Method for comprehensive analysis of pigment components in monascus rice and its application in discovery of active ingredients against alzheimer's disease

Abstract

The application discloses a method for comprehensively analyzing pigment components in Hongqu rice and application of the method in discovery of active components against Alzheimer's disease. The method first obtains mass spectrometry data of Hongqu rice extract through LC-MS / MS analysis; then, based on common fatty ketone side chains and characteristic skeletons of Hongqu rice pigments, four characteristic neutral losses including 98.0732 Da and two characteristic fragment ions of m / z 212.11 and m / z 215.11 are established as constituent units; the constituent units are used to screen target components from total data, and a molecular network is constructed for visual clustering; finally, combined with database comparison and calculation prediction, systematic identification of pigment components is realized. By using the method, 243 pigment components are successfully identified from Hongqu rice, including 185 potential new pigments. Further activity experiments show that the identified pigments, especially red pigments, have significant concentration-dependent inhibitory effects on L-glutamic acid-induced neuron injury and LPS-induced neuroinflammation, and exhibit good potential against Alzheimer's disease. The application overcomes the defects of low efficiency and incomplete coverage of traditional methods, and provides an efficient and comprehensive analysis method for deep development and quality control of Hongqu rice.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of natural product chemistry and analytical chemistry, specifically relating to a systematic, high-throughput method for identifying specific categories of compounds with similar structures in complex natural product systems. More specifically, this invention relates to a method for comprehensively analyzing pigment components in red yeast rice and its application in the discovery of active ingredients for combating Alzheimer's disease. Background Technology

[0002] Red yeast rice (RYR) is a traditional fermented food made from rice fermented with Monascus purpureus, with a history of consumption and medicinal use in Asia spanning over a thousand years. Modern research shows that red yeast rice is rich in various bioactive compounds, including monacolins (such as lovastatin), pigments, organic acids, and sterols. Among these, pigments are one of the key active components that contribute to the various health benefits of red yeast rice, such as lowering blood lipids, anti-inflammation, anti-oxidation, and neuroprotection.

[0003] A comprehensive and precise analysis of the pigment components in red yeast rice is a crucial prerequisite for elucidating its pharmacodynamic material basis, quality control, and the development of novel functional foods. However, this analytical work faces significant challenges: First, red yeast rice pigments are a class of complex and diverse polyketide compounds, mainly classified into red, yellow, and orange pigments based on their color and core structure, with a wide molecular weight distribution and numerous isomers; second, a large number of trace components with low content and novel structures are generated during fermentation, which may be key active substances but are difficult to capture and identify.

[0004] In terms of analytical techniques, early research mainly relied on traditional extraction and separation methods. This method separates individual pure compounds from complex matrices through repeated solvent extraction, column chromatography, preparative chromatography, and other steps, and then uses nuclear magnetic resonance (NMR) and other means for structural identification. Although this method can provide the most reliable structural information, its inherent limitations are becoming increasingly prominent in the face of modern high-throughput and comprehensive analytical needs: (1) extremely low efficiency, taking months or even years, and the throughput cannot meet the requirements of rapid screening; (2) serious information loss, the cumbersome separation process easily leads to the degradation or loss of chemically unstable or extremely small amounts of compounds, so that the finally identified compounds are only the "tip of the iceberg" of the total chemical composition; (3) poor purpose, belonging to "labor-intensive" blind separation, making it difficult to systematically track a certain type of specific components.

[0005] Therefore, there is an urgent need to develop a new strategy that can capture and identify all pigment components in red yeast rice more efficiently and comprehensively, so as to truly achieve in-depth and unbiased mining of pigment components in complex systems. Summary of the Invention

[0006] This invention aims to at least partially address one of the technical problems existing in the prior art. To this end, this invention provides a method for comprehensively analyzing pigment components in red yeast rice and its application in the discovery of active ingredients for combating Alzheimer's disease.

