Application of DUOX2 and related molecular network thereof in identification of ileum subtype and colon subtype Crohn disease

Abstract

The invention discloses an application of DUOX2 in identification of ileum subtype and colon subtype Crohn's disease. A group of new DUOX2-epi is analyzed and identified based on single cell transcriptome sequencing, the proportion of the cells in the Crohn disease inflammation tissue is obviously increased, and the proportion of the DUOX2-epi in the colon type Crohn disease inflammation tissue is obviously higher than that in the ileum type Crohn disease inflammation tissue; in addition, in ileum type and colon type Crohn diseases, interaction with surrounding cells is carried out through different mechanisms, so that the occurrence and development of the diseases are promoted. Therefore, based on a DUOX2-epi mediated intercellular communication network, a gene prediction module for predicting different subtypes of Crohn diseases and evaluating the treatment effect of a biological agent is developed, and based on a DUOX2 molecular network, an experimental basis and a research direction are provided for subsequent in-depth research, and a new strategy is expected to be provided for diagnosis and treatment of different subtypes of Crohn diseases.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and more specifically, the present invention relates to the application of DUOX2 and its related molecular network in distinguishing ileal subtype and colonic subtype Crohn's disease. Background Art

[0002] Crohn's disease (CD), formerly known as segmental enteritis, is characterized by segmental distribution of intestinal lesions. The most commonly affected intestinal segments are the terminal ileum and the adjacent colon, which distinguishes CD from another inflammatory bowel disease (IBD) - ulcerative colitis (UC). The Montreal classification can divide Crohn's disease into different phenotypes based on age, lesion range, and disease behavior. Currently, the most attention is paid to the ileal and colonic subtypes based on the differences in lesion location. The difference in the location of intestinal lesions leads to two treatment differences: in terms of overall mucosal healing after treatment, colonic mucosal ulcers are more likely to achieve mucosal healing and heal faster, while Crohn's disease with ileum involvement is more difficult to achieve mucosal healing and is more likely to develop into a complex phenotype; in terms of the efficacy of specific biological agents, patients with Crohn's disease with colon involvement can achieve mucosal healing faster when receiving biological agent treatment, while the efficacy of biological agents in patients with Crohn's disease with ileum involvement is significantly inferior to that in patients with Crohn's disease with colon involvement. In addition, Crohn's disease patients also show high heterogeneity in terms of individual treatment effects. In clinical practice, primary and secondary failure to respond to biologics are not uncommon, and different ileal / colonic Crohn's disease patients sometimes experience completely different treatment effects when using the same biologic treatment regimen. Therefore, it is crucial to clarify the causes of diversity in the pathogenesis and treatment response of ileal and colonic Crohn's disease, so as to guide the diagnosis of Crohn's disease and develop molecular markers to develop precise treatment plans for Crohn's disease.

[0003] The rapid development of high-throughput omics technologies has provided an unprecedented perspective for in-depth understanding of site-specific changes in the Crohn's disease intestine. A study using label-free proteomics technology compared the proteomic differences between the ulcer margins of the ileum and colon, providing protein-based evidence for some different pathophysiological processes. Another study used second-generation bulk sequencing technology to sequence the miRNA transcriptome of the ileum and colon of Crohn's disease, revealing 47 differentially expressed miRNAs and identifying miR-31-5p as a biomarker for the localization subtype in Crohn's disease. These studies provide valuable insights into the segment-specific characteristics of Crohn's disease. Recent single-cell RNA sequencing (scRNA-seq) can accurately characterize molecular processes at the single-cell level, thereby studying disease formation and resistance mechanisms in more detail, and providing more accurate and personalized biomarkers and therapeutic targets for disease diagnosis and treatment. Kong et al. (Kong L, Pokatayev V, Lefkovith A, et al. The landscape of immune dysregulation in Crohn's disease revealed through single-cell transcriptomic profiling in the ileum and colon. Immunity 2023; 56: 444-458e5.) mapped the immune dysregulation map in the ileum and colon of Crohn's disease through comprehensive scRNA-seq analysis, and deeply revealed the complex dysregulation of gastrointestinal immune response in Crohn's disease. Another scRNA-seq study on the ileum of Crohn's disease developed a unique cell module GIMATS (IgG plasma cells, inflammatory monocytes, activated T cells, and stromal cells) to predict the response of Crohn's disease patients to anti-TNF biologics. However, further research is still needed to explore the molecular mechanisms of site-specific therapeutic effects, so as to develop new personalized treatment strategies. One of the causes of inflammatory bowel disease (IBD) is the disruption of the immune balance between intestinal microorganisms and the body. There is a natural defense response in the intestinal epithelium: under microbial induction, NADPH dual oxidase (DUOX) on the top of the intestinal epithelial cell membrane is activated, releasing hydrogen peroxide into the epithelial mucus layer, thereby resisting the invasion of harmful intestinal microorganisms. The isoenzyme that performs this function in mammals is DUOX2. Previous studies have shown that DUOX2 variation is significantly associated with increased plasma IL17C levels, and abnormally elevated IL17C is associated with the expansion of specific pathogenic bacteria, suggesting that DUOX2 is associated with intestinal microbial disorders and an increased risk of IBD.The article "The gut microbiota engages different signaling pathways to induce Duox2expression in the ileum and colon epithelium" points out that dual oxidase 2 (DUOX2) is an important source of hydrogen peroxide in the small intestine and large intestine, and the intestinal flora induces the expression of DUOX2; in the ileum, the regulation of Duox2 expression involves tir domain-containing adaptor proteins, including interferon-β (TRIF) and canonical nuclear factor-κBp50 / p65 signaling pathways, while in the colon, the regulation of Duox2 expression involves MyD88 and p38 pathways, indicating that the intestinal microbiota uses two different signaling pathways to induce Duox2 expression in the ileum and colon epithelium. Although there are many literatures reporting on the relevant research of ileal and colonic CD, there are no relevant reports on the role of DUOX2 in ileal and colonic Crohn's disease. Summary of the invention

[0004] The purpose of the present invention is to overcome the above-mentioned defects and shortcomings in the prior art and to provide a preparation for detecting the abundance of DUOX2-epi in Crohn's disease inflammatory tissue for use in the preparation of products for identifying ileal subtype and colonic subtype Crohn's disease.

[0005] The second object of the present invention is to provide a use of a preparation for detecting the expression level of DUOX2 and / or DUOXA2 gene in Crohn's disease inflammatory tissue in the preparation of a product for distinguishing ileal subtype and colonic subtype Crohn's disease.

