Analysis method based on regulating effect of intestinal flora in longevity / aging process

Abstract

The invention relates to the technical field of analysis of intestinal flora structures, and discloses a method for analyzing an adjusting effect of intestinal flora in a longevity / aging process, which comprises the following steps: step 1, selecting research objects: randomly selecting 30 old people of more than 100 years old, and selecting 30 old people of 60-70 years old as a control group; 2, basic condition investigation and physical examination of a research object; and 3, detecting technical indexes of a research object. According to the invention, the change of intestinal micro-ecology of the 100-year old people is preliminarily discussed, and the dominant flora with longevity characteristics and the biomarkers are determined. The influence of aging on intestinal microecology is obvious, so that the diversity of intestinal flora is obviously reduced, and the structural proportion of the intestinal flora is also changed; the intestinal flora spectrums of the same age group are relatively similar, that is, the core flora exists; the'core flora 'of different age groups has difference. By combining genetic backgrounds, lifestyles and environmental factors of different samples, the influence of different lifestyles and environmental factors on the structure and longevity of intestinal bacteria is determined.

Description

Technical Field

[0001] This invention relates to the field of gut microbiota structure analysis technology, specifically to an analytical method based on the regulatory role of gut microbiota in the longevity / aging process. Background Technology

[0002] There are some existing studies on the gut microbiota of aging. These studies can be divided into two types. One type analyzes the impact and correlation of the geographical location, dietary structure, and lifestyle of centenarians on the composition of gut microbiota. The main purpose of this study is to find healthy lifestyles related to longevity. The other type compares the differences in gut microbiota between different age groups and centenarians in an attempt to isolate one or more longevity-related microbiota, thereby laying the foundation for the development of longevity probiotics.

[0003] Although existing research covers different countries and regions, studies on the gut microbiota of the elderly are relatively limited, and the sample size and age vary, with most studies focusing on individuals aged 60-89, and few on centenarians. The research methods used also differ, leading to inconsistent conclusions. Because gut microbiota is significantly influenced by geographical location, age, dietary structure, and lifestyle, there is currently a lack of relevant data on the gut microbiota of elderly people in my country, especially centenarians. Furthermore, existing research has not reached a definitive conclusion on the changes in gut microbiota after aging, nor has it clarified whether a certain dominant bacterial group associated with "longevity" exists. Whether such a dominant bacterial group plays a key role in the process of aging from a healthy state to death remains unclear, and the relationship between gut microbiota and longevity / aging remains to be elucidated. Therefore, we propose an analytical method based on the regulatory role of gut microbiota in the longevity / aging process. Summary of the Invention

[0004] The purpose of this invention is to provide an analytical method based on the regulatory role of gut microbiota in the longevity / aging process, in order to solve the problems mentioned in the background art.

[0005] To address the aforementioned technical problems, this invention provides the following technical solution: an analytical method based on the regulatory role of gut microbiota in the longevity / aging process, comprising the following specific steps:

[0006] Step 1: Selecting research subjects: 30 centenarians were randomly selected as the group, and 30 elderly people aged 60-70 years were selected as the control group;

[0007] Step 2: Basic Information Survey and Physical Examination of Research Subjects: The survey will be conducted through a combination of in-home interviews and questionnaires conducted at hospitals. The questionnaires will use a personal health questionnaire developed by the research team and will be conducted by uniformly trained investigators and physicians. Subsequently, standard stool samples will be collected from the research subjects.

[0008] Step 3: Detection and analysis of the gut microbiota structure of the research subjects:

[0009] S1. DNA extraction and 16S rRNA variable region amplification: Total genomic DNA of microorganisms was extracted from all fecal samples using a kit, and PCR amplification was performed using universal primers for the 16S rRNA variable region. After electrophoresis detection, high-throughput sequencing was performed.

[0010] S2. Sequencing Results Data Analysis:

[0011] S21. Gut Microbial Diversity Analysis: High-throughput sequencing of 16S rRNA genes was used to annotate and align the sequences in each sample library using OTUs. The microbial diversity and abundance of different microorganisms in the samples were calculated. Similarity analysis, difference analysis and diversity analysis between samples were performed using PCoA, NMDS analysis, linear discriminant contribution analysis and cluster analysis. The differences in the composition of the microbial community between different samples were compared to identify the significantly different species between samples or groups and to identify the dominant bacterial groups with longevity-related characteristics.

[0012] S22. Community functional difference analysis: After analyzing the composition of gene functions in the genomes of existing sequenced microorganisms, the composition of functional genes in the samples is inferred by the species composition obtained by 16S rRNA sequencing, thereby analyzing the functional differences between different samples and groups, including functional classification gene prediction and KEGG metabolic pathway prediction, and identifying longevity-related biomarkers.