[0007] According to one aspect of the present invention, a method for comprehensively analyzing pigment components in red yeast rice is provided, comprising the following steps: (a) pulverizing and extracting a red yeast rice sample to obtain a sample solution to be tested; (b) performing liquid chromatography-tandem mass spectrometry analysis on the sample solution to be tested obtained in step (a) to obtain a raw data file containing primary mass spectrometry and secondary mass spectrometry information; (c) converting the raw data file obtained in step (b) into mzML format, importing it into data analysis software, and screening based on preset characteristic neutral loss and / or characteristic fragment ions, wherein the characteristic neutral loss is 98.0732 Da, 114.1045 Da, 126.1045 Da or 142.1358 Da, and the characteristic fragment ions are m / z 212.11 or m / z 215.11, Screen out component data with at least one of the aforementioned characteristic neutral loss or characteristic fragment ions; (d) Upload the component data screened in step (c) to the molecular network platform for molecular network based on constituent units (BBMN) analysis, generate a molecular network diagram, and identify pigment components in red yeast rice by combining mass spectrometry data analysis and database comparison.

[0008] Preferably, in step (a), the extraction is performed by two shaking extractions using ethanol and 50% ethanol, and the combined extracts are then dried, reconstituted, and centrifuged.

[0009] Preferably, in step (b), the liquid chromatography uses a C18 column, and the mobile phase is an aqueous solution containing 0.1% formic acid and an acetonitrile solution containing 0.1% formic acid, with gradient elution.

[0010] Preferably, the gradient elution program is as follows: 0-0.5 min, 5% acetonitrile; 0.5-8.0 min, 5%-40% acetonitrile; 8.0-16.5 min, 40%-55% acetonitrile; 16.5-28.0 min, 55%-87% acetonitrile; 28.0-28.5 min, 87%-95% acetonitrile; 28.5-29.5 min, 95% acetonitrile.

[0011] Preferably, in step (b), the mass spectrometry analysis uses an electrospray ionization source, and in positive ion mode, data is acquired using autoMS / MS mode with collision energies of 20 eV and 30 eV.

[0012] Preferably, in step (c), the mass tolerance of the characteristic neutral loss and characteristic fragment ions is set to 0.02 Da and the ion abundance threshold is set to 2% in the data analysis software.

[0013] Preferably, in step (d), the molecular network platform is the Global Natural Product Social Molecular Network Platform (GNPS), and the parameters set when performing feature network analysis include: the parent ion mass tolerance and fragment ion tolerance are both 0.02 Da, the minimum cosine score is 0.70, and the minimum number of matched fragment ions is 3.

[0014] Preferably, in step (d), the identification includes importing the screened component data into molecular formula prediction software to calculate the molecular formula, identifying known pigments by comparing them with literature and mass spectrometry databases, and inferring the structure of unknown pigments based on molecular network clustering characteristics and secondary mass spectrometry structure analysis software.

[0015] According to another aspect of the present invention, the above method is provided for the application of identifying pigment components in red yeast rice that have anti-Alzheimer's activity.

[0016] Preferably, the pigment components identified by the method are used to prepare drugs or functional foods with neuroprotective and / or anti-neuroinflammatory effects, wherein the neuroprotective effect is evaluated using an L-glutamate-induced HT-22 cell injury model, and the anti-neuroinflammatory effect is evaluated using an LPS-induced BV-2 cell inflammation model.

[0017] The use of rubropunctamin (monascorubramine) and / or rubropunctatin (monascorubrin) in the preparation of drugs for treating or improving Alzheimer's disease. This invention reveals for the first time that two rubropunctamin (monascorubramine) and two rubropunctatin (monascorubrin) pigment components can exert anti-Alzheimer's activity through a dual mechanism of resisting neuronal damage and inhibiting neuroinflammation, providing a new source of compounds and a basis for action in the development of drugs or functional foods for the prevention and treatment of this disease.

[0018] The beneficial effects of this invention are mainly reflected in the following aspects: This invention creatively combines a molecular network strategy based on constituent units with deep mass spectrometry analysis targeting the structural characteristics of red yeast rice pigments, overcoming the bottlenecks of traditional extraction and separation methods such as "low efficiency, low throughput, and severe information loss," as well as the limitations of conventional molecular network strategies based on constituent units, which are overly dependent on preset features and may miss new skeletal components. It establishes a new, efficient, comprehensive, and accurate systematic identification method for specific compound families in complex matrices. This invention deeply analyzes the key characteristic structures (aliphatic ketone side chains) and characteristic skeletons (nitrogen / oxygen heterocycles) of red yeast rice pigments, transforming them into a unique mass spectrometry "characteristic fingerprint" (4 characteristic neutral loss + 2 characteristic fragment ions). Using this combination for data screening, intelligent and highly selective capture of target pigment components from massive mass spectrometry information is achieved, greatly improving the purposefulness and efficiency of the analysis. This method can effectively discover and infer a large number of novel, low-content unknown pigment analogs. In the examples, 185 potential new pigments were identified at once, fully demonstrating the powerful ability of this method in discovering new compounds. Attached Figure Description

[0019] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0020] Figure 1 This is a flowchart of a systematic analysis for identifying pigment components in red yeast rice using a molecular network method based on constituent units, provided in Embodiment 1 of the present invention.

