A data verification processing method and system for feline chronic inflammatory bowel disease

Abstract

The present invention relates to the field of biomedical technologies, and discloses a data verification and processing method for feline chronic inflammatory bowel disease, including the following steps: Sample collection and pretreatment: Collect feline fecal samples and tissue samples, extract microbial DNA and host RNA, and record the sample sources and detailed clinical information; Microbiota analysis: Perform 16S rRNA sequencing on the extracted microbial DNA. Also disclosed is a data verification and processing system for feline chronic inflammatory bowel disease, including: A data collection and storage module: Used to record sample sources, save sample information, and manage the DNA and RNA extraction results of samples. Through a comprehensive technical solution combining microbiota analysis and functional prediction based on 16S rRNA sequencing with qPCR verification and ELISA detection, a dynamically optimized feline IBD microbiota database is constructed, and personalized treatment suggestions are generated, forming a complete closed-loop system from data collection to treatment plan generation.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and specifically to a data verification processing method and system for feline chronic inflammatory bowel disease. Background Art

[0002] Feline chronic inflammatory bowel disease (IBD) is a common digestive tract disease in felines. Its complex pathological mechanism involves multiple factors such as genetics, environment, diet, and gut microbiota. However, at the present stage, there is still much room for improvement in the systematicness and scientificity of research and diagnosis and treatment of feline IBD, especially in the practical applications of accurate diagnosis and personalized treatment.

[0003] Existing research has shown that the dysregulation of gut microbiota is closely related to the occurrence and development of feline IBD. In a healthy state, the gut microbiota maintains balance and provides key functions such as energy metabolism and host immune regulation. However, in the state of IBD, this balance is broken, and some potential pathogenic bacteria (such as Helicobacter and Anaerobiospirillum) increase significantly, while some probiotics (such as Bacteroides) decrease significantly. This dysbiosis may directly or indirectly lead to impaired gut barrier function and exacerbated inflammatory responses. However, most existing technologies rely on 16S rRNA sequencing technology for taxonomic analysis of gut microbiota. This technology can reveal changes in the microbiota composition, but it cannot deeply analyze the dynamic changes in microbiota functions and their association with host inflammation, which makes it difficult for research to accurately explain the actual contribution of microbiota to the onset of IBD.

[0004] In addition, although existing functional prediction technologies (such as PICRUSt2) can speculate potential metabolic pathways based on microbiota classification, these prediction results usually lack subsequent experimental verification, resulting in doubts about their actual credibility. More importantly, these prediction results are often limited to the speculation of microbiota functions and cannot effectively combine host immune response data for correlation analysis. For example, whether the change in microbiota metabolic function affects the levels of host pro-inflammatory factors (such as TNF-α, IL-6) still lacks direct evidence, which to a certain extent restricts the transformation of research results into practical applications.

[0005] In terms of diagnosis and treatment, existing technologies mainly diagnose feline IBD through symptom assessment and traditional laboratory means, while ignoring the potential value of gut microbiota in disease diagnosis. For example, many diagnosis and treatment processes fail to effectively integrate the results of microbiota analysis, thus unable to provide a scientific basis for individualized treatment. For treatment methods, most use empirical combination of antibiotics and probiotics, but this mode lacks personalized guidance based on the structural and functional characteristics of microbiota, which may lead to random and unstable treatment effects. In some cases, due to the overuse of antibiotics, the dysbiosis is further exacerbated, and even the recovery of the disease is delayed.

[0006] Meanwhile, most existing microbiota databases are statically designed and difficult to reflect the dynamic changes during treatment. For example, after antibiotic treatment or probiotic intervention, the microbiota abundance and its functions may change significantly, but static databases cannot update these dynamic information in a timely manner, which causes clinicians unable to adjust treatment strategies based on the results provided by the database, reducing the value of the database in actual diagnosis and treatment.

[0007] Although some studies have started to attempt to combine high-throughput sequencing technology and function prediction tools at the current stage, there are still gaps in its overall technical system. For example, there is a lack of direct association between microbiota analysis results and treatment recommendations, and the complexity of data analysis also makes it difficult for clinicians to quickly master and apply. In addition, many studies only stay at the level of analyzing microbiota composition and basic functions, and fail to effectively integrate host immune data, which results in the inability of diagnosis and treatment means to achieve a complete closed-loop from data collection, analysis to treatment recommendations.

[0008] Based on this, the present invention proposes a data verification processing method and system for feline chronic inflammatory bowel disease. Summary of the Invention

[0009] Aiming at the deficiencies of the prior art, the present invention provides a data verification processing method and system for feline chronic inflammatory bowel disease, which solves the problems of lack of systematicness, dynamics and clinical application transformation among intestinal microbiota analysis, function prediction and personalized treatment recommendations in feline chronic inflammatory bowel disease.

[0010] To achieve the above objectives, the present invention is realized through the following technical solutions: A data verification processing method for feline chronic inflammatory bowel disease, comprising the following steps:

[0011] Sample collection and pretreatment: Collect cat fecal samples and tissue samples, extract microbial DNA and host RNA, and record the sample sources and detailed clinical information;

[0012] Microbiota analysis: Perform 16S rRNA sequencing on the extracted microbial DNA, and use data cleaning, clustering and annotation technologies to classify, annotate and analyze the diversity of the sample microbiota, and screen key microbiota;

[0013] Function prediction: Predict the metabolic function pathways of the microbiota based on the microbiota data, and identify the function pathways related to chronic inflammatory bowel disease;

[0014] Data verification: Use qPCR technology to verify the abundance of the screened key microbiota, and detect the levels of host-related inflammatory factors by ELISA or qPCR;

[0015] Database optimization: Construct a microbiota database for feline chronic inflammatory bowel disease, integrate microbiota classification, function pathways and clinical data, update regularly and verify the accuracy of the database through experiments;

[0016] Data analysis and output: Generate a standardized analysis report, including microbiota characteristics, functional pathway differences, and disease risk assessment, and provide personalized treatment recommendations.

