Application of biological factor developed based on MAMMOT in preparation of kit for sepsis identification

Abstract

The invention relates to the technical field of biological information, in particular to application of a biological factor developed based on MAMMOT in preparation of a kit for sepsis identification. The biological factors comprise a genome and a microbiome; the genome comprises a plurality of genes such as PMS2P4 and the like, and the microbiome comprises a plurality of bacteria such as Citrobacter werkmanii, Streptococcus oranis and the like, and a plurality of bacteriophages such as Escherichia virus Lambda, Campylobacter phage A11a and the like. The invention further discloses a preparation method of the microbiome. A plurality of sepsis-related cfDNA host methylation sites and a plurality of sepsis-related bacteria and bacteriophages are excavated for the first time, and the biological factors can be applied to sepsis identification and have important values in the fields of sepsis-related detection and patient death rate prediction.

Description

Technical Field

[0001] The present invention relates to the field of bioinformatics, and in particular to application of biological factors developed based on MAMMOT in preparing a kit for sepsis identification. Background Art

[0002] Sepsis is a life-threatening disease in which the body's dysregulated response to infection causes damage to its own tissues and organs. If not recognized and treated early, septic shock, multiple organ failure, and death may occur. Currently, treatment strategies for sepsis are limited, mainly supportive rather than curative treatments, focusing on anti-infection, maintaining organ perfusion and hemodynamic stability, and supporting organ function. Due to the heterogeneity of sepsis, it is still extremely difficult to accurately diagnose sepsis and judge its prognosis, and delays in diagnosis and intervention lead to high mortality. Currently, sepsis is identified by culture, quantitative PCR (qPCR), and various inflammatory markers such as procalcitonin and C-reactive protein. However, these methods are limited by a limited pathogen spectrum and low specificity. The most common assessment system currently used to evaluate the severity of organ dysfunction is the Sequential Organ Failure Assessment (SOFA) score. Summary of the invention

[0003] In order to solve the problems existing in the prior art, the present invention provides the use of biological factors developed based on MAMMOT in preparing a kit for sepsis identification.

[0004] The present invention is based on a highly sensitive, highly specific and accurate single-tube multimodal analysis of methylomics and metagenomics in one tube (MAMMOT) detection method to mine sepsis-related cfDNA host methylation sites and microbial species, which can be effectively used to identify whether sepsis occurs, the site of infection and the survival rate, and may provide clinical guidance for medication.

[0005] In a first aspect, the present invention provides the use of biological factors in preparing a kit for sepsis identification; The biological factors include differentially methylated regions and the microbiome; The differentially methylated regions include: chr7: 67292401-67292900, chrX: 149525801-149526300, chr20: 63537201-63537700, chr10: 42333001-42333500, chr3: 48566001-48566500, chr12: 6772401-6772900, chr9: 13223601-13224100, chr11-65492201-65492700, chr8: 144052201-144052700, chr13: 25095601-25096100 、chr17:81862801-81863300、chr17:10683601-10684100、chr17:10683801-10684300、chr12:10052601-10053100、chr2:127566601-127567100、chr1:25240201-25240700、chr7:149866801-149867300、chr17:44364001-44364500、chr14:3911360139114100 or chr22:29882601-29883100; The microbiome includes: bacteria and / or bacteriophages; The bacteria include: Citrobacter werkmanii, Streptococcus oralis, Paenibacillus psychroresistance , Enterobacteriaceae endosymbiont of Donacia fulgens、 Bradyrhizobium-sp.-PSBB068 Winogradskyella forsetii or Lachnoclostridium phytofermentans One or more of; The bacteriophage comprises: Escherichia virus Lambda、Escherichia virus HK630、Campylobacter phage A11a, Uncultured-Phage. One or more of Mycobacterium phage Hail, Streptococcus phage 142, Mycobacterium phage Xula, Tenacibaculum virus PTm1 or Komagataeibacter phagephiKM1.

[0006] Furthermore, the application includes: i) Identify whether it is sepsis; ii) Identify whether the site of sepsis infection is the abdomen or the lungs; iii) Identify mortality in patients with sepsis.

[0007] Furthermore, the biological factors can be applied as targets in the above applications.

