Method for treating piglet damp-heat diarrhea through multi-omics combined analysis of scutellaria baicalensis

Abstract

The invention discloses a method for treating piglet damp-heat diarrhea through multi-omics combined analysis. According to the method, faeces metabonomics, intestinal transcriptomics and intestinal flora 16s analysis are adopted, transcriptome data, metabolome data and intestinal flora data are screened according to a prime difference standard, then transcriptomics of a model group and a control group, faeces metabonomics and intestinal flora are subjected to conjoint analysis, an intersection is taken, and the faeces metabonomics of the model group and the control group, the faeces metabonomics of the model group and the control group and the intestinal flora of the model group and the control group are subjected carrying out conjoint analysis on transcriptomics of the Model group and HQ-M, faeces metabonomics and intestinal flora, and then taking an intersection; the method comprises the following steps of: selecting a plurality of metabolites and florae from a plurality of intestinal florae, respectively obtaining intersections, screening related genes, searching gene FPKM expression quantity in transcriptomics, and screening corresponding metabolites and florae in metabonomics and intestinal florae to obtain a gene-flora-metabolite related network diagram. Multi-omics combined analysis shows that the piglet diarrhea due to damp-heat is mainly manifested by inflammation and glucose and lipid metabolism disorder.

Description

Technical Field

[0001] This invention relates to the field of pharmaceutical technology, specifically to a method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis. Background Technology

[0002] Scutellaria baicalensis Georgi, a traditional Chinese medicine, has a long history of application in the field of Traditional Chinese Medicine (TCM). It is bitter and cold in nature, and enters the spleen, lung, large intestine, and small intestine meridians. It has the effects of clearing heat and detoxifying, drying dampness and purging fire, stopping bleeding and calming the fetus. The *Shennong Bencao Jing* (Shennong's Classic of Materia Medica) records that Scutellaria baicalensis "treats various types of jaundice due to heat, dysentery, diarrhea, promotes urination, treats amenorrhea, malignant sores, and ulcers." In TCM clinical practice, Scutellaria baicalensis is often used to treat various damp-heat syndromes, such as damp-heat diarrhea, jaundice, and cough due to lung heat. Its main active components include flavonoids, such as baicalin, baicalein, wogonin, and baicalein glycosides, which have broad biological activities, such as antibacterial, anti-inflammatory, antioxidant, and immunomodulatory effects. Although Scutellaria baicalensis has potential application value in treating damp-heat diarrhea in piglets, current research on its mechanism of action is relatively limited and insufficient. Most studies have only focused on in vitro experiments and simple animal studies of Scutellaria baicalensis extracts or single active ingredients, lacking a comprehensive and systematic understanding of its complex in vivo action network and molecular mechanisms. This has significantly limited the widespread clinical application and further development and utilization of Scutellaria baicalensis.

[0003] Metabolomics, through the analysis and comparison of metabolites, genes, and microorganisms, can discover biomarkers related to organismal states, disease progression, and drug responses. It has wide applications in biomedical research, drug development, food safety, and environmental monitoring, possessing significant scientific research and application value. Transcriptomics studies the types, structures, expression levels, and changes of transcripts under different physiological conditions, developmental stages, and disease states, thereby revealing the mechanisms of gene expression regulation. 16S rRNA genes are ubiquitous in bacteria and possess highly conserved and variable regions. Sequencing and analyzing these variable regions can determine bacterial species and relative abundance, thus understanding the composition and diversity of bacterial communities. Traditional Chinese medicine (TCM) syndromes are a stage in the disease process, characterized by specific location, etiology, nature, progression, and the strength of the body's resistance. They represent the overall state of the body's response and its movement and changes, aiming to study the life processes of organisms from a holistic and systemic perspective. The diversity of components and the multi-target nature of effects of traditional Chinese medicine (TCM) determine the complexity of its mechanisms of action. A complex regulatory network exists among components, pathways, and targets, involving synergistic or antagonistic interactions. This necessitates multi-level research methods and the processing of massive amounts of data to reveal specific changes in a particular process. Therefore, it is necessary to provide a method for analyzing the mechanism of action of Scutellaria baicalensis in treating damp-heat diarrhea in piglets using a multi-omics approach. This is of great significance for the further development of Scutellaria baicalensis in clinical applications and the treatment of diarrhea. Summary of the Invention

[0004] The purpose of this invention is to provide a method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis.

[0005] This invention is achieved by adopting the following technical solution:

[0006] A method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis includes the following steps:

[0007] S1, Fecal metabolomics

[0008] S11. Fecal pretreatment

[0009] Add 80% methanol solution to the feces at a material-to-liquid ratio of 1g:10μL, add small steel balls, pre-cool in a -40℃ refrigerator for 2 min, then place in a grinder and grind at a frequency of 60Hz for 2 min; extract by ultrasonication in an ice-water bath for 10 min; let stand overnight at -40℃; centrifuge at 12000rpm and 4℃ for 10 min, collect the supernatant, filter it using an organic phase pinhole filter, transfer it to a vial, and store at -80℃;

[0010] S12, Liquid Chromatography-Mass Spectrometry Analysis

[0011] Chromatographic conditions for fecal metabolomics: Column: ACQUITYUPLC HSS T3, 100 mm × 2.1 mm, 1.8 μm; Column temperature: 45 ℃; Mobile phase: 0.1% formic acid aqueous solution; Mobile phase: acetonitrile; Gradient elution; Flow rate: 0.35 mL / min; Injection volume: 2 μL.

[0012] The elution gradient program is as follows:

[0013]

[0014] The mass spectrometry conditions are as follows:

[0015]

[0016]

[0017] S13, Data Preprocessing

[0018] Compound identification is based on multiple dimensions, including retention time, exact mass number, secondary fragments, and isotopic distribution. HMDB, Lipidmaps, METLIN databases, and LuMet-Animal 3.0 database are used for identification analysis. The extracted data are processed for missing values, zero value replacement, score scoring and filtering, and data merging.