[0017] Preferably, the sample collection includes fecal samples, intestinal tissue samples, and blood samples of IBD cats. Among them, the fecal samples are collected through a sterile preservation tube and stored at low temperature, the intestinal tissue samples are processed and placed in an RNA protection reagent, and the blood samples are used to isolate serum for detecting inflammatory factors.

[0018] Preferably, the microbiota analysis includes the following steps:

[0019] Perform paired-end sequencing on the V4 region of the 16S rRNA gene using the Illumina NovaSeq platform;

[0020] Use QIIME2 to clean, dereplicate, and cluster the sequencing data to generate ASV sequences, and perform taxonomic annotation based on the Greengenes or SILVA database;

[0021] Calculate Alpha diversity indices, including Shannon index and Simpson index;

[0022] Perform Beta diversity analysis through Bray-Curtis distance to evaluate the differences in microbiota structure between IBD cats and healthy cats.

[0023] Preferably, the function prediction is based on the PICRUSt2 tool and includes the following:

[0024] Predict the functional metabolic pathways of the microbiota, and identify the lipopolysaccharide biosynthesis, steroid metabolism, and short-chain fatty acid metabolism pathways related to chronic inflammatory bowel disease;

[0025] Combine the results of microbiota difference analysis to locate the metabolic function characteristics of key microbiota, and further associate with the functional pathways related to chronic inflammatory bowel disease.

[0026] Preferably, the data verification includes the following steps:

[0027] Use qPCR technology to verify the abundance changes of key microbiota, and the key microbiota include Helicobacter and Anaerobiospirillum;

[0028] Use ELISA method to detect the concentrations of TNF-α and IL-6 pro-inflammatory factors in the host serum;

[0029] Use qPCR to detect the mRNA expression levels of TNF-α, IL-6, and IL-10 genes in the host intestinal tissue to evaluate the association between the microbiota and host inflammation.

[0030] Preferably, the database optimization includes the following:

[0031] Integrate sequencing data, functional prediction results, and experimental verification data to construct a microbiota database for feline chronic inflammatory bowel disease;

[0032] The database content includes microbiota classification, microbiota abundance distribution, functional pathway characteristics, and clinical association information;

[0033] Regularly update the database and perform regression testing and model calibration through newly added experimental data.

[0034] A data verification processing system for feline chronic inflammatory bowel disease, comprising:

[0035] Data acquisition and storage module: used to record the sample source, save sample information, and manage the DNA and RNA extraction results of the samples;

[0036] Microbiota analysis module: used to perform 16S rRNA sequencing on the sample microbial DNA and perform classification annotation and diversity analysis on the microbiota;

[0037] Functional prediction module: based on the microbiota analysis results, predict the metabolic functional pathways and identify the functional abnormal pathways related to the disease;

[0038] Data verification module: verify the reliability of the microbiota function prediction through qPCR and ELISA techniques and correlate with host inflammatory factors;

[0039] Database management module: store microbiota classification, functional pathways, and clinical data, and support data update and analysis;

[0040] Output and application module: generate a standardized analysis report and provide personalized treatment recommendations

[0041] Preferably, the microbiota analysis module includes:

[0042] Data processing unit: used to clean, dereplicate, and generate ASV sequences for the 16S rRNA gene sequencing data;

[0043] Diversity calculation unit: used to calculate the Alpha diversity and Beta diversity indices of the samples;

[0044] Differential microbiota screening unit: used to screen key differential microbiota and correlate with metabolic functional pathways.

[0045] Preferably, the data verification module includes:

[0046] qPCR verification unit: used to verify the abundance changes of the target microbiota through qPCR technology, and the target microbiota includes Helicobacter and Anaerobiospirillum.

[0047] The ELISA detection unit is used to detect the concentrations of pro-inflammatory factors TNF-α and IL-6 in serum;

[0048] The functional pathway verification unit is used to verify the correlation between the microbial community functional pathway and host inflammation in combination with the functional prediction results.

[0049] Preferably, the analysis report generated by the output and application module includes:

[0050] The microbial community structure characteristics of the sample;

[0051] Key differential microbial communities and functional pathways related to IBD;

[0052] Analysis of host inflammatory factor levels and their association with the microbial community;

[0053] Disease risk assessment and personalized treatment recommendations.

[0054] The present invention provides a data verification and processing method and system for feline chronic inflammatory bowel disease. It has the following beneficial effects:

[0055] 1. The present invention adopts a comprehensive technical solution combining 16S rRNA sequencing-based microbial community analysis and functional prediction with qPCR verification and ELISA detection, achieving the technical effect of accurately analyzing the microbial community characteristics and functional abnormalities related to feline chronic inflammatory bowel disease (IBD). Compared with the prior art technical solutions that solely use microbial community sequencing for classification or functional prediction, it solves the deficiencies of being unable to effectively verify the reliability of predicted functions and lacking the association analysis with host inflammatory factors, providing a solid technical support for more comprehensive disease research and diagnosis.

[0056] 2. The present invention realizes efficient data management and prediction capabilities by constructing and dynamically optimizing and updating a microbial community database for feline IBD. It achieves the technical effect of automatically generating personalized treatment recommendations in microbial community classification, abundance distribution, and functional pathway analysis. Compared with the limitations of the prior art in which microbial community analysis stays at static data recording and simple comparison, it solves the technical defect of being unable to reflect the correlation between the dynamic changes of the microbial community and disease development, significantly improving the clinical applicability and decision support capabilities of the database.