[0008] Further, the differentially methylated regions used for application i) include one or more of: chr7: 67292401-67292900, chrX: 149525801-149526300, chr20: 63537201-63537700, chr10: 42333001-42333500, chr3: 48566001-48566500, chr12: 6772401-6772900 or chr9: 13223601-13224100; Bacteria used for application i) include: Citrobacter werkmanii、Streptococcus oralis、 Paenibacillus psychroresistance or Enterobacteriaceae endosymbiont of Donacia fulgens One or more of; Bacteriophages used for application i) include: Smallpox virus Lambda、Smallpox virus HK630 or Campylobacter phage A11a One or more of .

[0009] Furthermore, those who meet any of the following conditions have a higher likelihood of developing sepsis: (1) For chr7: 67292401-67292900, the methylation level is lower; (2) For chrX:149525801-149526300, the methylation level is lower; (3) For chr20:63537201-63537700, the methylation level is even lower; (4) For chr10:42333001-42333500, the methylation level is even lower; (5) For chr3:48566001-48566500, the methylation level is even lower; (6) For chr12: 6772401-6772900, the methylation level is even lower; (7) For chr9:13223601-13224100, the methylation level is even lower; (8) For Citrobacter workman The relative abundance is lower. (9) For Streptococcus oralis The relative abundance is lower. (10) For Paenibacillus psychroresistance The relative abundance is lower. (11) For Enterobacteriaceae endosymbiont of Donacia fulgens The relative abundance is higher. (12) For Escherichia virus Lambda The relative abundance is lower. (13) For Escherichia virus HK630 The relative abundance is lower. (14) For Campylobacter phage A11a The relative abundance is lower.

[0010] Further, the differentially methylated regions for application ii) include one or more of: chr11-65492201-65492700, chr8: 144052201-144052700, chr13: 25095601-25096100, chr17: 81862801-81863300, chr17: 10683601-10684100 or chr17: 10683801-10684300.

[0011] The microbial group used for application ii) includes: bacteria and / or bacteriophages; The bacteria are Bradyrhizobium sp.-PSBB068 The bacteriophage is Uncultured-Phage .

[0012] Furthermore, if any of the following conditions are met, the infection site is more likely to be in the abdomen than in the lungs: (1) For chr11-65492201-65492700, the methylation level is higher; (2) For chr8:144052201-144052700, the methylation level is higher; (3) For chr13:25095601-25096100, the methylation level is even lower; (4) For chr17:81862801-81863300, the methylation level is even lower; (5) For chr17:10683601-10684100, the methylation level is higher; (6) For chr17:10683801-10684300, the methylation level is higher; (7) For Bradyrhizobium sp.-PSBB068 The relative abundance is lower. (8) For Uncultured-Phage The relative abundance is higher.

[0013] Further, the differentially methylated regions for application iii) include one or more of: chr12: 10052601-10053100, chr2: 127566601-127567100, chr1: 25240201-25240700, chr7: 149866801-149867300, chr17: 44364001-44364500, chr14: 39113601-39114100, or chr22: 29882601-29883100; Bacteria used for application iii) include: Winogradskyella forsetii or Lachnoclostridium phytofermentans One or more of; Bacteriophages used in application iii) include: Mycobacterium phage Hail , Streptococcus phage 142 , Mycobacterium phage Dissolve , Tenacibaculum virus PTm1 or Commagataeibacter phage phiKM1 One or more of .

[0014] Furthermore, the mortality rate is higher if any of the following conditions are met: (1) For chr12:10052601-10053100, the methylation level is higher; (2) For chr2:127566601-127567100, the methylation level is even lower; (3) For chr1:25240201-25240700, the methylation level is higher; (4) For chr7:149866801-149867300, the methylation level is higher; (5) For chr17:44364001-44364500, the methylation level is higher; (6) For chr14:39113601-39114100, the methylation level is higher; (7) For chr22: 29882601-29883100, the methylation level is higher; (8) For Winogradskyella forsetii The relative abundance is lower. (9) For Lachnoclostridium phytofermentans The relative abundance is lower. (10) For Mycobacterium phage Hail The relative abundance is higher. (11) For Streptococcus phage 142 The relative abundance is higher. (12) For Mycobacterium phage Xula The relative abundance is lower. (13) For Tenacibaculum virus PTm1 The relative abundance is lower. In a second aspect, the present invention provides a combination of biological factors, including differentially methylated regions and a microbiome; The differentially methylated regions include: chr7: 67292401-67292900, chrX: 149525801-149526300, chr20: 63537201-63537700, chr10: 42333001-42333500, chr3: 48566001-48566500, chr12: 6772401-6772900, chr9: 13223601-13224100, chr11-65492201-65492700, chr8: 144052201-144052700, chr13: 25095601-25096100 、chr17:81862801-81863300、chr17:10683601-10684100、chr17:10683801-10684300、chr12:10052601-10053100、chr2:127566601-127567100、chr1:25240201-25240700、chr7:149866801-149867300、chr17:44364001-44364500、chr14:3911360139114100 or chr22:29882601-29883100; The microbiome includes: bacteria and / or bacteriophages; The bacteria include: Citrobacter werkmanii, Streptococcus oralis, Paenibacillus psychroresistens , Enterobacteriaceae endosymbiont of Donacia fulgens, Bradyrhizobium-sp.-PSBB068, Winogradskyella forsetii, Lachnoclostridium Phytofermentans or Komagataeibacter One or more of; The bacteriophage comprises: Escherichia virus Lambda, Escherichia virus HK630, Campylobacter phage A11a、Uncultured-Phage、 One or more of Mycobacterium phage Hail, Streptococcus phage 142, Mycobacterium phage Xula, Tenacibaculum virus PTm1 or Komagataeibacter phagephiKM1.

