Method for researching influence mechanism of high-fat diet on health of old people by using mouse model

Abstract

The invention discloses a research method for researching an influence mechanism of high-fat diet on old-age health by using a mouse model, which comprises the following steps: S1, constructing a high-fat diet-induced mouse model: from normal diet feeding of mice to an old mouse stage, dividing the mice into two groups, and respectively performing high-fat diet feeding and normal diet feeding; s2, detecting physiological and functional indexes of the mice in the high-fat diet feeding group and the normal diet feeding group; s3, microbial diversity analysis: performing 16S rRNA sequencing to analyze the community structure of the intestinal microbiota of the mouse; s4, carrying out non-targeted metabonomics analysis: S4.1, carrying out intestinal tract non-targeted metabonomics sequencing; s4.2, carrying out non-targeted metabonomics sequencing on the liver tissues; s5, carrying out transcriptomics analysis; s6, performing conjoint analysis: performing the conjoint analysis on every two results of transcriptomics analysis, non-targeted metabonomics analysis and microbial diversity analysis; and S7, evaluating the influence of the high-fat diet on the health of the old rats by using the data obtained through analysis.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to a research method for studying the mechanism of the influence of a high-fat diet on the health of the elderly by using a mouse model. Background Art

[0002] Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases in the world and a major public health problem. In 2020, experts reclassified NAFLD as metabolic-associated fatty liver disease (MAFLD). Liver steatosis ≥ 5% combined with overweight or obesity, type 2 diabetes, or metabolic syndrome (Mets) can be diagnosed as MAFLD.

[0003] Dietary factors, especially a high-fat diet, play a key role in the progression of non-alcoholic fatty liver disease. Cellular senescence is a process in which the ability of cell proliferation and differentiation and physiological functions gradually decline. The elderly population has decreased metabolic efficiency and changes in lipid metabolism, making them susceptible to the harmful effects of a high-fat diet. In addition, the high incidence of co-morbidities such as obesity, diabetes, and cardiovascular disease further exacerbates the complex effects of a high-fat diet on liver health in this population. With the aging of the population, the incidence of metabolic diseases has increased year by year. As one of the important causes of metabolic diseases, the mechanism of a high-fat diet has not yet been fully clarified. Existing studies have shown that a high-fat diet can affect the metabolic function of the liver, but the specific mechanism still needs further exploration.

[0004] At present, some studies have explored the effect of a high-fat diet on hepatic steatosis in young mice through transcriptomics, non-targeted metabolomics, and single-omics or multiple-omics in microbial diversity sequencing. Some studies have also explored the effect of a high-fat diet on the health of old mice through single-omics. However, traditional single-omics analysis may not be able to fully evaluate and analyze the complexity of disease mechanisms, and a more comprehensive evaluation model is needed. Summary of the invention

[0005] The purpose of the present invention is to provide a research method for studying the mechanism of the effect of high-fat diet on the health of the elderly using a mouse model in response to the above problems.

[0006] In order to achieve its purpose, the present invention adopts the following technical solution:

[0007] The first aspect of the present invention provides a research method for studying the mechanism of the effect of a high-fat diet on the health of the elderly using a mouse model, comprising the following steps:

[0008] S1. Construction of a high-fat diet-induced mouse model: Mice were fed a normal diet until they reached the old stage, and then divided into two groups, one fed with a high-fat diet and the other fed with a normal diet;

[0009] S2. Detection of physiological and functional indicators of mice in the high-fat diet group and the normal diet group:

[0010] S2.1. Analyze the effects of a high-fat diet on physiological and morphological changes in aged mice;

[0011] S2.2. Effects of high-fat diet on liver function and blood lipid levels in aged mice;

[0012] S3. Microbial diversity analysis:

[0013] S3.1. Analyze the community structure of mouse intestinal microbiota by 16S rRNA sequencing: extract the mouse intestinal tissue genome, perform PCR amplification, construct sequencing library, and perform high-throughput sequencing data analysis;

[0014] S4. Non-targeted metabolomics analysis:

[0015] S4.1. Intestinal non-targeted metabolomics sequencing: Analysis of mouse colon tissue metabolites based on ultra-high performance liquid chromatography-Fourier transform mass spectrometry;

[0016] S4.2 Non-targeted metabolomics sequencing of liver tissue: LC-MS / MS analysis of mouse liver tissue;

[0017] S5. Transcriptomic analysis:

[0018] S5.1. Liver transcriptome sequencing: Perform eukaryotic mRNA sequencing on mouse liver tissue; measure the expression level of each transcript by TPM method, find differentially expressed genes DEGs between two samples, quantify gene abundance using RSEM, perform differential expression analysis using DEGseq or DESeq2, and conduct functional enrichment studies to determine whether DEGs are highly enriched in GO keywords and metabolic pathways relative to the whole transcriptome background;

[0019] S6. Combined analysis: Combine the results of transcriptomics analysis, non-targeted metabolomics analysis, and microbial diversity analysis in pairs;

[0020] S6.1. Joint analysis of transcriptome and liver metabolism: Identify the common enriched pathways of pathways with significantly enriched genes in the gene set and pathways with significantly enriched metabolites in the metabolic set, and create a metabolic set for the differential metabolites on the common enriched pathways;

[0021] S6.2. Joint analysis of liver metabolism and intestinal metabolism: Create a metabolic set of differential metabolites present in both liver metabolism and intestinal metabolism;

[0022] S6.3. Perform intersection analysis on the differential metabolites on pathways that are co-enriched in transcriptomics and liver metabolomics with the differential metabolites that exist in both liver metabolism and intestinal metabolism to obtain common metabolites and determine all pathways enriched in the common metabolites.

[0023] S6.4. Pearson correlation coefficient was used for quantitative analysis to draw a heat map of the correlation between genes and metabolites in different pathways enriched with common metabolites;

[0024] S6.5. Joint analysis of intestinal metabolism and microbial diversity: Spearman correlation analysis was used to determine the potential association between differentially abundant genera and differential metabolites, and metabolites were associated with microbial communities at the genus level. Heat maps were drawn to analyze the correlation between metabolites and microbial communities.