[0013] S23. Quantitative analysis of representative communities: For species with significant differences, establish a quantitative analysis method and use qPCR to quantitatively analyze representative communities in fecal samples to provide support for using changes in gut microbiota as an indicator of aging and a measure of longevity.

[0014] S24. Analysis of longevity-related environmental factors: Canonical correspondence analysis and redundancy analysis were used to examine the genetic background and lifestyle of individuals, as well as the relationship between environmental factors and longevity / aging and gut microbiota.

[0015] According to the above technical solution, all research subjects in step one meet the following criteria:

[0016] a) No serious liver or kidney dysfunction, pancreatitis, colitis, gastrointestinal ulcers, or bleeding;

[0017] b) No tumors, and no immune abnormalities following liver or kidney transplantation;

[0018] c) No diarrhea has occurred in the past 3 months;

[0019] d) Patients with hypertension and coronary heart disease are in a stable period of disease;

[0020] e) No history of currently taking traditional Chinese medicine;

[0021] f) No history of antibiotic use in the past 3 months.

[0022] According to the above technical solution, the content of the personal health questionnaire in step two includes:

[0023] a) General information: including name, gender, place of origin, date of birth, method of birth, whether breastfed, residential community, education level, occupation, and contact number;

[0024] b) Chronic diseases and family history: hypertension, coronary heart disease, stroke, diabetes, hyperlipidemia, hyperuricemia, COPD;

[0025] c) Chronic disease treatment status: including various chronic disease treatment plans, treatment process, treatment adherence, and current control status of various clinical indicators;

[0026] d) Previous use of antibiotics, probiotics and their products, traditional Chinese medicine and health products, including specific drug names, methods of administration and time of administration;

[0027] e) Lifestyle history: dietary habits, smoking history, drinking history, daily routine, sleep schedule, and physical exercise.

[0028] According to the above technical solution, in step two, fresh fecal samples are collected and sent for testing immediately. If they cannot be sent for testing immediately, the fresh fecal samples are stored in an environment of -80℃.

[0029] According to the above technical solution, in step three, the 16S rRNA is located on the small subunit of the prokaryotic ribosome, including 10 conserved regions and 9 hypervariable regions. 16S rDNA amplicon sequencing is performed, one or several variable regions are selected, universal primers are designed using the conserved regions for PCR amplification, and then the hypervariable regions are sequenced and analyzed for bacterial species identification.

[0030] According to the above technical solution, the raw data processing method obtained from 16S rDNA amplicon sequencing firstly splices and filters the raw data to obtain effective data; then, based on the effective data, noise reduction is performed using DADA2 or deblur to obtain the final ASVs.

[0031] Based on the above technical solution, the annotation results of amplicon are associated with the corresponding functional database, and the functional prediction analysis of the microbial community in the ecological sample is performed using PICRUSt2, BugBase, and Tax4fun software.

[0032] According to the above technical solution, if there is no grouping information or the number of samples in the group is less than 3, the analysis of differences between Beta diversity groups, the analysis of differences in α diversity, the test of the significance of differences in community results between groups, and the analysis of significant differences in species between groups cannot be performed.

[0033] If there is no grouping information or fewer than 3 biological replicates, then the significance test of differences in community structure between groups and the analysis of species differences between groups cannot be performed.

[0034] Environmental factor association analysis requires the client to provide environmental factor data; if the number of samples is greater than 24, micropita analysis will be performed by default.

[0035] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0036] 1) Obtain relevant data on the gut microbiota of centenarians.

[0037] 2) The study identified dominant bacterial communities and biomarkers associated with longevity in centenarians. The results of this study are groundbreaking and highly innovative in the relevant research field.

[0038] 3) It is preliminarily proposed that characteristic changes in the gut microbiota of the elderly can serve as a new indicator for evaluating aging and a measure of longevity.

[0039] 4) Discover longevity biomarkers, which can serve as early warning indicators of aging.

[0040] 5) It was found that aging has a significant impact on the gut microbiota, leading to a significant reduction in gut microbiota diversity and changes in its structural proportions; aging can cause changes in the gut "core microbiota". The gut "core microbiota" profiles are relatively similar in the same age group, while the gut "core microbiota" profiles of different age groups are different.