[0006] The third object of the present invention is to provide a preparation for detecting the expression level of PI3 and / or SAA1 gene in DUOX2-epi in Crohn's disease inflammatory tissue in the preparation of a product for distinguishing ileal subtype and colonic subtype Crohn's disease.

[0007] The fourth object of the present invention is to provide a method for preparing a product for identifying ileal subtype and colonic subtype Crohn's disease by using a preparation for detecting DUOX2-epi interacting cells in inflammatory tissue of Crohn's disease.

[0008] A fifth object of the present invention is to provide a gene prediction module consisting of DUOX2, DUOXA2 and signal molecules involved in the colon or ileum cell-cell communication network.

[0009] A sixth object of the present invention is to provide a gene prediction module scoring system based on the above gene prediction module.

[0010] A seventh object of the present invention is to provide an application of the above-mentioned gene prediction module and gene prediction module scoring system.

[0011] The above-mentioned object of the present invention is achieved through the following technical solutions:

[0012] The present invention uses single-cell sequencing technology to study the heterogeneity of the ileum and colon in Crohn's disease, and discovers an epithelial cell subpopulation (DUOX2-epi) that highly expresses DUOX2 and DUOXA2 (dual oxidase maturation factor 2). This DUOX2-epi subpopulation is mainly distributed in the tip epithelium of the inflamed colon, and DUOX2-epi has a potential role in responding to microbial infections and triggering inflammatory processes associated with Crohn's disease (CD). In the ileum and colon of CD inflammation, DUOX2-epi highly expresses genes related to pathways related to bacterial defense response, antimicrobial humoral response, and inflammatory response regulation. Studies have shown that DUOX2-epi in colonic and ileal Crohn's disease triggers inflammatory responses through different mechanisms, and the proportion of DUOX2-epi in colonic Crohn's disease inflammatory tissue is significantly higher than that in ileal Crohn's disease inflammatory tissue.

[0013] Therefore, the present invention provides the use of a preparation for detecting the abundance of DUOX2-epi in Crohn's disease inflammatory tissue in the preparation of a product for identifying ileal subtype and colonic subtype Crohn's disease, wherein the DUOX2-epi is an epithelial cell subpopulation with high expression of DUOX2 and DUOXA2.

[0014] Furthermore, the preparation includes but is not limited to single-cell RNA sequencing preparation and immunofluorescence staining.

[0015] Furthermore, in the immunofluorescence staining method, the primary antibody for identifying DUOX2-epi includes anti-DUOX2 and epithelial-specific antigen antibody.

[0016] Preferably, the epithelial-specific antigen antibody is anti-EPCAM / CD326.

[0017] The present invention also provides the use of a preparation for detecting the expression level of DUOX2 and / or DUOXA2 gene in Crohn's disease inflammatory tissue in the preparation of a product for distinguishing ileal subtype and colonic subtype Crohn's disease.

[0018] Furthermore, the preparation includes but is not limited to real-time fluorescence quantitative PCR.

[0019] The cell sequencing results of the present invention also showed that DUOX2-epi in the inflamed colon showed abnormally high expression of PI3 and SAA1, while DUOX2-epi in the inflamed ileum showed abnormally high expression of DEFA6. Cell-cell communication network analysis showed that colon DUOX2-epi mainly affected monocytes through SAA1-FPR2 and ANXA1-FPR1 / 2 ligand-receptor interactions, and interacted with venous endothelial cells (VECs) through the CXCL1 / 3-ACKR1 ligand-receptor pair. Among them, the ligand receptor of SAA1-FPR2 can promote the recruitment of monocytes to the site of mucosal injury, and may promote mucosal healing in the case of mucosal homeostasis imbalance; while ileal DUOX2-epi directly interacted with Treg cells through the CXCL16-CXCR6 ligand-receptor pair. After binding with CXCR6, CXCL16 activated Treg and promoted its migration to the inflammatory site. At the same time, ileal DUOX2-epi can also interact through the chemokine ligand CXCL9-11 binding to the Th1 receptor CXCR3, and the chemokine ligand CXCL16 binding to the Th1 receptor CXCR6.

[0020] Therefore, the present invention provides the use of a preparation for detecting the expression level of PI3 and / or SAA1 gene in DUOX2-epi in Crohn's disease inflammatory tissue in the preparation of a product for identifying ileal subtype and colonic subtype Crohn's disease, wherein the DUOX2-epi is an epithelial cell subpopulation with high expression of DUOX2 and DUOXA2.

[0021] Furthermore, the preparation includes but is not limited to real-time fluorescence quantitative PCR.

[0022] The present invention also provides the use of a preparation for detecting interacting cells of DUOX2-epi in Crohn's disease inflammatory tissue in the preparation of a product for identifying ileal subtype and colonic subtype Crohn's disease, wherein the DUOX2-epi is an epithelial cell subpopulation with high expression of DUOX2 and DUOXA2.

[0023] Furthermore, the interacting cells are one or more of Treg cells, Th1 cells, monocytes or venous endothelial cells.

[0024] Furthermore, the preparation includes but is not limited to multiple immunohistochemical analysis reagents. The primary antibodies include anti-DUOX2, anti-EPCAM, anti-SAA1, anti-CD14, anti-FPR2, anti-FOXP3, anti-CXCL16, and anti-CXCR6.

[0025] Cell-cell communication networks involving DUOX2-epi in the colon and ileum can help predict the location-specific effects of biological therapies. To investigate the relationship between the cell-cell communication (CCC) network and the efficacy of biological therapies for Crohn's disease in the ileum and colon, a gene prediction module was developed for biological therapies at different locations based on the cell-cell communication network regulated by DUOX2-epi, which consisted of DUOX2, DUOXA2, and signaling molecules involved in the CCC network in the colon or ileum, namely the colon / ileum module scoring system. Among them, the colon / ileum module score of CD patients was significantly higher than that of the normal control group. The colon module score could effectively predict the response effect of anti-Infliximab (anti-TNF) biological agents and Ustekinumab (anti-IL-12 / 23), while the ileum module score showed a slightly weaker correlation.