[0057] 3. The present invention adopts a complete closed-loop system combining microbial community analysis, functional prediction, and personalized treatment recommendations, achieving the technical effect of covering the entire process from data collection to treatment plan generation. Compared with the non-systematic method of relying on manual experience for treatment recommendations in the prior art, it solves the problems of lacking scientific basis, insufficient personalization, and difficulty in tracking treatment effects, significantly improving the precision diagnosis and treatment capabilities for feline IBD, and at the same time improving the quality of health management for diseased cats. Description of the Drawings

[0058] Figure 1 is a flowchart of the method of the present invention;

[0059] Figure 2 is a flowchart of the system of the present invention;

[0060] Figure 3 is a framework diagram of the flora analysis module of the present invention;

[0061] Figure 4 is a framework diagram of the data verification module of the present invention. Detailed implementation manners

[0062] Next, in combination with the accompanying drawings of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0063] Please refer to the attached Figure 1 , the embodiment of the present invention provides a data verification processing method for feline chronic inflammatory bowel disease, including the following steps:

[0064] Sample collection and pretreatment: Collect feline fecal samples and tissue samples, extract microbial DNA and host RNA, and record the sample sources and detailed clinical information;

[0065] Sample collection includes fecal samples, intestinal tissue samples and blood samples of IBD cats. Among them, fecal samples are collected through sterile preservation tubes and stored at low temperature. Intestinal tissue samples are placed in RNA protection reagents after processing. Blood samples are used to separate serum for detecting inflammatory factors.

[0066] Specifically, in some embodiments, the sample collection includes fecal samples, intestinal tissue samples and blood samples.

[0067] Specifically, the collected fecal samples need to be directly collected from the fresh feces of cats through sterile sampling tubes. It is recommended that the sampling amount be 10 to 20 grams. After sample collection, they are immediately stored at 4°C for short-term or -80°C for long-term. The low-temperature preservation of fecal samples helps to maximize the protection of the original state of the intestinal flora and avoid the change of the flora structure caused by external environmental factors.

[0068] For intestinal tissue samples, generally, intestinal tissues (such as small intestine or colon) from cats diagnosed with IBD need to be immediately placed in an RNA protection reagent (such as RNAlater) after collection, and then stored at -80°C. This method can significantly reduce the risk of RNA degradation and provide reliable sample quality for subsequent extraction of host RNA.

[0069] As an option, for blood samples, 5 mL of fresh blood is collected through a blood collection tube and quickly centrifuged (usually at 3000×g for 10 minutes) to separate the serum. The separated serum should be aliquoted and stored to avoid repeated freezing and thawing affecting the sample integrity. The serum samples are used for subsequent detection of inflammatory factors, including but not limited to quantitative determination of pro-inflammatory factor levels such as TNF-α and IL-6.

[0070] In this example, extracting microbial DNA from fecal samples is an important step, mainly completed using a commercial fecal DNA extraction kit. To improve the efficiency and quality of DNA extraction, it is recommended to add appropriate mechanical lysis steps, such as using a bead mill to break the samples. During the extraction process, it is necessary to pay attention to removing PCR inhibitors (such as polysaccharides and humic acids) to ensure the success rate of subsequent 16S rRNA sequencing.

[0071] For intestinal tissue samples, the conventional method for extracting host RNA is to use Trizol reagent. Specifically, after grinding and pulverizing the tissue samples in liquid nitrogen, an appropriate amount of Trizol reagent is added to fully lyse the tissue, and the RNA, DNA, and protein phases are separated through a liquid separation step. Subsequently, RNA is precipitated and purified using isopropanol, washed, and dissolved in RNase-free water. After RNA extraction is completed, its purity and concentration are measured using a NanoDrop spectrophotometer. Usually, the OD260 / 280 value is required to be between 1.8 and 2.1 to ensure that the RNA quality meets the requirements of subsequent experiments.

[0072] Generally, after fecal DNA extraction is completed, quality detection is required. The purity (OD260 / 280 ratio) of DNA is initially detected using a NanoDrop ND-1000 spectrophotometer, and the integrity of DNA is observed through agarose gel electrophoresis. In addition, to ensure that there are no obvious contaminants in the extracted DNA, a fluorescent dye (such as PicoGreen) can be used to quantitatively determine the DNA concentration to further calibrate the sequencing input amount.

[0073] For host RNA, quality detection is also a necessary step. In one possible implementation, a Bioanalyzer or an RNA electrophoresis analyzer is used to evaluate the integrity of RNA to obtain an RNA integrity number (RIN value). Usually, an RIN value greater than 7 is required, indicating that the RNA has not been severely degraded and can be used for downstream gene expression analysis.

[0074] In this embodiment, all sample collection and processing information needs to be recorded in detail as part of the experimental data. This information includes, but is not limited to, the cat's identification number, sample source (feces, intestinal tissue, serum), sampling time, sampling location, storage conditions, and its basic information (such as breed, age, gender, eating habits, medical history, etc.). The purpose of this detailed record is to ensure the traceability of the samples and provide a reference background for the interpretation of subsequent analysis results.

[0075] As a possible extension, after the microbial DNA in the fecal samples is extracted, it can be selectively used for metagenomic analysis. This analysis method allows for improving the accuracy of flora annotation by amplifying multiple gene fragments. In addition, for the host RNA extracted from intestinal tissue samples, cytokine gene expression profiling can also be performed to more comprehensively evaluate the host's inflammatory response to IBD.

[0076] In some embodiments, the formula for RNA purity detection is:

[0077] RNA purity (OD260 / 280) =

[0078] Where:

[0079] The OD260 value is the absorbance of RNA at a wavelength of 260 nm;

[0080] The OD280 value is the absorbance of RNA at a wavelength of 280 nm.

[0081] Generally, an OD260 / 280 value between 1.8 and 2.1 indicates a high RNA purity. If there is a deviation, there may be proteins or other contaminants, and further purification is required.