[0019] S14, Data Analysis

[0020] Partial least squares-discriminant analysis was used, and partial least squares regression was employed to establish a model relating metabolite expression levels to sample groupings. The p-value < 0.05 and VIP > 1 criteria were used to screen differentially expressed metabolites. The common and specific differentially expressed metabolites among the differential comparison groups were analyzed using Venn plots.

[0021] S2, Intestinal Transcriptomics

[0022] S21. Extraction of ileal mRNA and construction of transcriptome library

[0023] Take 50-100 mg of tissue and place it in a 10 mL centrifuge tube containing 1 mL of Trizol. Homogenize thoroughly and transfer to a 1.5 mL centrifuge tube. Let stand for 5 min. Add 200 μL of chloroform, invert and mix well. Let stand at room temperature for 10 min. Centrifuge at 4℃ and 13000 rpm for 15 min. Transfer the supernatant to another new 1.5 mL centrifuge tube, add an equal volume of isopropanol, mix well, and let stand at -20℃ for 30 min. Centrifuge at 4℃ and 13000 rpm for another 15 min. Discard the supernatant. Add 500 μL of 75% ethanol to wash the precipitate. Centrifuge at 4℃ and 13000 rpm for 5 min. Discard the supernatant and retain the precipitate. Dry. Centrifuge briefly, aspirate the supernatant, dissolve in an appropriate amount of H2O until completely dissolved, and store at -80℃. The extracted RNA was tested by agarose gel electrophoresis to detect RNA degradation and contamination. RNA purity was determined by detecting the OD260 / 280 ratio. Construct a transcriptome library using the kit according to the instructions.

[0024] S22, RNA sequencing and differentially expressed gene analysis

[0025] Library sequencing was performed using the Llumina Novaseq 6000 sequencing platform, generating 150bp paired-end reads. The raw reads in FASTQ format were processed using FASTP software to remove low-quality reads, resulting in clean reads for subsequent data analysis. HISAT2 software was used for reference genome alignment and gene expression level calculation, with read counts for each gene obtained using HTSeq-count. R v3.2.0 was used for PCA analysis and plotting to assess sample biological repeatability. Differentially expressed genes were analyzed using DESeq2 software, with genes meeting the criteria of q-value < 0.05 and FC > 2.0 (or FC < 0.5) defined as differentially expressed genes. R v3.2.0 was used for hierarchical cluster analysis of differentially expressed genes to demonstrate gene expression patterns in different sample combinations. The R package ggradar was used to create radar plots of the top 30 genes to show changes in upregulated or downregulated gene expression. Subsequently, GO and KEGG analyses were performed on differentially expressed genes based on the hypergeometric distribution algorithm. Enrichment analyses using Pathway, Reactome, and WikiPathways were performed to filter for saliency-enriched feature items; enrichment analysis cyclographs were plotted on the saliency-enriched feature items using Rv3.2.0.

[0026] S3, 16s analysis of gut microbiota

[0027] S31. DNA extraction and PCR amplification

[0028] Genomic DNA was extracted from the samples using the MagPure Soil DNA LQ Kit according to the manufacturer's instructions. DNA concentration and purity were assessed using NanoDrop 2000 and agarose gel electrophoresis. The extracted DNA was stored at -20°C. Using the extracted genomic DNA as a template, PCR amplification of the bacterial 16S rRNA gene was performed using barcode-specific primers and Takara Ex Taq high-fidelity enzyme. The V3-V4 variable region of the 16S rRNA gene was amplified using universal primers for bacterial diversity analysis.

[0029] S32, Library Construction and Sequencing

[0030] The first-round PCR amplification products were detected by agarose gel electrophoresis, then purified using AMPure XP beads. The purified products were used as templates for the second-round PCR amplification. The products were purified again using magnetic beads, and the purified second-round products were quantified using Qubit. The concentration was then adjusted for sequencing. Sequencing was performed using the Illumina NovaSeq 6000 sequencing platform, generating 250bp paired-end reads.

[0031] S33, Data Analysis

[0032] After the data was processed, the Cutadapt software was first used to cut out the primer sequences from the raw data sequences. Then, DADA2 was used to perform quality filtering, noise reduction, splicing, and chimera removal quality control analysis on the qualified paired-end raw data from the previous step according to the default parameters of QIIME 2, resulting in representative sequences and an ASV abundance table. Representative sequences for each ASV were selected using the QIIME 2 software package, and all representative sequences were compared and annotated with the Silva database. Species alignment and annotation were analyzed using the default parameters of the q2-feature-classifier software. α and β diversity analyses were performed using QIIME 2 software. α diversity of the samples was assessed using alpha diversity including the Chao1 index and Shannon index. β diversity of the samples was assessed using unweighted Unifrac principal coordinate analysis based on the unweighted Unifrac distance matrix calculated by R. Differential analysis was performed using the ANOVA / Kruskal Wallis / T test / Wilcoxon statistical algorithm based on the R package. Differential analysis of species abundance spectra was performed using LEfSe.

[0033] S4, Omics Joint Analysis

[0034] Transcriptomic, metabolomic, and gut microbiota data were screened according to the original omics difference criteria. Then, the intersection of transcriptomic and fecal metabolomic and gut microbiota analyses of the Model and Control groups was obtained to obtain the set of changed genes A. The intersection of transcriptomic and fecal metabolomic and gut microbiota analyses of the Model and HQ-M groups was obtained to obtain the set of changed genes B. The intersection of A and B was obtained to obtain the set of returned genes C. Relevant genes were screened in C, gene FPKM expression levels were found in transcriptomics, and corresponding metabolites and microbiota were screened in metabolomics and gut microbiota to obtain a gene-microbiota-metabolite correlation network diagram.

[0035] The methanol solution described in this invention also contains 4 μg / mL L-2-chlorophenylalanine.

[0036] The tissue described in step S21 of this invention is as follows: the ileum sample is placed in a mortar pre-cooled with liquid nitrogen and ground with a pestle, with liquid nitrogen added continuously until it is ground into powder.