[0026] Therefore, the present invention also provides a gene prediction module, which is composed of DUOX2, DUOXA2 and signal molecules involved in the colon or ileum cell-cell communication network; the signal molecules are a combination of SAA1, ANXA1, FPR1, FPR2, CCL4, CCR5, CXCL1, CXCL3, ACKR1, CXCL2 or a combination of CCL3L1, CCR1, CCL3, IL1B, IL1R2, CXCL9, CXCR3, ACKR1, CXCL8, CXCL5, CXCL3, CXCL2, CXCL1, CXCL10, CXCL11, CXCL16, CXCR6, CCR5, CCL4, FPR1, and FPR2.

[0027] The present invention also provides a gene prediction module scoring system, comprising a data input module, a data calculation module and a result output module; the data input module is used to input the gene expression spectrum of the above-mentioned gene prediction module in Crohn's disease inflammatory tissue; the data calculation module uses a single sample gene set enrichment analysis method to respectively calculate the network gene score of the gene expression spectrum in Crohn's disease inflammatory tissue; the result output module is used to output the network gene score.

[0028] Furthermore, the single sample gene set enrichment analysis method adopts GSVA R package (v1.42.0).

[0029] The present invention also provides the use of the above gene prediction module or gene prediction module scoring system in predicting the therapeutic effect of biological therapy on ileal subtype and colonic subtype Crohn's disease.

[0030] The present invention also provides the use of the above gene prediction module or gene prediction module scoring system in screening drugs for treating ileal subtype or colonic subtype Crohn's disease.

[0031] Furthermore, the drug is a drug that blocks the cell-cell communication network in ileal subtype or colonic subtype Crohn's disease.

[0032] The present invention also provides the use of the above gene prediction module or gene prediction module scoring system in identifying ileal subtype or colonic subtype Crohn's disease.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention provides the use of a preparation for detecting the abundance of DUOX2-epi in Crohn's disease inflammatory tissue in the preparation of a product for identifying ileal subtype and colonic subtype Crohn's disease. The present invention identified a new group of DUOX2-epi based on single-cell transcriptome sequencing analysis, the proportion of which was significantly upregulated in Crohn's disease inflammatory tissue, and the proportion of DUOX2-epi in colonic Crohn's disease inflammatory tissue was significantly higher than that in ileal Crohn's disease inflammatory tissue, and in ileal and colonic Crohn's disease, it interacts with surrounding cells through different mechanisms to promote the occurrence and development of the disease. Therefore, based on the intercellular communication network mediated by DUOX2-epi, the present invention has developed a gene prediction module for predicting different subtypes of Crohn's disease and evaluating the therapeutic effects of biological agents. The molecular network based on DUOX2 provides an experimental basis and research direction for subsequent in-depth research, and is expected to provide new strategies for the diagnosis and treatment of different subtypes of Crohn's disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 Single-cell sequencing analysis of validation tissues for ileum and colon. Figure 1 Figure 1 shows the workflow of biopsy collection and study design; B shows the UMAP map of all qualified cells from the ileum (lower right), colon (upper left), and ileum and colon (middle), colored according to their corresponding cell lineages; C shows the UMAP map of all cells from the ileum and colon; D shows the heat map of the expression of cell type-specific markers in each cell subset; E shows the expression of cell type-specific markers in each cell subset by R o / e The index (i.e., the ratio of observed to predicted values) assesses the tissue enrichment of each cell subtype in different tissues; the ratio of observed to expected cell numbers was calculated by the chi-square test. Note: Epi cells, epithelial cells; Fibro, fibroblasts; Endo, endothelial cells; plasma cells, plasma cells; MNPs, mononuclear phagocytes; mast cells, mast cells; PLC, proliferating lymphocytes; PMC, proliferating myeloid cells.

[0036] Figure 2 For DUOX2 + Identification and characterization of epithelial cells. Figure 2A is a UMAP map showing the inflammatory response score of epithelial cells in scRNA-seq data; B is the microbial score in epithelial cells; C is the PI score in epithelial cells. D is a UMAP map showing the expression and distribution of DUOX2; E is a UMAP map showing the expression and distribution of DUOXA2; F is a UMAP map showing epithelial cells from ileal and colonic Crohn's disease obtained by Kong et al.; G is a representative co-staining of EPCAM and DUOX2 in inflammatory CD colon; H is a real-time fluorescence quantitative PCR to detect the transcriptional expression of DUOX2 / DUOXA2, cI (n=10), iI (n=8), HC (n=13), HI (n=13); I is a scoring method for the association of inflammatory bowel disease / Crohn's disease in different cell subsets using scDRS; the box plot shows the median of the 1.5×interquartile range. Myeloid cells are represented by orange text, T cells by green text, B cells by blue text, epithelial cells by red text, and stromal cells by purple text; J is DUOX2 + and DUOX2 - Differentially expressed genes (DEGs) between epithelial cells. Red and blue bubbles represent DUOX2, + Significantly up-regulated and down-regulated genes in epithelial cells; K is DUOX2 + GO enrichment terms of significantly upregulated genes in epithelial cells.

[0037] Figure 3 The responses of ileal and colonic DUOX2-epi to inflammation and microbiota are mediated by different molecules. Figure 3 A in the figure represents the expression of DUOX2 in different tissues in the study published by Kong et al. +Error bar graph of epithelial cell percentage; B is the expression level of DUOX2 and DUOXA2 in inflammatory CD and corresponding normal controls from GEO bulk RNA-seq or microarray datasets; C is the relative expression changes of DUOX2 and DUOXA2 detected by real-time fluorescence quantitative PCR; D is the co-localization of immunofluorescence staining EPCAM and DUOX2 in HC (a), HI (c), cI (e), and iI (g), marked with yellow rectangles, and b, d, f, and h represent their magnified areas; E is the mean fluorescence intensity (MFI) of EPCAM and DUOX2 double-positive sites in HC, HI, cI, and iI in the colon, using Image J. ΔMFI quantification, the average MFI in cI was subtracted from the average MFI in HC; ΔMFI in the ileum followed the same procedure as in the colon; F is a heat map showing the expression of selected DEGs in different groups of DUOX2-epi cells (in-house scRNA-seq data); GH is the expression level of PI3, SAA family genes, and DEFA6 in inflammatory CD and corresponding normal controls from the GEO chip dataset; IJ is the relative change of PI3 (I) and SAA1 (J) expression determined by real-time qPCR; KL is the PI score (K) and microbial score (L) of different groups of DUOX2-epi in the in-house scRNA-seq data. *, p<0.05; **, p<0.01; ***, p<0.001; ****, p<0.0001.

