diglutathione-semine are positively correlated with the risk of microcystin-LR exposure.

[0014] Preferably, the expression levels of the phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine are negatively correlated with the risk of microcystin-LR exposure.

[0015] Fifthly, the present invention also provides an application of the metabolic biomarker in constructing a microcystin-LR environmental exposure risk assessment model.

[0016] In a sixth aspect, the present invention also provides a model for assessing the environmental exposure risk of microcystin-LR, wherein the judgment criteria of the model are as follows: In the sample to be tested, if the expression levels of at least 5-8 of the metabolic markers—farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, and diglutathione-semine—are increased, and the expression levels of at least 1-3 of the metabolic markers—phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine—are decreased, then the sample to be tested is determined to have a risk of MC-LR exposure.

[0017] The present invention discloses the following technical effects: 1. This invention establishes for the first time a method for assessing MC-LR environmental exposure risk based on intestinal metabolic markers of black soldier fly larvae. There are currently no reports on using changes in endogenous metabolites in the gut of black soldier fly larvae to assess MC-LR exposure risk. This invention expands the application of black soldier flies in the field of environmental monitoring and has significant technological innovation.

[0018] 2. The combination of intestinal metabolic markers of black soldier fly larvae screened in this invention can effectively distinguish between the MC-LR exposure group and the control group, and the changes in differential metabolites are statistically significant. P <0.05). The method of this invention has high accuracy and sensitivity, and can reliably identify metabolic disturbances caused by MC-LR exposure, avoiding the risk of false positives or false negatives caused by single indicator detection.

[0019] 3. This invention screened and identified a set of characteristic metabolic biomarkers associated with MC-LR exposure, including 13 upregulated metabolites and three downregulated lipid biomarkers: phosphatidylcholine (PC), phosphatidylethanolamine (PE), and phosphatidylserine (PS), which have good combination discrimination ability.

[0020] 4. This invention uses black soldier fly larvae as indicator organisms, which can accurately reflect the comprehensive exposure effect of MC-LR on organisms in the environment, superior to methods that rely solely on chemical concentration detection (the latter only reflects instantaneous exposure levels). As environmental insects, the metabolic response of black soldier flies is closely related to the actual toxic effects of pollutants in the ecosystem; therefore, the assessment results of this invention are more ecologically relevant. Furthermore, the method of this invention can comprehensively evaluate the environmental exposure risk of MC-LR, providing a scientific basis for pollution monitoring, risk warning, and ecological management of MC-LR in water bodies, sediments, and organic waste, and has good potential for widespread application. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 The results of the multivariate statistical analysis comparing the control group and the exposed group are shown below; where ac represents the PCA scatter plot (a), OPLS-DA score plot (b), and OPLS-DA model validation plot (c) under positive ion mode; df represents the PCA scatter plot (d), OPLS-DA score plot (e), and OPLS-DA model validation plot (f) under negative ion mode. Figure 2 Volcano diagram showing the differences in intestinal metabolites between the control and exposed groups of black soldier fly larvae. Detailed Implementation

[0023] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as a limitation of the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0024] It should be understood that the terminology used in this invention is merely for describing particular embodiments and is not intended to limit the invention. Furthermore, with respect to numerical ranges in this invention, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Any stated value or intermediate value within a stated range, as well as each smaller range between any other stated value or intermediate value within said range, is also included in this invention. The upper and lower limits of these smaller ranges may be independently included or excluded from the range.

[0025] Unless otherwise stated, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. While only preferred methods and materials have been described herein, any methods and materials similar or equivalent to those described herein may be used in the implementation or testing of this invention. All references to this specification are incorporated by way of citation to disclose and describe methods and / or materials associated with those references. In the event of any conflict with any incorporated reference, the content of this specification shall prevail.

[0026] Various modifications and variations can be made to the specific embodiments described in this specification without departing from the scope or spirit of the invention, as will be apparent to those skilled in the art. Other embodiments derived from this specification will also be apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0027] The terms “include,” “including,” “have,” “contain,” etc., used in this article are all open-ended terms, meaning that they include but are not limited to.

[0028] Example 1 Screening Experiment A method for assessing environmental exposure risk based on intestinal metabolic markers of black soldier fly larvae using the MC-LR model includes the following steps: S1. Black soldier fly larvae were exposed to feed containing MC-LR. Accurately weigh an appropriate amount of MC-LR standard (CAS: 101043-37-2, purity 99.9%), add ultrapure water, gently shake to fully wet the powder, and sonicate in an ice bath for 20 min to promote dissolution, preparing a 50 µg / mL MC-LR stock solution. Dilute the stock solution with ultrapure water to prepare a 400 µg / L MC-LR working solution, denoted as M400, with ultrapure water as the control group (denoted as M0). All the above MC-LR solutions were aliquoted and stored at -20℃ to avoid repeated freeze-thaw cycles.