[0037] The kit described in step S21 of this invention is the VAHTS Universal V5 RNA-seq Library Prep kit.

[0038] The reference genome mentioned in step S22 of this invention is the genome information of a pig published by NCBI with the number GCF_000003025.6, which serves as a reference database.

[0039] The universal primer mentioned in step S31 of this invention is:

[0040] 343F: 5'-TACGGGRAGGCAGCAG-3';

[0041] 798R: 5'-AGGGTATCTAATCCT-3'.

[0042] The first round of PCR system in step S32 of this invention is as follows:

[0043]

[0044] The cycling conditions for the first round of PCR in step S32 of this invention are as follows:

[0045]

[0046]

[0047] The second round PCR system described in step S32 of this invention is as follows:

[0048]

[0049] The cycling conditions for the second round of PCR described in step S32 of this invention are as follows:

[0050]

[0051] Compared with the prior art, the present invention has the following advantages:

[0052] Using multi-omics analysis, this invention revealed that damp-heat diarrhea in piglets is mainly characterized by inflammation and disordered glucose and lipid metabolism. Scutellaria baicalensis treats damp-heat diarrhea in piglets by altering the expression of inflammation-related genes ACE, HMOX1, LGALS7, PIK3C2G, SIGLEC14, and metabolism-related genes G6PC, CYP4F2, and DBP, thereby changing the metabolism of substances such as 10-nitrolinoleic acid, arachidonic acid ethanolamine, D-mannose, and uridine 5'-monophosphate, thus producing a therapeutic effect. Simultaneously, it alters the intestinal flora structure, primarily Firmicutes (Eubacterium spp., Christensenaceae, and Bacillus zurichae), thereby regulating inflammation and metabolism. The effects of Scutellaria baicalensis on the PI3K / Akt / Nrf2 / HO-1 pathway and the metabolism of substances such as linoleic acid and arachidonic acid may be key targets and material bases for its therapeutic effect on damp-heat diarrhea. This is of great significance for the further development of Scutellaria baicalensis in clinical applications and the treatment of diarrhea. Attached Figure Description

[0053] Figure 1 Fecal metabolomics PLS-DA analysis diagram;

[0054] Figure 2 VEEN diagram of pig fecal metabolomics;

[0055] Figure 3 Inter-sample correlation test;

[0056] Figure 4 : Statistical graph of differentially expressed genes;

[0057] Figure 5 Electrophoresis diagram of PCR amplification;

[0058] Figure 6 Gene intersection A (model group and normal group);

[0059] Figure 7 Gene intersection B (model group and HQ-M);

[0060] Figure 8 Gene intersection C (gene intersection A and gene intersection B);

[0061] Figure 9 Gene-microbiota-metabolite network diagram. Detailed Implementation

[0062] The technical solution of the present invention will be further described in detail below through specific embodiments.

[0063] Example 1

[0064] S1, Fecal metabolomics

[0065] S11. Fecal pretreatment

[0066] Add 80% methanol solution (containing 4 μg / mL L-2-chlorophenylalanine) to the feces at a material-to-liquid ratio of 1 g:10 μL, add small steel balls, pre-cool in a -40℃ refrigerator for 2 min, then grind in a grinder at a frequency of 60 Hz for 2 min; ultrasonically extract in an ice-water bath for 10 min; let stand overnight at -40℃; centrifuge at 12000 rpm at 4℃ for 10 min, collect the supernatant, filter it using an organic phase pinhole filter, transfer it to a vial, and store at -80℃;

[0067] S12, Liquid Chromatography-Mass Spectrometry Analysis

[0068] Chromatographic conditions for fecal metabolomics: Column: ACQUITYUPLC HSS T3, 100 mm × 2.1 mm, 1.8 μm; Column temperature: 45 ℃; Mobile phase: 0.1% formic acid aqueous solution; Mobile phase: acetonitrile; Gradient elution; Flow rate: 0.35 mL / min; Injection volume: 2 μL.

[0069] The elution gradient program is as follows:

[0070]

[0071] The mass spectrometry conditions are as follows:

[0072]

[0073] S13, Data Preprocessing

[0074] Compound identification is based on multiple dimensions such as retention time, exact mass number, secondary fragments, and isotope distribution. HMDB, Lipidmaps, METLIN databases, and LuMet-Animal 3.0 database are used for identification analysis. The extracted data are processed for missing values, zero value replacement, score filtering, and data merging.

[0075] S14, Data Analysis

[0076] Partial least squares-discriminant analysis was used, and partial least squares regression was employed to establish a model relating metabolite expression levels to sample groupings. The p-value < 0.05 and VIP > 1 criteria were used to screen for differentially expressed metabolites. The common and specific differentially expressed metabolites in each differential comparison group were analyzed using Venn plots.

[0077] S2, Intestinal Transcriptomics

[0078] S21. Extraction of ileal mRNA and construction of transcriptome library

[0079] The ileum sample was placed in a mortar pre-cooled with liquid nitrogen and ground with a pestle, with liquid nitrogen added continuously until it was ground into powder. Take 50-100 mg of tissue and place it in a 10 mL centrifuge tube containing 1 mL of Trizol. Homogenize thoroughly and transfer to a 1.5 mL centrifuge tube. Let stand for 5 min. Add 200 μL of chloroform, invert and mix well. Let stand at room temperature for 10 min. Centrifuge at 4℃ and 13000 rpm for 15 min. Transfer the supernatant to another new 1.5 mL centrifuge tube, add an equal volume of isopropanol, mix well, and let stand at -20℃ for 30 min. Centrifuge again at 4℃ and 13000 rpm for 15 min, and discard the supernatant. Add 500 μL of 75% ethanol to wash the precipitate. Centrifuge at 4℃ and 13000 rpm for 5 min, discard the supernatant, retain the precipitate, and dry. Centrifuge briefly, aspirate the supernatant, dissolve in an appropriate amount of H2O until completely dissolved, and store at -80℃. The extracted RNA was analyzed by agarose gel electrophoresis to detect RNA degradation and contamination. RNA purity was determined by detecting the OD260 / 280 ratio. VAHTS Universal V5 RNA-seq was used. Construct transcriptome libraries using the LibraryPrep kit according to the instructions;

[0080] S22, RNA sequencing and differentially expressed gene analysis

[0081] The library was sequenced using the Llumina Novaseq 6000 sequencing platform, generating 150bp paired-end reads. The raw reads in FASTQ format were processed using FASTP software to remove low-quality reads, resulting in clean reads for subsequent data analysis. HISAT2 software was used for reference genome alignment and gene expression level calculation, with read counts for each gene obtained via HTSeq-count. PCA analysis and mapping of the genes were performed using R v3.2.0 to assess biological repeatability of the samples. The reference genome was the genomic information of pigs published by NCBI with the accession number GCF_000003025.6.