[0082] Flora analysis: Perform 16S rRNA sequencing on the extracted microbial DNA, and use data cleaning, clustering, and annotation techniques to classify and annotate the sample flora and analyze its diversity, and screen for key flora;

[0083] The flora analysis includes the following steps:

[0084] Perform paired-end sequencing of the V4 region of the 16S rRNA gene using the Illumina NovaSeq platform;

[0085] Use QIIME2 to clean, de-chimera, and cluster the sequencing data to generate ASV sequences, and perform taxonomic annotation based on the Greengenes or SILVA database;

[0086] Calculate Alpha diversity indices, including the Shannon index and the Simpson index;

[0087] Beta diversity analysis was performed using the Bray-Curtis distance to evaluate the differences in the microbial community structure between IBD cats and healthy cats.

[0088] Specifically, generally, microbial community analysis mainly includes three core processes: generation of sequencing data, data cleaning and taxonomic annotation, and diversity calculation. First, high-throughput sequencing was performed on the V4 region of the microbial 16S rRNA gene using the Illumina NovaSeq platform to generate data. Subsequently, bioinformatics tools were used to clean the raw data and perform de-chimeric operations to obtain high-quality ASV (amplicon sequence variant) sequences. In addition, the ASV sequences were taxonomically annotated based on the Greengenes or SILVA databases to ensure the accuracy and integrity of the microbial community classification information. Finally, by calculating Alpha and Beta diversities, the microbial richness and the differences in community structure between samples were further evaluated.

[0089] In this example, 16S rRNA gene sequencing was performed using the Illumina NovaSeq platform, and the V4 region was selected as the amplification target region. The amplification of the V4 region is a conventional choice in 16S rRNA sequencing, with a moderate sequence length and relatively high species classification resolution. The primers used for sequencing were 515F ( -GTGCCAGCMGCCGCGGTAA- ) and 806R ( -GGACTACHVGGGTWTCTAAT- ). The amplification products were generated with a read length of 2 × 150 bp by paired-end sequencing. Generally, to ensure the sequencing depth, the sequencing reads of each sample needed to reach more than 30,000 to cover most of the low-abundance microbial communities.

[0090] In some examples, for samples with a relatively high complexity of specific microbial communities, the sequencing depth can be further increased or the number of samples can be increased to ensure the representativeness and statistical significance of the experimental results.

[0091] After sequencing, data cleaning is a crucial step. Specifically, QIIME2 was used to process the raw sequencing data, and the steps included removing low-quality sequences (Q value below 30), trimming adapter sequences, and de-chimerization. The de-chimerization was performed using the UCHIME or DADA2 algorithm to ensure that only high-quality sequences were included in the downstream analysis.

[0092] In this embodiment, the cleaned sequences are subjected to ASV clustering. ASV is a refined taxonomic unit that can accurately reflect the true diversity of the microbial community. Compared with the traditional OTU, it does not rely on artificially set clustering thresholds, so it has higher biological resolution. To ensure the accuracy of classification, in this embodiment, the Greengenes or SILVA database is selected to classify and annotate the ASV sequences, and the annotation results cover multiple taxonomic levels such as phylum, class, order, family, and genus.

[0093] After the classification and annotation are completed, it is necessary to further evaluate the diversity of the microbial community and the differences between samples.

[0094] In a possible implementation, Alpha diversity is used to characterize the richness and evenness of the microbial community within a single sample. The specific calculation metrics include the Shannon index and the Simpson index:

[0095] The Shannon index ( ) calculation formula is:

[0096]

[0097] where S is the number of species detected in the sample, is the relative abundance of the i-th species. The higher the Shannon index, the higher the diversity of the microbial community.

[0098] The Simpson index (D) calculation formula is:

[0099]

[0100] The Simpson index reflects the dominance of the main species in the sample. The closer the value is to 1, the more uniform the microbial community.

[0101] In some embodiments, other Alpha diversity metrics can be calculated according to experimental needs, such as the Chao1 index, which is used to evaluate the possibility of undetected species in the sample.

[0102] As an option, to evaluate the differences in the microbial community structure between IBD cats and healthy cats, Beta diversity analysis is used. Specifically, in this embodiment, the Bray-Curtis distance is used to calculate the similarity between samples:

[0103]

[0104] where, is the Bray-Curtis distance between sample i and sample j, and They are the abundance values of the k-th bacterial species in sample i and sample j, respectively. The smaller the distance value, the higher the similarity of the microbial community structure between the two samples.

[0105] In the analysis, Bray-Curtis distance was used for principal coordinate analysis (PCoA) to visualize the structural differences of the sample microbiota. PCoA can reduce the dimensionality of high-dimensional data to two or three dimensions, clearly presenting the microbiota distribution patterns between IBD cats and healthy cats.

[0106] In some embodiments, to further verify the reliability of the analysis results, PERMANOVA (multivariate analysis of variance based on distance matrix) can be introduced to perform a statistical test on the between-group differences among the microbiota.

[0107] Function prediction: Predict the metabolic function pathways of the microbiota based on the microbiota data, and identify the function pathways related to chronic inflammatory bowel disease;

[0108] The function prediction is based on the PICRUSt2 tool and includes the following:

[0109] Predict the functional metabolic pathways of the microbiota, and identify the lipopolysaccharide biosynthesis, steroid metabolism, and short-chain fatty acid metabolism pathways related to chronic inflammatory bowel disease;

[0110] Combined with the results of microbiota difference analysis, locate the metabolic function characteristics of key microbiota, and further associate with the function pathways related to chronic inflammatory bowel disease.

[0111] Specifically, in this embodiment, the usage process of PICRUSt2 includes gene function prediction, pathway annotation, and functional association analysis. Specifically, the ASV sequences obtained from the microbiota analysis step are first mapped to a reference database (such as Greengenes13.5 or SILVA database), and the closest reference sequence is assigned to each ASV. Subsequently, based on the genomic annotations of these reference sequences, the abundance distribution of the gene families corresponding to the ASVs is inferred.

[0112] As a possible implementation, the calculation of predicting the gene family abundance is based on the following formula:

[0113]

[0114] Where is the abundance value of gene family i, is the relative abundance of ASVj in the sample, is the abundance value of gene family i in the reference genome corresponding to ASVj. Through the above formula, the abundance distribution of all gene families in each sample can be calculated.

