Data verification processing method and system for cat chronic inflammatory bowel disease

Abstract

The invention relates to the technical field of biomedicine, and discloses a data verification processing method for cat chronic inflammatory bowel disease, which comprises the following steps: sample collection and pretreatment: collecting cat feces samples and tissue samples, extracting microbial DNA and host RNA, and recording sample sources and detailed clinical information; the extracted microbial DNA is subjected to 16SrRNA sequencing, the invention further discloses a data verification processing system for the cat chronic inflammatory bowel disease, and the data verification processing system comprises a data acquisition and storage module which is used for recording a sample source, storing sample information and managing DNA and RNA extraction results of a sample. Through a comprehensive technical scheme of combining flora analysis and function prediction based on 16SrRNA sequencing with qPCR verification and ELISA detection, a dynamically optimized cat IBD flora database is constructed, generation of personalized treatment suggestions is realized, and a complete closed-loop system from data acquisition to treatment scheme generation is formed.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular 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 cats, and its complex pathological mechanism involves multiple factors such as genetics, environment, diet, and intestinal flora. However, at this stage, there is still much room for improvement in the systematic and scientific research and diagnosis and treatment of feline IBD, especially in the practical application of accurate diagnosis and personalized treatment.

[0003] Existing studies have shown that intestinal flora imbalance is closely related to the occurrence and development of feline IBD. In a healthy state, the intestinal flora maintains balance and provides key functions such as energy metabolism and host immune regulation. However, in the IBD state, this balance is broken, some potential pathogens (such as Helicobacter and anaerobic spirochetes) increase significantly, while some probiotics (such as Bacteroides) decrease significantly. This dysbiosis may directly or indirectly lead to impaired intestinal barrier function and aggravated inflammatory response. However, most existing technologies rely on 16SrRNA sequencing technology to perform taxonomic analysis of intestinal flora. This technology can reveal changes in flora composition, but cannot deeply analyze the dynamic changes in flora function and its association with host inflammation, which makes it difficult for research to accurately explain the actual contribution of flora to the onset of IBD.

[0004] In addition, although existing functional prediction technologies (such as PICRUSt2) can infer potential metabolic pathways based on microbial classification, these prediction results usually lack subsequent experimental verification, which leads to doubts about their actual credibility. More importantly, these prediction results are often limited to the speculation of microbial functions and cannot effectively combine the host's immune response data for association analysis. For example, whether changes in the metabolic function of the microbiome affect the host's pro-inflammatory factor levels (such as TNF-α, IL-6) still lack direct evidence support, 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 methods, while ignoring the potential value of intestinal flora in disease diagnosis. For example, many diagnosis and treatment processes fail to effectively integrate the results of flora analysis, thus failing to provide a scientific basis for individualized treatment. For treatment methods, most of them use the empirical combination of antibiotics and probiotics, but this model lacks personalized guidance based on the structure and functional characteristics of the flora, which may lead to random and unstable treatment effects. In some cases, the excessive use of antibiotics further aggravated the dysbiosis and even delayed the recovery of the disease.

[0006] At the same time, most existing microbiome databases are statically designed and difficult to reflect dynamic changes during treatment. For example, after antibiotic treatment or probiotic intervention, the abundance and function of the microbiome may change significantly, but static databases cannot update this dynamic information in a timely manner, which makes it impossible for clinicians 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 begun to try to combine high-throughput sequencing technology with functional prediction tools, there are still gaps in the overall technical system. For example, there is a lack of direct correlation between microbiome analysis results and treatment recommendations, and the complexity of data analysis also makes it difficult for clinicians to quickly master and apply them. In addition, many studies only stay at the level of analysis of microbiome composition and basic functions, and fail to effectively integrate host immune data, which makes it impossible for diagnostic and treatment methods to achieve a complete closed loop from data collection and 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] In view of the shortcomings of the existing technology, the present invention provides a data verification and processing method and system for chronic inflammatory bowel disease in cats, which solves the problem of lack of systematicity, dynamics and clinical application transformation between intestinal flora analysis, functional prediction and personalized treatment recommendations in chronic inflammatory bowel disease in cats.

[0010] To achieve the above objectives, the present invention is implemented by the following technical scheme: A data verification and processing method for chronic inflammatory bowel disease in cats, comprising the following steps: 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 microbiota based on microbiota data and identify functional pathways related to chronic inflammatory bowel disease; Data verification: qPCR technology was used to verify the abundance of the selected key bacterial communities, and ELISA or qPCR was used to detect the levels of host-related inflammatory factors; 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 microbiome characteristics, functional pathway differences, and disease risk assessment, and provide personalized treatment recommendations.

[0011] Preferably, 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 temperatures, 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.

[0012] Preferably, 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.