[0082] Differentially expressed genes were analyzed using DESeq2 software, where genes meeting the criteria of q-value < 0.05 and FC > 2.0 (or FC < 0.5) were defined as differentially expressed genes. Hierarchical clustering analysis was performed on differentially expressed genes using R v3.2.0 to demonstrate gene expression patterns in different groups and samples. Radar plots were generated for the top 30 genes using the R package ggradar to show changes in the expression of upregulated or downregulated genes. Subsequently, enrichment analyses based on GO, KEGGPathway, Reactome, and WikiPathways were performed on differentially expressed genes using the hypergeometric distribution algorithm to screen for significant enrichment functional items. Enrichment analysis cyclographs were generated for significant enrichment functional items using Rv3.2.0.

[0083] S3, 16s analysis of gut microbiota

[0084] S31. DNA extraction and PCR amplification

[0085] Genomic DNA was extracted from the samples using the MagPure Soil DNA LQ Kit according to the instructions. DNA concentration and purity were detected using NanoDrop 2000 and agarose gel electrophoresis. The extracted DNA was stored at -20°C. Using the extracted genomic DNA as a template, PCR amplification of the bacterial 16S rRNA gene was performed using barcode-specific primers and Takara Ex Taq high-fidelity enzyme. The V3-V4 variable region of the 16S rRNA gene was amplified using universal primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3') for bacterial diversity analysis.

[0086] S32, Library Construction and Sequencing

[0087] The first-round PCR amplification products were detected by agarose gel electrophoresis, then purified using AMPure XP beads. The purified products were used as templates for the second-round PCR amplification. The products were purified again using magnetic beads, and the purified second-round products were quantified using Qubit. The concentration was then adjusted for sequencing. Sequencing was performed using the Illumina NovaSeq 6000 sequencing platform, generating 250bp paired-end reads.

[0088] The first round of PCR system is as follows:

[0089]

[0090] The conditions for the first round of PCR cycling were as follows:

[0091]

[0092] The second round of PCR system is as follows:

[0093]

[0094] The conditions for the second round of PCR cycling were as follows:

[0095]

[0096]

[0097] S33, Data Analysis

[0098] After the data was processed, the Cutadapt software was first used to cut out the primer sequences from the raw data sequences. Then, DADA2 was used to perform quality filtering, noise reduction, splicing, and chimera removal quality control analysis on the qualified paired-end raw data from the previous step according to the default parameters of QIIME 2, resulting in representative sequences and an ASV abundance table. Representative sequences for each ASV were selected using the QIIME 2 software package, and all representative sequences were compared and annotated with the Silva database. Species alignment and annotation were analyzed using the default parameters of the q2-feature-classifier software. α and β diversity analyses were performed using QIIME 2 software. α diversity of the samples was assessed using alpha diversity including the Chao1 index and Shannon index. β diversity of the samples was assessed using unweighted Unifrac principal coordinate analysis based on the unweighted Unifrac distance matrix calculated by R. Differential analysis was performed using the ANOVA / Kruskal Wallis / T test / Wilcoxon statistical algorithm based on the R package. Differential analysis of species abundance spectra was performed using LEfSe.

[0099] S4, Omics Joint Analysis

[0100] Transcriptomic, metabolomic, and gut microbiota data were screened according to omics difference criteria. Then, the intersection of transcriptomic and fecal metabolomic and gut microbiota analyses of the Model and Control groups was obtained to obtain the set of altered genes A. The intersection of transcriptomic and fecal metabolomic and gut microbiota analyses of the Model and HQ-M groups was obtained to obtain the set of altered genes B. The intersection of A and B was obtained to obtain the set of retrospective genes C. Relevant genes were screened in C, gene FPKM expression levels were found in transcriptomics, and corresponding metabolites and microbiota were screened in metabolomics and gut microbiota to obtain a gene-microbiota-metabolite correlation network diagram.

[0101] To further verify the feasibility of the present invention, the inventors conducted a series of experiments, as follows:

[0102] 1. Experimental Materials

[0103] 1.1 Experimental Materials

[0104] The piglet feces and ileum tissue were samples preserved from the experimental part of the modeling and drug administration technology scheme in a patent application filed by the same applicant on the same day (a method for preparing a Guizhou miniature piglet damp-heat diarrhea model).

[0105] 1.2 Test Drugs and Reagents

[0106] Table 1. Reagents and instruments used in fecal metabolomics

[0107]

[0108]

[0109] Table 2 Transcriptomics Reagents and Instruments

[0110]

[0111]

[0112] Table 3. Reagents and Instruments for Intestinal Microbiology Analysis

[0113]

[0114] 2 Experimental Methods

[0115] 2.1 Experimental Methods for Fecal Metabolomics

[0116] 2.1.1 Fecal pretreatment

[0117] Weigh 60 mg of sample into a 1.5 mL centrifuge tube, add two small steel balls and 600 μL of methanol-water (V:V = 4:1, containing L-2-chlorophenylalanine mixed internal standard, 4 μg / mL); pre-cool in a -40 °C freezer for 2 min, then grind in a grinder (60 Hz, 2 min); sonicate in an ice-water bath for 10 min; incubate overnight at -40 °C; centrifuge for 10 min (12000 rpm, 4 °C), aspirate 150 μL of supernatant with a syringe, filter through a 0.22 μm organic phase pinhole filter, transfer to an LC vial, and store at -80 °C until LC-MS analysis.