[0025] S7. Use the data obtained from the analysis to evaluate the impact of a high-fat diet on the health of elderly mice.

[0026] Preferably, in S3.1, after the sequencing data is quality controlled by fastp software, the original paired-end sequencing reads are assembled using the FLASH program, and then denoised by the DADA2 plug-in in the Qiime2 process to obtain ASVs; in order to reduce the impact of sequencing depth on subsequent Alpha and Beta diversity studies, each sample sequence is rarefied to 20,000, with an average sequence coverage of 99.09%; finally, based on the Silva 16S rRNA gene database, the Qiime2 classifier is used to conduct species taxonomy research on ASVs, and the PICRUSt2 algorithm is used for 16S function prediction research.

[0027] Preferably, in S4.1, the data analysis process includes: using Progenesis The QI metabolomics processing software performed baseline filtering, peak identification, integration, retention time correction and peak alignment on the raw LC-MS data to generate a data matrix containing peak intensity, retention time and mass-to-charge ratio; metabolite information was obtained by comparing the MS and MSMS spectral data in the HMDB, Metlin public metabolic database and Majorbio's own library; the data matrix was uploaded to the Majorbio cloud platform, and the 80% rule was used to remove missing values ​​and fill them with the minimum value in the original matrix, and then the sum was normalized to reduce errors; after removing variables with relative standard deviations (RSDs) greater than 30% in QC samples, log10 transformation was performed; the preprocessed data matrix was subjected to principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) using the ropls package of the R language, and the model stability was evaluated using the seven-fold cross-validation method; the significantly differential metabolites (VIP>1 and p<0.05) were screened according to the variable importance projection (VIP) value of the OPLS-DA model and the p value of the Student's t test; finally, the KEGG database was used to annotate the metabolic pathways involved in the differential metabolites.

[0028] Preferably, in S4.1, the chromatographic conditions are as follows: mobile phase B is 47.5% acetonitrile, 47.5% isopropanol and 5% water, the water contains 0.1% formic acid, mobile phase A is 95% water and 5% acetonitrile, the acetonitrile contains 0.1% formic acid; an ACQUITY UPLCHSS T3 chromatographic column is used; the injection volume is 3 μL, and the column temperature is maintained at 40°C;

[0029] The mass spectrometer was set up with electrospray ionization, and data were acquired in positive and negative ion scanning modes with a mass scanning range of 70–1050 m / z. The collision energy was cycled between 20-40-60 eV, the ion transfer tube temperature was 325 °C, the auxiliary gas heater temperature was 425 °C, the sheath gas flow rate was 50 psi, and the auxiliary spray voltage was 3500 V in both positive and negative modes. The primary mass spectrometer resolution was 60,000, the secondary mass spectrometer resolution was 7500, and the DDA data acquisition method was used.

[0030] Preferably, in S4.2, the LC-MS raw data is imported into the metabolomics processing software for preprocessing, and matched with multiple metabolite databases to identify metabolites; the preprocessed data matrix is ​​uploaded to the cloud platform for further analysis, including data cleaning, normalization and logarithmic processing; finally, the significantly different metabolites are screened out by principal component analysis, orthogonal least partial squares discriminant analysis and other methods, and the KEGG database is used for metabolic pathway annotation and enrichment analysis to determine the biological pathways most relevant to the experimental treatment.

[0031] Preferably, in S5.1, the functional enrichment study includes GO and KEGG analysis, KOBAS is used for KEGG pathway analysis, and Goatools is used for GO functional enrichment.

[0032] Preferably, in S6.3, all pathways enriched with common metabolites are determined according to the screening criterion of P-value < 0.05.

[0033] Preferably, in S6.4, the screening criteria are set as a correlation P value less than 0.05 and a correlation absolute value greater than 0.8.

[0034] The beneficial effects of the present invention are:

[0035] This invention has established a research method based on multi-omics analysis and using mouse models to study the mechanism of the impact of high-fat diet on the health of the elderly. The goal is to deeply and comprehensively understand the multi-dimensional impact of high-fat diet on the elderly body, reveal the dynamic changes of biological molecules in the elderly body under the intervention of high-fat diet, the regulatory pattern of gene expression, the regulatory mechanism of metabolic pathways, etc., so as to further explore the comprehensive impact of high-fat diet on the physiological function, metabolic state, immune response and disease risk of the elderly body. Through this method, we hope to provide a powerful tool for in-depth research on the impact of high-fat diet on the health of the elderly body, and also provide new ideas and methods for the prevention and treatment of geriatric diseases related to high-fat diet. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Shows the effects of a high-fat diet on the physiological and morphological changes in aged mice.

[0037] Figure 2 Shows the effects of a high-fat diet on liver function and blood lipid levels in aged mice.

[0038] Figure 3 Shows the effects of a high-fat diet on the intestinal flora.

[0039] Figure 4 The effects of a high-fat diet on the metabolism of colonic contents are shown.

[0040] Figure 5 Shown are the effects of a high-fat diet on liver metabolism.

[0041] Figure 6 The effects of a high-fat diet on liver transcriptomics are shown.

[0042] Figure 7 It is the result of the combined analysis of transcriptomics and metabolomics.

[0043] Figure 8 It is the result of the combined analysis of transcriptomics, metabolomics, and microbial diversity. DETAILED DESCRIPTION

[0044] The present invention will be further described below in conjunction with embodiments, but the present invention is not limited thereto.

[0045] The experimental methods in the following examples are conventional methods unless otherwise specified.

[0046] Example 1

[0047] 1 Animal experiments

[0048] The experimental animals used were male CBA / CaJ mice approved by the Institutional Animal Care and Use Committee (IACUC) provided by the Experimental Animal Center of Chongqing Medical University. For a period of two years, the mice were kept in a strictly controlled environment with a 12-hour light and dark cycle, a temperature maintained at 21±2°C, and a humidity maintained in the range of 50±10%. At the same time, regular feed was provided to the mice. Subsequently, we divided the mice into two groups. One group was fed a high-fat diet (HFD D09100310; provided by Research Diets; 40% fat content, 20% fructose content, and 2% cholesterol content), while the other group continued to be fed with regular feed as a control group. After a period of 14 weeks, the mice were killed by cervical dislocation, and their liver and blood samples were collected.