[0013] Preferably, the function prediction is performed based on the PICRUSt2 tool, including the following contents: Predict the functional metabolic pathways of the microbiota and identify the lipopolysaccharide biosynthesis, steroid metabolism, and short-chain fatty acid metabolism pathways associated with chronic inflammatory bowel disease; Combined with the results of microbial difference analysis, the metabolic functional characteristics of key microbiota were located and further associated with the functional pathways of chronic inflammatory bowel disease.

[0014] Preferably, 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.

[0015] Preferably, the database optimization includes the following contents: 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.

[0016] A data validation processing system for feline chronic inflammatory bowel disease, comprising: Data collection and storage module: used to record sample sources, save sample information, and manage DNA and RNA extraction results of samples; Microbial community analysis module: used to perform 16SrRNA sequencing on sample microbial DNA, and to classify, annotate and analyze the diversity of the microbial community; Function prediction module: predict metabolic functional pathways based on microbiome analysis results and identify abnormal functional pathways related to diseases; Data verification module: Verify the reliability of microbial function prediction through qPCR and ELISA technology, and associate it with host inflammatory factors; Database management module: stores bacterial flora classification, functional pathways and clinical data, and supports data update and analysis; Output and application module: Generate standardized analysis reports and provide personalized treatment recommendations Preferably, the flora analysis module includes: A data processing unit, used for cleaning, removing chimeras and generating ASV sequences for 16SrRNA gene sequencing data; Diversity calculation unit, used to calculate the Alpha diversity and Beta diversity indicators of the sample; The differential bacterial flora screening unit is used to screen key differential bacterial flora and associate them with metabolic functional pathways.

[0017] Preferably, the data verification module includes: qPCR verification unit, used to verify the abundance changes of target bacteria by qPCR technology, the target bacteria include Helicobacter and Anaerobic Spirillum; ELISA test unit, used to detect the concentration of pro-inflammatory factors TNF-α and IL-6 in serum; The functional pathway verification unit is used to verify the association between the bacterial flora functional pathway and host inflammation in combination with the functional prediction results.

[0018] Preferably, the analysis report generated by the output and application module includes: The bacterial community structure characteristics of the samples; Key differential bacterial communities and functional pathways associated with IBD; Analysis of host inflammatory factor levels and their association with microbiota; Disease risk assessment and personalized treatment recommendations.

[0019] The present invention provides a data verification processing method and system for chronic inflammatory bowel disease in cats. It has the following beneficial effects: 1. The present invention adopts a comprehensive technical solution of flora analysis and function prediction based on 16SrRNA sequencing combined with qPCR verification and ELISA detection, achieving the technical effect of accurately analyzing the flora characteristics and functional abnormalities associated with chronic inflammatory bowel disease (IBD) in cats. Compared with the technical solution of using flora sequencing alone for classification or function prediction in the prior art, it solves the shortcomings of its inability to effectively verify the reliability of the prediction function and the lack of analysis of association with host inflammatory factors, providing solid technical support for more comprehensive disease research and diagnosis.

[0020] 2. The present invention achieves efficient data management and prediction capabilities by constructing a microbiome database for cat IBD and dynamically optimizing and updating it. The technical effect of automatically generating personalized treatment recommendations in microbiome classification, abundance distribution, and functional pathway analysis is achieved. Compared with the limitations of microbiome analysis in the prior art that remains in static data recording and simple comparison, the technical defect that it cannot reflect the dynamic changes of the microbiome and the lack of correlation between disease development is solved, and the clinical applicability and decision support capabilities of the database are significantly improved.

[0021] 3. The present invention adopts a complete closed-loop system that combines flora analysis, function prediction and personalized treatment recommendations, achieving a 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 lack of scientific basis, insufficient personalization and difficulty in tracking treatment effects, significantly improves the ability to accurately diagnose and treat feline IBD, and at the same time improves the quality of health management of sick cats. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a flow chart of the method of the present invention; Figure 2 is a flow chart of the system of the present invention; Figure 3 This is a framework diagram of the flora analysis module of the present invention; Figure 4 It is a framework diagram of the data verification module of the present invention. DETAILED DESCRIPTION

[0023] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0024] Please see attached Figure 1 The embodiment of the present invention provides a data verification and processing method for chronic inflammatory bowel disease in cats, comprising the following steps: 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; Sample collection includes fecal samples, intestinal tissue samples and blood samples of IBD cats. Fecal samples are collected through sterile preservation tubes and stored at low temperature. Intestinal tissue samples are placed in RNA protection reagent after processing. Blood samples are used to separate serum for inflammatory factor detection.

[0025] Specifically, in some embodiments, sample collection includes stool samples, intestinal tissue samples, and blood samples.