[0041] 6) This study, by investigating changes in the gut microbiota of the elderly, represents an innovative attempt to seek ways to promote health and delay aging by improving the gut microbiota. The research findings can lay the foundation for subsequent research, development, and widespread clinical application of novel longevity prebiotic / probiotic protection technologies to specifically improve the gut microbiota and formulate new anti-aging treatment strategies. Attached Figure Description

[0042] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 This is a flowchart of the 16S amplicon sequencing information analysis process in this invention; Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1 The present invention provides the following technical solution:

[0046] Equipment includes: a blood biochemistry testing center laboratory; a -80℃ ultra-low temperature freezer; a 12-lead conventional electrocardiogram (ECG) analyzer; an ultrasound analyzer, etc. Also equipped are a liquid (gas) chromatography-mass spectrometry (LC-MS) system, a high-performance liquid chromatograph (HPLC), an Olympus inverted fluorescence microscope, a Zeiss laser confocal microscope, a Bio-red electrophoresis apparatus, an automated gel imaging system, an RT-PCR instrument, a plasmid transfection instrument, a computer imaging and analysis system, a Thermo cryogenic ultracentrifuge, an ultrathin cryostat, and other instruments and equipment for cell and molecular biology experiments, immunohistochemistry experiments, etc. It also possesses equipment for gut microbiota structure analysis, including the Eppendorf Mastercycler Personal and Mastercycler Gradient gene amplification systems, Eppendorf centrifuges (Eppendorf-5415D, Eppendorf-5417R), a multi-purpose high-speed refrigerated centrifuge (CT15RT), a BIO-RAD electrophoresis system, a gel imaging system (UVP-GDS-8000), a sterilizer (SA-300YF), a dust-proof PCR workbench (Holten PCR Mini, Captair Bicocap RNA / DNA), and the DCode gene mutation detection system (BIO-RAD).

[0047] This study analyzes the regulatory role of gut microbiota in longevity / aging processes. It utilizes 16S rDNA high-throughput sequencing, genomics, and metabolomics to compare the differences in gut microbiota between centenarians and younger elderly individuals (aged 60-70). The aim is to explore the correlation between gut microbiota and longevity, identify dominant gut microbiota associated with longevity, and provide a theoretical basis for developing novel longevity prebiotic / probiotic protection technologies, formulating precise intervention programs for gut microbiota imbalance in the elderly, and specifically improving gut microbiota for anti-aging.

[0048] Specific technical approach:

[0049] Step 1: Study Subjects. Thirty centenarians were randomly selected, and another 30 younger elderly individuals (aged 60-70 years) were selected as the control group. All subjects met the following criteria: ① No severe liver or kidney dysfunction, or digestive system diseases such as pancreatitis, colitis, peptic ulcers, or bleeding; ② No tumors, or post-liver or kidney transplant immune abnormalities; ③ No diarrhea in the past 3 months; ④ Patients with hypertension, coronary heart disease, or other chronic diseases were in a stable phase; ⑤ No history of taking any traditional Chinese medicine; ⑥ No history of antibiotic use in the past 3 months.

[0050] Step Two: Basic Information Survey and Physical Examination of Research Subjects: This step combines in-home interviews or questionnaire surveys conducted by patients at hospitals with physical examinations. The questionnaires use a personal health questionnaire developed by the research team, administered by uniformly trained investigators and physicians, followed by the collection of standard stool samples. The questionnaire included the following information: ① General information: including name, gender, place of origin, date of birth, mode of delivery (vaginal delivery / cesarean section), whether breastfeeding, residential community, education level, occupation, and contact number; ② Chronic diseases and family history: history of chronic diseases such as hypertension, coronary heart disease, stroke, diabetes, hyperlipidemia, hyperuricemia, and COPD; ③ Chronic disease treatment: including treatment plans for various chronic diseases, treatment process, treatment adherence, and current control status of various clinical indicators (such as blood pressure, blood lipids, blood sugar, and uric acid); ④ Past use of antibiotics, probiotics and their products, traditional Chinese medicine, and health products, including specific drug names, methods of administration, and duration of use; ⑤ Lifestyle history: dietary habits, smoking history, alcohol consumption history, and daily routine (work and rest schedule, physical activity, etc.).

[0051] Sample collection and processing: Collect fresh fecal samples and send them for testing immediately. If immediate testing is not possible, store them in a refrigerator at -80°C.

[0052] Step 3: Detection and analysis of the gut microbiota structure of the research subjects:

[0053] S1 DNA extraction and 16S rRNA variable region amplification: Total genomic DNA of microorganisms was extracted from all fecal samples using a kit, and PCR amplification was performed using universal primers for the 16S rRNA variable region (V3-V4 region or V1-V3 region). After electrophoresis detection, high-throughput sequencing was performed.

[0054] S2 sequencing results data analysis:

[0055] S21 Gut Microbial Diversity Analysis: High-throughput 16S rRNA sequencing was used to annotate and align the sequences obtained from each sample library using OTUs, calculating the microbial diversity and abundance of different microorganisms in the samples. Multivariate statistical methods, including PCoA (principal coordinates analysis), NMDS (non-metric multidimensional scaling analysis), linear discriminant analysis (LDA), and cluster analysis, were employed to analyze similarity, differences, and diversity among samples. This compared the differences in microbial community composition between different samples, identified significantly different species among samples or groups, and determined the dominant bacterial groups associated with longevity.