[0118] 2.1.2 Liquid Chromatography-Mass Spectrometry Analysis

[0119] Chromatographic conditions for fecal metabolomics: Column: ACQUITYUPLC HSS T3 (100mm×2.1mm, 1.8um); Column temperature: 45℃; Mobile phase: A-water (containing 0.1% formic acid), B-acetonitrile; Flow rate: 0.35mL / min; Injection volume: 2μL.

[0120] Table 4. Fecal metabolomics elution gradient

[0121]

[0122]

[0123] Table 5 Mass Spectrometry Conditions

[0124]

[0125] 2.1.3 Data Preprocessing

[0126] Compound identification was based on multiple dimensions, including retention time (RT), exact mass number, secondary fragmentation, and isotopic distribution. The Human Metabolome Database (HMDB), Lipidmaps (v2.3), METLIN database, and the local LuMet-Animal 3.0 database were used for identification analysis. The extracted data underwent missing value handling, zero-value replacement, score scoring and filtering, and data merging.

[0127] 2.1.4 Data Analysis

[0128] Partial least squares-discriminant analysis (PLS-DA) was used to establish a model of the relationship between metabolite expression levels and sample groupings using partial least squares regression. Differential metabolites were screened using the criteria of P-value < 0.05 and VIP > 1. The common and specific differential metabolites among the differential comparison groups were analyzed by using Venn plots.

[0129] 2.2 Experimental Methods of Intestinal Transcriptomics

[0130] 2.2.1 Extraction of ileal mRNA and construction of transcriptome library

[0131] Ileal samples were placed in a mortar pre-cooled with liquid nitrogen and ground with a pestle, continuously adding liquid nitrogen until a powder was formed. 50–100 mg of the tissue was placed in a 10 mL centrifuge tube containing 1 mL of Trizol, homogenized thoroughly, and then transferred to a 1.5 mL centrifuge tube. The mixture was allowed to stand for 5 min. 200 μL of chloroform was added, and the mixture was inverted and incubated at room temperature for 10 min. The mixture was then centrifuged at 4 °C and 13,000 rpm for 15 min. The supernatant was transferred to a new 1.5 mL centrifuge tube, and an equal volume of isopropanol was added. The mixture was mixed and incubated at -20 °C for 30 min. The mixture was then centrifuged again at 4 °C and 13,000 rpm for 15 min, and the supernatant was discarded. The precipitate was washed with 500 μL of 75% ethanol. The precipitate was centrifuged at 4 °C and 13,000 rpm for 5 min, the supernatant was discarded, and the precipitate was dried. After a brief centrifugation, the supernatant was aspirated. The precipitate was dissolved in an appropriate amount of H₂O until completely dissolved and stored at -80 °C. The extracted RNA was analyzed by agarose gel electrophoresis to detect the degree of RNA degradation and contamination, and the purity of the RNA was determined by detecting the OD260 / 280 ratio.

[0132] Transcriptome libraries were constructed using the VAHTS Universal V5 RNA-seq Library Prep kit according to the instructions.

[0133] 2.2.2 RNA sequencing and differentially expressed gene analysis

[0134] The library was sequenced using the Llumina Novaseq 6000 sequencing platform, generating 150 bp paired-end reads. The raw reads in FASTQ format were processed using FASTP software to remove low-quality reads, resulting in clean reads for subsequent data analysis. HISAT2 software was used for reference genome alignment and gene expression levels (FPKM) calculation, and read counts for each gene were obtained using HTSeq-count. R (v3.2.0) was used to perform PCA analysis and plotting of the gene counts to assess sample biological repeatability. The reference genome was the swine genome information (NCBI ID GCF_000003025.6) as a reference database.

[0135] Differentially expressed genes (DEGs) were analyzed using DESeq2 software. Genes meeting the criteria of q-value < 0.05 and FC > 2.0 (or FC < 0.5) were defined as differentially expressed genes. Hierarchical cluster analysis of DEGs was performed using R (v 3.2.0) to visualize gene expression patterns across different groups and samples. A radar plot of the top 30 genes was generated using the R package ggradar to show changes in the expression of upregulated or downregulated genes.

[0136] Subsequently, enrichment analyses of differentially expressed genes were performed using the hypergeometric distribution algorithm based on GO, KEGG Pathway, Reactome, and WikiPathways to screen for significantly enriched functional items. R (v 3.2.0) was used to construct enrichment analysis cyclographs for the significantly enriched functional items.

[0137] 2.3 16s analysis of gut microbiota

[0138] 2.3.1 DNA extraction and PCR amplification

[0139] Genomic DNA was extracted from the samples using the MagPure Soil DNA LQ Kit (Magan) according to the manufacturer's instructions. DNA concentration and purity were assessed using a NanoDrop 2000 (Thermo Fisher Scientific, USA) and agarose gel electrophoresis. The extracted DNA was stored at -20°C. Using the extracted genomic DNA as a template, PCR amplification of the bacterial 16S rRNA gene was performed using barcode-enabled specific primers and Takara Ex Taq high-fidelity enzyme. The V3-V4 variable region of the 16S rRNA gene was amplified using universal primers 343F (5'-TACGGRAGGCAGCAG-3') and 798R (5'-AGGGTATCTAATCCT-3') for bacterial diversity analysis.

[0140] 2.3.2 Library Construction and Sequencing

[0141] PCR amplification products were detected by agarose gel electrophoresis. The products were then purified using AMPure XP beads and used as templates for two rounds of PCR amplification. After purification with magnetic beads, the purified products from the second round were quantified using Qubit sequencing, and the concentration was adjusted for sequencing. Sequencing was performed using an Illumina NovaSeq 6000 sequencing platform, generating 250 bp paired-end reads. The conditions for the first and second rounds of PCR are shown in Tables 6–9.