[0026] Specifically, the fecal samples collected need to be collected directly from fresh feces of cats through sterile sampling tubes. The recommended sampling volume is 10 to 20 grams. After the samples are collected, they are immediately stored at 4°C for short-term storage or -80°C for long-term storage. Low-temperature storage of fecal samples helps to maximize the protection of the original state of the intestinal flora and avoid changes in the flora structure caused by external environmental factors.

[0027] For intestinal tissue samples, in general, intestinal tissue (such as small intestine or colon) from cats diagnosed with IBD needs to be placed in RNA protection reagents (such as RNAlater) immediately 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 host RNA extraction.

[0028] As an option, 5 mL of fresh blood is collected through a blood collection tube and quickly centrifuged (generally 3000×g, 10 minutes) to separate the serum. The separated serum should be stored in aliquots to avoid repeated freezing and thawing that affects the integrity of the sample. Serum samples are used for subsequent inflammatory factor detection, including but not limited to the quantitative determination of pro-inflammatory factor levels such as TNF-α and IL-6.

[0029] In this embodiment, extracting microbial DNA from stool samples is an important step, and the extraction is mainly completed using a commercial stool DNA extraction kit. In order to improve the efficiency and quality of DNA extraction, it is recommended to add an appropriate mechanical lysis step, such as using a bead mill to crush the sample. During the extraction process, attention should be paid to removing PCR inhibitors (such as polysaccharides and humic acid) to ensure the success rate of subsequent 16SrRNA sequencing.

[0030] For intestinal tissue samples, the conventional method for extracting host RNA is to use Trizol reagent. Specifically, after the tissue sample is placed in liquid nitrogen and ground, an appropriate amount of Trizol reagent is added to fully lyse the tissue, and the RNA, DNA, and protein phases are separated through the separation step. The RNA is then purified by isopropanol precipitation, washed, and dissolved in RNase-free water. After the RNA is extracted, its purity and concentration are determined by a NanoDrop spectrophotometer. The OD260 / 280 value is usually required to be between 1.8 and 2.1 to ensure that the RNA quality meets the needs of subsequent experiments.

[0031] In general, quality testing is required after fecal DNA extraction is completed. A NanoDrop ND-1000 spectrophotometer is used to perform a preliminary test on the purity of the DNA (OD260 / 280 ratio), and agarose gel electrophoresis is used to observe the integrity of the DNA. In addition, to ensure that there are no obvious contaminants in the extracted DNA, fluorescent dyes (such as PicoGreen) can be used to quantitatively measure the DNA concentration and further calibrate the sequencing input amount.

[0032] For host RNA, quality testing is also a necessary step. In one possible implementation, the integrity of RNA is evaluated using a Bioanalyzer or RNA electrophoresis analyzer to obtain an RNA integrity number (RIN value). Usually, a 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.

[0033] In this example, all sample collection and processing information must be recorded in detail as part of the experimental data. This information includes but is not limited to the cat's number, sample source (feces, intestinal tissue, serum), sampling time, sampling location, storage conditions and 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 sample and provide a reference background for the interpretation of subsequent analysis results.

[0034] As a possible extension, microbial DNA in stool samples can be extracted and selectively used for metagenomic analysis. This analysis method allows the accuracy of microbiota annotation to be improved by amplifying multiple gene fragments. In addition, for host RNA extracted from intestinal tissue samples, expression profile analysis of cytokine genes can also be performed to more comprehensively evaluate the host's inflammatory response to IBD.

[0035] In some embodiments, the formula used for RNA purity detection is: RNA purity (OD260 / 280) =

[0036] in: The OD260 value is the absorbance of RNA at a wavelength of 260 nm; The OD280 value is the absorbance of RNA at a wavelength of 280 nm.

[0037] It is generally believed that an OD260 / 280 value between 1.8 and 2.1 indicates high RNA purity. If there is a deviation, proteins or other contaminants may be present and further purification is required.

[0038] 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; Microbiome analysis includes 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.

[0039] Specifically, in general, microbial community analysis mainly includes three core processes: sequencing data generation, data cleaning and classification annotation, and diversity calculation. First, the V4 region of the microbial 16SrRNA gene is sequenced by high-throughput sequencing on the IlluminaNovaSeq platform to generate data. Subsequently, bioinformatics tools are used to clean and remove chimeras from the raw data to obtain high-quality ASV (specific taxonomic unit) sequences. In addition, the ASV sequences are classified and annotated based on the Greengenes or SILVA database to ensure the accuracy and completeness of the microbial community classification information. Finally, by calculating Alpha and Beta diversity, the richness of the microbial community and the differences in community structure between samples are further evaluated.