[0115] In functional annotation, PICRUSt2 uses the KEGG database to associate predicted gene families with known metabolic pathways, generating a pathway abundance table for each sample. In some embodiments, particular attention is paid to pathways closely related to chronic inflammatory bowel disease. For example:

[0116] The lipopolysaccharide biosynthesis pathway, mainly participated by Gram-negative bacteria, is a source of pro-inflammatory factors related to IBD;

[0117] The steroid metabolism pathway is closely related to the endocrine regulation and immune response of the host;

[0118] The short-chain fatty acid metabolism pathway, including products such as butyric acid and acetic acid, has been proven to have anti-inflammatory and intestinal barrier repair effects.

[0119] Generally, statistical methods such as the Mann-Whitney U test are used for the significance analysis of functional pathways to compare the differences in the abundances of specific pathways between the IBD group and the healthy group. Through this analysis, functionally specific up-regulated or down-regulated pathways in the IBD group can be screened out.

[0120] In some embodiments, the results of functional prediction can be combined with the analysis of microbial community differences to locate the action points of key microbial communities in functional pathways. Specifically, key microbial communities screened by LEfSe (linear discriminant analysis effect size), such as Helicobacter and Anaerobiospirillum, can be further associated with the lipopolysaccharide biosynthesis and steroid metabolism pathways. PICRUSt2 prediction shows that the gene abundances of these microbial communities in the relevant pathways are significantly higher than those in the healthy group, providing direct evidence for the up-regulation of these pathways.

[0121] As an option, Spearman correlation analysis can also be used to evaluate the relationship between the abundances of functional pathways and microbial communities. Assuming that the correlation coefficient between a certain microbial community and a pathway is positive and the statistical significance p < 0.05, it can be speculated that this microbial community may make a greater metabolic contribution to this pathway.

[0122] To verify the accuracy of functional prediction, in this embodiment, the results predicted by PICRUSt2 were cross-validated with metagenomic data. In some embodiments, by directly measuring the metagenomic functional pathways of the IBD group and the healthy group, it was found that the abundance of the lipopolysaccharide synthesis pathway was significantly correlated with the results predicted by PICRUSt2 (Pearson correlation coefficient r > 0.8). This verification method can enhance the reliability of functional prediction and provide a basis for downstream verification.

[0123] In a possible implementation, to make the functional prediction results more interpretable, multi-dimensional correlation analysis can also be performed in combination with host inflammatory factor data (such as TNF-α and IL-6 concentrations). For example, by performing regression analysis on the abundance of the lipopolysaccharide synthesis pathway and TNF-α levels, potential causal relationships between pathway metabolism and host inflammatory responses can be revealed.

[0124] Data verification: Use qPCR technology to verify the abundance of the key microbiota selected, and detect the levels of host-related inflammatory factors by ELISA or qPCR;

[0125] Data verification includes the following steps:

[0126] Use qPCR technology to verify the abundance changes of the key microbiota, and the key microbiota include Helicobacter and Anaerobiospirillum;

[0127] Use ELISA method to detect the concentrations of TNF-α and IL-6 pro-inflammatory factors in the host serum;

[0128] Use qPCR to detect the mRNA expression levels of TNF-α, IL-6, and IL-10 genes in the host intestinal tissue to evaluate the correlation between the microbiota and host inflammation.

[0129] Specifically, in this embodiment, qPCR technology is used to verify the abundance of the key microbiota selected in the functional prediction results. The key microbiota include Helicobacter and Anaerobiospirillum, which are closely related to pathways such as lipopolysaccharide biosynthesis and steroid metabolism in functional prediction. qPCR is a highly sensitive quantitative method that can accurately measure the abundance changes of these microbiota in IBD cats and healthy cats.

[0130] Specifically, the qPCR verification process is as follows:

[0131] The microbial DNA extracted from the fecal sample is used as a template.

[0132] Use specific primers targeting the 16S rRNA gene sequences of the target microbiota, and the primer sequences are designed as follows:

[0133] Helicobacter primers: Forward -AGGGTGAGGAGTGTGAG- ; Reverse -CGGTTTGGTCGTAAAACG- .

[0134] Anaerobiospirillum primers: Forward -ATGAGCTCCGAGATGTTG- ; Reverse -CCTTCTGCTGGAAGATCA- .

[0135] Prepare a 25 μL reaction system including 10 ng template DNA, SYBR Green dye, primers (0.5 μM) and deionized water.

[0136] The thermal cycle conditions were set as follows: pre-denaturation at 95°C for 5 min, 95°C for 15 s, 60°C for 30 s, and 72°C for 30 s, for 40 cycles.

[0137] The ΔΔCt method was used to calculate the relative abundance of the target bacterial community, and healthy cats were used as the control group:

[0138] Ct

[0139] Ct

[0140] Relative abundance Ct

[0141] In one possible implementation, in order to further improve the reliability of the results, the number of repeated experiments can be increased, and different sample sources can be independently verified.

[0142] As an alternative, host inflammatory factor detection mainly uses ELISA to quantify the concentration of TNF-α and IL-6 in serum. These factors are important pro-inflammatory signaling molecules in the pathological process of IBD, and their concentration changes can reflect the activity of the host immune response.

[0143] The specific implementation is as follows:

[0144] 5 mL of serum sample was extracted for ELISA testing.

[0145] Use a commercial ELISA kit (such as ThermoFisher TNF-α ELISA Kit) for detection. The steps include:

[0146] Standards and diluted serum samples were added to a 96-well plate, with 100 μL added to each well.

[0147] According to the instructions of the kit, capture antibody and detection antibody were added, and incubation and washing were performed.

[0148] The reaction was developed using TMB substrate and the absorbance was read at a wavelength of 450 nm.

[0149] The actual concentrations of TNF-α and IL-6 in serum were calculated according to the standard curve in pg / mL.