[0056] S22 Community Functional Difference Analysis: After analyzing the gene function composition of existing sequenced microbial genomes, the composition of functional genes in the samples was inferred from the species composition obtained by 16S rRNA sequencing. This allowed for the analysis of functional differences between different samples and groups, including functional classification (COG) gene prediction and KEGG metabolic pathway prediction, to identify longevity-related biomarkers.

[0057] S23 Representative Community Quantitative Analysis: For species with significant differences, a quantitative analysis method was established, and qPCR was used to quantitatively analyze representative communities in fecal samples. This provides support for using changes in gut microbiota as an indicator of aging and a measure of longevity.

[0058] S24 Longevity-Related Environmental Factor Analysis: Canonical correspondence analysis (CCA) and redundancy analysis (RDA) were used to examine the genetic background and lifestyle of individuals, as well as the relationship between environmental factors and longevity and gut microbiota.

[0059] 16S rRNA is located on the small subunit of the prokaryotic ribosome and includes 10 conserved regions and 9 hypervariable regions. The conserved regions show little variation among bacteria, while the hypervariable regions are genus- or species-specific and vary depending on phylogenetic relationships. Therefore, 16S rDNA can serve as a characteristic nucleic acid sequence for revealing biological species and is considered the most suitable indicator for bacterial phylogeny and classification. 16S rDNA amplicon sequencing typically involves selecting one or several variable regions, using universal primers designed for PCR amplification of the conserved regions, and then sequencing and analyzing the hypervariable regions for species identification. 16S rDNA amplicon sequencing technology has become an important tool for studying the composition and structure of microbial communities in environmental samples (Caporaso et al., 2011; Youssef Netal., 2009; Hess Metal., 2011). Small fragment libraries are constructed based on the characteristics of the amplified regions, and the libraries are sequenced at both ends using the Illumina sequencing platform. After Reads splicing and filtering, OTUs (Operational Taxonomic Units) clustering or ASVs (Amplicon Sequence Variations) noise reduction, the obtained effective data are then annotated with species and subjected to abundance analysis to reveal the species composition of the samples. Further α-diversity and β-diversity analysis can not only uncover the differences in community structure among samples, but also perform personalized analysis and in-depth data mining according to project needs.

[0060] After the DNA sample amplification test is qualified, the PCR products are mixed and purified, and then the entire library preparation work is completed through steps such as end repair, addition of A tail, addition of sequencing adapter, and purification.

[0061] The raw sequencing data contains a certain proportion of interference data (Dirty Data). To make the information analysis results more accurate and reliable, the raw data is first spliced ​​and filtered to obtain the effective data (Clean Data). Then, based on the effective data, noise reduction is performed using DADA2 or deblur (DADA2 is used by default) (LiMetal., 2020) to obtain the final ASVs. For the obtained ASVs (Callahan BJetal., 2017), on the one hand, species annotation is performed on the representative sequence of each ASV to obtain the corresponding species information and species-based abundance distribution. At the same time, ASVs are analyzed for abundance, alpha diversity calculation, Venn diagrams, and petal diagrams to obtain information on species richness and evenness within the sample, as well as information on common and unique ASVs among different samples or groups. On the other hand, multiple sequence alignment can be performed on ASVs to construct phylogenetic trees. Dimensionality reduction analysis such as PCoA, PCA, and NMDS, and sample clustering tree display can be used to explore the differences in community structure among different samples or groups. To further explore the differences in community structure among the grouped samples, statistical analysis methods such as T-test, MetaStat, and LEfSe were used to test the significance of differences in species composition and community structure among the grouped samples. The annotation results of amplicon sequencing can also be correlated with corresponding functional databases; software such as PICRUSt2, BugBase, and Tax4fun can be used to perform functional prediction analysis of microbial communities in ecological samples.

[0062] If there is no grouping information or fewer than 3 samples within a group, Beta diversity difference analysis, alpha diversity difference analysis, significance test for differences in community results between groups, and significance analysis for differences in species between groups cannot be performed. If there is no grouping information or fewer than 3 biological replicates, significance test for differences in community structure between groups and analysis of species differences between groups cannot be performed. Environmental factor association analysis requires the client to provide environmental factor data. If the number of samples is greater than 24, micropita analysis will be performed by default.

[0063] It should be noted that:

[0064] Analysis results:

[0065] Data processing: First, the raw data for each sample was obtained by splitting the data according to the barcode, and then the barcode and primers were removed. Next, the R1 and R2 sequence data were assembled using FLASH software. The assembled tags were then quality-controlled to obtain Clean Tags, which were then filtered for chimeras to obtain effective tags for subsequent analysis.