[0049] 2 Experimental methods

[0050] 2.1 Histological analysis

[0051] To determine the status of hepatic steatosis, the liver tissues of mice were paraffin-embedded, fixed with 4% paraformaldehyde solution, and then stained with HE. Afterwards, the samples were analyzed using an optical microscope.

[0052] 2.2 16S rRNA sequencing analysis

[0053] Extraction of mouse intestinal tissue genome, PCR amplification, sequencing library construction and high-throughput sequencing data analysis were performed. The specific steps include:

[0054] First, the intestinal tissues of mice were taken after euthanasia, sterilized and stored at -80°C. Next, the whole genomic DNA was extracted, and its purity and content were evaluated using NanoDrop 2000, and its integrity was evaluated by 1% agarose gel electrophoresis. Then, PCR amplification was performed, including a three-minute denaturation stage at 95°C and 27 cycles of denaturation, annealing and extension steps, and finally a final extension of 10 minutes at 72°C. The samples were stored at 10°C until the end of the experiment. The PCR experiment was performed using TransGenAP221-02: TransStart Fastpfu DNA polymerase in a specific reaction system, and the products were stored at 4°C. Each sample was repeated three times, and the PCR product was recovered and purified by 2% agarose gel electrophoresis to confirm the band size and calculate the amount of recovered compounds.

[0055] Subsequently, the sequencing library was constructed using the NEXTFLEX RapidDNA-Seq Kit and purified PCR products, and sequencing was performed using the IlluminaNextSeq 2000PE300 platform. After the sequencing data was quality controlled by fastp software, the original paired-end sequencing reads were assembled using the FLASH program, and then denoised using the DADA2 plug-in in the Qiime2 process to obtain ASVs (amplicon sequence variants). In order to reduce the impact of sequencing depth on subsequent Alpha and Beta diversity studies, each sample sequence was rarefied to 20,000, with an average sequence coverage of 99.09%. Finally, based on the Silva 16SrRNA gene database, the Qiime2 classifier was used to study the species taxonomy of ASVs, and the PICRUSt2 algorithm was used for 16S function prediction.

[0056] 2.3 Non-targeted intestinal metabolomics sequencing

[0057] Metabolites of mouse colon tissue were analyzed based on ultra-high performance liquid chromatography-Fourier transform mass spectrometry (UHPLC-Q Exactive HF-X) technology.

[0058] The colon tissue of mice was removed after anesthesia, placed in a sterile tube and frozen at -80°C. 30±5 mg of tissue sample was taken and placed in a 2 ml centrifuge tube together with a 6 mm diameter grinding bead. Subsequently, 250 μl of extraction solution (methanol: water = 4:1 (v:v)) containing 0.02 mg / ml internal standard (L-2-chlorophenylalanine) was added, and dissolved and crushed using a frozen tissue grinder at -10°C and 50 Hz for 6 minutes. After that, low-temperature ultrasonic extraction was performed at 5°C and 40 kHz for 30 minutes, and then allowed to stand at -20°C for 30 minutes. After the sample was centrifuged at 13,000g and 4°C for 15 minutes, the supernatant was taken and injected into a sample injection bottle equipped with an inner insert for testing. At the same time, 20 μl of supernatant was taken from every 5 samples and mixed to make a quality control (QC) sample to evaluate the stability of the entire detection process.

[0059] The chromatographic conditions were as follows: mobile phase B was 47.5% acetonitrile, 47.5% isopropanol, and 5% water (containing 0.1% formic acid), and mobile phase A was 95% water and 5% acetonitrile (containing 0.1% formic acid); an ACQUITY UPLC HSS T3 column (100 mm × 2.1 mm, 1.8 μm, Waters, Milford, USA) was used; the injection volume was 3 μL, and the column temperature was maintained at 40°C. The mass spectrometer was set up using electrospray ionization, data were acquired in positive and negative ion scanning modes, and the mass scanning range was 70–1050 m / z; the collision energy was cycled between 20-40-60 eV, the ion transfer tube temperature was 325°C, the auxiliary gas heater temperature was 425°C, the sheath gas flow rate was 50 psi, and the auxiliary spray voltage in both positive and negative modes was 3500 V; the primary mass spectrometer resolution was 60,000, the secondary mass spectrometer resolution was 7500, and the DDA data acquisition method was used.

[0060] The data analysis process included baseline filtering, peak identification, integration, retention time correction, and peak alignment of the raw LC-MS data using Progenesis QI metabolomics processing software (Waters, Milford, USA) to generate a data matrix containing peak intensity, retention time, and mass-to-charge ratio (m / z). Metabolite information was obtained by comparing MS and MSMS spectral data in the HMDB, Metlin public metabolic database, and Majorbio's own library. The data matrix was uploaded to the Majorbio cloud platform, and the 80% rule was used to remove missing values ​​and fill them with the minimum value in the original matrix, and then sum normalization was performed to reduce errors. After removing variables with relative standard deviations (RSDs) greater than 30% in QC samples, log10 transformation was performed. The preprocessed data matrix was subjected to principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) using the ropls package in R language, and the model stability was evaluated using the seven-fold cross-validation method. The significantly differential metabolites (VIP>1 and p<0.05) were screened according to the variable importance projection (VIP) value of the OPLS-DA model and the p value of the Student's t-test. Finally, the KEGG database was used to annotate the metabolic pathways involved in the differential metabolites.