[0015] For the first time, the present invention generated a local phage BLAST database by self-building library method, then removed any sequence with human genome homology, and captured reads covering highly conserved sequences among bacterial phages by secondary BLAST, and aligned the consistent reads (HIFI reads) generated by PacBio CCS mode to 8848 high-quality phage genomes (Chinese Human Gut Virome-High-Quality, CHGV-HQ). This largely eliminated the impact of methylation on the data.

[0016] Based on single-sample whole genome bisulfite sequencing (WGBS), MAMMOT integrates methylomes and microbial metagenomics, and preliminarily evaluates the applicability of MAMMOT in sepsis diagnosis and patient mortality prediction.

[0017] The present invention selected the remaining plasma samples of the same batch of patients with sepsis (a total of 48 cases) to extract cfDNA without methylation treatment, and directly constructed a single-stranded library. The correlation between the GC content of the sample sequences and the sample abundance of the two groups (methylation treatment group and non-methylation treatment control group) was analyzed, and the accuracy of the microbial metagenome in MAMMOT was preliminarily proved.

[0018] The present invention has the following technical effects: The present invention has for the first time discovered multiple sepsis-related cfDNA host methylation sites (genes), bacteria and bacteriophages. Based on the methylation differences of these genes and the abundance difference detection of bacteria and bacteriophages, the diagnosis of sepsis, the diagnosis of infection sites and the prediction of patient mortality can be effectively achieved, which is of great significance in the field of sepsis-related detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0020] Figure 1 This is the application queue and flow chart provided in Example 1 of the present invention.

[0021] Figure 2 This is a promoter region enrichment diagram provided in Example 1 of the present invention; A: GO; B: KEGG; C: Reactome, the bubble size indicates the number of genes involved in the pathway, and the color from blue to red indicates PThe values ​​are from large to small.

[0022] Figure 3 It is an exon region enrichment diagram provided in Example 1 of the present invention; wherein A: GO; B: KEGG; C: Reactome, the bubble size indicates the number of genes involved in the pathway, and the colors from blue to red indicate the P value from large to small.

[0023] Figure 4 It is a biological pathway enrichment diagram of promoter genes adjacent to DMRs provided in Example 1 of the present invention; wherein A: Abdominal vs. Pulmonary is enriched and down-regulated in DMRs of GO database; B: Abdominal vs. Pulmonary is enriched and up-regulated in DMRs of GO database; C: Abdominal vs. Pulmonary is enriched and down-regulated in DMRs of Reactome database; D: Death vs. Live is enriched and up-regulated in DMRs of GO database; the size of the bubble indicates the number of genes involved in the pathway, and the color from blue to red indicates P The values ​​are from large to small.

[0024] Figure 5 This is the enrichment map of the DMRs adjacent to the exon region of Abdominal vs. Pulmonary provided in Example 1 of the present invention; A: GO; B: Reactome; C: cnetplot analysis of Reactome; the size of the bubble indicates the number of genes involved in the pathway, and the color from blue to red indicates P Values ​​from large to small ( Figure 5 Different colors in C represent different pathways), and the number of cluster lines indicates the degree of correlation between genes.