[0040] In this example, the 16SrRNA gene sequencing was performed using the Illumina NovaSeq platform, and the V4 region was selected as the amplification target region. Amplification of the V4 region is a common choice in 16SrRNA sequencing, and its sequence length is moderate and the species classification resolution is high. The primers used for sequencing are 515F ( -GTGCCAGCMGCCGCGGTAA- ) and 806R ( -GGACTACHVGGGTWTCTAAT- ). The amplified product generates a read length of 2×150bp through double-end sequencing. Generally, to ensure the sequencing depth, the sequencing reads of each sample need to reach more than 30,000 to cover most of the low-abundance bacterial communities.

[0041] In some embodiments, for samples with a high complexity of a specific microbial community, the sequencing depth can be further improved or the number of samples can be increased to ensure the representativeness and statistical significance of the experimental results.

[0042] After sequencing is completed, data cleaning is a key step. Specifically, QIIME2 is used to process the raw sequencing data, including removing low-quality sequences (Q value less than 30), cutting adapter sequences, and removing chimeras. Chimera removal uses UCHIME or DADA2 algorithms to ensure that only high-quality sequences are included in downstream analysis.

[0043] In this embodiment, the cleaned sequences are clustered into ASVs. ASV is a refined classification unit that can accurately reflect the true diversity of microbial communities. Compared with traditional OTUs, it does not rely on artificially set clustering thresholds and therefore has a higher biological resolution. In order to ensure the accuracy of the classification, the Greengenes or SILVA database is selected in this embodiment to classify and annotate the ASV sequences, and the annotation results cover multiple classification levels such as phylum, class, order, family, and genus.

[0044] After the taxonomic annotation is completed, the diversity of the bacterial community and the differences between samples need to be further evaluated.

[0045] In one possible implementation, Alpha diversity is used to characterize the richness and uniformity of the bacterial community within a single sample. Specific calculation indicators include the Shannon index and the Simpson index: Shannon Index ( ) is calculated as:

[0046] 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 bacterial community.

[0047] The Simpson index (D) is calculated as:

[0048] The Simpson index reflects the dominance of the main species in the sample. The closer the value is to 1, the more uniform the bacterial community is.

[0049] In some embodiments, calculation of other Alpha diversity indicators, such as Chao1 index, can be added according to experimental requirements to evaluate the possibility of undetected species in the sample.

[0050] As an option, Beta diversity analysis was used to evaluate the differences in microbiota structure between IBD cats and healthy cats. Specifically, the Bray-Curtis distance was used in this example to calculate the similarity between samples:

[0051] in, is the Bray-Curtis distance between sample i and sample j, and are the abundance values ​​of the kth bacterial species in sample i and sample j, respectively. The smaller the distance value, the higher the similarity of the bacterial community structure between the two samples.

[0052] In the analysis, principal coordinate analysis (PCoA) was performed using Bray-Curtis distance to visualize the structural differences in the sample microbiota. PCoA can reduce high-dimensional data to two-dimensional or three-dimensional space, clearly showing the distribution pattern of microbiota between IBD cats and healthy cats.

[0053] In some embodiments, in order to further verify the reliability of the analysis results, PERMANOVA (multivariate analysis of variance based on a distance matrix) can be introduced to perform statistical tests on the inter-group differences between bacterial communities.

[0054] Function prediction: predict the metabolic functional pathways of the microbiota based on microbiota data and identify functional pathways related to chronic inflammatory bowel disease; Function prediction is based on the PICRUSt2 tool, which includes the following: Predict the functional metabolic pathways of the microbiota and identify the lipopolysaccharide biosynthesis, steroid metabolism, and short-chain fatty acid metabolism pathways associated with chronic inflammatory bowel disease; Combined with the results of microbial difference analysis, the metabolic functional characteristics of key microbiota were located and further associated with the functional pathways of chronic inflammatory bowel disease.

[0055] Specifically, in this embodiment, the use process of PICRUSt2 includes gene function prediction, pathway annotation and functional association analysis. Specifically, the ASV sequence obtained from the flora analysis step is first mapped to a reference database (such as Greengenes13.5 or SILVA database), and each ASV is assigned its nearest reference sequence. Subsequently, based on the genome annotations of these reference sequences, the abundance distribution of the gene family corresponding to the ASV is inferred.

[0056] As a possible implementation, the calculation of predicted gene family abundance is based on the following formula:

[0057] in, 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 inferred.

[0058] In functional annotation, PICRUSt2 uses the KEGG database to associate predicted gene families with known metabolic pathways to generate a pathway abundance table for each sample. In some embodiments, special attention is paid to pathways closely related to chronic inflammatory bowel disease. For example: The lipopolysaccharide biosynthesis pathway, which is mainly participated by Gram-negative bacteria, is the source of pro-inflammatory factors associated with IBD; The steroid metabolism pathway is closely related to the host's endocrine regulation and immune response; Short-chain fatty acid metabolic pathways, including products such as butyrate and acetate, have been shown to have anti-inflammatory and intestinal barrier repair effects.