[0061] 2.4 Non-targeted metabolomics sequencing of liver tissue

[0062] After killing the mice, the liver tissue was taken, placed in a sterile tube and frozen at -80°C. 50±5 mg of liver tissue was accurately weighed and placed in a 2 ml centrifuge tube, and a grinding bead with a diameter of 6 mm was added. 400 μl of extraction solution (methanol to water volume ratio of 4:1) was prepared, which contained four internal standard substances (such as L-2-chlorophenylalanine at a concentration of 0.02 mg / ml) for metabolite extraction. The mixture was ground for six minutes using a frozen tissue grinder at -10°C and 50 Hz. Thirty minutes later, low-temperature ultrasonic extraction was performed at 40 kHz and 5°C. After the extract was allowed to stand at -20°C for 30 minutes, it was centrifuged at 13,000g for 15 minutes at 4°C, and the supernatant was injected into a sample injection bottle equipped with an inner insert for testing. At the same time, equal volumes of all sample metabolites were mixed to prepare quality control samples (QC). During the instrument analysis process, a QC sample was inserted into every 5 samples to examine the repeatability of the entire analysis process.

[0063] The samples were analyzed by LC-MS / MS using the Thermo Fisher ultra-high performance liquid chromatography tandem Fourier transform mass spectrometry system. Chromatographic and mass spectrometric conditions were set in detail to optimize separation and detection. After completion of the machine, the LC-MS raw data was imported into the metabolomics processing software for preprocessing and matched with multiple metabolite databases to identify metabolites. The preprocessed data matrix was uploaded to the cloud platform for further analysis, including data cleaning, normalization, and logarithmic processing. Finally, significant differential metabolites were screened out by principal component analysis, orthogonal least partial squares discriminant analysis, and metabolic pathway annotation and enrichment analysis were performed using the KEGG database to determine the biological pathways most relevant to the experimental treatment.

[0064] 2.5 Liver transcriptome sequencing

[0065] Eukaryotic mRNA sequencing of mouse liver tissue was performed based on the Illumina platform. The specific steps are as follows: liver tissue was removed from the mouse and stored in a sterile tube at -80°C. Reagents were used to extract total RNA from tissue samples according to the supplier's instructions. RNA integrity was assessed by agarose gel electrophoresis, RNA concentration and purity were determined by Nanodrop 2000, and RIN values ​​were calculated by Agilent 5300 Bioanalyzer. When constructing the sequencing experiment library, the Illumina NovaSeq reagent kit method was used, requiring a total RNA amount of 1 μg, a concentration of ≥30 ng / μl, a RIN value of ≥6.5, and an OD260 / 280 ratio between 1.8 and 2.2. mRNA was isolated from total RNA using poly(A) selection and oligo(dT) magnetic beads, followed by fragmentation. Double-stranded cDNA was synthesized using the kit and random hexamer primers. After end repair, phosphorylation, and "A" base addition, a 300 bp cDNA target fragment was selected on a 2% low-range super agarose gel and amplified by PCR for 15 cycles. After quantification using Qubit 4.0, paired-end RNA-seq sequencing was performed on a NovaSeq Xplus machine. The raw paired-end data were trimmed and quality checked using Fastp, and each clean read was individually aligned to the reference genome using HISAT2 software in strand-specific mode. Afterwards, StringTie was used to construct aligned reads for each sample. The expression level of each transcript was measured by the TPM method, and differentially expressed genes (DEGs) between the two samples were found. Gene abundance was quantified using RSEM, and genes with |log2FC|>1 and FDR<0.05 (for DESeq2) or ≤0.001 (for DEGseq) were considered to have significantly changed expression. Differential expression analysis was performed using DEGseq or DESeq2, and functional enrichment studies, including GO and KEGG analysis, were performed to determine whether the DEGs were highly enriched in GO keywords and metabolic pathways relative to the entire transcriptome background. Multiple test correction techniques were used to obtain corrected P values ​​<0.05. KOBAS was used for KEGG pathway analysis, and Goatools was used for GO functional enrichment. All alternative splicing events present in the sample were determined using rMATS technology, including splicing modifications such as exon inclusion, exon exclusion, alternate 5' and 3' splice sites, and intron retention events. Only isoforms with different splice junctions or similar to the reference were considered.

[0066] 2.6 Statistical analysis

[0067] All results are expressed as mean ± standard error (SEM). For unpaired tests (quantitative liver histology), P values ​​less than 0.05 were considered statistically significant (two-sided), except for transcriptome sequencing, metabolite analysis, and gut microbiota analysis.

[0068] 3 Experimental results

[0069] 3.1 Aged mice developed severe metabolic-associated fatty liver disease (MAFLD) after being fed a high-fat diet

[0070] 3.1.1 Effects of high-fat diet on physiological and morphological changes in aged mice

[0071] After conducting a high-fat diet (HFD) feeding experiment, we observed a series of significant changes. First, compared with the old mice in the standard diet (SD) group, the old mice in the HFD group showed signs of sparse hair. This change in appearance directly reflects the impact of high-fat diet on the skin and hair health of old mice. At the same time, the old mice in the HFD group not only appeared larger in size ( Figure 1 A), and their body weight was significantly higher than that of the SD group ( Figure 1 B), which strongly suggests that a high-fat diet promotes weight gain. It is worth noting that although the weight of the elderly mice in the HFD group increased significantly, their food intake was unexpectedly lower than that in the SD group (P < 0.05). Figure 1 C), this finding suggests that a high-fat diet may promote weight gain by increasing the energy density of food rather than increasing food intake. After 14 weeks of high-fat diet intervention, the ratio of liver weight to body weight in the HFD group was significantly higher than that in the SD group (p<0.05) ( Figure 1 D), which suggests that a high-fat diet may induce fat accumulation or structural changes in the liver. In order to further explore the specific effects of a high-fat diet on liver tissue morphology, we used H&E staining technology to carefully observe the liver samples of elderly mice. The results clearly showed that compared with the SD group, the liver of the HFD group showed obvious fatty degeneration, which was manifested by the presence of round lipid droplets of varying sizes and no coloring scattered in the cytoplasm of hepatocytes ( Figure 1 E). This pathological change directly confirms that a high-fat diet can lead to fat deposition in the liver, which in turn may increase the risk of a series of metabolic diseases.