[0066] Species Visualization: Noise Reduction and Species Annotation. The DADA2 method (Callahan B.J. et al., 2016) is primarily used for noise reduction. It no longer clusters based on similarity, but only performs dereplication, essentially clustering at 100% similarity. Each deduplicated sequence generated after DADA2 noise reduction is called an ASV (Amplicon Sequence Variants), or characteristic sequence (corresponding to the OTU representative sequence), and the abundance table of these sequences in the sample is called the characteristic table (corresponding to the OTU table). The DADA2 method is more sensitive and specific than the traditional OTU method, capable of detecting true biological variations missed by the OTU method, while outputting fewer false sequences (Callahan B.J. et al., 2019). Furthermore, ASVs replacing OTUs improve the accuracy, comprehensiveness, and reproducibility of marker gene data analysis (Amir A. et al., 2017).

[0067] The classify-sklearn algorithm of QIIME2 (Bokulich N.A. et al., 2018; Bolyen E.E. et al., 2019) was used to annotate each ASV using a pre-trained NaiveBayes classifier.

[0068] Based on the ASVs annotation results and the characteristic tables of each sample, species abundance tables at the kingdom, phylum, class, order, family, genus, and species levels are obtained. These abundance tables with annotation information are the core content of amplicon analysis. Depending on different experimental objectives, one or several species of interest can be selected from the species abundance tables at each taxonomic level (usually focusing on the phylum and genus levels). Further research can then be conducted by combining the results of species composition and differential analysis of different samples (groups), cluster analysis, and other methods.

[0069] Based on species annotation results at different taxonomic levels, each sample or group was selected at each taxonomic level.

[0070] Generate a cumulative bar chart of relative abundance of the top 10 species in (Phylum, Class, Order, Family, Genus, Species) to visually view the species with higher relative abundance and their proportion at different taxonomic levels for each sample.

[0071] Based on the species annotations and abundance information at the genus level for all samples, the top 35 genera in terms of abundance were selected. Based on their abundance information in each sample, they were clustered at the species level and a heat map was drawn to facilitate the discovery of the concentration of species in each sample.

[0072] Based on the obtained feature sequence results and research needs, analyze the common and unique feature sequences among different samples (groups). When the number of samples (groups) is less than or equal to 5, a Venn graph is drawn. When the number of samples (groups) is greater than 5, a petal graph is displayed.

[0073] To further investigate the phylogenetic relationships of species at the genus level, representative sequences of the top 100 genera were obtained through multiple sequence alignment.

[0074] Alpha diversity analysis: AlphaDiversity is used to analyze the diversity of microbial communities within a sample (LiBetal., 2013). Single-sample diversity analysis (Alpha diversity) can reflect the richness and diversity of microbial communities within a sample. This includes using a series of statistical analysis indices, species diversity curves, and species cumulative box plots to assess the differences in species richness and diversity of microbial communities in each sample.

[0075] Alpha Diversity Index: A statistical analysis of the Alpha Diversity index for different samples.

[0076] Species diversity curve: The dilution curve is a common curve describing the diversity of samples within a group. It is constructed by randomly sampling a certain amount of sequencing data from the sample, calculating their alpha diversity index values, and constructing the curve using the amount of sequencing data sampled and the corresponding index value (cutoff = 50726).

[0077] Species accumulation boxplots are analyses that depict the increase in species diversity as sample size increases. They are effective tools for investigating the species composition of a sample and predicting species richness. In biodiversity and community surveys, they are widely used to determine sample size adequacy and to estimate species richness. Therefore, species accumulation boxplots can not only determine sample size adequacy, but also, assuming a sufficient sample size, predict species richness (by default, analysis is performed with a sample size of 10 or more).

[0078] Hierarchical clustering curves are generated by sorting characteristic sequences in a sample from highest to lowest relative abundance (or the number of sequences they contain), obtaining corresponding ranking numbers. These ranking numbers are then plotted on the x-axis, and the relative abundance (or the relative percentage of sequences within that rank) on the y-axis. Connecting these points with a line graph creates the RankAbundance curve, which visually reflects the richness and evenness of species in the sample. In Alpha diversity index difference analysis, box plots visually reflect the median, dispersion, maximum, minimum, and outliers of species diversity within each group. Simultaneously, T-tests, Wilcox rank-sum tests, and Tukey tests (T-test and Wilcox rank-sum tests are used when there are only two groups; Tukey and Kruskal-Wallis rank-sum tests are used when there are more than two groups) are used to analyze whether the differences in species diversity between groups are significant.