[0150] Generally, multiple control groups need to be set up in the experiment, including positive control, negative control, and healthy cat group, to exclude the influence of non-specific reactions on the test results.

[0151] In a possible implementation, the mRNA expression levels of TNF-α, IL-6, and IL-10 genes in the host intestinal tissue are detected by qPCR technology. These genes play important roles in the inflammatory response, where TNF-α and IL-6 are pro-inflammatory factors, and IL-10 is an anti-inflammatory factor, which can reveal the dynamic balance state of the host inflammatory response.

[0152] The specific implementation is as follows:

[0153] Extract tissue RNA: Use the Trizol method to extract intestinal tissue RNA. After isopropanol precipitation and 75% ethanol washing, dissolve the RNA in RNase-free water.

[0154] Reverse transcription into cDNA: Use HiScript Reverse Transcriptase for reverse transcription. Add RNA template, random primers, and reverse transcriptase according to the reaction system and incubate at 42 °C for 30 minutes.

[0155] Configure the qPCR reaction system: Include cDNA template, SYBR Green dye, specific primers, and PCR buffer.

[0156] Thermal cycling program: Pre-denature at 95 °C for 5 minutes, 95 °C for 15 seconds, 60 °C for 30 seconds, 72 °C for 30 seconds, and cycle 40 times.

[0157] Use GAPDH as the internal reference gene and calculate the relative expression level of the target gene by the ΔΔCt method.

[0158] The calculation formula for mRNA expression level is:

[0159]

[0160] In some embodiments, the mRNA expression levels of inflammatory genes can be further analyzed for correlation with the microbiota abundance data. For example, the Spearman correlation test can be used to evaluate the association between the abundance of Helicobacter and the expression level of TNF-α.

[0161] Database optimization: Construct a microbiota database for feline chronic inflammatory bowel disease, integrate microbiota classification, functional pathways, and clinical data, update it regularly, and verify the accuracy of the database through experiments;

[0162] Database optimization includes the following:

[0163] Integrate sequencing data, functional prediction results, and experimental verification data to construct a microbiota database for feline chronic inflammatory bowel disease;

[0164] The database content includes microbiota classification, microbiota abundance distribution, functional pathway characteristics, and clinical association information;

[0165] Regularly update the database and perform regression testing and model calibration by adding new experimental data.

[0166] Specifically, in this embodiment, the construction of the database is based on the collected fecal samples and intestinal tissue samples. By integrating microbiota sequencing data, functional prediction results, and experimental verification data, systematically record the microbiota characteristics and functional information related to IBD.

[0167] Specifically, the construction of the database includes the following steps:

[0168] Integrate sequencing data: Incorporate the microbiota classification information obtained in the aforementioned 16S rRNA sequencing step into the database, covering multiple taxonomic levels such as phylum, class, order, family, and genus. Pay special attention to the key microbiota significantly associated with IBD, such as Helicobacter and Anaerobiospirillum.

[0169] Functional prediction results: Add the results of the functional metabolic pathways predicted by the PICRUSt2 tool to the database, and focus on recording the abundance information of the lipopolysaccharide biosynthesis, steroid metabolism, and short-chain fatty acid metabolism pathways, as well as the association of these pathways with IBD.

[0170] Experimental verification data: Incorporate the microbiota abundance and inflammatory factor expression levels verified by qPCR and ELISA into the database. These data provide experimental support for the reliability of the functional prediction results.

[0171] As a possible implementation, the database content can be divided into the following parts:

[0172] Microbiota classification information:

[0173] Include the taxonomic level information of the microbiota at the phylum, class, order, family, and genus levels, as well as the abundance data of each taxonomic unit. Specifically, such as:

[0174] Data format: [Taxonomic unit]-[Abundance percentage]

[0175] For example: Helicobacter-15%.

[0176] Functional pathway characteristics:

[0177] Include the metabolic pathway information related to the microbiota function. For example:

[0178] Abundance of lipopolysaccharide biosynthesis pathway: 30%

[0179] Abundance of steroid metabolism pathway: 12%

[0180] Abundance of short-chain fatty acid metabolism pathway: 20%

[0181] Clinical correlation data:

[0182] Record the association between the abundance of the microbiota and clinical symptoms, such as the correlation coefficient between the abundance of Helicobacter and the concentration of TNF-α. The data format is as follows:

[0183] Microbiota abundance: 20% - TNF-α concentration: 50 pg / mL - Correlation coefficient: r = 0.85

[0184] Time series data:

[0185] The database supports recording the dynamic changes of the microbiota at different time points, facilitating the evaluation of the effectiveness of treatment interventions. For example:

[0186] Day 1: Helicobacter - 15%

[0187] Day 7: Helicobacter - 5%

[0188] Generally, the database needs to be updated by supplementing regular experimental data and analysis results to maintain its applicability to actual cases.

[0189] In this embodiment, the process of database update includes:

[0190] Integration of new data: Incorporate new sample sequencing data, functional prediction results, and experimental verification results every quarter, and add new features of IBD cats to the database. For example:

[0191] New sample data: It is found that the abundance of a certain microbiota (such as Anaerobiospirillum) increases significantly.

[0192] Discovery of new functional pathways: Key genes in the short-chain fatty acid metabolism pathway are further confirmed.

[0193] Dynamic data management: The database supports dynamic updates, fusing new data with existing data and validating old data. To ensure data consistency, data comparison can be performed through manual inspection or automated verification tools.

[0194] As a possible implementation, the database needs to verify the accuracy of predictions through regression testing and model calibration. The calibration process is described in detail below:

[0195] Regression testing: Based on the newly added experimental data, perform a linear regression analysis on the predicted values and actual values of the microbiota and functional pathways. For example:

[0196] The correlation coefficient between the predicted value of Helicobacter abundance and the qPCR measurement value reached 0.9, indicating that the prediction model has high accuracy.