[0072] In summary, our study not only revealed the significant effects of a high-fat diet on the appearance, body weight, food intake, and liver weight to body weight ratio of elderly mice, but also further confirmed its adverse changes in liver tissue morphology through histological analysis, providing strong evidence to support understanding the association between a high-fat diet and health risks.

[0073] 3.1.2 Effects of high-fat diet on liver function and blood lipid levels in aged mice

[0074] After comparing and analyzing the liver damage and metabolic indicators of the aged mice in the standard diet group (SD group) and the high-fat diet group (HFD group), we observed the following significant differences: By detecting liver function markers, we found that the levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in the HFD group were significantly increased compared with those in the SD group ( Figure 2 A, 2B), which strongly suggests that high-fat diet induces substantial liver damage in aged mice. The blood glucose concentration in the HFD group was significantly higher than that in the SD group ( Figure 2 C), which not only reflects the disruption of glucose metabolism in elderly mice by high-fat diet, but also indicates the potential risk of metabolic syndrome, such as the emergence of insulin resistance or prediabetes. Blood lipid analysis showed that the total cholesterol (TC) content in the HFD group was significantly higher than that in the SD group ( Figure 2 D), which is an important marker for increased risk of cardiovascular disease. At the same time, high-density lipoprotein cholesterol (HDL-C) remained high in the SD group, while it was relatively low in the HFD group ( Figure 2 E), indicating that a high-fat diet reduces the proportion of "good cholesterol" in the blood, further exacerbating the threat to cardiovascular health. The LDL-C level in the SD group was significantly lower than that in the HFD group ( Figure 2 F), this finding directly points to the fact that a high-fat diet promotes the accumulation of "bad cholesterol", which is a direct factor in the increased risk of atherosclerosis and cardiovascular disease.

[0075] In summary, these research results clearly show that a high-fat diet not only causes liver damage in elderly mice, but also seriously interferes with their metabolic homeostasis, especially by increasing blood sugar, total cholesterol and low-density lipoprotein cholesterol levels, and reducing high-density lipoprotein cholesterol levels, significantly increasing the risk of cardiovascular disease. These findings provide an important basis for understanding the negative impact of a high-fat diet on the health of the elderly population, and lay an experimental foundation for the subsequent development of intervention strategies for metabolic diseases and liver protection.

[0076] 3.2 Effects of a high-fat diet on intestinal flora

[0077] We explored the effect of a high-fat diet (HFD) on the structure of the intestinal microbial community. The overall microbial structure was analyzed by detecting the Alpha diversity of the colonic flora, and the samples were analyzed for diversity based on the construction of the ASV set. The results showed that the Shannon index at the ASV level of the index group difference test was not statistically significant between the two groups, indicating that the diversity of microorganisms after high-fat diet feeding remained relatively stable between the groups ( Figure 3A). The dilution curve of the Sobs index at the ASV level shows that the curve tends to be flat towards the end, and the species diversity of each group gradually reaches stability, indicating that the amount of sequencing data is sufficient and the sequencing data is reasonable ( Figure 3 B). Based on the principal coordinate analysis (PcoA) of the relative abundance at the genus level between the groups, it was found that the intestinal microbiota of the SD group and the HFD group were significantly separated ( Figure 3 C). We performed an in-depth phylum-level analysis of the community composition of colonic contents, and the results showed that Firmicutes and Bacteroidetes were dominant in the SD group, while Proteobacteria and Verrucomicrobia were dominant in the HFD group ( Figure 3 D). In order to more specifically depict the community characteristics at the genus level, we further studied the top 20 bacterial genera in relative abundance and displayed them intuitively through heat maps. The results showed that the dominant bacterial communities in the SD group mainly included Lactobacillus, norank_f_Muribaculaceae, and Bacillus, while in the HFD group, Escherichia coli-Shigella, Akkermansia muciniphila, Dunaliella, and Heterobacterium became the dominant bacterial communities ( Figure 3 E). To dig deeper into the differences among species, we used the Wilcoxon rank sum test at the genus level. At a 95% confidence level, we found that the proportions of Escherichia coli-Shigella, Dunaliella, and Ruminococcus_gnavus_group were significantly increased in the HFD group, and the proportions of Lachnospiraceae_NK4A136_group and Muribaculum were significantly increased in the SD group ( Figure 3 F). Finally, we performed a current discriminant analysis (LDA). Combined with the effect size measurement, characteristic taxa with significant differences in abundance were identified. The results of taxa with LDA scores greater than 2.0 showed that Shigella and Dunaliella showed the highest abundance in the HFD group, and Lachnospiraceae_NK4A136_group showed the highest abundance in the SD group ( Figure 3 G). The above results reveal the profound impact of a high-fat diet on certain species of intestinal microbiota, and also provide strong support for subsequent functional studies.

[0078] 3.3 Effects of a high-fat diet on colonic content metabolism

[0079] The colon contents collected from the HFD group and the SD group were analyzed using non-targeted metabolomics technology based on liquid chromatography-mass spectrometry (LC-MS). The differential metabolites between the two groups were screened using the orthogonal partial least squares discriminant analysis (OPLS-DA) method, and the results showed that the two groups were significantly divided into groups, and the overall levels of the two groups were significantly different ( Figure 4A). Further visualization analysis using the volcano plot showed that compared with the SD group, the HFD group had 1,325 significantly upregulated metabolites and 320 significantly downregulated metabolites. The screening of these significantly differential metabolites strictly followed the statistical standards of P-value < 0.05 and variable projection importance VIP > 1 ( Figure 4 B). In addition, the KEGG pathways involved in these differential metabolites were enriched and analyzed. The figure shows the top 20 pathways with enriched P-values. The results showed that a high-fat diet had a significant effect on arachidonic acid metabolism, steroid hormone biosynthesis, 5-hydroxytryptamine synapses, bile secretion and other pathways (P < 0.05) ( Figure 4 C) To identify the metabolites that contributed most to the classification, the variable projection importance VIP analysis method of the first principal component was used. According to the weighted coefficients of the OPLS-DA model, the VIP score was used to rank the metabolites' contribution to the discrimination of the HFD group and the SD group. It was found that the five metabolites that contributed most to the difference between the two groups were Dibucaine, Apigenin 7-sulfate, Coriandrone B,

[0080] N-(6-Cyano-3-hydroxy-2,2-dimethyl-3,4-dihydrochromen-4-yl)-N-methylethanesulfonamide, N5-(4-Methoxybenzyl)glutamine( Figure 4 D) These findings provide important clues for studying the effects of a high-fat diet on colonic metabolism.