[0038] Figure 4 DUOX2-epi in the colon and ileum exerts its effects through shared and specific cell-cell communication networks. Figure 4 A in the figure shows the number of interactions between cells in group iI. DUOX2-epi is the signal sender, and cell subsets of T cell, B cell, myeloid cell, and stromal cell lineages are signal receivers. The numbers on the green edge indicate the number of interactions between DUOX2-epi and a given cell subset; B is Figure 4 The number of cell-to-cell interactions after sorting in A is shown in a bar graph; C is a circular graph showing the number of cell-to-cell interactions in group cI. DUOX2-epi is the signal sender, and cell subsets of the T cell, B cell, myeloid cell, and stromal cell lineages are the signal receivers. The numbers on the orange edge indicate the number of interactions between DUOX2-epi and a given cell subset; D is Figure 4The number of cell-cell interactions after sorting in C is visualized by a bar graph; E is the cell receptor-ligand pairs upregulated between DUOX2-epi cells and other cell subsets in the cI and iI groups. FI is the colocalization analysis of DUOX2-epi (blue dots) and interacting cell clusters (yellow dots) mediated by CXCL16-CXCR6 (F) and SAA1-FPR2 (H) ligand-receptor interactions in cI (n=5) and iI (n=5). The nearest interacting cells are connected by white lines. The average distance between the two interacting populations through CXCL16-CXCR6 (G) and SAA1-FPR2 (I) was determined by proximity analysis in Halo software.

[0039] Figure 5 Gene modules organized by distinct cell-cell communication networks predict treatment response in ileal and colonic CD. Figure 5 AB in the figure are schematic diagrams of the cell-cell communication network between DUOX2-epi cells and their signaling receptors in the ileum group (A) and colon group (B); the dotted line indicates that the mutual pathways in the colon group and the ileum group are the same; the blue line indicates colon-specific cell interactions, and the ligand-receptor pair indicates the key interaction; the red line indicates ileum-specific cell interactions, and the bound ligand-receptor pair indicates the key interaction; CE shows the comparison of colon module scores between CD colon and corresponding normal controls and between responders and non-responders who received biological treatment; FG shows the comparison of ileum module scores between CD ileum and corresponding normal controls and between responders and non-responders who received biological treatment. Note: R, responder; NR, non-responder.

[0040] Figure 6 The AUC value was used to measure the predictive performance of the colon and ileum modules in distinguishing Crohn's disease and the efficacy of biological agents. Figure 6 AE in is the diagnostic performance of the colon and ileum module for Crohn's disease; FJ is the predictive performance of the colon and ileum module for the efficacy of biological agents. DETAILED DESCRIPTION

[0041] The present invention is further described below in conjunction with the accompanying drawings and specific examples, but the examples do not limit the present invention in any form. Unless otherwise specified, the reagents, methods and equipment used in the present invention are conventional reagents, methods and equipment in the art.

[0042] Unless otherwise specified, the reagents and materials used in the following examples are commercially available.

[0043] 1. Sample Source

[0044] The present invention recruited 4 healthy individuals (healthy volunteers were recruited during routine colonoscopy) and 9 Crohn's disease patients who were not treated with biologics, glucocorticoids, immunosuppressants, and antibiotics at Renji Hospital in Shanghai, China from October 2021 to March 2022, and collected their normal colon, normal ileum, inflamed, and adjacent non-inflamed Crohn's disease colon and ileum tissues for single-cell RNA sequencing (scRNA-seq). The diagnosis of Crohn's disease was based on the European Crohn's and Colitis Organization guidelines. All samples were collected with informed consent and approved by the Institutional Review Board of Renji Hospital.

[0045] Example 1 Single-cell sequencing analysis of ileal and colonic inflammatory tissues

[0046] 1. Experimental Methods

[0047] 1. Tissue isolation, preparation and activity assessment

[0048] Fresh biopsies were immediately immersed in sCelLiveTM tissue preservation solution (Singleron Biotech, Nanjing, China) within 30 minutes after endoscopy. The specimens were washed three times with Hanks balanced salt solution (HBSS) and then stored in a Singleron PythoN TM The automated tissue dissociation system (Singleron) was used for digestion at 37°C for 15 minutes with 2 mL of sCelLiveTM tissue dissociation solution (Singleron). After digestion, the cells were centrifuged at 500 × g for 5 minutes, and the resulting suspension was gently resuspended in PBS. The samples were then stained with trypan blue (Sigma, USA), and cell viability was assessed by microscopic examination.

[0049] 2. Library preparation and scRNA-seq

[0050] Using Singleron Single Cell Processing System (Singleron) was used to prepare a single cell suspension (1×10 5 cells / mL) were loaded into the microfluidic device. scRNA-seq libraries were generated using the protocol outlined in the Single Cell RNA Library Kit (Singleron). Each library was then diluted to 4 nM and a sample pool was created for subsequent sequencing. The pooled samples were sequenced on an Illumina HiSeq X with paired-end reads of 150 bp.

[0051] 3. scRNA-seq data preprocessing and quality control

[0052] Raw reads were processed using the CeleScope pipeline (Singleron) to generate gene expression profiles. Cell barcodes and unique molecular identifiers (UMIs) were extracted after filtering out reads without poly T tails. After the adapter and poly A tail were cut using fastp V1, the second read segment (read two) was aligned to the GRCh38 reference genome using ensemble version 92 gene annotation (fastp 2.5.3a and featureCounts1.6.2). Reads with the same cell barcode, UMI, and gene were grouped together, and the number of UMIs per gene in each cell was calculated. The UMI count table for each cell barcode was used as input for further analysis using the Seurat R package (v4.2.0). As part of the quality control procedure, each sample dataset was initially processed individually. Cells expressing less than 250 or more than 6000 genes, or with a mitochondrial RNA percentage of more than 50%, were considered to be of poor quality and were subsequently filtered out. DoubletFinde (https: / / github.com / chris-mcginnis-ucsf / DoubletFinder) with default parameters was used to detect doublets in each sample, and cells identified as doublets were filtered out.

[0053] 4. Data integration, dimensionality reduction and cell clustering

[0054] The scRNA detection data were normalized using the LogNormalize method in the NormalizeData function. The top 2000 variable genes were selected using the FindVariableFeatures function, and the scaled variable gene matrix was subjected to principal component analysis (PCA) using the RunPCA function. Harmony v0.1.0 was used for data integration and batch effect removal. The first 20 principal components were used for dimensionality reduction using the FindNeighbors function, and the cells were clustered using the FindClusters function of the Louvain algorithm. Cell populations were visualized using the uniform manifold approximation and projection (UMAP) based on the first 20 principal components. Subsequent cell subtyping for each major cell type followed the same approach as above.