[0025] Figure 6 This is the enrichment map of the DMRs adjacent exon region of Death vs. Live provided in Example 1 of the present invention; A: GO; B: Reactome; C: cnetplot analysis of Reactome; the size of the bubble indicates the number of genes involved in the pathway, and the color from blue to red indicates P Values ​​from large to small ( Figure 6 Different colors in C represent different pathways), and the number of cluster lines indicates the degree of correlation between genes.

[0026] Figure 7 This is the GSEA diagram of differentially methylated genes in the promoter region provided in Example 1 of the present invention; A: GO enrichment; BH: GO gene set; the bubble size indicates the number of genes involved in the pathway, and the color from blue to red indicates PThe values ​​go from large to small, with positive values ​​of NES indicating that the gene set is enriched at the top of the list (up-regulated), and negative values ​​indicating that the gene set is enriched at the bottom of the list (down-regulated).

[0027] Figure 8 This is the GSEA diagram of differentially methylated genes in the exon region provided in Example 1 of the present invention; A: GO enrichment; BJ: GO gene set; the bubble size indicates the number of genes involved in the pathway; the color from blue to red indicates P The values ​​are from large to small; positive values ​​of NES indicate that the gene set is enriched at the top of the list (up-regulated), and negative values ​​indicate that the gene set is enriched at the bottom of the list (down-regulated).

[0028] Fig. 9 This is a GSEA diagram of differentially methylated genes in the promoter region provided in Example 1 of the present invention; A: enrichment in GO; B: enrichment in Reactome; CD: gene set in GO; EF: Reactome gene set; the size of the bubble indicates the number of genes involved in the pathway; the color from blue to red indicates P The values ​​go from large to small, with positive values ​​of NES indicating that the gene set is enriched at the top of the list (up-regulated), and negative values ​​indicating that the gene set is enriched at the bottom of the list (down-regulated).

[0029] Fig.10 This is a GSEA diagram of differentially methylated genes in the exon region provided in Example 1 of the present invention; A: enrichment in GO; B: enrichment in Reactome; CD: gene set in GO; EF: Reactome gene set; the size of the bubble indicates the number of genes involved in the pathway; the color from blue to red indicates P The values ​​go from large to small, with positive values ​​of NES indicating that the gene set is enriched at the top of the list (up-regulated), and negative values ​​indicating that the gene set is enriched at the bottom of the list (down-regulated).

[0030] Fig.11 This is a GSEA diagram of differentially methylated genes in the promoter region provided in Example 1 of the present invention; A: enrichment in GO; B: enrichment in Reactome; C: gene set in GO; D: Reactome gene set; the size of the bubble indicates the number of genes involved in the pathway; the color from blue to red indicates P The values ​​go from large to small, with positive values ​​of NES indicating that the gene set is enriched at the top of the list (up-regulated), and negative values ​​indicating that the gene set is enriched at the bottom of the list (down-regulated).

[0031] Fig.12 This is a GSEA diagram of differentially methylated genes in the exon region provided in Example 1 of the present invention; A: enrichment in GO; B: enrichment in Reactome; C: gene set in GO; DF: Reactome gene set; the size of the bubble indicates the number of genes involved in the pathway; the color from blue to red indicatesP The values ​​go from large to small, with positive values ​​of NES indicating that the gene set is enriched at the top of the list (up-regulated), and negative values ​​indicating that the gene set is enriched at the bottom of the list (down-regulated).

[0032] Fig.13 It is a classifier performance diagram for the relationship between plasma cfDNA host methylation and sepsis and non-sepsis diagnosis provided in Example 1 of the present invention; wherein A: the ordinate represents DMRs, and the column length represents the importance of random forest features; B: ROC curve, the abscissa is specificity, and the ordinate is sensitivity.

[0033] Fig.14 It is a classifier performance diagram for the relationship between plasma cfDNA host methylation and the abdominal and pulmonary infection sites of sepsis provided in Example 1 of the present invention; wherein A: the ordinate represents DMRs, and the column length represents the importance of random forest features; B: ROC curve, the abscissa is specificity, and the ordinate is sensitivity.

[0034] Fig.15 It is a classifier performance diagram for the relationship between plasma cfDNA host methylation and patient mortality provided in Example 1 of the present invention; wherein A: the ordinate represents DMRs, and the column length represents the importance of random forest features; B: ROC curve, the abscissa is specificity, and the ordinate is sensitivity.