[0059] Generally, the significance analysis of functional pathways uses statistical methods, such as the Mann-Whitney U test, to compare the differences in the abundance of specific pathways between the IBD group and the healthy group. Through this analysis, functional pathways that are specifically upregulated or downregulated in the IBD group can be screened.

[0060] In some embodiments, the results of functional prediction can be combined with bacterial community difference analysis to locate the action points of key bacterial communities in functional pathways. Specifically, key bacterial communities screened by LEfSe (linear discriminant analysis effect size), such as Helicobacter and Anaerobic Spirillum, can be further associated with lipopolysaccharide biosynthesis and steroid metabolism pathways. PICRUSt2 prediction shows that the gene abundance of these bacterial communities in related pathways is significantly higher than that of the healthy group, providing direct evidence for the upregulation of these pathways.

[0061] As an option, Spearman correlation analysis can also be used to evaluate the relationship between the abundance of functional pathways and the abundance of bacterial communities. Assuming that the correlation coefficient between a bacterial community and a pathway is positive and statistically significant at p < 0.05, it can be inferred that the bacterial community may contribute more to the metabolism of the pathway.

[0062] In order to verify the accuracy of the function prediction, the results predicted by PICRUSt2 were cross-validated with the metagenomic data in this embodiment. 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 function prediction and provide a basis for downstream verification.

[0063] In one possible implementation, in order to make the functional prediction results more interpretable, multidimensional association analysis can also be performed in combination with host inflammatory factor data (such as TNF-α and IL-6 concentrations). For example, by regressing the abundance of the lipopolysaccharide synthesis pathway with the TNF-α level, the potential causal relationship between pathway metabolism and host inflammatory response can be revealed.

[0064] Data verification: qPCR technology was used to verify the abundance of the selected key bacterial communities, and ELISA or qPCR was used to detect the levels of host-related inflammatory factors; Data validation includes 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.

[0065] Specifically, in this embodiment, qPCR technology is used to verify the abundance of key bacterial groups selected in the functional prediction results. The key bacterial groups 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 bacterial groups in IBD cats and healthy cats.

[0066] Specifically, the qPCR validation process is as follows: Microbial DNA extracted from stool samples was used as template.

[0067] Use specific primers for the 16SrRNA gene sequence of the target bacteria, and design the primer sequences as follows: Helicobacter Primers: Forward -AGGGTGAGGAGTGTGAG- ; Reverse -CGGTTTGGTCGTAAAACG- .

[0068] Anaerobic Spirillum Primers: Forward -ATGAGCTCCGAGATGTTG- ; Reverse -CCTTCTGCTGGAAGATCA- .

[0069] Prepare a 25 μL reaction system including 10 ng template DNA, SYBR Green dye, primers (0.5 μM) and deionized water.

[0070] 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.

[0071] The ΔΔCt method was used to calculate the relative abundance of the target bacterial community, and healthy cats were used as the control group: Ct

[0072] Ct

[0073] Relative abundance Ct 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.

[0074] 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.

[0075] The specific implementation is as follows: 5 mL of serum sample was extracted for ELISA testing.

[0076] Use a commercial ELISA kit (such as ThermoFisher TNF-α ELISA Kit) for detection. The steps include: Standards and diluted serum samples were added to a 96-well plate, with 100 μL added to each well.

[0077] According to the instructions of the kit, capture antibody and detection antibody were added, and incubation and washing were performed.

[0078] The reaction was developed using TMB substrate and the absorbance was read at a wavelength of 450 nm.

[0079] The actual concentrations of TNF-α and IL-6 in serum were calculated according to the standard curve in pg / mL.

[0080] Generally, the experiment needs to set up multiple control groups, including positive control, negative control and healthy cat group, to eliminate the influence of nonspecific reactions on the test results.

[0081] In one 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 an important role in the inflammatory response, among which TNF-α and IL-6 are pro-inflammatory factors, and IL-10 is an anti-inflammatory factor, which can reveal the dynamic equilibrium state of the host inflammatory response.

[0082] The specific implementation is as follows: Extraction of tissue RNA: The intestinal tissue RNA was extracted using the Trizol method. After isopropanol precipitation and 75% ethanol washing, the RNA was dissolved in RNase-free water.

[0083] Reverse transcription to 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.

[0084] Configure the qPCR reaction system: including cDNA template, SYBRGreen dye, specific primers and PCR buffer.