[0197] Model correction formula: For microbial communities or functional pathways with large deviations in predicted values, the following formula can be used for correction:

[0198]

[0199] Where: is the corrected functional abundance; is the initial predicted value of PICRUSt2; is the experimental measurement value; k is the correction coefficient, determined based on historical data, usually between 0.5 and 1.

[0200] Model iteration: As the amount of experimental data increases, optimize the model parameters through machine learning algorithms (such as random forest or XGBoost) to further improve the prediction accuracy.

[0201] The database is not only a tool for data storage, but can also be directly applied to disease risk assessment and personalized treatment recommendations. The following are some practical application scenarios:

[0202] Disease risk assessment:

[0203] Based on the correlation between the microbial community and inflammatory factors in the database, assess the risk of developing IBD in cats. For example, when the abundance of Helicobacter is greater than 20% and the abundance of the lipopolysaccharide pathway is greater than 25%, it indicates a high risk.

[0204] Personalized treatment recommendations:

[0205] The database can generate treatment plan recommendations based on the dynamic changes of the microbial community. For example, for IBD cats with too high Helicobacter abundance, it is recommended to combine antibiotic and probiotic treatment.

[0206] Data analysis and output: Generate a standardized analysis report, including microbial community characteristics, functional pathway differences, and disease risk assessment, and provide personalized treatment recommendations.

[0207] Specifically, in this embodiment, the personalized treatment recommendation module takes the microbial community abundance and functional pathway information as the core basis, and combines the host's inflammatory factor level and clinical manifestations to generate specific treatment recommendations.

[0208] Specifically:

[0209] Treatment plan for abnormal Helicobacter abundance:

[0210] When database analysis reveals that the abundance of Helicobacter in IBD cats is significantly increased (such as an abundance greater than 20%), and the abundance of the lipopolysaccharide synthesis pathway increases by more than 25%, it indicates that this bacterial community may be the key pathogenic bacterium for disease exacerbation.

[0211] The following treatment strategies are recommended:

[0212] Use narrow-spectrum antibiotics (such as amoxicillin) to inhibit the excessive proliferation of Helicobacter.

[0213] Meanwhile, supplement probiotics (such as Lactobacillus or Bifidobacterium) to restore the intestinal microecological balance.

[0214] In terms of diet, it is recommended to use special cat food with low fat and high fiber to reduce the intestinal inflammation burden.

[0215] Treatment plan for abnormal short-chain fatty acid metabolism:

[0216] When functional prediction shows that the short-chain fatty acid (such as butyric acid) metabolism pathway is significantly downregulated and the abundance of functional bacteria (such as Bacteroides and Ruminococcaceae) decreases, it indicates that the intestinal barrier function may be impaired.

[0217] The following treatment strategies are recommended:

[0218] Supplement sodium butyrate or prebiotics (such as inulin) to stimulate the recovery of beneficial intestinal flora.

[0219] Increase soluble dietary fiber (such as oats) in the diet to promote the production of short-chain fatty acids.

[0220] Use intestinal protectants containing probiotics (such as Saccharomyces boulardii) to assist in restoring the intestinal barrier function.

[0221] Treatment plan for elevated inflammatory factor levels:

[0222] If it is detected that the levels of TNF-α and IL-6 in the host serum are significantly increased, and the bacterial community data analysis indicates an enhanced activity of the lipopolysaccharide synthesis pathway, it can be speculated that intestinal flora dysbiosis induces a systemic inflammatory response.

[0223] The following treatment strategies are recommended:

[0224] Use non-steroidal anti-inflammatory drugs (such as meloxicam) to control inflammation in the short term.

[0225] Combine probiotics and prebiotics to reduce the abundance of pro-inflammatory bacteria (such as Anaerobiospirillum) in the intestine.

[0226] In terms of diet, it is recommended to adopt a diet plan that is easy to digest, high in protein, and low in fat to reduce the intestinal burden.

[0227] In a possible implementation, personalized treatment recommendations are presented in a standardized form through an analysis report. The report content is structured and mainly includes the following parts:

[0228] Analysis of the microbial community characteristics:

[0229] Including the taxonomic distribution of the microbial community, the abundance of dominant microbial communities, and the dynamic changes of key microbial communities.

[0230] For example:

[0231] Abundance of Helicobacter: 20% (significantly increased)

[0232] Abundance of Ruminococcaceae: 5% (significantly decreased)

[0233] Differences in functional pathways:

[0234] Analyze the significant differences in functional metabolic pathways between IBD cats and healthy cats.

[0235] For example:

[0236] Abundance of lipopolysaccharide synthesis pathway: 25% (higher than 10% in the healthy group)

[0237] Abundance of short-chain fatty acid metabolic pathway: 15% (lower than 30% in the healthy group)

[0238] Disease risk assessment:

[0239] Output a disease risk score according to the database model. For example:

[0240] Risk score: 85 (high risk)

[0241] Main risk factors: high abundance of Helicobacter and upregulation of lipopolysaccharide synthesis pathway.

[0242] Personalized treatment recommendations:

[0243] Generate specific treatment strategies by combining microbial community and host data. For example:

[0244] Antibiotics: It is recommended to use amoxicillin at a dose of 10 mg / kg, twice a day.

[0245] Probiotics: It is recommended to use Lactobacillus preparations at 1×109 CFU per day.

[0246] Diet: It is recommended to use low-fat and high-fiber cat food (brand A).

[0247] Generally, personalized treatment recommendations need to be adjusted according to the dynamic update of the database and the changes in the microbial community during the treatment process.

[0248] In this embodiment, dynamic adjustment is achieved through the following methods:

[0249] Data is updated regularly:

[0250] It is recommended to re - collect samples every 1 - 2 weeks to analyze the changes in the abundance of the flora and functional pathways.

[0251] For example:

[0252] Sampling in the second week showed that the abundance of Helicobacter decreased from 20% to 10%, indicating the effectiveness of the treatment.

[0253] If the TNF - α level remains high, the dose of anti - inflammatory drugs needs to be increased.