[0142] Table 6. PCR system for the first round

[0143]

[0144] Table 7. PCR Cycling Conditions for Round 1

[0145]

[0146] Table 8. PCR system for the second round

[0147]

[0148]

[0149] Table 9. Cycling conditions for the second round of PCR.

[0150]

[0151] 2.3.3 Data Analysis

[0152] After the data was processed, the Cutadapt software was first used to cut out the primer sequences from the raw data sequences. Then, DADA2 was used to perform quality control analyses on the qualified paired-end raw data from the previous step, including quality filtering, noise reduction, splicing, and chimera removal, according to the default parameters of QIIME 2, to obtain representative sequences and an ASV abundance table. Representative sequences for each ASV were selected using the QIIME 2 software package, and all representative sequences were aligned and annotated against the Silva (version 138) database. Species alignment and annotation were analyzed using the default parameters of the q2-feature-classifier software. Alpha and β diversity analyses were performed using QIIME 2 software. Alpha diversity of the samples was assessed using alpha diversity including the Chao1 index and the Shannon index. β diversity of the samples was assessed using unweighted unifrac principal coordinate analysis (PCoA) based on the unweighted unifrac distance matrix calculated in R. Difference analysis was performed using the ANOVA / Kruskal-Wallis / T-test / Wilcoxon statistical algorithm based on the R package. Differential analysis of species abundance spectra was performed using LEfSe.

[0153] 2.4 Omics-based joint analysis

[0154] Transcriptomic, metabolomic, and gut microbiota data were screened according to the original omics difference criteria. Then, the intersection of transcriptomic and fecal metabolomic and gut microbiota analyses of the Model and Control groups was obtained to obtain the set of changed genes A. The intersection of transcriptomic and fecal metabolomic and gut microbiota analyses of the Model and HQ-M groups was obtained to obtain the set of changed genes B. The intersection of A and B was obtained to obtain the set of returned genes C. Relevant genes were screened in C, gene FPKM expression levels were found in transcriptomics, and corresponding metabolites and microbiota were screened in metabolomics and gut microbiota to obtain a gene-microbiota-metabolite correlation network diagram.

[0155] 3 Results

[0156] 3.1 Fecal metabolomics results

[0157] 3.1.1 PLS-DA Analysis

[0158] The results are as follows Figure 1As shown, Partial Least Squares Discriminant Analysis (PLS-DA) is a supervised statistical method for discriminant analysis. This method uses partial least squares regression to establish a model relating metabolite expression levels to sample categories, thereby predicting sample categories. PLS-DA models for each comparison group were established, and the model evaluation parameters (R², Q²) were obtained through 7-fold cross-validation (seven rounds of cross-validation; when the number of biological replicates n <= 3, k rounds of cross-validation are performed, k = 2n). The closer R² and Q² are to 1, the more stable and reliable the model. The figure shows the model's reliability.

[0159] 3.1.2 Screening for Differential Metabolites

[0160] The number of metabolites screened according to the screening criteria of P-value < 0.05 and VIP > 1 is shown in Table 10.

[0161] Table 10. Screening of differentially expressed metabolites in fecal metabolomics

[0162]

[0163] The top 50 differentially expressed metabolites in feces between the HQ-M group and the Model group are shown in Table 11.

[0164] Table 11 Top 50 Differential Metabolites in Feces Between HQ-M Group and Model Group

[0165]

[0166]

[0167]

[0168] 3.1.3 Venn diagram

[0169] The number of unique and common metabolites in each group is displayed using a Veen diagram, such as Figure 2 As shown.

[0170] Results: There are a total of 88 elements across the three groups. Model-vs-Control has 129 elements. HQ-M-vs-Model has 80 elements. HQ-M-vs-Control has 155 elements.

[0171] 3.2 Results of ileal transcriptomics experiments

[0172] 3.2.1 RNA extraction and detection

[0173] The RNA extraction results for each group are shown in Table 12. The A260 / 280 ratios were all ≥2.00 and the 28S / 18S ratios were ≥0.7, indicating that the RNA sample detection results met the requirements for transcriptome library construction. Control 1-5 are the five random sample numbers within the blank group, and similarly, HQ-M1-M5 are the five random samples within the group.

[0174] Table 12 RNA quality test results

[0175]

[0176]

[0177] 3.2.2 Statistical Analysis of Sample Sequencing Data

[0178] The quality assessment of the sample sequencing output data is detailed in Table 13. Among all individuals sequenced, the proportion of Q30 (accuracy 99.9%) reads reached over 93%, indicating that the data output quality was good and met the sequencing requirements.

[0179] Table 13 Statistical analysis of sample sequencing data

[0180]

[0181] 3.2.3 Alignment results of sequencing fragments with reference genes

[0182] Hisat2 was used to align the filtered sequencing data with a specified reference genome to obtain the location information on the reference genome or genes, as well as the sequence characteristics unique to the sequencing samples. The comparison results are shown in Table 14. In all sequencing individuals, the percentage of sequencing fragments that could be located on the reference genome (Total mapped) was over 70%, which meets the requirements for data analysis.

[0183] Table 14. Results of Sequencing Data Alignment with Reference Genome

[0184]

[0185]

[0186] 3.2.4 Inter-sample correlation test

[0187] In biological experiments, intragroup biological replication is essential, and sample correlation analysis in high-throughput sequencing is a crucial indicator for assessing the reliability and rationality of experimental samples. A higher correlation coefficient indicates smaller individual differences between samples in each group and higher similarity in gene expression levels. Therefore, in high-throughput sequencing, the correlation coefficient between biologically replicated samples must be at least greater than 0.8. The correlation analysis of individual piglets in each group in this experiment is as follows: Figure 3As shown, the differences between groups were slightly greater than the differences within groups, which meets the requirements for subsequent experiments.