[0085] Thermal cycle program: pre-denaturation at 95°C for 5 min, 95°C for 15 s, 60°C for 30 s, 72°C for 30 s, and 40 cycles.

[0086] GAPDH was used as the internal reference gene, and the ΔΔCt method was used to calculate the relative expression of the target gene.

[0087] The calculation formula of mRNA expression is:

[0088] In some embodiments, the mRNA expression levels of inflammatory genes can be further correlated with the bacterial abundance data, for example, the correlation between the abundance of Helicobacter and the expression level of TNF-α can be evaluated by the Spearman correlation test.

[0089] 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; 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.

[0090] Specifically, in this embodiment, the construction of the database is based on the collected stool samples and intestinal tissue samples, and the microbial characteristics and functional information related to IBD are systematically recorded by integrating microbial sequencing data, functional prediction results and experimental verification data.

[0091] Specifically, the construction of the database includes the following steps: Integration of sequencing data: The bacterial taxonomy information obtained in the above 16SrRNA sequencing step is incorporated into the database, covering multiple taxonomic levels such as phylum, class, order, family, and genus. Special attention is paid to key bacterial groups significantly associated with IBD, such as Helicobacter and Anaerobic Spirillum.

[0092] Functional prediction results: The functional metabolic pathways predicted by the PICRUSt2 tool were added to the database, with a focus on recording the abundance information of lipopolysaccharide biosynthesis, steroid metabolism, and short-chain fatty acid metabolism pathways, as well as the association between these pathways and IBD.

[0093] Experimental validation data: The abundance of bacterial flora and expression levels of inflammatory factors verified by qPCR and ELISA were included in the database. These data provided experimental support for the reliability of the functional prediction results.

[0094] As a possible implementation, the database content can be divided into the following parts: Bacteria classification information: Including the taxonomic information of the phylum, class, order, family, and genus of the bacterial community, as well as the abundance data of each taxonomic unit. For example: Data format: [classification unit]-[abundance percentage] For example: Helicobacter spp. - 15%.

[0095] Functional pathway characteristics: Includes information on metabolic pathways related to bacterial flora functions. For example: Abundance of lipopolysaccharide biosynthesis pathway: 30% Steroid metabolic pathway abundance: 12% Abundance of short-chain fatty acid metabolic pathway: 20% Clinically relevant data: Record the association between bacterial abundance and clinical symptoms, such as the correlation coefficient between Helicobacter abundance and TNF-α concentration. The data format is as follows: Bacterial abundance: 20% - TNF-α concentration: 50pg / mL - Correlation coefficient: r=0.85 Time Series Data: The database supports the recording of dynamic changes in bacterial flora at different time points, which facilitates the evaluation of the effects of therapeutic interventions. For example: Day 1: Helicobacter - 15% Day 7: Helicobacter - 5% In general, the database needs to be regularly supplemented with experimental data and updated with analysis results to maintain its applicability to actual cases.

[0096] In this embodiment, the process of updating the database includes: New data integration: 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: New sample data: The abundance of a certain bacterial group (such as Anaerobic Spirillum) was found to have increased significantly.

[0097] New functional pathway discovery: Key genes in the short-chain fatty acid metabolism pathway were further confirmed.

[0098] Dynamic data management: The database supports dynamic updates, integrating new data with existing data and verifying old data. To ensure data consistency, data can be compared through manual inspection or automated verification tools.

[0099] As a possible implementation method, the database needs to verify the accuracy of the prediction through regression testing and model correction. The correction process is described in detail below: Regression test: Based on the newly added experimental data, linear regression analysis is performed on the predicted and actual values ​​of the bacterial flora and functional pathways. For example: The correlation coefficient between the predicted value of Helicobacter abundance and the qPCR measured value reached 0.9, indicating that the prediction model has high accuracy.

[0100] Model correction formula: For bacterial communities or functional pathways with large deviations in predicted values, correction can be made using the following formula:

[0101] in: is the corrected functional abundance; is the initial prediction value of PICRUSt2; is the experimentally determined value; k is the correction factor, which is determined based on historical data and is usually between 0.5 and 1.

[0102] Model iteration: As the amount of experimental data increases, the model parameters are optimized through machine learning algorithms (such as random forest or XGBoost) to further improve the prediction accuracy.

[0103] The database is not only a tool for data storage, but can also be directly used for disease risk assessment and personalized treatment recommendations. The following are some practical application scenarios: Disease risk assessment: The risk of IBD cats was assessed based on the correlation between the microbiota and inflammatory factors in the database. For example, when the abundance of Helicobacter was greater than 20% and the abundance of the lipopolysaccharide pathway was greater than 25%, it indicated a high risk.