[0021] Figure 2 This is a bar chart showing the neuroprotective effects of six representative red yeast rice pigments in an L-glutamate-induced mouse hippocampal neuron (HT-22) cell damage model, as provided in Example 2 of the present invention.

[0022] Figure 3 This is a bar chart showing the inhibitory effects of six representative red yeast rice pigments on the secretion of key inflammatory factors (NO, TNF-α, IL-6) in a lipopolysaccharide (LPS)-induced mouse microglia (BV-2) cell inflammation model, provided in Example 2 of the present invention. Detailed Implementation

[0023] The following examples are provided to enable those skilled in the art to better understand the present invention. It should be noted that, unless otherwise specified, the raw materials, reagents, or devices mentioned in the following examples are commercially available or obtained through known existing methods.

[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. Every smaller range between any stated value or intermediate value within a stated range, and 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 readily apparent to those skilled in the art. This specification and embodiments are merely exemplary.

[0027] To provide a new strategy for more intelligent and comprehensive capture and identification of all pigment components in red yeast rice, and to truly achieve in-depth and unbiased mining of pigment compounds in complex systems, this invention must address the problem of accurately defining the key constituent units of specific compound groups such as red yeast rice pigments. This requires starting from the key structural features and basic skeletons of known pigments, and establishing a set of features that can simultaneously cover the neutral loss of features and the combination of feature fragment ions, through a combination of theoretical derivation and mass spectrometry verification of standards. Secondly, after obtaining a preliminary list of compounds containing the features of the preset constituent units, it is necessary to use the visualization clustering function of molecular networks to intuitively display the structural relationships between these components, thereby distinguishing known pigments and discovering new analogues with tight clusters. Finally, for the large number of nodes in the network that are not annotated by known databases, it is necessary to further integrate molecular formula prediction, fragment ion derivation, and computer-aided structure prediction tools to reasonably infer and classify their potential structures. Therefore, this patent aims to provide a complete experimental scheme from feature screening, network clustering to structure inference, to achieve comprehensive and in-depth analysis of pigment components in red yeast rice.

[0028] Example 1

[0029] The present invention provides a comprehensive method for analyzing pigment components in red yeast rice, such as... Figure 1As shown, the procedure includes the following steps: (a) pulverizing and extracting the red yeast rice sample to obtain a sample solution to be tested; (b) performing liquid chromatography-tandem mass spectrometry analysis on the sample solution to be tested obtained in step (a) to obtain a raw data file containing primary mass spectrometry and secondary mass spectrometry information; (c) converting the raw data file obtained in step (b) into mzML format, importing it into data analysis software, and screening based on preset characteristic neutral loss and / or characteristic fragment ions, wherein the characteristic neutral loss is 98.0732 Da, 114.1045 Da, 126.1045 Da or 142.1358 Da, and the characteristic fragment ion is m / z 212.11 or m / z 215.11, and screening out component data with at least one of the characteristic neutral loss or characteristic fragment ions; (d) uploading the component data screened in step (c) to the Global Natural Products Social Molecular Network Platform for molecular network analysis based on constituent units, generating a molecular network diagram, and identifying pigment components in red yeast rice by combining mass spectrometry data analysis and database comparison.

[0030] I. Sample Preparation

[0031] The purpose of this step is to prepare a high-quality sample solution that can maximally cover the different polar pigment components in red yeast rice and is compatible with the liquid chromatography-mass spectrometry system for subsequent analysis. Specifically, extraction efficiency is increased by pulverization, and stepwise extraction with different proportions of ethanol is used to ensure sufficient dissolution of pigments across a wide polarity range. After merging, the solvent system is changed by drying, and finally, insoluble particles are removed by centrifugation to obtain a clear and concentrated analyte solution. This study uses a stepwise extraction strategy with pure ethanol and 50% ethanol to account for the polarity differences of different pigment components (pigments are usually low- or medium-polar compounds). This method can effectively cover a wide range of target compounds from low to medium polarity, thereby ensuring sufficient dissolution of pigment components. If only pure ethanol is used for extraction, the total number of pigments detected by subsequent LC-MS / MS analysis and BBMN identification will be significantly reduced.