[0055] 5. Differentially expressed gene (DEG) analysis and cell type annotation

[0056] To identify differentially expressed genes between cell clusters, the FindAllMarkers function in Seurat was used, and genes detected in more than 25% of cells in each cluster were tested. Gene expression was visualized using the FeaturePlot function. DEGs for each cluster were examined based on previously described cell type-specific canonical cell-specific genes to identify cell lines and sublineages. The FindMarkers function was used to detect DEGs of DUOX2-epi between groups, with a threshold set to |log2(fold change)(FC)| value>1 and an adjusted P value<0.01 to screen for significant DEGs.

[0057] 6. Distribution of cell clusters in tissues

[0058] The STARTRAC-dist index was used to analyze the tissue preference of cell clusters in different tissues. The formula is as follows: STARTRAC -dist =Ro / e=observed / expected, where Ro / e represents the ratio of the observed cell number to the expected cell number in a given cell cluster and tissue. When the STARTRAC-dist index>1, it indicates that the cell cluster is enriched in a specific tissue.

[0059] 7. Pathway enrichment analysis and characteristic gene set enrichment scoring

[0060] The significant DEGs between groups were used as inputs for enrichment GO functions in clusterProfiler R package (v4.4.4) to identify enriched biological functions and related signaling pathways. Functional scores (inflammatory score, microbial score, parainflammatory score (PI score), and crypt axis score) were generated by AUCell v1.16.0 to investigate specific biological states of single cells. The corresponding gene sets were curated from the Molecular Signature Database (MSigDB) (https: / / www.gsea-msigdb.org / gsea / msigdb).

[0061] 8. Single cell disease association

[0062] The single-cell disease relevance score (scDRS) method was used to analyze IBD disease relevance at the single-cell level and evaluate the multi-gene disease enrichment of single cells in scRNA-seq data.

[0063] 9. Quantification and statistical analysis

[0064] Statistical analysis was performed using R software (version 4.1.0, https: / / www.r-project.org / ) and GraphPad Prism 9.0 software. Student's t test and analysis of variance (ANOVA) were used to test normally distributed continuous data; when continuous data followed a non-normal distribution, the Mann-Whitney test and Kruskal-Wallis test were used. Statistical significance was considered when two-sided P < 0.05.

[0065] 10. Immunofluorescence staining

[0066] Paraffin-embedded and formalin-fixed colon and ileum specimens from CD patients and healthy individuals were used for immunofluorescence staining, including 23 cases of inflammatory CD colon, 25 cases of inflammatory CD ileum, 24 cases of normal colon, and 21 cases of normal ileum. The primary antibodies used to identify DUOX2-epi were as follows: anti-DUOX2 (#NB110-61576SS, novusbio; 1:300), anti-EPCAM / CD326 (#21050-1-AP, proteintech; 1:800). Tissue sections were washed with 1% PBS-Tween-20 and treated with 2% hydrogen peroxide. Subsequently, they were blocked with 3% goat serum and incubated with specific primary antibodies for 2 hours at room temperature, followed by goat anti-rabbit IgG H&L (HRP) secondary antibodies (#ab205718, abcam; 1:2000). The mean fluorescence intensity (MFI) of positively stained cells was quantified using Image J software (NIH).

[0067] 11. RNA extraction and quantitative reverse transcription PCR (qRT-PCR)

[0068] Total RNA was extracted using Trizol reagent (Invitrogen, USA) and then transcribed into cDNA using Gene Amp PCR System 9700 (Applied Biosystems). TM 5 Real-time fluorescence PCR System (Applied Biosystems) was used for qRT-PCR detection. The relative expression level of mRNA was determined by comparative CT method and normalized to the housekeeping gene β-actin. The primers used for qRT-PCR are shown in Table 1.

[0069] Table 1 Primers for qRT-PCR

[0070]

[0071] 2. Experimental Results

[0072] 1. Single-cell atlas of Crohn's disease reveals changes in cellular composition between the ileum and colon

[0073] like Figure 1 To systematically investigate the factors that contribute to the differences in ileal and colonic mucosal healing in CD patients, single-cell transcriptome analysis was performed on all viable cells isolated from 21 ileal and colonic tissues obtained from a cohort of nine CD patients and four healthy individuals ( Figure 1 A). After quality control, a total of 97,728 cells were retained, which were derived from colonic Crohn's disease inflammatory tissue (cI, n=4), colonic Crohn's disease inflammatory adjacent normal tissue (cN, n=4), ileal Crohn's disease inflammatory tissue (iI, n=5), ileal Crohn's disease inflammatory adjacent normal tissue (iN, n=4), normal control colon tissue (HC, n=2) and normal control ileum tissue (HI, n=2). Through standardized dimensionality reduction and clustering procedures, 10 distinct clusters were identified and subsequently labeled using typical cell type markers as epithelial cells (Epi cells), fibroblasts, endothelial cells, T cells, B cells, plasma cells, mononuclear phagocytes (MNPs), mast cells, proliferative lymphocytes (PLCs), and proliferative myeloid cells (PMCs) ( Figure 1 B) In the inflamed ileum or colon, the proportions of cell types including fibroblasts, endothelial cells, plasma cells, mononuclear phagocytes, mast cells, and proliferative myeloid cells were increased, while the proportion of epithelial cells was decreased, compared with non-inflamed and normal tissues. The proportions of T cells, B cells, and PLCs were increased in cI but decreased in iI.

[0074] To further clarify the heterogeneity of cell types, unsupervised cluster analysis was performed on the five major cell clusters (epithelial cells, T cells, B cells, stromal cells, and MNPs), and 51 subclusters were identified based on the expression of typical and specifically expressed marker genes, including 15 epithelial cell clusters, 7 mononuclear phagocyte clusters, 13 T cell clusters, 5 B cell clusters, and 11 stromal cell clusters ( Figure 1CD). With the exception of the Paneth and DUOX2-epi cell clusters, most epithelial subsets were less abundant in inflamed tissues from either the ileum or colon than in non-inflamed and normal tissues. Mononuclear phagocytes, especially monocytes, were primarily enriched in the inflamed ileum and colon. Most T- and B-cell subsets were dominant in the inflamed colon compared with the normal colon, whereas the opposite distribution was observed in the ileum. Some immune cell clusters, including CD8MT_high, CD4 Trm, CD8 Trm, and central B cells, were found to be highly enriched in non-inflamed tissues. Some stromal cell clusters (Cycling fib, Myofib, AEC, VEC, CapEC) were enriched in cI but not in iI, showing different properties in the ileum and colon ( Figure 1 E).