[0081] 3.4 Effects of a high-fat diet on liver metabolism

[0082] Using non-targeted metabolomics technology based on liquid chromatography-mass spectrometry (LC-MS), we deeply analyzed the metabolism of the liver of aged mice. Through partial least squares discriminant analysis (PLS-DA), we successfully screened out metabolites with significant differences between the two groups. These metabolites with significant differences and stable high expression are likely to be involved in important metabolic pathways and play extremely important biological functions. It can be clearly seen from the PLS-DA diagram that the separation degree of the two groups of samples is quite large and the classification effect is very significant, which highlights the significant differences between different groups related to liver metabolism ( Figure 5 A). The volcano plot shows that compared with the SD group, the HFD group had 147 significantly upregulated metabolites and 227 significantly downregulated metabolites ( Figure 5B). In order to more intuitively show the effects of different diets on liver metabolites, a cluster heat map analysis was performed on the differential metabolites of the HFD group and the SD group. The cluster heat map showed clear clustering and demonstrated the metabolic characteristics under different dietary conditions ( Figure 5 C). By comparing with the KEGG pathway database, it was found that the most significantly enriched pathways in liver metabolism included retinol metabolism, one-carbon pool formed by folic acid, anti-folate resistance, and retrograde endocannabinoid signaling pathways ( Figure 5 D) Through VIP plot analysis, the five metabolites with the greatest differences between the two groups were identified, namely 24-Epibrassinolide,

[0083] 3-Hydroxy-6,8-dimethoxy-7(11)-eremophilen-12,8-olide、5-Amino-6-ribitylamino uracil、

[0084] 2,3-Dinor-TXB1, 2-Anthraquinonesulfonic acid( Figure 5 E). These findings provide new clues and ideas for in-depth research on liver metabolic mechanisms and related diseases.

[0085] 3.5 Effects of a high-fat diet on liver transcriptomics

[0086] Transcriptomic analysis of the livers of the HFD and SD groups was performed. We used the principal component analysis method, and the results showed that there was a significant separation between the two groups of samples ( Figure 6 A). Through differentially expressed gene analysis, a total of 3835 differentially expressed genes (DEGs) were identified, of which 1924 differentially expressed genes were significantly up-regulated and 1911 differentially expressed genes were significantly down-regulated ( Figure 6 B). In order to have a more comprehensive understanding of the biological functions of these differentially expressed genes, the KEGG database was referenced and a KEGG classification statistical bar chart of differentially expressed genes (i.e., KEGG histogram) was drawn. The chart clearly shows the top 30 KEGG pathways in terms of abundance. The analysis results show that there are 9 pathways involved in human diseases, 7 in organic systems, 4 in cellular processes, 2 in environmental information processing, 3 in genetic information processing, and 5 in metabolic pathways ( Figure 6 C). Further KEGG enrichment analysis revealed that the up-regulated differentially expressed genes were mainly enriched in specific biological pathways, including adherens junction pathway, MAPK signaling pathway, proteoglycan pathway in cancer, Rap1 signaling pathway, FoxO signaling pathway, etc. ( Figure 6 D). At the same time, KEGG enrichment analysis showed that the down-regulated differentially expressed genes were mainly enriched in ribosome pathway, oxidative phosphorylation pathway, novel coronavirus infection, Parkinson's disease and other related pathways ( Figure 6 E). The above findings provide valuable reference information for subsequent functional research and clinical application.

[0087] 3.6 Joint analysis of transcriptomics, metabolomics, and microbial diversity

[0088] In order to further explore the mechanism of action of high-fat diet on metabolic-related fatty liver disease in elderly mice, we used a joint analysis method of transcriptomics, metabolomics, and microbial diversity. Through KEGG pathway enrichment analysis, we conducted in-depth research on the screened differential gene sets and differential metabolic sets. Using the hypergeometric distribution algorithm, we successfully identified pathways with significantly enriched genes in the gene set and pathways with significantly enriched metabolites in the metabolic set. By comparing these two sets, we found that there are 8 pathways that are significantly enriched by both the gene set and the metabolic set ( Figure 7 A). These commonly enriched pathways included thermogenesis, retrograde endocannabinoid signaling, hepatocellular carcinoma, retinol metabolism, EGFR tyrosine kinase inhibitor resistance, non-small cell lung cancer, bile secretion, and arginine and proline metabolism ( Figure 7 B). The discovery of these enriched pathways laid a solid foundation for us to deeply analyze the effects of high-fat diet on metabolic diseases in elderly mice.

[0089] We then created a metabolite set based on the differential metabolites on the eight pathways that were co-enriched by transcriptomics and liver metabolomics, and created another metabolite set based on the differential metabolites that existed in both liver metabolism and intestinal metabolism. Intersection analysis of the two metabolite sets revealed that there were three common metabolites between them ( Figure 7 C). To further explore the functional characteristics of these three metabolites, we further identified all pathways enriched by them based on the screening criteria of P-value < 0.05. The results showed that a total of 9 pathways were significantly associated with these three metabolites. Four of these 9 pathways coincided with the 8 pathways we previously co-enriched through transcriptomics and liver metabolomics. These 4 key pathways are bile secretion, retinol metabolism, thermogenesis, and retrograde endocannabinoid signaling ( Figure 7 D).