[0188] 3.2.5 Statistical Analysis of Differential Genes

[0189] Gene expression levels were calculated using FPKM values. Genes meeting the criteria of q-value < 0.05 and |log2FC| > 1.0 were identified as differentially expressed genes. The statistical analysis of differentially expressed genes is as follows: Figure 4 As shown, compared with the Model group, 234 genes were upregulated and 182 genes were downregulated in the HQ-M group; compared with the Control group, 322 genes were upregulated and 424 genes were downregulated in the Model group; and compared with the Control group, 552 genes were upregulated and 337 genes were downregulated in the HQ-M group.

[0190] 3.3 Results of fecal microbial testing

[0191] 3.3.1 DNA Extraction and Detection

[0192] The results of fecal microbial DNA extraction and quality control are shown in Table 15, and the PCR amplification and electrophoresis results are as follows: Figure 5 As shown, the bands are uniform in size and have appropriate concentration, indicating that the extracted DNA is of acceptable quality and can be used for subsequent experiments.

[0193] Table 15 Results of intestinal microbial DNA extraction

[0194]

[0195]

[0196] 3.4 Multi-omics Joint Analysis

[0197] 3.4.1 Joint analysis of model group and normal group

[0198] After combined analysis of transcriptomics, fecal metabolomics, and gut microbiota of the model group and the normal group, the intersection of the results was taken to obtain the set of altered genes, A. Figure 6 As shown.

[0199] 3.4.2 Joint Analysis of Model Group and HQ-M

[0200] After combining transcriptomics, fecal metabolomics, and gut microbiota analysis of the model group and HQ-M, the intersection of these analyses was taken to obtain the set of altered genes, B. Figure 7 As shown.

[0201] 3.4.3 Gene-Microbiome-Metabolite Network Diagram

[0202] Taking the intersection of A and B yields the callback gene set C; genes are screened from C, and corresponding metabolites and microbial communities are selected in omics to create a relevant network diagram. Gene set C is as follows: Figure 8As shown, the corresponding metabolites and microbial communities are shown in Tables 16 and 17, and the relevant network diagram is shown in... Figure 9 As shown.

[0203] Table 16 Model - Normal

[0204]

[0205]

[0206] Table 17 Model - Treatment

[0207]

[0208]

[0209] Multi-omics analysis revealed that damp-heat diarrhea in piglets is mainly characterized by inflammation and disordered glucose and lipid metabolism. Scutellaria baicalensis (Huang Qin) treats damp-heat diarrhea in piglets by altering the expression of inflammation-related genes ACE, HMOX1, LGALS7, PIK3C2G, SIGLEC14, and metabolism-related genes G6PC, CYP4F2, and DBP, thereby changing the metabolism of substances such as 10-nitrolinoleic acid, arachidonic acid ethanolamine, D-mannose, and uridine 5'-monophosphate, thus producing a therapeutic effect. It also alters the intestinal flora structure, primarily Firmicutes (Eubacterium spp., Christensenaceae, and Bacillus zurichae), thereby regulating inflammation and metabolism. The effects of Scutellaria baicalensis on the PI3K / Akt / Nrf2 / HO-1 pathway and the metabolism of substances such as linoleic acid and arachidonic acid may be key targets and material bases for its therapeutic effect on damp-heat diarrhea. These findings are significant for the further development of Scutellaria baicalensis in clinical applications and the treatment of diarrhea.

[0210] Although the present invention has been described in detail above with general descriptions, specific embodiments, and experiments, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, all such modifications or improvements made without departing from the spirit of the present invention fall within the scope of protection claimed by the present invention.

Claims

1. A method for treating damp-heat diarrhea in piglets using multi-omics analysis of Scutellaria baicalensis, characterized in that, Includes the following steps: S1, Fecal metabolomics S11. Fecal pretreatment Add 80% methanol solution to the feces at a material-to-liquid ratio of 1g:10μL, add small steel balls, pre-cool in a -40℃ refrigerator for 2 min, then grind in a grinder at a frequency of 60Hz for 2 min; extract by ultrasonication in an ice-water bath for 10 min; let stand overnight at -40℃; centrifuge at 12000rpm and 4℃ for 10 min, collect the supernatant, filter it using an organic phase pinhole filter, transfer it to a vial, and store it at -80℃; S12, Liquid Chromatography-Mass Spectrometry Analysis Chromatographic conditions for fecal metabolomics: Column: ACQUITYUPLC HSS T3, 100 mm × 2.1 mm, 1.8 μm; Column temperature: 45 ℃; Mobile phase: 0.1% formic acid aqueous solution; Mobile phase: acetonitrile; Gradient elution; Flow rate: 0.35 mL / min; Injection volume: 2 μL. The elution gradient program is as follows: The mass spectrometry conditions are as follows: S13, Data Preprocessing Compound identification is based on multiple dimensions, including retention time, exact mass number, secondary fragments, and isotopic distribution. HMDB, Lipidmaps, METLIN databases, and LuMet-Animal 3.0 database are used for identification analysis. The extracted data are processed for missing values, zero value replacement, score scoring and filtering, and data merging. S14, Data Analysis Partial least squares-discriminant analysis was used, and partial least squares regression was employed to establish a model relating metabolite expression levels to sample groupings. The p-value < 0.05 and VIP > 1 criteria were used to screen differentially expressed metabolites. The common and specific differentially expressed metabolites among the differential comparison groups were analyzed using Venn plots. S2, Intestinal Transcriptomics S21. Extraction of ileal mRNA and construction of transcriptome library Take 50–100 mg of tissue and place it in a 10 mL centrifuge tube containing 1 mL of Trizol. Homogenize thoroughly and transfer to a 1.5 mL centrifuge tube. Let stand for 5 min. Add 200 μL of chloroform, invert and mix well. Let stand at room temperature for 10 min. Centrifuge at 4 °C and 13,000 rpm for 15 min. Transfer the supernatant to another new 1.5 mL centrifuge tube, add an equal volume of isopropanol, mix well, and let stand at -20 °C for 30 min. Centrifuge at 4 °C and 13,000 rpm for another 15 min. Discard the supernatant. Add 500 μL of 75% ethanol to wash the precipitate. Centrifuge at 4 °C and 13,000 rpm for 5 min. Discard the supernatant and retain the precipitate. Dry. Centrifuge briefly, aspirate the supernatant, dissolve in an appropriate amount of H2O until completely dissolved, and store at -80 °C. The extracted RNA was analyzed by agarose gel electrophoresis to detect RNA degradation and contamination. RNA purity was determined by detecting the OD260 / 280 ratio. Construct a transcriptome library using the kit according to the instructions. S22, RNA sequencing and differentially expressed gene analysis The library was sequenced using the Llumina Novaseq 6000 sequencing platform, generating 150bp paired-end reads. The raw reads in FASTQ format were processed using FASTP software to remove low-quality reads, resulting in clean reads for subsequent data analysis. HISAT2 software was used for reference genome alignment and gene expression level calculation, with read counts for each gene obtained using HTSeq-count. R v3.2.0 was used for PCA analysis and plotting to assess biological repeatability of the samples. Differentially expressed genes were analyzed using DESeq2 software, where genes meeting the criteria of q-value < 0.05 and FC > 2.0, or FC < 0.5, were defined as differentially expressed genes. R v3.2.0 was used for hierarchical cluster analysis of differentially expressed genes to demonstrate gene expression patterns in different sample combinations. The ggradar package in R was used to create radar plots of the top 30 genes to show changes in upregulated or downregulated gene expression. Subsequently, GO and KEGG analyses were performed on the differentially expressed genes based on the hypergeometric distribution algorithm. Enrichment analyses using Pathway, Reactome, and WikiPathways were performed to filter for saliency-enriched feature items; enrichment analysis cyclographs were plotted on the saliency-enriched feature items using Rv3.2.