[0075] In summary, the types of cells enriched in ileal and colonic subtypes of Crohn's disease are significantly different, and it is speculated that the pathogenesis of ileal and colonic subtypes of Crohn's disease is also different.

[0076] 2. DUOX2 associated with inflammation and microbes + Identification of epithelial cells

[0077] Epithelial cells play a key role in the pathogenesis of CD by regulating immune responses and microbial interactions. Figure 2 As shown, to identify dysregulated epithelial cells associated with inflammation and microbes in the inflamed ileum and colon, gene sets reported to be associated with inflammation, parainflammation, and microbial infection in IBD were collected and the enrichment scores of these gene sets in all epithelial cell subsets were calculated. It was found that DUOX2-epi showed the highest enrichment scores of these gene sets in all epithelial subsets ( Figure 2 AC). Therefore, we focused on this unique epithelial subset, which is characterized by high expression of DUOX2 and DUOXA2 ( Figure 2 DE). Using the public scRNA-seq dataset from Kong et al. ( Figure 2 F) and immunofluorescence (IF) staining of freshly collected CD tissues ( Figure 2 G) verified the presence of DUOX2-epi. In addition, compared with normal intestinal tissue, the scRNA-seq analysis, IF staining and real-time qPCR analysis of the present invention and the public demonstrated that the proportion of DUOX2-epi cells in inflammatory intestinal lesions of CD patients was increased ( Figure 1 E. Figure 2 GH, Figure 3A). Among them, the proportion of DUOX2-epi in the inflammatory tissue of colonic Crohn's disease is higher than that of ileal Crohn's disease. Further using the genome-wide association study (GWAS) gene features associated with IBD, the single-cell disease relevance score (scDRS) algorithm was used to evaluate the disease relevance of all cell subtypes. Consistent with previous studies, T cells and macrophages are the cell types most strongly associated with IBD ( Figure 2 I).

[0078] To further explore the function of DUOX2-epi cells, we performed a comparative analysis of the transcriptional profiles of DUOX2-epi cells and other epithelial cell populations, and identified 216 differentially expressed genes (DEGs), including PI3, SAA1, and NOS2 ( Figure 2 J). Gene ontology (GO) gene set enrichment analysis showed that signaling pathways regulating immune response, bacterial defense response, tissue homeostasis, and acute inflammatory response were significantly enriched ( Figure 2 K), further confirming the potential role of DUOX2-epi in responding to microbial infections and triggering inflammatory processes associated with CD.

[0079] 3. Different mechanisms by which ileal and colonic DUOX2-epi respond to microbial stimulation and induce inflammatory responses

[0080] like Figure 3 As shown, in the present invention and the disclosed scRNA-seq data, it was observed that the proportion of DUOX2-epi in cI tissues was significantly higher than that in iI tissues ( Figure 1 E, Figure 3 A). Public bulk transcriptome sequencing datasets (GSE16879, GSE179285, GSE66407, and GSE75214) ( Figure 3 B) and real-time qPCR analysis ( Figure 3 C) further supports this observation. Immunofluorescence staining of freshly collected normal / inflamed ileum and colon tissues also showed that the proportion of DUOX2-epi in cI was much higher than that in iI ( Figure 3 DE). Numerous studies have highlighted that mucosal healing of CD can be achieved faster and more rapidly in the colon compared with the ileum. It is speculated that ileal and colonic DUOX2-epi may play an important role in this site-specific therapeutic effect.

[0081] Next, we compared the transcriptional profiles of DUOX2-epi in the colon and ileum to determine whether the molecular events of DUOX2-epi differ in the different locations ( Figure 3F). In both the ileum and colon of CD inflammation, DUOX2-epi highly expressed genes related to pathways associated with bacterial defense responses, antimicrobial humoral responses, and regulation of inflammatory responses. However, there were differences in the genes associated with these pathways in the ileum and colon. Among them, PI3 encodes an antimicrobial peptide induced by bacterial lipopolysaccharide and cytokines; intestinal SAA1 can regulate bacterial responses to adapt to inflammation, and serum SAA1 levels are considered to be a biomarker for monitoring CD disease; DEFA6 is mainly distributed in the ileum, and DEFA6 may be involved in the host defense mechanism of the human intestine, and may be involved in the protection against microbial pathogens and the maintenance of intestinal homeostasis. The results of the study showed that DUOX2-epi in the inflamed colon showed abnormally high expression of PI3 and SAA1, while DUOX2-epi in the inflamed ileum showed abnormally high expression of DEFA6 ( Figure 3 FH). Real-time fluorescence quantitative PCR further verified that the expression of PI3 and SAA1 in the inflamed colon was significantly upregulated compared with that in the inflamed ileum ( Figure 3 IJ).

[0082] Parainflammation (PI) is a low-grade inflammatory response between basal homeostasis and chronic inflammation. Compared with normal tissues, the inflamed ileum and colon showed higher PI scores and microbial scores, among which the PI score of the inflamed ileum was significantly higher than that of the colon ( Figure 3 KL). DUOX2 plays a role in regulating the crosstalk between the gut microbiota and the mucosa and is essential for maintaining immune homeostasis. In the setting of mucosal dysbiosis, increased exposure of epithelial cells to pathogens can trigger a compensatory DUOX2 response and be enhanced by the IL-23 / IL-22 cascade, a known target of ustekinumab. A strong correlation was found between DUOX2-epi and parainflammation. In conclusion, DUOX2-epi may be at the intersection of microbiota and inflammation, constituting mucosal dysbiosis in CD.

[0083] In summary, the proportion of DUOX2-epi in Crohn's disease inflammatory tissue was significantly higher than that in the normal tissue of the corresponding intestinal segment, and the proportion of DUOX2-epi in colonic Crohn's disease inflammatory tissue was significantly higher than that in ileal Crohn's disease inflammatory tissue; DUOX2-epi showed abnormally high expression of PI3 and SAA1, while DUOX2-epi in inflamed ileum showed abnormally high expression of DEFA6; it is speculated that ileal and colonic DUOX2-epi may have different mechanisms in responding to microbial stimulation and initiating inflammatory responses.