[0029] BSFLs were derived from a stable population raised in our laboratory for over three years. Four-day-old black soldier fly larvae from the same batch, exhibiting consistent hatching and developmental stages, were selected as experimental material. The rearing substrate was conventional wheat bran, treated at 80℃ for 60 min in a drying oven before use. The eggs were evenly distributed in rearing boxes containing conventional wheat bran feed and incubated at 30℃ and 60% relative humidity. Nine g of dried wheat bran was added to 400 µg / L MC-LR working solution to adjust the substrate moisture content to 70%, serving as the MC-LR exposure group (denoted as M400). An equal volume of ultrapure water was added to the control group, with all other treatment conditions identical to the exposure group (denoted as M0). Four-day-old black soldier fly larvae were randomly divided into the control group and the MC-LR group, with three biological replicates in each group. Each replicate was inoculated with 10 larvae and exposed continuously for 12 days at 30℃ and 60% relative humidity, with fresh feed provided daily.

[0030] S2. Collection of intestinal tissue from black soldier fly larvae After MC-LR exposure, the larvae in each group were washed with ultrapure water, then disinfected with 75% ethanol, and transferred to a sterile laminar flow hood. The larvae were disinfected again with 75% ethanol and thoroughly rinsed with sterile water. Dissection was performed on ice. Using sterile instruments, transverse incisions were made at the ends of the larval segments, and the intact intestinal tissue was carefully separated and removed. Adhering fat bodies and Malpighian tubules were removed from the intestinal surface. After rinsing with sterile water and drying with sterile filter paper, the intestines of 10 larvae from the same treatment group were collected into one sample, immediately flash-frozen in liquid nitrogen, and stored at -80°C for later use. Sterile instruments were changed between different groups to prevent cross-contamination.

[0031] S3. Extraction of intestinal metabolites Add an appropriate amount of pre-chilled metabolite extraction buffer (0.3 mg / mL L-2-chlorophenylalanine dissolved in methanol as an internal standard, plus 80% methanol aqueous solution) and magnetic beads to the intestinal sample. Incubate the sample at -20℃ for 2 min, then grind at 60 Hz for 2 min, sonicate on ice for 10 min, and incubate at -20℃ for 30 min to precipitate the protein. Centrifuge the mixture at 4℃ and 13000 rpm for 10 min and collect the supernatant. Dry the supernatant in a freeze dryer. After drying, add 80% methanol aqueous solution, vortex to mix, sonicate on ice for 3 min, and then incubate at -20℃ for 2 h to precipitate the protein. Centrifuge the sample at 4℃ and 13000 rpm for 10 min again and collect the supernatant. Filter the supernatant through a 0.22 µm filter membrane and store at -80℃.

[0032] S4. Metabolite detection The extract of the metabolites to be tested obtained in step S3 was detected by liquid chromatography-mass spectrometry to obtain raw data, and data processing such as peak extraction and peak alignment was performed.

[0033] Detection was performed using a Waters Acquity l-Class PLUS ultra-high performance liquid chromatography system tandem with a Waters Xevo G2-XS QTOF high-resolution mass spectrometer. The Waters Acquity UPLC HSS T3 column had dimensions of 1.8 μm and 2.1 mm × 100 mm. Metabolomics data were acquired in both positive ion mode (POS) and negative ion mode (NEG). Each sample was analyzed independently in both ion modes. Raw data acquired using MassLynx V4.2 were processed using Progenesis QI software for peak extraction, peak alignment, and other data processing.

[0034] S5. Screening of differential metabolic biomarkers Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed on the data from positive and negative ion modes, respectively. The reliability of the model was verified using OPLS 1.6.2 software with 7 cross-validations and 200 permutation tests. The data from positive and negative ion modes were then combined for differential metabolite mining, with a VIP ≥ 1 as the screening criterion. P <0.05, |FC|≥2.

[0035] The results showed that, compared with the M0 group, the expression levels of farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, and diglutathione-semine in the M400 group showed an up-regulation trend, while the expression levels of phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine showed a down-regulation trend (see Table 1 for details). Figures 1-2 ).

[0036] Table 1. Differential expression patterns of key response metabolites in the M0 and M400 groups. Establishment of S6.MC-LR Exposure Risk Assessment Model Using the differentially expressed metabolic markers obtained in step S5 as characteristic variables, a risk assessment model for MC-LR exposure in black soldier fly larvae was established. When at least 5-8 of the 13 upregulated metabolites described in step S5 are upregulated, and at least 1-3 of phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine are downregulated, the sample to be tested is determined to have MC-LR exposure risk.

[0037] In this embodiment, the model established based on the above differential metabolic biomarkers can effectively distinguish between the control group and the MC-LR exposure group, indicating that the biomarker combination can serve as a basis for identifying the MC-LR exposure risk of black soldier fly larvae.

[0038] S7. Assessment of MC-LR Exposure Risk in the Environment Black soldier fly larvae samples from the environment to be evaluated, the substrate to be treated, or the breeding batch to be tested were collected for intestinal sampling, metabolite extraction, detection, and differential analysis according to the methods in steps S2 to S5. The risk of MC-LR exposure in the subjects to be evaluated was identified based on the model established in step S6.