[0032] To achieve the above objectives, the red yeast rice sample was thoroughly pulverized into a fine powder using a grinder, and 50 mg of powder was accurately weighed into a 1.5 mL centrifuge tube. Extraction was performed by shaking with 1 mL of ethanol and 1 mL of 50% ethanol for 1 hour, respectively. The two extracts were then combined, dried under a nitrogen atmosphere, and redissolved in 1 mL of acetonitrile. The sample was centrifuged at 13,500 rpm for 10 minutes, and 50 μL of the supernatant was analyzed by LC-MS / MS.

[0033] II. LC-MS / MS Analysis

[0034] The purpose of this step is to obtain precise primary mass spectrometry molecular ion information of each component in red yeast rice extract, along with its corresponding secondary mass spectrometry fragment maps containing rich structural details, based on high-performance liquid chromatography (HPLC) separation. By optimizing the chromatographic gradient to achieve effective separation of complex components, and utilizing high-resolution mass spectrometry to acquire data at two different collision energies, the aim is to provide high-quality, complete raw mass spectrometry data for subsequent compound screening, network construction, and structural identification.

[0035] To achieve the above objectives, analysis was performed using an Agilent 1290 Infinity liquid chromatography system. Sample separation was performed using a Waters ACQUITY UPLC BEH C18 column (2.1 × 100 mm, 1.7 µm). The mobile phase consisted of 0.1% formic acid aqueous solution (A) and 0.1% formic acid acetonitrile solution (B). The gradient elution program was as follows: 0–0.5 min, 5% B; 0.5–8.0 min, 5%–40% B; 8.0–16.5 min, 40%–55% B; 16.5–28.0 min, 55%–87% B; 28.0–28.5 min, 87%–95% B; 28.5–29.5 min, 95% B; the flow rate was 0.3 mL / min. Mass spectrometry analysis was performed using an Agilent 6550 UHD high-precision quadrupole time-of-flight mass spectrometer equipped with a dual-jet electrospray ionization source. The drying gas flow rate was 10 L / min, temperature 325 °C, nebulizer pressure 35 psi, sheath gas flow rate 10 L / min, temperature 350 °C, capillary voltage 5000 V, and nozzle voltage 350 V. Analysis was performed in positive ion mode using auto MS / MS, with collision energies specifically optimized to a combination of 20 eV and 30 eV. Positive ion mode significantly improves the mass spectrometry response and yields more comprehensive compound information. Crucially, the energy combination of 20 eV and 30 eV was systematically experimentally verified as the optimal conditions for the method of this invention, producing the most complete secondary mass spectra, thereby greatly improving the accuracy and efficiency of subsequent component screening and structural identification. If the optimization conditions are deviated from, for example, the collision energy is reduced to 10 eV or 15 eV, the precursor ion fragmentation will be insufficient, and the quality of the secondary mass spectrum will be significantly reduced. On the other hand, if the energy is increased to 40 eV or above, the fragment ion information will be incomplete, especially the fragment ion signal in the high-mass region will be significantly weakened, which will also have an adverse effect on subsequent analysis.

[0036] III. Molecular Network Analysis Based on Constituent Units

[0037] The aim of this step is to utilize a systematic data mining strategy to rapidly, comprehensively, and accurately identify and characterize all pigment components from complex LC-MS / MS data. The core of this method lies in: first, establishing a set of characteristic mass spectrometry fingerprints (constituent units) that uniquely point to red yeast rice pigment compounds through theoretical and experimental verification; then, intelligently screening all potential pigment components from the total data using these fingerprints; subsequently, constructing a molecular network based on mass spectrometry similarity to visualize their structural correlations and aid in classification; and finally, combining database comparison and computational prediction to confirm known pigments and infer the structures of a large number of unknown or novel pigments.