[0084] Example 2 Cell-cell communication network between colon and ileum DUOX2-epi cells

[0085] 1. Experimental Methods

[0086] 1. Regulatory network inference

[0087] The activity of the transcription factor (TF) regulatory network in the selected cell cluster is inferred by SCENIC (Single-Cell regulatory network Inference and Clustering) (v1.3.1) calculation method. First, the co-expression module between TF and its target gene (regulator) is determined using GENIE3 (v1.12.0). Subsequently, the direct target of TF is selected based on DNA-motif analysis using RcisTarget (v1.10.1). Finally, AUCell (v1.16.0) is used to calculate the regulator activity score of a single cell. Cell type specific regulators are identified by regulator specificity scores (Regulon Specificity Score, RSS).

[0088] 2. Cell-cell interaction analysis

[0089] Cellular interactions between different cell subsets were analyzed using CellChat (v1.6.1), a toolkit that predicts intercellular communication probabilities and major signal flows based on the expression of specific ligand-receptor pairs. The size parameter in the computeCommunProb function was set to TRUE. Intercellular communications of different groups were first analyzed separately and then combined for subsequent comparative analysis.

[0090] 3. Multiplex immunohistochemistry

[0091] Multiplex immunohistochemical analysis revealed and validated the cellular interactions between DUOX2-epi and monocytes and Tregs mediated by ligand-receptor pairs. Inflammatory tissues of the colon (n=5) and ileum (n=5) were collected from CD patients in Renji Hospital for this experiment. The specimens were collected within 30 minutes after endoscopy and fixed in formalin for 48 hours. The specimens were embedded in paraffin according to the routine method. The paraffin blocks were cut into 4 μm thick slides and pasted on the slide glass. These paraffin sections were washed in TBST buffer and then transferred to preheated EDTA solution (100°C) and then heat-treated for 15 minutes using a microwave set at 20% maximum power. The slides were cooled to room temperature in the same solution, incubated with primary antibodies, and then treated with peroxidase-conjugated (HRP) secondary antibodies (#DS9800, Leica) for 10 minutes and paired with TSA 6-color kit (M-D110061-50T, Yuanxibio). The primary antibodies used for each staining panel are listed in Table 2. After all sequential staining steps were completed, each slide was then treated with 2 drops of SN470 (A11010-100T; Yuanxibio), washed with distilled water, and manually covered. The slides were air-dried and then scanned using a panoramic MIDI tissue imaging system (3DHISTECH). Cell distances were analyzed using the "Proximity Analysis" model in Halo pathology software (Indica labs, USA).

[0092] Table 2 Antibodies used for mIHC detection

[0093]

[0094] 2. Experimental Results

[0095] 1. Colonic and ileal DUOX2-epi cells exhibit shared and specific cell-cell communication networks

[0096] Based on CellChat analysis, the present invention found that there were significant differences in the interactions between DUOX2-epi cells and other cells in the microenvironment of colon and ileum tissue under inflammatory conditions ( Figure 4 Specifically, in the inflamed ileal microenvironment, a significant increase in chemokines was observed, through which DUOX2-epi cells directly interact with T cells, for example, DUOX2-epi cells interact with Treg cells (regulatory T cells) via the CXCL16-CXCR6 receptor ligand pair ( Figure 4 E). This phenomenon was significantly less observed in colon tissue. Multiple immunohistochemical experiments confirmed that CXCL16+DUOX2-epi cells and CXCR6+Treg cells were closer in the inflamed ileal tissue microenvironment than in the inflamed colon tissue microenvironment ( Figure 4 FG).

[0097] Both colonic and ileal DUOX2-epi cells showed strong interactions with venous endothelial cells (VECs), macrophages, and monocytes in their microenvironment, but this interaction involved position-specific receptor-ligand pairs ( Figure 4 AE). Colonic DUOX2-epi cells prefer to use the SAA1-FPR2 and ANXA1-FPR1 / 2 receptor ligand pairs to interact with monocytes, and the CXCL1 / 3-ACKR1 receptor ligand pair to interact with venous endothelial cells (VECs) ( Figure 4 E) Multiple immunohistochemical experiments confirmed that SAA1+DUOX2 in the inflammatory colon tissue microenvironment was significantly higher than that in the inflammatory ileum tissue microenvironment. + -epi cells and FPR2 monocytes are closer ( Figure 4 HI).

[0098] Further analysis of the interactions between monocytes and other cells revealed that they communicated closely with T cells and VECs in inflamed colon and ileal tissues. The most frequent communication between monocytes and Treg cells was observed, mediated by chemokines and their receptors, as well as the interaction of TNF-TNFRSF1B. + Similar ligand-receptor interactions were also observed in the communication between monocytes and VECs (a subtype of helper T cells). In addition, strong signaling mediated by chemokine-ACKR1 interactions was found between monocytes and VECs. These results indicate that although DUOX2-epi cells in the colon do not directly interact with T cells, they can further influence inflammatory responses involving T cells through monocytes, suggesting a differential role for DUOX2-epi cells in the development and progression of Crohn's disease with impaired intestinal mucosal barrier.

[0099] Example 3 Prediction of location-specific therapeutic response in colonic and ileal CD via distinct cell-cell communication networks

[0100] 1. Experimental Methods

[0101] 1. Calculation of cell-cell communication (CCC) network scores for ileal and colonic CD

[0102] After understanding the cellular communication network in which DUOX2-epi is a signal sender, the relationship between CCC and the efficacy of biological treatment for ileal and colonic CD was investigated. Signaling molecules involved in the communication network centered on DUOX2-epi were extracted to form the ileal and colonic CCC network gene sets (i.e., provided Table 3, Colon_module is the gene set used to construct the colon prediction module, and Ileum_module is the gene set used to construct the ileum prediction module). Based on the gene expression profiles of these gene sets, the single sample gene set enrichment analysis (ssGSEA) method in the GSVA R package (v1.42.0) was used to calculate the CCC network gene score for a given sample. The CCC network gene scores were then compared between responders and non-responders who received biological treatment.