[0079] Beta Diversity Analysis: Beta Diversity compares the microbial community composition of different samples. First, based on the species annotation results and abundance information of characteristic sequences for all samples, characteristic sequence information of the same taxa is merged to obtain a species abundance table (Profiling Table). Simultaneously, using the phylogenetic relationships between characteristic sequences, the Unifrac distance (Unweighted Unifrac) is further calculated (Lozupone, Ce., 2005; Lozupone, Ce., 2011). Unifrac distance is a distance calculation method that uses evolutionary information between microbial sequences in each sample to calculate the distance between samples; for two or more samples, a distance matrix is ​​obtained. Then, using the abundance information of characteristic sequences, the Unifrac distance (Unweighted Unifrac) is further used to construct the Weighted Unifrac distance (Lozupone, Ce., 2007). Finally, differences between different samples (groups) were identified through Beta diversity index intergroup difference analysis, multivariate statistical methods such as principal component analysis (PCA), principal coordinates analysis (PCoA), and non-metric multi-dimensional scaling (NMDS).

[0080] In the Beta diversity study, four indicators were used to measure the dissimilarity coefficient between two samples: Weightedunifrac distance, Unweightedunifrac distance, Jaccard distance, and Bray-curtis distance. The smaller the value, the smaller the difference between the two samples in terms of species diversity.

[0081] To study the similarity between different samples, cluster analysis can be performed on the samples to construct a clustering tree. In environmental biology, UPGMA...

[0082] (UnweightedPair-groupMethodwithArithmeticMean)

[0083] UPGMA (Backeljau Te et al., 1996) is a commonly used clustering analysis method, originally developed to solve classification problems. The basic idea of ​​UPGMA is: first, cluster the two samples with the smallest distance together to form a new node (new sample), with its branch point located at half the distance between the two samples; then calculate the average distance between the new "sample" and all other samples, and find the two smallest distances to cluster again; repeat this process until all samples are clustered together, ultimately obtaining a complete cluster tree. UPGMA clustering analysis is performed using the Weighted Unifrac distance matrix, and the clustering results are integrated with the relative abundance of species at the phylum level for each sample.

[0084] PCoA Analysis: Principal Coordinates Analysis (PCoA) (Minchin, P. Real., 1987) extracts the most important elements and structures from multidimensional data through a series of eigenvalues ​​and eigenvector rankings. We performed PCoA analysis based on weighted unifrac distance and unweighted unifrac distance, selecting the principal coordinate combination with the highest contribution for plotting. Closer sample distances indicate more similar species composition; therefore, samples with high community structural similarity tend to cluster together, while samples with significant community differences will be farther apart. PCoA is presented in both two-dimensional and three-dimensional formats; the two-dimensional PCoA plot uses the first and second principal coordinates.

[0085] PCA analysis: Principal Component Analysis (PCA) (Jolliffe I et al., 1986) is a method based on Euclidean distance and variance decomposition to reduce the dimensionality of multidimensional data, thereby extracting the most important elements and structures in the data (Avershina E et al., 2013). Applying PCA analysis can extract two coordinate axes that best reflect the differences between samples, thus reflecting the differences in multidimensional data on a two-dimensional coordinate graph, and revealing simple patterns in a complex data context. The more similar the community composition of the samples, the closer they are in the PCA graph.

[0086] NMDS Analysis: Metric-Free Multidimensional Calibration (NMDS)

[0087] Non-Metric Multi-Dimensional Scaling (NMDS) (Kruskal J. Betal., 1964) is a metric used in ecological research for ordination. NMDS is a non-linear model based on the weighted and unweighted unifrac distances, representing the species information in the sample as points on a two-dimensional plane. Its design aims to overcome the shortcomings of linear models (including PCA and PCoA) and better reflect the non-linear structure of ecological data (Noval Rivas Metal., 2013). Applying NMDS analysis, the species information in the sample is represented as points in a multi-dimensional space, and the degree of difference between different samples is reflected by the distance between points, indicating between-group and within-group differences.

[0088] Statistical tests: For grouped projects, the Adonis and Anosim analysis methods can be used to analyze whether the differences in community structure between groups are significant. Through statistical analysis, species with significant differences in abundance between groups can be identified, and the enrichment of these species in different groups can be obtained.

[0089] Anosim analysis (Chapman & McGeal., 1999) is a nonparametric test used to examine whether the differences between groups are significantly greater than the differences within groups, thus determining whether the grouping is meaningful. We use the rank of the Bray-Curtis distance to test the significance of differences between groups. The R-value is between (-1, 1). An R-value greater than 0 indicates that the differences between groups are greater than the differences within groups. An R-value less than 0 indicates that the differences within groups are greater than the differences between groups. The confidence level of the statistical analysis is expressed by the P-value, with P < 0.05 indicating statistical significance.