[0038] To achieve the above objectives, firstly, the constituent units of the pigments were established. The aim was to identify key mass spectrometry features for uniquely screening red yeast rice pigments from mass spectrometry data. This was achieved by analyzing the common characteristic structures (aliphatic ketone side chains) and characteristic skeletons (nitrogen / oxygen heterocycles) of known pigments, converting them into observable characteristic neutral loss and fragment ions in mass spectrometry. This established an accurate and reliable set of markers for subsequent high-throughput screening. Firstly, the structures of all currently reported pigments were characterized, revealing that aliphatic ketones are a common structural feature, including four types: hexanone, octanone, 2-heptanone, and 2-nonanone. Typically, the α-position of the carbonyl group is easily broken in mass spectrometry; therefore, it was speculated that these carbonyl-containing aliphatic ketones might produce specific neutral losses in secondary mass spectrometry. Next, secondary mass spectrometry of standards confirmed that the aliphatic ketones of all pigments would break and produce corresponding neutral losses in secondary mass spectrometry, specifically 98.0732 Da (hexanone, ...). ), 114.1045 Da (octanone, ), 126.1045 Da (2-heptanone, ) or 142.1358 Da (2-nonanone, Therefore, these four neutral losses were set as common building blocks for the pigments. In addition to shared structural features, yellow, orange, and red pigments also have different basic skeletons. The red pigment skeleton contains nitrogen heterocycles, while the yellow and orange pigments contain oxygen heterocycles. This structural difference leads to the generation of different characteristic fragment ions. Fragments generated by its basic skeleton are frequently observed in the secondary mass spectra of red pigments at m / z 212.11 (…). Fragment ions frequently appear at m / z 215.11 in the secondary mass spectra of yellow and orange pigments. Verification revealed that this fragment ion is generated from the basic framework of yellow or orange pigments. Therefore, the two characteristic fragment ions, m / z 212.11 and m / z 215.11, were selected as another building block for red and yellow / orange pigments, respectively.

[0039] Secondly, data processing and screening are performed to automatically and efficiently target and screen the full-scan LC-MS / MS data using the mass spectrometry feature markers established in step (a). By setting reasonable mass tolerances and abundance thresholds, all mass spectra that produce any preset feature signal are quickly captured, thereby efficiently screening target compound groups and generating standardized data files suitable for subsequent network analysis. The LC-MS / MS data files are converted to mzML format using MSConvert software and then imported into MS-DIAL software. Click Search, and then in the MS / MS fragment searcher directory, set the above four neutral loss features and two feature fragment ions, with a tolerance of 0.02 Da, an ion abundance threshold of 2%, Alignment spots selected as the Search viewer, and "or" selected as the Search option, then click Start. Components exhibiting one of the above four neutral loss features or one of the two feature fragment ions are screened out, and the data is exported as mgf and txt files. In particular, the setting of the ion abundance threshold is a key parameter affecting the accuracy of the screening. Through repeated verification, 2% has been found to be the optimal balance between precise screening and comprehensive coverage in this invention: when the threshold is set to 2%, the system can effectively focus on the target pigment family, screening out 312 nodes with a false positive rate of only 22.12%; if the threshold is lowered to 1% or lower, instrument noise will cause non-pigment compounds to be misidentified as fragment ions, leading to a significant increase in interference (screening nodes increase to 692, false positive rate rises to 64.88%), seriously affecting the reliability of subsequent structure identification; while if the threshold is increased to 2.5% or higher, some low-abundance pigment signals will be lost, resulting in a decrease in coverage. Therefore, the 2% threshold selected in this invention is a key optimized parameter that has been repeatedly verified and can balance comprehensiveness and accuracy.

[0040] Thirdly, a molecular network based on constituent units was constructed. The aim was to cluster and visualize the screened pigment component data based on the similarity of their secondary mass spectra. By constructing a molecular network, the structural correlations between pigment components (such as homologues and isomers) could be intuitively revealed, aiding in the classification of compounds by family and providing intuitive guidance for discovering novel analogs clustered around known pigments. The mgf and txt files were uploaded to the Global Natural Products Social Molecular Network Platform (GNPS, https: / / gnps.ucsd.edu) for feature network analysis. The parameters were set as follows: both the parent ion mass tolerance and fragment ion tolerance were 0.02 Da; the minimum cosine score was 0.70; and the minimum number of matching fragment ions was 3. The resulting BBMN had 312 nodes, representing 312 possible pigment components. The BBMN results were visualized using Cytoscape.