[0090] In order to further explore the correlation between three specific metabolites and related genes in four key metabolic pathways, we used the Pearson correlation coefficient for quantitative analysis and drew a heat map based on it. The screening criteria were set as a correlation P value less than 0.05 and a correlation absolute value greater than 0.8, and four correlation heat maps were successfully constructed. It can be clearly observed from the heat map that there is a significant positive correlation between the metabolite MG (0:0 / 20:4 (5Z, 8Z, 11Z, 14Z) / 0:0) in the thermogenesis pathway and the mt-Nd5 and mt-Nd6 genes. However, this metabolite showed a significant negative correlation with genes such as different subunits of ATP synthase (including Atp5f1d, Atp5f1e, Atp5mc1, etc.), cytochrome c oxidase (such as Cox4i1, Cox6a1, etc.), multiple subunits of NADH dehydrogenase (coenzyme Q), and multiple components of mitochondrial respiratory chain complex III ( Figure 8 A). This metabolite is also involved in the retrograde endocannabinoid signaling pathway, showing a significant negative correlation with genes encoding multiple subunits of the mitochondrial respiratory chain complex I, while maintaining a significant positive correlation with mt-Nd5 and mt-Nd6 ( Figure 8 B). In the retinol metabolic pathway, we found that the metabolite 4-Hydroxyretinoic acid was significantly negatively correlated with genes such as Aox3 and Dhrs9, and significantly positively correlated with genes such as Cyp26a1 and Cyp3a44 ( Figure 8 C). In addition, in the bile secretion pathway, the metabolite folic acid was significantly negatively correlated with genes such as Abcg2 and Slc5a1, and significantly positively correlated with genes such as Adcy2 and Slc51b ( Figure 8 D).

[0091] Next, we conducted an in-depth joint analysis of intestinal metabolism and microbial diversity. Spearman correlation analysis was used to determine the potential association between differentially abundant genera and differential metabolites. Based on the significance threshold (P<0.05), 16 genus-level microbiota were found to be significantly different between the two groups. The three metabolites were associated with the 16 genus-level microbiota, and a heat map was drawn ( Figure 8E). It can be seen from the figure that the metabolite folic acid is significantly negatively correlated with multiple microbial communities norank_f__Desulfovibrionaceae, Gordonibacter, UBA1819, Anaerofustis, Lactococcus, Clostridioides, Mucispirillum, and Ruminococcus_gnavus_group; the metabolite MG (0:0 / 20:4(5Z,8Z,11Z,14Z) / 0:0) is significantly negatively correlated with Ileibacterium; the metabolite 4-Hydroxyretinoic acid is significantly positively correlated with Dubosiella and significantly negatively correlated with Ileibacterium.

[0092] 4 Analysis

[0093] Through in-depth joint analysis, we revealed the potential mechanism of high-fat diet on the health of elderly mice. The results showed that high-fat diet changes the composition of intestinal microorganisms, thereby regulating metabolite levels, ultimately affecting gene transcription and health status. This discovery provides a new perspective for understanding the various health problems faced by elderly mice, such as obesity, metabolic syndrome and cardiovascular disease.

[0094] First, we found that a high-fat diet exerts its effects through thermogenesis pathway. High-fat intake changes the abundance of gut microbiota, specifically promoting the growth of Ileibacterium. This bacterium is associated with specific metabolites.

[0095] The levels of MG (0:0 / 20:4 (5Z, 8Z, 11Z, 14Z) / 0:0) were negatively correlated, and the increase in its number led to a decrease in the level of the metabolite MG (0:0 / 20:4 (5Z, 8Z, 11Z, 14Z) / 0:0). This decrease further affected the transcription of genes related to energy metabolism, such as ATP synthase and mitochondrial respiratory chain complexes, suggesting that energy metabolism may be abnormal, which may lead to obesity and insulin resistance in elderly mice.

[0096] Secondly, similar effects were observed in the retrograde endocannabinoid signaling pathway. Ileibacterium affected this pathway by reducing MG(0:0 / 20:4(5Z,8Z,11Z,14Z) / 0:0) levels, which were negatively correlated with genes encoding subunits of the mitochondrial respiratory chain complex I, suggesting that the transcription of these genes may be upregulated, which may interfere with mitochondrial function. This is closely related to health problems such as accelerated aging process and decreased nervous system function in aged mice.

[0097] In addition, in the retinol metabolic pathway, we found that Dubosiella microorganisms may promote the metabolic process of retinol, thereby increasing the level of 4-Hydroxyretinoic acid, while Ileibacterium bacteria showed a trend of reducing its level. This change in metabolite levels further affected gene transcription in the retinol metabolic pathway, which may affect the visual health, skin health and immune function of aged mice.

[0098] Finally, in terms of the bile secretion pathway, we found that various microbial communities may affect the secretion and composition of bile by reducing folate levels. When folate levels decrease, the transcription of genes such as Abcg2 and Slc5a1 may be upregulated. Abnormal bile secretion may lead to health problems such as abnormal cholesterol metabolism and fatty liver in aged mice.

[0099] In summary, the present invention deeply reveals a series of mechanisms by which a high-fat diet regulates the composition of intestinal microorganisms, further changes metabolite levels, and ultimately affects gene transcription and overall health status. These mechanisms are closely related to a variety of health problems faced by elderly mice, such as obesity, metabolic syndrome, and cardiovascular disease. Through this discovery, we not only gained a deep insight into the complex interactions between metabolites, genes, and microbial communities, but also provided a new method for evaluating the impact of a high-fat diet on elderly health. These research results are expected to provide new ideas for biomedical research and valuable clues for application development, thereby promoting the advancement of health sciences.

Claims

1. A research method for studying the mechanism of the effect of high-fat diet on the health of the elderly using a mouse model, characterized in that: The steps include: S1. Construction of a high-fat diet-induced mouse model: Mice were fed a normal diet until they reached the old stage, and then divided into two groups, one fed with a high-fat diet and the other fed with a normal diet; S2. Detection of physiological and functional indicators of mice in the high-fat diet group and the normal diet group: S2.

1. Analyze the effects of a high-fat diet on physiological and morphological changes in aged mice; S2.

2. Effects of high-fat diet on liver function and blood lipid levels in aged mice; S3. Microbial diversity analysis: S3.

1. Analyze the community structure of mouse intestinal microbiota by 16S rRNA sequencing: extract the mouse intestinal tissue genome, perform PCR amplification, construct sequencing library, and perform high-throughput sequencing data analysis; S4. Non-targeted metabolomics analysis: S4.