[0035] Fig.16 It is a classifier performance diagram for the relationship between bacterial and fungal metagenomes and sepsis and non-sepsis provided in Example 1 of the present invention; wherein A: the ordinate represents the bacterial or fungal species, and the column length represents the importance of the random forest feature; B: ROC curve, the abscissa is the specificity, and the ordinate is the sensitivity.

[0036] Fig.17 It is a classifier performance diagram for the relationship between bacterial and fungal metagenomes and the sites of abdominal and pulmonary infection in sepsis provided in Example 1 of the present invention; wherein A: the ordinate represents the bacterial or fungal species, and the column length represents the importance of the random forest feature; B: ROC curve, the abscissa is the specificity, and the ordinate is the sensitivity.

[0037] Fig.18 It is a classifier performance diagram for the relationship between bacterial and fungal metagenomes and patient mortality provided in Example 1 of the present invention; wherein A: the ordinate represents the bacterial or fungal species, and the column length represents the importance of the random forest feature; B: ROC curve, the abscissa is the specificity, and the ordinate is the sensitivity.

[0038] Fig.19It is a classifier performance diagram for the relationship between bacteriophage and its host bacterial metagenome and sepsis and non-sepsis patients provided in Example 1 of the present invention; wherein A: the ordinate represents the bacteriophage or its host bacteria, and the column length represents the importance of the random forest feature; B: ROC curve, the abscissa is the specificity, and the ordinate is the sensitivity.

[0039] Fig. 20 It is a classifier performance diagram for the relationship between bacteriophages and their host bacteria and the abdominal and pulmonary infection sites of sepsis provided in Example 1 of the present invention; wherein A: the ordinate represents the bacteriophage or its host bacteria, and the column length represents the importance of the random forest feature; B: ROC curve, the abscissa is the specificity, and the ordinate is the sensitivity.

[0040] Fig.21 It is a classifier performance diagram for the relationship between bacteriophage and its host bacteria and patient mortality provided in Example 1 of the present invention; wherein A: the ordinate represents the bacteriophage or its host bacteria, and the column length represents the importance of the random forest feature; B: ROC curve, the abscissa is the specificity, and the ordinate is the sensitivity.

[0041] Fig. 22 It is a performance diagram of the MAMMOT-based sepsis and non-sepsis diagnostic classifier provided in Example 1 of the present invention; wherein A: the ordinate represents DMRs or microorganisms, and the column length represents the importance of random forest features; B: ROC curve, the abscissa is specificity, and the ordinate is sensitivity.

[0042] Fig.23 It is a separator performance diagram for classification of abdominal and pulmonary infection sites of sepsis based on MAMMOT provided in Example 1 of the present invention; wherein A: the ordinate represents DMRs or microorganisms, and the column length represents the importance of random forest features; B: ROC curve, the abscissa is specificity, and the ordinate is sensitivity.

[0043] Fig.24 It is a classifier performance diagram for predicting patient mortality based on MAMMOT provided in Example 1 of the present invention; wherein A: the ordinate represents DMRs or microorganisms, and the column length represents the importance of random forest features; B: ROC curve, the abscissa is specificity, and the ordinate is sensitivity. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] The experimental methods involved in the following examples, unless otherwise mentioned, are all conventional methods in the art, for example, reference may be made to experimental manuals in the art, or the conditions recommended in the manufacturer's instructions.

[0046] Unless otherwise specified, the experimental materials and reagents involved in the following examples can be obtained from commercial sources.

[0047] Example 1

[0048] 1. Establishment of the test population

[0049] The present invention collected a total of 240 peripheral blood specimens from multiple sepsis patients (Sepsis), ICU (Intensive Care Unit) non-sepsis patients (Non_Sepsis) and healthy adults (Health) (from Northern Guangdong People's Hospital). According to the test results and final diagnosis, 134 sepsis patients, 40 ICU non-sepsis patients and 12 healthy adults, totaling 186 cases, were finally included in the analysis. The experiment of the present invention has been approved by the Ethics Committee of Northern Guangdong People's Hospital. All participants signed an informed consent form before enrollment. The research team is responsible for collecting and collating the clinical information of the patients, and conducting telephone follow-up on patients who have signed the informed consent form. The inclusion and exclusion criteria of this study are as follows: 1.1 Sepsis patient inclusion criteria: i) Patients aged ≥18 years; ii) infection + sequential organ failure assessment score (SOFA score) ≥ 2; iii) No antibiotics were taken in the week before sampling.