[0103] 2. Experimental Results

[0104] The results are as follows Figure 5 As shown, distinct shared cell-cell communication (CCC) networks were found in the inflamed colon and ileum affected by Crohn's disease. As signal senders, DUOX2-epi cells primarily communicated with VECs and monocytes in the colon and ileum, but through distinct ligand-receptor pairs ( Figure 5 AB). In the ileum, DUOX2-epi cells exhibited enhanced interactions with Th1 and Treg cells and displayed a more extensive cytokine-mediated communication network compared with the colon, indicating a higher complexity of inflammatory responses in the ileal microenvironment. For example, DUOX2-epi cells communicated through the binding of chemokine ligands CXCL9-11 to the Th1 receptor CXCR3, and chemokine ligand CXCL16 to the Th1 receptor CXCR6 ( Figure 5 A). Importantly, these interactions were absent in the colon, where DUOX2-epi cells normally interact with T cells indirectly through monocytes ( Figure 5 B) These findings provide a deeper understanding of the subtle interactions of DUOX2 in different intestinal segments affected by Crohn's disease.

[0105] Therefore, to investigate the relationship between CCC and the efficacy of biological treatments for ileal and colonic Crohn's disease, a CCC network scoring system was developed to predict biological treatment responses based on a gene prediction module consisting of DUOX2, DUOXA2, and signaling molecules involved in the colon or ileal CCC network, namely the colon / ileum module score (Table 3). Public datasets containing treatment outcomes for colon or ileal Crohn's disease were used for evaluation. Four eligible datasets were analyzed, including two Infliximab (anti-TNF) datasets (GSE16879, GSE52746) and two Ustekinumab (anti-IL-12 / 23) datasets (GSE207022, GSE112366). The results showed that the colon / ileum module score could distinguish CD patients from healthy individuals. Specifically, the colon / ileum module score was significantly higher in CD patients than in normal controls. Interestingly, the colon module score showed superior performance in predicting treatment efficacy compared with the ileum module score, regardless of the type of biological agent used ( Figure 5 CG). Given the higher abundance of DUOX2-epi cells in the colon, the superior predictive performance of the colon module score for biotherapeutic efficacy may be attributed to the greater specificity of DUOX2-epi cells in the colon.

[0106] Among them, the AUC value is an important indicator to measure the classification performance of the model. It is defined as the area under the ROC curve. The value range of AUC is 0 to 1. The larger the value, the better the classification performance of the model. When AUC is 1, it means perfect prediction; when AUC is 0.5, it means that the prediction performance of the model is equivalent to random guessing. Figure 6 It can be seen that, using the above database analysis, the AUC value range of the colon module for distinguishing colonic CD patients from healthy individuals is 0.808-1, indicating that the colon module is more accurate in distinguishing CD patients from healthy individuals ( Figure 6 AC); the AUC value of the ileum module for distinguishing ileal CD patients from healthy individuals ranged from 0.848 to 0.991, indicating that the ileum module was also relatively accurate in distinguishing CD patients from healthy individuals ( Figure 6 Similarly, when the colon module was used to distinguish the predictive performance of the efficacy of biological agents (i.e., distinguishing responders from non-responders), the AUC values ​​ranged from 0.761 to 0.964 ( Figure 6 FH); when the ileum module was used to distinguish the predictive performance of the efficacy of biological agents (i.e., distinguishing responders from non-responders), the AUC values ​​ranged from 0.624 to 0.713 ( Figure 6 IJ), therefore, the colon module showed superior predictive performance for treatment efficacy in distinguishing the efficacy of biologics.

[0107] In-depth investigation of the cellular and molecular interactions mediated by DUOX2-epi in the microenvironment of different intestinal segments is essential for tailoring personalized ileal / colonic treatments.

[0108] Table 3 Gene sets used to calculate ileum module scores and colon module scores

[0109]

[0110]

[0111]

Claims

1. Use of a preparation for detecting the abundance of DUOX2-epi in Crohn's disease inflammatory tissue in the preparation of a product for distinguishing ileal subtype and colonic subtype Crohn's disease, characterized in that: The DUOX2-epi is an epithelial cell subpopulation with high expression of DUOX2 and DUOXA2.

2. Use of a preparation for detecting the expression level of DUOX2 and / or DUOXA2 genes in inflammatory tissues of Crohn's disease in the preparation of a product for distinguishing ileal subtype and colonic subtype of Crohn's disease.

3. Use of a preparation for detecting the expression level of PI3 and / or SAA1 gene in DUOX2-epi in Crohn's disease inflammatory tissue in the preparation of a product for distinguishing ileal subtype and colonic subtype Crohn's disease, characterized in that: The DUOX2-epi is an epithelial cell subpopulation with high expression of DUOX2 and DUOXA2.

4. Use of a preparation for detecting DUOX2-epi interacting cells in inflammatory tissue of Crohn's disease in the preparation of a product for distinguishing ileal subtype and colonic subtype of Crohn's disease, characterized in that: The DUOX2-epi is an epithelial cell subpopulation with high expression of DUOX2 and DUOXA2.

5. The use according to claim 4, characterized in that: The interacting cells are one or more of Treg cells, Th1 cells, monocytes or venous endothelial cells.

6. A gene prediction module, characterized in that: The module is composed of DUOX2, DUOXA2 and signal molecules involved in the colon or ileum cell-cell communication network; the signal molecules are a combination of SAA1, ANXA1, FPR1, FPR2, CCL4, CCR5, CXCL1, CXCL3, ACKR1, CXCL2 or a combination of CCL3L1, CCR1, CCL3, IL1B, IL1R2, CXCL9, CXCR3, ACKR1, CXCL8, CXCL5, CXCL3, CXCL2, CXCL1, CXCL10, CXCL11, CXCL16, CXCR6, CCR5, CCL4, FPR1, and FPR2.

7. A gene prediction module scoring system, characterized in that: It comprises a data input module, a data calculation module and a result output module; the data input module is used to input the gene expression spectrum of the gene prediction module according to claim 6 in the Crohn's disease inflammatory tissue; the data calculation module uses a single sample gene set enrichment analysis method to calculate the network gene score of the gene expression spectrum in the Crohn's disease inflammatory tissue; the result output module is used to output the network gene score.

8. Use of the gene prediction module according to claim 6 or the gene prediction module scoring system according to claim 7 in predicting the therapeutic effect of biological therapy on ileal subtype and colonic subtype Crohn's disease.

9. Use of the gene module according to claim 6 or the gene prediction module scoring system according to claim 7 in screening drugs for treating ileal subtype or colonic subtype Crohn's disease.

10. Use of the gene module according to claim 6 or the gene prediction module scoring system according to claim 7 in distinguishing ileal subtype and colonic subtype Crohn's disease.

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