1. Intestinal non-targeted metabolomics sequencing: Analysis of mouse colon tissue metabolites based on ultra-high performance liquid chromatography-Fourier transform mass spectrometry; S4.2 Non-targeted metabolomics sequencing of liver tissue: LC-MS / MS analysis of mouse liver tissue; S5. Transcriptomic analysis: S5.

1. Liver transcriptome sequencing: Perform eukaryotic mRNA sequencing on mouse liver tissue; measure the expression level of each transcript by TPM method, find differentially expressed genes DEGs between two samples, quantify gene abundance using RSEM, perform differential expression analysis using DEGseq or DESeq2, and conduct functional enrichment studies to determine whether DEGs are highly enriched in GO keywords and metabolic pathways relative to the whole transcriptome background; S6. Combined analysis: Combine the results of transcriptomics analysis, non-targeted metabolomics analysis, and microbial diversity analysis in pairs; S6.

1. Joint analysis of transcriptome and liver metabolism: Identify the common enriched pathways of pathways with significantly enriched genes in the gene set and pathways with significantly enriched metabolites in the metabolic set, and create a metabolic set for the differential metabolites on the common enriched pathways; S6.

2. Joint analysis of liver metabolism and intestinal metabolism: Create a metabolic set of differential metabolites present in both liver metabolism and intestinal metabolism; S6.

3. Perform intersection analysis on the differential metabolites on pathways that are co-enriched in transcriptomics and liver metabolomics with the differential metabolites that exist in both liver metabolism and intestinal metabolism to obtain common metabolites and determine all pathways enriched in the common metabolites. S6.

4. Pearson correlation coefficient was used for quantitative analysis to draw a heat map of the correlation between genes and metabolites in different pathways enriched with common metabolites; S6.

5. Joint analysis of intestinal metabolism and microbial diversity: Spearman correlation analysis was used to determine the potential association between differentially abundant genera and differential metabolites, and metabolites were associated with microbial communities at the genus level. Heat maps were drawn to analyze the correlation between metabolites and microbial communities. S7. Use the data obtained from the analysis to evaluate the impact of a high-fat diet on the health of elderly mice.

2. The research method according to claim 1, characterized in that: In S3.1, after the sequencing data was quality controlled by fastp software, the original paired-end sequencing reads were assembled using the FLASH program, and then denoised using the DADA2 plug-in in the Qiime2 process to obtain ASVs; in order to reduce the impact of sequencing depth on subsequent Alpha and Beta diversity studies, each sample sequence was rarefied to 20,000, with an average sequence coverage of 99.09%; finally, based on the Silva 16S rRNA gene database, the Qiime2 classifier was used to conduct species taxonomy research on ASVs, and the PICRUSt2 algorithm was used for 16S functional prediction research.

3. The research method according to claim 1, characterized in that: In S4.1, the data analysis process includes: using Progenesis The QI metabolomics processing software performed baseline filtering, peak identification, integration, retention time correction and peak alignment on the raw LC-MS data to generate a data matrix containing peak intensity, retention time and mass-to-charge ratio; metabolite information was obtained by comparing the MS and MSMS spectral data in the HMDB, Metlin public metabolic database and Majorbio's own library; the data matrix was uploaded to the Majorbio cloud platform, and the 80% rule was used to remove missing values ​​and fill them with the minimum value in the original matrix, and then the sum was normalized to reduce errors; after removing variables with relative standard deviations (RSD) greater than 30% in QC samples, log10 transformation was performed; the preprocessed data matrix was subjected to principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) using the ropls package in the R language, and the model stability was evaluated using the seven-fold cross-validation method; the significantly differential metabolites were screened according to the variable importance projection VIP value of the OPLS-DA model and the p-value of the Student's t-test, VIP>1 and p<0.05; finally, the KEGG database was used to annotate the metabolic pathways involved in the differential metabolites.

4. The research method according to claim 1, characterized in that: In S4.1, the chromatographic conditions are as follows: mobile phase B is 47.5% acetonitrile, 47.5% isopropanol and 5% water, the water contains 0.1% formic acid, and mobile phase A is 95% water and 5% acetonitrile, the acetonitrile contains 0.1% formic acid; an ACQUITYUPLC HSS T3 column is used; the injection volume is 3 μL, and the column temperature is maintained at 40°C; The mass spectrometer was set up with electrospray ionization, and data were acquired in positive and negative ion scanning modes with a mass scanning range of 70–1050 m / z. The collision energy was cycled between 20-40-60 eV, the ion transfer tube temperature was 325 °C, the auxiliary gas heater temperature was 425 °C, the sheath gas flow rate was 50 psi, and the auxiliary spray voltage was 3500 V in both positive and negative modes. The primary mass spectrometer resolution was 60,000, the secondary mass spectrometer resolution was 7500, and the DDA data acquisition method was used.

5. The research method according to claim 1, characterized in that: In S4.2, the LC-MS raw data were imported into the metabolomics processing software for preprocessing and matched with multiple metabolite databases to identify metabolites; the preprocessed data matrix was uploaded to the cloud platform for further analysis, including data cleaning, normalization and logarithmic processing; finally, the significantly different metabolites were screened out by principal component analysis, orthogonal least partial squares discriminant analysis and other methods, and the KEGG database was used for metabolic pathway annotation and enrichment analysis to determine the biological pathways most relevant to the experimental treatment.

6. The research method according to claim 1, characterized in that: In S5.1, the functional enrichment study included GO and KEGG analysis, KOBAS was used for KEGG pathway analysis, and Goatools was used for GO functional enrichment.

7. The research method according to claim 1, characterized in that: In S6.3, all pathways enriched by common metabolites were determined based on the screening criteria of P-value < 0.

05.

8. The research method according to claim 1, characterized in that: In S6.4, the screening criteria were set as a correlation P value less than 0.05 and an absolute value of the correlation greater than 0.8.

Images