[0104] Personalized treatment recommendations: The database can generate treatment recommendations based on the dynamic changes in the microbiota. For example, for IBD cats with excessive abundance of Helicobacter, combined treatment with antibiotics and probiotics is recommended.

[0105] Data analysis and output: Generate standardized analysis reports, including microbiome characteristics, functional pathway differences, and disease risk assessment, and provide personalized treatment recommendations.

[0106] Specifically, in this embodiment, the personalized treatment recommendation module takes the flora abundance and functional pathway information as the core basis, and combines the host's inflammatory factor levels and clinical manifestations to generate specific treatment recommendations.

[0107] Specifically: Treatment options for abnormal abundance of Helicobacter spp.: When database analysis found that the abundance of Helicobacter in IBD cats was significantly increased (such as an abundance greater than 20%) and the abundance of the lipopolysaccharide synthesis pathway increased by more than 25%, it suggested that this bacterial community might be a key pathogen causing the worsening of the disease.

[0108] The following treatment strategies are recommended: Use narrow-spectrum antibiotics (eg, amoxicillin) to suppress overgrowth of Helicobacter species.

[0109] At the same time, supplement with probiotics (such as Lactobacillus or Bifidobacterium) to restore the balance of intestinal microecology.

[0110] In terms of diet, it is recommended to use special cat food that is low in fat and high in fiber to reduce the burden of intestinal inflammation.

[0111] Treatment options for abnormal short-chain fatty acid metabolism: When functional prediction shows that the short-chain fatty acid (such as butyrate) metabolic pathway is significantly downregulated and the abundance of functional bacteria (such as Bacteroides and Ruminococcaceae) is decreased, it suggests that the intestinal barrier function may be impaired.

[0112] The following treatment strategies are recommended: Supplement with sodium butyrate or prebiotics (such as inulin) to stimulate the restoration of beneficial intestinal flora.

[0113] Increase soluble dietary fiber (such as oats) in your diet to promote the production of short-chain fatty acids.

[0114] Use intestinal protectants containing probiotics (such as Saccharomyces boulardii) to help restore intestinal barrier function.

[0115] Treatment options for elevated levels of inflammatory factors: If the test finds that the levels of TNF-α and IL-6 in the host serum are significantly elevated, and the microbial data analysis suggests that the activity of the lipopolysaccharide synthesis pathway is enhanced, it can be inferred that intestinal microbial imbalance has induced a systemic inflammatory response.

[0116] The following treatment strategies are recommended: Use nonsteroidal anti-inflammatory drugs (such as meloxicam) for short-term control of inflammation.

[0117] Combination of probiotics and prebiotics to reduce the abundance of pro-inflammatory bacteria in the gut, such as anaerobic Spirillum.

[0118] It is recommended to adopt an easily digestible, high-protein, low-fat diet to reduce the burden on the intestines.

[0119] In one 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: Bacterial flora characteristics analysis: Including the taxonomic distribution of bacterial communities, the abundance of dominant bacterial communities, and the dynamic changes of key bacterial communities.

[0120] For example: Helicobacter abundance: 20% (significantly increased) Ruminococcaceae abundance: 5% (significant decrease) Functional pathway differences: The significant differences in functional metabolic pathways between IBD cats and healthy cats were analyzed.

[0121] For example: Abundance of lipopolysaccharide synthesis pathway: 25% (higher than 10% of the healthy group) Abundance of short-chain fatty acid metabolic pathway: 15% (lower than 30% of the healthy group) Disease risk assessment: Output disease risk scores based on the database model. For example: Risk score: 85 (high risk) Main risk factors: high abundance of Helicobacter spp. and upregulation of lipopolysaccharide synthesis pathway.

[0122] Personalized treatment recommendations: Combine microbiome and host data to generate specific treatment strategies. For example: Antibiotics: Amoxicillin is recommended at a dose of 10 mg / kg twice a day.

[0123] Probiotics: It is recommended to use Lactobacillus preparations, 1×109 CFU per day.

[0124] Diet: Low-fat, high-fiber cat food (Brand A) is recommended.

[0125] In general, personalized treatment recommendations need to be adjusted based on dynamic updates of the database and changes in the bacterial flora during treatment.

[0126] In this embodiment, dynamic adjustment is achieved by: Data is updated regularly: It is recommended to re-collect samples every 1-2 weeks to analyze changes in bacterial abundance and functional pathways.

[0127] For example: Sampling in the second week showed that the abundance of Helicobacter dropped from 20% to 10%, indicating that the treatment was effective.

[0128] If TNF-α levels remain high, the dose of anti-inflammatory drugs may need to be increased.

[0129] Long-term monitoring: After treatment, it is recommended to conduct a flora test every 3-6 months to prevent recurrence.

[0130] The database can store microbial data at multiple time points to form a complete health status curve for risk prediction and long-term management.