[0090] MRPP: MRPP analysis (O'Reilly F. Je et al., 1980) is similar to Anosim, but it is a parametric test based on the Bray-Curtis distance. It is used to analyze whether differences in microbial community structure between groups are significant, and is usually used in conjunction with dimensionality reduction plots such as PCA, PCoA, and NMDS. A smaller ObserveDelta value indicates smaller within-group differences, while a larger Expectdelta value indicates larger between-group differences. An A value greater than 0 indicates that between-group differences are greater than within-group differences, and an A value less than 0 indicates that within-group differences are greater than between-group differences. A Significance value less than 0.05 indicates a significant difference.

[0091] Adonis (StatMetal., 2013), also known as permutational MANOVA or nonparametric MANOVA, is a nonparametric multivariate ANOVA method based on the Bray-Curtis distance. This method can analyze the explanatory power of different grouping factors on sample differences and uses permutation tests to analyze the statistical significance of groupings.

[0092] (Anderson MJ et al., 2000; McArdlee et al., 2001; Wartone et al., 2012; Zapala M et al., 2006).

[0093] Simper (Similarity percentage) (Warton D. et al., 2012) is a decomposition of the Bray-Curtis difference index, which can quantify the contribution of each species to the difference between two groups. The results show the top 10 species and their abundances in terms of contribution to the difference between the two groups.

[0094] T-test: The T-test is a method to test whether the difference is significant. In actual analysis, the T-test can be used to identify species with significant differences between groups at each taxonomic level (pvalue < 0.05).

[0095] MetaStat: To study species with significant differences between groups, starting from species abundance tables at different levels, the MetaStat method is used to perform hypothesis testing on the species abundance data between groups to obtain p-values. Species with significant differences between groups are then selected based on the p-values, and box plots of the abundance distribution of the differential species are drawn between groups.

[0096] LEfSe: LEfSe (LDAEffectSize) (Segata Netal., 2011) is an analytical tool for discovering and interpreting high-dimensional biomarkers. It can be used to compare two or more groups, emphasizing statistical significance and biological relevance, and is able to identify biomarkers with statistically significant differences between groups. This allows researchers to identify features of varying abundance and associated categories.

[0097] Random Forest is a classic machine learning model based on classification tree algorithms, proposed by Leo Breiman (2001). As a supervised machine learning method, it has important applications in pattern recognition. Its principle involves dividing data into training and testing sets. The classification function is continuously trained using the training set to achieve optimal classification performance, and then the classification performance is validated using the testing set. For a well-established model, its performance can be evaluated using cross-validation or receiver operating characteristic curve (ROC) methods. In ecological research, the Random Forest algorithm primarily constructs classification models and selects features (biomarkers) that play a crucial role in classification or grouping.

[0098] The analysis of random forests based on species abundance involved selecting different numbers of species according to gradients at different taxonomic levels to construct random forest models and plotting ROC curves. Each model was then cross-validated (default 10-fold), and important species were selected using MeanDecreaseAccuracy and MeanDecreaseGin.

[0099] Mean Decrease Accuracy measures the decrease in prediction accuracy of a random forest when the values ​​of a variable are made random. A higher value indicates greater importance of the variable. Mean Decrease Gini calculates the impact of each variable on the heterogeneity of observations at each node of the classification tree using the Gini index, thus comparing the importance of variables. A higher value indicates greater importance of the variable. a) x-axis: mean decrease accuracy, y-axis: top 50 most important species; b) x-axis: mean decrease Gini index, y-axis: top 50 most important species.

[0100] This invention preliminarily explores the changes in the gut microbiota of centenarians, identifying dominant bacterial communities and biomarkers characteristic of longevity. Aging significantly impacts the gut microbiota, leading to a marked decrease in gut microbiota diversity and changes in its structural proportions. Gut microbiota profiles are relatively similar within the same age group, exhibiting a "core microbiota." However, the "core microbiota" differs across age groups. By considering the genetic background, lifestyle, and environmental factors of different samples, this invention clarifies the impact of various environmental factors (lifestyle and environmental factors) on gut microbiota structure and longevity.