0. S3, 16s analysis of gut microbiota S31. DNA extraction and PCR amplification Genomic DNA was extracted from the samples using the MagPure Soil DNA LQ Kit according to the manufacturer's instructions. DNA concentration and purity were assessed using NanoDrop 2000 and agarose gel electrophoresis. The extracted DNA was stored at -20°C. Using the extracted genomic DNA as a template, PCR amplification of the bacterial 16S rRNA gene was performed using barcode-specific primers and Takara Ex Taq high-fidelity enzyme. The V3-V4 variable region of the 16S rRNA gene was amplified using universal primers for bacterial diversity analysis. S32, Library Construction and Sequencing The first-round PCR amplification products were detected by agarose gel electrophoresis, then purified using AMPure XP beads. The purified products were used as templates for the second-round PCR amplification. The products were purified again using magnetic beads, and the purified second-round products were quantified using Qubit, then the concentration was adjusted for sequencing. Sequencing was performed using the Illumina NovaSeq 6000 sequencing platform, generating 250bp paired-end reads. S33, Data Analysis After the data was processed, the Cutadapt software was first used to cut out the primer sequences from the raw data sequences. Then, DADA2 was used to perform quality filtering, noise reduction, splicing, and chimera removal analysis on the qualified paired-end raw data from the previous step according to the default parameters of QIIME 2, resulting in representative sequences and an ASV abundance table. Representative sequences for each ASV were selected using the QIIME 2 software package, and all representative sequences were aligned and annotated with the Silva database. Species alignment and annotation were analyzed using the default parameters of the q2-feature-classifier software. α and β diversity analyses were performed using QIIME 2 software. α diversity of the samples was assessed using alpha diversity including the Chao1 index and Shannon index. β diversity of the samples was assessed using unweighted Unifrac principal coordinate analysis based on the unweighted Unifrac distance matrix calculated by R. Differential analysis was performed using the ANOVA / Kruskal Wallis / T test / Wilcoxon statistical algorithm based on the R package. Differential analysis of species abundance spectra was performed using LEfSe. S4, Omics Joint Analysis The transcriptomic, metabolomic, and gut microbiota data were screened according to the obtained omics difference criteria. Then, the transcriptomic, fecal metabolomic, and gut microbiota analyses of the Model and Control groups were combined and the intersection was taken to obtain the set of changed genes A. The transcriptomic, fecal metabolomic, and gut microbiota analyses of the Model and HQ-M groups were combined and the intersection was taken to obtain the set of changed genes B. The intersection of A and B was taken to obtain the set of returned genes C. Relevant genes were screened in C, the expression levels of gene FPKM were found in transcriptomics, and the corresponding metabolites and microbiota were screened in metabolomics and gut microbiota to obtain the gene-microbiota-metabolite correlation network diagram.

2. The method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis according to claim 1, characterized in that, The methanol solution is a methanol solution containing 4 μg / mL L-2-chlorophenylalanine.

3. The method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis according to claim 1, characterized in that, The tissue described in step S21 is prepared by placing the ileum sample in a mortar pre-cooled with liquid nitrogen and grinding it with a pestle, continuously adding liquid nitrogen until it is ground into powder.

4. The method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis according to claim 1, characterized in that, The kit described in step S21 is the VAHTS Universal V5 RNA-seq Library Prep kit.

5. The method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis according to claim 1, characterized in that, The reference genome mentioned in step S22 is the genome information of a pig published by NCBI with the number GCF_000003025.6, which is used as a reference database.

6. The method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis according to claim 1, characterized in that, The universal primer mentioned in step S31 is: 343F: 5'-TACGGGRAGGCAGCAG-3'; 798R: 5'-AGGGTATCTAATCCT-3'.

7. The method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis according to claim 1, characterized in that, The first round of PCR system described in step S32 is as follows:

8. A method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis according to claim 1, characterized in that, The cycling conditions for the first round of PCR described in step S32 are as follows:

9. A method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis according to claim 1, characterized in that, The second round of PCR system described in step S32 is as follows:

10. A method for treating damp-heat diarrhea in piglets using multi-omics combined analysis of Scutellaria baicalensis according to claim 1, characterized in that, The cycling conditions for the second round of PCR described in step S32 are as follows:

Images