[0041] Fourthly, pigment identification aims to systematically identify each node (compound) in the molecular network. First, known pigments are quickly identified through precise mass number matching and database comparison. For unknown nodes in the network, high-precision molecular formula prediction, secondary mass spectrometry fragmentation analysis, and computer-aided structure inference tools are combined. The structural family information provided by network clustering is comprehensively utilized to rationally assign their potential chemical structures or propose novel structural hypotheses, ultimately achieving a comprehensive identification of the entire pigment group. The mgf format data screened in (b) was imported into BUDDY software for molecular formula calculation. Subsequently, 58 known pigments were identified through comparison with literature and databases. Apart from these 58 nodes, the remaining nodes in BBMN could not be matched with any reported pigments, therefore they may be potential new pigments. Subsequently, using SIRIUS, a software capable of inferring structures based on secondary mass spectrometry and a large compound structure library, and combining the clustering characteristics of BBMN, the structures of these potential new pigments were preliminarily inferred. As a result, a total of 243 pigments in red yeast rice were identified, including 218 red pigments, 17 yellow pigments, and 8 orange pigments. Of these, 185 are potential new pigments, and 34 are discovered for the first time in RYR.

[0042] Example 2

[0043] Validation of the anti-Alzheimer's activity of representative red yeast rice pigments

[0044] This embodiment aims to verify the anti-Alzheimer's disease (AD) related biological activities of the red yeast rice pigment components identified by the method of this invention, specifically including neuroprotective and anti-neuroinflammatory effects. Six representative compounds were selected from the pigments identified in Example 1, including two red pigments (rubropunctamin and monascorubramine), two yellow pigments (monascin and ankaflavin), and two orange pigments (rubropunctatin and monascorubrin). Stock solutions were prepared using dimethyl sulfoxide (DMSO) and diluted with cell culture medium before use, with the final DMSO concentration in the culture medium not exceeding 0.1%. The cell lines used were mouse hippocampal neurons HT-22 and mouse microglia BV-2.

[0045] 1. Main reagents

[0046] DMEM medium, fetal bovine serum (FBS), penicillin-streptomycin solution, L-glutamate, lipopolysaccharide (LPS, derived from E. coli O55:B5), dexamethasone (DXMS, positive control), CCK-8 cell proliferation and toxicity assay kit, nitric oxide (NO) assay kit, mouse tumor necrosis factor-α (TNF-α) ELISA kit, and mouse interleukin-6 (IL-6) ELISA kit.

[0047] 2. Experimental Methods

[0048] 2.1 Evaluation of neuroprotective effect (based on HT-22 cell model): Excess L-glutamate was used to induce excitotoxicity and oxidative stress damage in HT-22 cells, simulating the neuronal death process in AD. The protective ability of pigment was evaluated by detecting cell viability.

[0049] (1) Cell culture and seeding: HT-22 cells were cultured in DMEM complete medium containing 10% FBS and 1% penicillin antibiotics at 37°C. Cultured under controlled conditions. Collect cells in the logarithmic growth phase, digest and count them, then distribute per well. Cells were seeded at a density of 100 μL per well in 96-well plates and cultured for 24 hours to allow the cells to adhere.

[0050] (2) Grouping and drug administration: A normal control group, a model group, a positive control group, and experimental groups for each concentration of the sample were set up. The old culture medium was discarded and the cells were treated separately: 100 μL of fresh complete culture medium was added to the normal control group and the model group; 100 μL of culture medium containing different final concentrations (5, 10, 20 μM) of the test pigment was added to each experimental group. The cells were put back into the incubator for pretreatment for 4 hours.

[0051] (3) Modeling and culture: After pretreatment, 10 μL of L-glutamic acid solution (final concentration of 5 mM) was added to each well of the model group and each experimental group, and an equal volume of culture medium was added to the normal control group. Continue to culture for 24 hours.

[0052] (4) Cell viability assay: Add 10 μL of CCK-8 reagent to each well, gently shake to mix, and continue incubation in an incubator for 1 hour. Measure the absorbance (OD) value of each well at a wavelength of 450 nm using a microplate reader.

[0053] (5) Data processing: Cell viability (%) = [(OD experimental group - OD blank well) / (OD normal control group - OD blank well)] × 100%. The experiment was independently repeated 3 times.

[0054] 2.2 Evaluation of anti-neuroinflammatory effects (based on BV-2 cell model): BV-2 microglia were activated using LPS to induce the release of a large number of inflammatory factors (NO, TNF-α, IL-6) to simulate the neuroinflammatory state in AD. The anti-inflammatory ability of the pigment was evaluated by detecting the secretion level of these factors.