[0131] Please see attached Figure 2 , a data validation processing system for feline chronic inflammatory bowel disease, comprising: Data collection and storage module: used to record sample sources, save sample information, and manage DNA and RNA extraction results of samples; Microbial community analysis module: used to perform 16SrRNA sequencing on sample microbial DNA, and to classify, annotate and analyze the diversity of the microbial community; Function prediction module: predict metabolic functional pathways based on microbiome analysis results and identify abnormal functional pathways related to diseases; Data verification module: Verify the reliability of microbial function prediction through qPCR and ELISA technology, and associate it with host inflammatory factors; Database management module: stores bacterial flora classification, functional pathways and clinical data, and supports data update and analysis; Output and application module: Generate standardized analysis reports and provide personalized treatment recommendations.

[0132] Please see attached Figure 3 , the flora analysis module includes: A data processing unit, used for cleaning, removing chimeras and generating ASV sequences for 16SrRNA gene sequencing data; Diversity calculation unit, used to calculate the Alpha diversity and Beta diversity indicators of the sample; The differential bacterial flora screening unit is used to screen key differential bacterial flora and associate them with metabolic functional pathways.

[0133] Please see attached Figure 4 , the data validation module includes: qPCR verification unit, used to verify the abundance changes of target bacteria by qPCR technology, the target bacteria include Helicobacter and Anaerobic Spirillum; ELISA test unit, used to detect the concentration of pro-inflammatory factors TNF-α and IL-6 in serum; The functional pathway verification unit is used to verify the association between the bacterial flora functional pathway and host inflammation in combination with the functional prediction results.

[0134] The analysis reports generated by the output and application modules include: The bacterial community structure characteristics of the samples; Key differential bacterial communities and functional pathways associated with IBD; Analysis of host inflammatory factor levels and their association with microbiota; Disease risk assessment and personalized treatment recommendations.

[0135] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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 microbiota based on microbiota data and identify functional pathways related to chronic inflammatory bowel disease; Data verification: qPCR technology was used to verify the abundance of the selected key bacterial communities, and ELISA or qPCR was used to detect the levels of host-related inflammatory factors; 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 microbiome characteristics, functional pathway differences, and disease risk assessment, and 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 function prediction is based on the PICRUSt2 tool, including the following contents: Predict the functional metabolic pathways of the microbiota and identify the lipopolysaccharide biosynthesis, steroid metabolism, and short-chain fatty acid metabolism pathways associated with chronic inflammatory bowel disease; Combined with the results of microbial difference analysis, the metabolic functional characteristics of key microbiota were located and further associated with the functional pathways of chronic inflammatory bowel disease.

5. 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.

6. 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.

7. A data verification and processing system for feline chronic inflammatory bowel disease, characterized in that: include: Data collection and storage module: used to record sample sources, save sample information, and manage DNA and RNA extraction results of samples; Microbial community analysis module: used to perform 16SrRNA sequencing on sample microbial DNA, and to classify, annotate and analyze the diversity of the microbial community; Function prediction module: predict metabolic functional pathways based on microbiome analysis results and identify abnormal functional pathways related to diseases; Data verification module: Verify the reliability of microbial function prediction through qPCR and ELISA technology, and associate it with host inflammatory factors; Database management module: stores bacterial flora classification, functional pathways and clinical data, and supports data update and analysis; Output and application module: Generate standardized analysis reports and provide personalized treatment recommendations.

8. A data verification and processing system for feline chronic inflammatory bowel disease according to claim 7, characterized in that: The flora analysis module comprises: A data processing unit, used for cleaning, removing chimeras and generating ASV sequences for 16SrRNA gene sequencing data; Diversity calculation unit, used to calculate the Alpha diversity and Beta diversity indicators of the sample; The differential bacterial flora screening unit is used to screen key differential bacterial flora and associate them with metabolic functional pathways.

9. A data verification and processing system for feline chronic inflammatory bowel disease according to claim 7, characterized in that: The data verification module comprises: qPCR verification unit, used to verify the abundance changes of target bacteria by qPCR technology, the target bacteria include Helicobacter and Anaerobic Spirillum; ELISA test unit, used to detect the concentration of pro-inflammatory factors TNF-α and IL-6 in serum; The functional pathway verification unit is used to verify the association between the bacterial flora functional pathway and host inflammation in combination with the functional prediction results.

10. A data verification and processing system for feline chronic inflammatory bowel disease according to claim 7, characterized in that: The analysis report generated by the output and application module includes: The bacterial community structure characteristics of the samples; Key differential bacterial communities and functional pathways associated with IBD; Analysis of host inflammatory factor levels and their association with microbiota; Disease risk assessment and personalized treatment recommendations.

Images