[0254] Long - term monitoring:

[0255] After the treatment is completed, it is recommended to conduct a flora test every 3 - 6 months to prevent recurrence.

[0256] The database can store the flora data at multiple time points to form a complete health status curve for risk prediction and long - term management.

[0257] Please refer to the appendix Figure 2 , a data verification processing system for feline chronic inflammatory bowel disease, including:

[0258] Data collection and storage module: used to record the sample source, save sample information, and manage the DNA and RNA extraction results of the samples;

[0259] Flora analysis module: used to perform 16S rRNA sequencing on the microbial DNA of the samples, and conduct taxonomic annotation and diversity analysis on the flora;

[0260] Function prediction module: based on the results of flora analysis, predict the metabolic functional pathways and identify the functional abnormal pathways related to diseases;

[0261] Data verification module: verify the reliability of flora function prediction through qPCR and ELISA techniques, and correlate with host inflammatory factors;

[0262] Database management module: stores the flora classification, functional pathways, and clinical data, and supports data update and analysis;

[0263] Output and application module: generates a standardized analysis report and provides personalized treatment recommendations.

[0264] Please refer to the appendix Figure 3 , the flora analysis module includes:

[0265] Data processing unit: used to clean, de - chimerize, and generate ASV sequences for the 16S rRNA gene sequencing data;

[0266] A diversity calculation unit for calculating the Alpha diversity and Beta diversity indices of samples;

[0267] A differential flora screening unit for screening key differential flora and associating metabolic functional pathways.

[0268] Please refer to the appendix Figure 4 , and the data verification module includes:

[0269] A qPCR verification unit for verifying the abundance changes of target flora through qPCR technology, and the target flora includes Helicobacter and Anaerobiospirillum;

[0270] An ELISA detection unit for detecting the concentrations of pro-inflammatory factors TNF-α and IL-6 in serum;

[0271] A functional pathway verification unit for verifying the association between the flora functional pathway and host inflammation in combination with the functional prediction results.

[0272] The analysis report generated by the output and application module includes:

[0273] The flora structure characteristics of the sample;

[0274] Key differential flora and functional pathways related to IBD;

[0275] The host inflammatory factor levels and their association analysis with the flora;

[0276] Disease risk assessment and personalized treatment suggestions.

[0277] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A data verification and processing method for feline chronic inflammatory bowel disease, characterized in that: The following steps are involved: Sample collection and pretreatment: Collect cat fecal samples and tissue samples, extract microbial DNA and host RNA, and record the source of the samples and detailed clinical information; Microbial flora analysis: 16SrRNA sequencing of the extracted microbial DNA was performed, and data cleaning, clustering and annotation techniques were used to classify and annotate the sample flora and analyze its diversity, and to screen key flora; Function prediction: predict the metabolic functional pathways of the flora based on flora data, identify the lipopolysaccharide biosynthesis, steroid metabolism and short-chain fatty acid metabolism pathways related to chronic inflammatory bowel disease, and locate the metabolic functional characteristics of key flora based on the results of flora difference analysis, and further associate them with the functional pathways of chronic inflammatory bowel disease; Data verification: qPCR technology was used to verify the abundance changes of Helicobacter and Anaerobic Spirillum, ELISA was used to detect the concentrations of TNF-α and IL-6 in host serum, and qPCR was used to detect the mRNA expression levels of TNF-α, IL-6, and IL-10 genes in host intestinal tissues; Database optimization: Build a microbiome database for feline chronic inflammatory bowel disease, integrate microbiome classification, functional pathways and clinical data, regularly update and verify the accuracy of the database through experiments; Data analysis and output: Generate standardized analysis reports, including microbial characteristics, functional pathway differences and disease risk assessment. According to the dynamic changes in the abundance of Helicobacter in the microbial characteristic analysis and the up-regulation results of the lipopolysaccharide synthesis pathway in the functional pathway analysis, combined with the down-regulation results of the short-chain fatty acid metabolic pathway, provide personalized treatment recommendations.

2. A data verification and processing method for feline chronic inflammatory bowel disease according to claim 1, characterized in that: The sample collection includes fecal samples, intestinal tissue samples and blood samples of IBD cats, wherein the fecal samples are collected through sterile preservation tubes and stored at low temperature, the intestinal tissue samples are placed in RNA protection reagents after processing, and the blood samples are used to separate serum for inflammatory factor detection.

3. A data verification and processing method for feline chronic inflammatory bowel disease according to claim 1, characterized in that: The flora analysis comprises the following steps: Paired-end sequencing of the 16S rRNA gene V4 region was performed using the Illumina NovaSeq platform; Use QIIME2 to clean, remove chimeras, and cluster sequencing data to generate ASV sequences, which are then annotated based on the Greengenes or SILVA databases; Calculate Alpha diversity indicators, including Shannon index and Simpson index; Beta diversity analysis was performed using Bray-Curtis distance to assess differences in microbiota structure between IBD cats and healthy cats.

4. A data verification and processing method for feline chronic inflammatory bowel disease according to claim 1, characterized in that: The data verification comprises the following steps: qPCR technology was used to verify the abundance changes of key bacterial groups, including Helicobacter and Anaerobic Spirillum; The concentrations of TNF-α and IL-6 pro-inflammatory factors in host serum were detected by ELISA method; qPCR was used to detect the mRNA expression levels of TNF-α, IL-6, and IL-10 genes in the host intestinal tissue to evaluate the association between the flora and host inflammation.

5. A data verification and processing method for feline chronic inflammatory bowel disease according to claim 1, characterized in that: The database optimization Includes the following: Integrate sequencing data, functional prediction results and experimental verification data to build a bacterial flora database for feline chronic inflammatory bowel disease; The database content includes bacterial taxonomy, bacterial abundance distribution, functional pathway characteristics and clinical association information; The database is updated regularly, and regression testing and model correction are performed by adding new experimental data.

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