[0055] (1) Preliminary cytotoxicity experiment: BV-2 cells were divided into two groups per well. Cells were seeded at a density of 1,000 cells / well in 96-well plates and cultured for 24 hours. Different concentrations (5, 10, 20 μM) of pigment or culture medium were added to the groups, and after culturing for 4 hours, LPS solution (final concentration 0.5 μg / mL) was added and cultured for 20 hours. Cell viability was detected using the CCK-8 assay to ensure that the test concentration and experimental conditions were not toxic to the cells.

[0056] (2) Anti-inflammatory experiment grouping and administration: BV-2 cells were seeded as in (1). A normal control group, a model group, a positive control group (20 μM DXMS), and experimental groups for each concentration of the sample were set up. After replacing the culture medium with fresh medium, the normal control group and the model group were given complete culture medium, while the positive control group and each experimental group were given culture medium containing the corresponding compound. Pretreatment was carried out for 4 hours.

[0057] (3) Inflammation induction: Except for the normal control group, LPS solution was added to each well of the other groups to make the final concentration 0.5 μg / mL, and the normal control group was added with an equal volume of culture medium. Continue to culture for 20 hours.

[0058] (4) Sample collection and detection: Take some wells, add CCK-8 reagent for detection, and ensure that the drug itself does not affect cell survival during the establishment of the inflammation model. After the culture is completed, carefully aspirate the cell supernatant from the remaining wells, centrifuge at 4°C and 2000×g for 10 minutes to remove cell debris, aliquot and store at -80°C for later testing.

[0059] (5) Quantitative analysis of inflammatory factors: The concentration of nitrite (a stable metabolite of NO) in the supernatant was determined strictly according to the instructions of the nitrate reduction method NO detection kit. The corresponding mouse TNF-α and IL-6 ELISA kits were used for the same procedure, and the concentration of inflammatory factors in the cell supernatant was calculated using a standard curve.

[0060] (6) Data processing: The levels of inflammatory factors were calculated using a standard curve and expressed as concentrations. The experiment was independently repeated three times.

[0061] 2.3 Statistical Analysis

[0062] All data are expressed as mean ± standard deviation (mean ± SD). Statistical analysis was performed using GraphPad Prism 8.0 software. Two-way ANOVA was used to compare differences between groups. Statistical significance was defined as: , , .

[0063] 3. Experimental Results

[0064] 3.1 Results of neuroprotective effects

[0065] like Figure 2 As shown, compared with the normal control group, the survival rate of HT-22 cells in the model group treated with 5 mM L-glutamate for 24 hours significantly decreased to approximately 51.5%, indicating that the neurological injury model was successfully established. Pretreatment with six representative pigments all exhibited varying degrees of neuroprotective effects, with the effects being concentration-dependent. At a low concentration of 5 μM, both red pigments and the yellow pigment monascin showed significant protective activity (P < 0.05). At a concentration of 20 μM, except for the orange pigment monascorubrin (containing octanone), all other tested pigments significantly increased cell survival (P < 0.001).

[0066] Pigments containing pentanone side chains generally exhibit better neuroprotective activity than their counterparts containing octanone side chains. Among different colored pigments, red pigments show the strongest protective effect, followed by yellow pigments, while orange pigments exhibit relatively weaker activity.

[0067] 3.2 Results of anti-neuroinflammatory effects

[0068] like Figure 3 As shown, preliminary cytotoxicity experiments confirmed that 5-20 μM concentrations of the test pigment had no significant effect on the viability of BV-2 cells, ruling out the interference of drug toxicity on subsequent anti-inflammatory experiments. After LPS stimulation, the levels of NO, TNF-α, and IL-6 in the cell supernatant of the model group increased sharply by tens of times compared with the normal control group, indicating that the neuroinflammation model was successfully established.

[0069] Each pigment treatment inhibited the excessive secretion of the aforementioned inflammatory factors induced by LPS in a concentration-dependent manner. At the highest tested concentration (20 μM), the inhibitory effects of the red and yellow pigments on NO, TNF-α, and IL-6 were comparable to those of the positive control drug dexamethasone (20 μM), with no statistically significant difference.