[0101] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An analytical method based on the regulatory role of gut microbiota in the longevity / aging process, characterized by: The specific steps include the following: Step 1: Selecting research subjects: 30 centenarians were randomly selected as the group, and 30 elderly people aged 60-70 years were selected as the control group; Step 2: Basic Information Survey and Physical Examination of Research Subjects: The survey will be conducted through a combination of in-home interviews and questionnaires conducted at hospitals. The questionnaires will use a personal health questionnaire developed by the research team and will be conducted by uniformly trained investigators and physicians. Subsequently, standard stool samples will be collected from the research subjects. Step 3: Detection and analysis of the gut microbiota structure of the research subjects: S1. DNA extraction and 16S rRNA variable region amplification: Total genomic DNA of microorganisms was extracted from all fecal samples using a kit, and PCR amplification was performed using universal primers for the 16S rRNA variable region. After electrophoresis detection, high-throughput sequencing was performed. S2. Sequencing Results Data Analysis: S21. Gut Microbial Diversity Analysis: High-throughput sequencing of 16S rRNA genes was used to annotate and align the sequences in each sample library using OTUs. The microbial diversity and abundance of different microorganisms in the samples were calculated. Similarity analysis, difference analysis and diversity analysis between samples were performed using PCoA, NMDS analysis, linear discriminant contribution analysis and cluster analysis. The differences in the composition of the microbial community between different samples were compared to identify the significantly different species between samples or groups and to identify the dominant bacterial groups with longevity-related characteristics. S22. Community functional difference analysis: After analyzing the composition of gene functions in the genomes of existing sequenced microorganisms, the composition of functional genes in the samples is inferred by the species composition obtained by 16S rRNA sequencing, thereby analyzing the functional differences between different samples and groups, including functional classification gene prediction and KEGG metabolic pathway prediction, and identifying longevity-related biomarkers. S23. Quantitative analysis of representative communities: For species with significant differences, establish a quantitative analysis method and use qPCR to quantitatively analyze representative communities in fecal samples to provide support for using changes in gut microbiota as an indicator of aging and a measure of longevity. S24. Analysis of longevity-related environmental factors: Canonical correspondence analysis and redundancy analysis were used to examine the genetic background and lifestyle of individuals, as well as the relationship between environmental factors and longevity / aging and gut microbiota.

2. The analytical method based on the regulatory role of gut microbiota in the longevity / aging process according to claim 1, characterized in that: All research subjects in step one meet the following criteria: a) No serious liver or kidney dysfunction, pancreatitis, colitis, gastrointestinal ulcers, or bleeding; b) No tumors, and no immune abnormalities following liver or kidney transplantation; c) No diarrhea has occurred in the past 3 months; d) Patients with hypertension and coronary heart disease are in a stable period of disease; e) No history of currently taking traditional Chinese medicine; f) No history of antibiotic use in the past 3 months.

3. The analytical method based on the regulatory role of gut microbiota in the longevity / aging process according to claim 1, characterized in that: The personal health questionnaire in step two includes the following: a) General information: including name, gender, place of origin, date of birth, method of birth, whether breastfed, residential community, education level, occupation, and contact number; b) Chronic diseases and family history: hypertension, coronary heart disease, stroke, diabetes, hyperlipidemia, hyperuricemia, COPD; c) Chronic disease treatment status: including various chronic disease treatment plans, treatment process, treatment adherence, and current control status of various clinical indicators; d) Previous use of antibiotics, probiotics and their products, traditional Chinese medicine and health products, including specific drug names, methods of administration and time of administration; e) Lifestyle history: dietary habits, smoking history, drinking history, daily routine, sleep schedule, and physical exercise.

4. The analytical method based on the regulatory role of gut microbiota in the longevity / aging process according to claim 1, characterized in that: In step two, collect fresh fecal samples and send them for testing immediately. If immediate testing is not possible, store the fresh fecal samples at -80°C.

5. The analytical method based on the regulatory role of gut microbiota in the longevity / aging process according to claim 1, characterized in that: In step three, the 16S rRNA is located on the small subunit of the prokaryotic ribosome, including 10 conserved regions and 9 hypervariable regions. 16S rDNA amplicon sequencing is performed, one or several variable regions are selected, universal primers are designed using the conserved regions for PCR amplification, and then the hypervariable regions are sequenced for analysis and bacterial identification.

6. The analytical method based on the regulatory role of gut microbiota in the longevity / aging process according to claim 5, characterized in that: The raw data processing method obtained from 16S rDNA amplicon sequencing involves first splicing and filtering the raw data to obtain effective data; then, based on the effective data, noise reduction is performed using DADA2 or deblur to obtain the final ASVs.

7. The analytical method based on the regulatory role of gut microbiota in the longevity / aging process according to claim 6, characterized in that: The annotation results of the amplicon were correlated with the corresponding functional databases, and the microbial communities in the ecological samples were analyzed for functional prediction using PICRUSt2, BugBase, and Tax4fun software.

8. The analytical method based on the regulatory role of gut microbiota in the longevity / aging process according to claim 7, characterized in that: If there is no grouping information or the number of samples within a group is less than 3, then the analysis of differences between Beta diversity groups, the analysis of differences in α diversity, the test of the significance of differences in community results between groups, and the analysis of significant differences in species between groups cannot be performed. If there is no grouping information or fewer than 3 biological replicates, the significance test of differences in community structure between groups and the analysis of species differences between groups cannot be performed. Correlation analysis of environmental factors requires the client to provide environmental factor data; If the number of samples is greater than 24, micropita analysis will be performed by default.

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