[0039] If the expression levels of some metabolites among farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, and diglutathione-semine are increased compared to the control group, and the expression level of at least one lipid among phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine shows a downward trend compared to the control group, then the sample is considered to have MC-LR exposure risk.

[0040] S8. Performance Evaluation Indicators Statistics from the test set: True positive (TP): Actual exposure and positive result; False positive (FP): No actual exposure but positive result; True negative (TN): No actual exposure and negative result; False negative (FN): Actual exposure but negative result.

[0041] And calculate: Accuracy = (TP + TN) / (TP + TN + FP + FN); Sensitivity = TP / (TP + FN); Specificity = TN / (TN + FP); The results showed that the number of MC-LR exposed samples that were correctly identified as positive (TP=3) and the number of samples that were incorrectly identified as negative (FN=0) were positive. For unexposed control samples, the number of samples that were incorrectly identified as positive (FP=0) and the number of samples that were correctly identified as negative (TN=3) were negative (see Table 2). Based on this, the accuracy, sensitivity, and specificity of the calculation model were 1, 1, and 1 respectively (see Table 3).

[0042] Table 2 Test Set Confusion Matrix Table 3 Summary of Performance Indicators This embodiment demonstrates that the risk assessment model established based on the above-mentioned differential metabolic biomarkers can achieve early identification of MC-LR exposure risk.

[0043] Example 2 Verification Experiment This embodiment verifies the accuracy of the markers selected in Example 1. The experimental steps are the same as in Example 1.

[0044] Independent batch larval validation includes the following steps: S1. Independent Sample Acquisition Different batches of first-instar larvae of black soldier flies were taken and set up as a control group and a high-concentration MC-LR exposure group according to the method in Example 1, with 3 biological replicates in each group.

[0045] S2. Blind Detection and Judgment The sample numbering was completed by a third party and kept blinded from the testing personnel; the testing personnel completed the intestinal sample metabolic testing according to S1-S5 of Example 1 without knowing the grouping information.

[0046] S3. Description of External Validation Results According to Example 1, S8 statistical analysis of TP, TN, FP, FN and calculation accuracy, sensitivity and specificity were performed.

[0047] Blinded external validation results show that, in independent batches of samples, the model of this invention correctly identified 3 exposed samples and 3 control samples, with 0 false positives and 0 false negatives, achieving an accuracy of 1, a sensitivity of 1, and a specificity of 1 (see Tables 4-5).

[0048] Table 4 Validation set confusion matrix Table 5 Summary of Performance Indicators The above results demonstrate that the combination of metabolic biomarkers and the risk assessment model screened in this invention are reproducible and stable.

[0049] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A metabolic biomarker for assessing the environmental exposure risk of microcystin-LR, characterized in that, The metabolic markers include farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, diglutathione-semine, phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine.

2. The metabolic biomarker according to claim 1, characterized in that, The expression levels of the aforementioned farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, and diglutathione-semine were positively correlated with the risk of microcystin-LR exposure.

3. The metabolic biomarker according to claim 1, characterized in that, The expression levels of the phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine were negatively correlated with the risk of microcystin-LR exposure.

4. The use of a reagent for detecting the expression level of the metabolic marker of claim 1 in the preparation of products for assessing the risk of environmental exposure to microcystin-LR.

5. A product for assessing the environmental exposure risk of microcystin-LR, characterized in that, Includes reagents for detecting the expression levels of the metabolic markers described in claim 1.

6. A method for assessing the environmental exposure risk of microcystin-LR, characterized in that, Includes the following steps: Metabolites from the intestinal tissue of black soldier fly larvae were extracted from the test sample, and the content of the metabolic markers described in claim 1 in the intestinal tissue metabolites of black soldier fly larvae was detected to assess the environmental exposure risk of microcystin-LR.

7. The method according to claim 6, characterized in that, The expression levels of the aforementioned farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, and diglutathione-semine were positively correlated with the risk of microcystin-LR exposure.

8. The method according to claim 6, characterized in that, The expression levels of the phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine were negatively correlated with the risk of microcystin-LR exposure.

9. The application of the metabolic biomarker of claim 1 in constructing a microcystin-LR environmental exposure risk assessment model.

10. A model for assessing the environmental exposure risk of microcystin-LR, characterized in that, The criteria for judging the model are: In the sample to be tested, if the expression levels of at least 5-8 of the metabolic markers described in claim 1—farnesyl pyrophosphate, deoxycytidine, deoxyuridine, D-fructose, D-glucose, L-arginine phosphate, L-aspartic acid, L-histidine, L-isoleucine, L-phenylalanine, L-proline, methionine, and diglutathione-semine—are increased, and the expression levels of at least 1-3 of the metabolic markers—phosphatidylcholine, phosphatidylethanolamine, and phosphatidylserine—are decreased, then the sample to be tested is determined to have a risk of MC-LR exposure.

Images