[0070] In summary, the inhibitory effects of the three inflammatory factors showed that the red pigment exhibited the strongest anti-neuroinflammatory activity, followed by the yellow pigment, while the orange pigment had the weakest activity.

[0071] 4. Conclusion

[0072] This embodiment demonstrates, using a standard in vitro cell model, that the pigments in red yeast rice systematically identified by the method of this invention, particularly the red and yellow pigments, possess clear anti-Alzheimer's disease-related activities. They effectively counteract L-glutamate-induced neuronal excitotoxicity and inhibit the excessive secretion of key inflammatory factors by LPS-activated microglia. These results not only validate the biological functional relevance of the identified components but also provide important experimental evidence for the development of red yeast rice as a potential functional food or drug lead compound for anti-AD.

[0073] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for comprehensively analyzing pigment components in red yeast rice, characterized in that, Includes the following steps: (a) The red yeast rice sample was crushed and extracted to obtain the sample solution to be tested; (b) Perform liquid chromatography-tandem mass spectrometry analysis on the sample solution obtained in step (a) to obtain raw data files containing primary mass spectrometry and secondary mass spectrometry information; (c) Convert the raw data file obtained in step (b) into mzML format, import it into data analysis software, and screen out the component data with at least one of the aforementioned neutral loss or fragment ions based on the preset feature neutral loss and / or feature fragment ions; (d) Upload the component data screened in step (c) to the molecular network platform for molecular network analysis based on constituent units, generate a molecular network diagram, and identify the pigment components in red yeast rice by combining mass spectrometry data analysis and database comparison.

2. The method according to claim 1, characterized in that, In step (a), the extraction is performed by two shaking extractions using ethanol and 50% ethanol. The extracts are then combined, dried, reconstituted, and centrifuged.

3. The method according to claim 1, characterized in that, In step (b), the liquid chromatography uses a C18 column, and the mobile phase is an aqueous solution containing 0.1% formic acid and an acetonitrile solution containing 0.1% formic acid, with gradient elution.

4. The method according to claim 3, characterized in that, The gradient elution program is as follows: 0-0.5 min, 5% acetonitrile; 0.5-8.0 min, 5%-40% acetonitrile; 8.0-16.5 min, 40%-55% acetonitrile; 16.5-28.0 min, 55%-87% acetonitrile; 28.0-28.5 min, 87%-95% acetonitrile; 28.5-29.5 min, 95% acetonitrile.

5. The method according to claim 1, characterized in that, In step (b), the mass spectrometry analysis uses an electrospray ionization source, and in positive ion mode, data is acquired using auto MS / MS mode with collision energies of 20 eV and 30 eV.

6. The method according to claim 1, characterized in that, In step (c), the characteristic neutral loss is 98.0732 Da, 114.1045 Da, 126.1045 Da, or 142.1358 Da, and the characteristic fragment ion is m / z 212.11 or m / z 215.11; in the data analysis software, the mass tolerance for the characteristic neutral loss and characteristic fragment ion is set to 0.02 Da, and the ion abundance threshold is 2%.

7. The method according to claim 1, characterized in that, In step (d), the molecular network platform is the Global Natural Product Social Molecular Network Platform (GNPS). When performing feature network analysis, the parameters set include: the parent ion mass tolerance and fragment ion tolerance are both 0.02 Da, the minimum cosine score is 0.70, and the minimum number of matched fragment ions is 3.

8. The method according to claim 1, characterized in that, In step (d), the identification includes importing the screened component data into molecular formula prediction software to calculate the molecular formula, identifying known pigments by comparing them with literature and mass spectrometry databases, and inferring the structure of unknown pigments based on molecular network clustering characteristics and secondary mass spectrometry structure analysis software.

9. The application of the method according to any one of claims 1-8 in identifying pigment components in red yeast rice that have anti-Alzheimer's activity, wherein, The pigment components identified by the method are used to prepare drugs or functional foods with neuroprotective and / or anti-neuroinflammatory effects, wherein the neuroprotective effect is evaluated by an L-glutamate-induced HT-22 cell injury model, and the anti-neuroinflammatory effect is evaluated by an LPS-induced BV-2 cell inflammatory factor secretion model.

10. Use of red pigments (rubropunctamin, monascorubramine) and / or orange pigments (rubropunctatin, monascorubrin) in the preparation of medicaments for the treatment or improvement of Alzheimer's disease.

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