[0050] 1.2 Inclusion criteria for non-Sepsis patients: i) Patients aged ≥18 years; ii) similar severity of illness to Sepsis in the ICU; iii) Failure to meet the Sepsis criteria.

[0051] 1.3 Inclusion criteria for healthy physical examination subjects

[0052] i) Patients aged ≥18 years; ii) No serious internal or external diseases such as infection, surgery, immune disease, etc.; iii) General physical examination indicators are normal, such as creatinine, uric acid, platelets, white blood cells, alanine aminotransferase, etc.

[0053] 1.4 Determination of infection site: With etiological testing as the gold standard, refer to the standards for healthcare-associated infection and infection in acute care settings, and finally the primary infection site is classified by the attending clinical physician (such as patients with sepsis with primary abdominal infection vs. patients with pulmonary infection, Abdominal vs. Pulmonary). Clinical outcomes of patients: Death and survival within 28 days after Sepsis and Non_Sepsis sampling studies (Death vs. Live).

[0054] 1.5 Exclusion criteria: i) Patients with malignant tumors or severe hematological diseases; ii) patients with autoimmune diseases who are receiving glucocorticoids or other immunosuppressive drugs; iii) patients who died within 24 hours of admission; iv) Patients with acquired immunodeficiency syndrome (AIDS).

[0055] 1.6 Exit criteria for this study: i) Cases whose final clinical diagnosis after enrollment does not meet the inclusion criteria or meets the exclusion criteria; ii) The subject requests to withdraw the informed consent.

[0056] 2. Establishment of experimental process

[0057] After obtaining informed consent from the patients, 5 mL of antecubital venous blood was drawn from each group. Plasma and blood cells were separated by centrifugation; plasma free DNA was extracted, and the concentration was measured using the Qubit™ Flex instrument. The bisulfite conversion and single-stranded library construction methods were used to capture the methylation treatment and annotation of small DNA fragments. The samples were sent to Guangzhou Weiyuan Gene Technology Co., Ltd. for sequencing. After sequencing, all biological samples and sequencing data were returned to the Laboratory Department (Department of Laboratory Science) of Yuebei People's Hospital, and the sequencing results were further analyzed and interpreted. First, 306 cases were initially included in the study, 120 cases were screened and excluded, and 186 cases were finally included in the analysis. Then, a classifier model was constructed by machine learning such as forward feature selection, RF and LR for sepsis diagnosis and patient mortality prediction (Sepsis vs. Non_Sepsis, Abdominal vs. Pulmonary and Death vs. Live), and the ROC curve was used to evaluate the established classification performance. The remaining plasma samples of the same batch of patients with sepsis (48 cases in total) were selected to extract cfDNA without methylation treatment, and single-stranded library construction was directly performed. The correlation between the GC content of the sample sequences and the sample abundance of the two groups (methylation treatment group and non-methylation treatment control group) was analyzed to prove the accuracy of the microbial metagenome in MAMMOT. Figure 1 .

[0058] 3. Experimental results

[0059] 3.1 Enrichment of DMRs in promoter and exon regions

[0060] (1) Enrichment of sepsis-related DMRs in promoter and exon regions

[0061] The promoter is a DNA sequence that RNA polymerase recognizes, binds to, and initiates transcription, while the exon is the sequence that appears last in mature RNA. To view the sepsis-related DMRs in the promoter and exon regions, the differentially enriched genes between Sepsis and Healthy control were analyzed. Figure 2 and Figure 3 The promoter results showed that they were mainly enriched in immune response, ribosomal RNA processing, mitochondria, transcription and translation, etc. The exon results were concentrated on phagocytosis, immune response and membrane internal and external components. For example, ALK and AIM2 and promoter MNDA Genes, etc., are involved in various stages of sepsis, from immune response to acute inflammation to the regulation of cell apoptosis.

[0062] (2) Enrichment of promoter regions adjacent to DMRs in sepsis diagnosis and patient mortality prediction

[0063] Considering the clinical significance of sepsis diagnosis and patient prognosis, Sepsis vs. Non_Sepsis, Abdominal vs. Pulmonary, and Death vs. Live were analyzed. However, no significantly enriched pathways were found in the Sepsis vs. Non_Sepsis cohort. A total of 8 Abdominal vs. Pulmonary enriched pathways were found ( Figure 4 A, B, C in the results), the results showed that it may be related to the microtubule organizing center attachment point and nuclear membrane protein complex in GO (associated genes: SYNE2, SYNE4, SPAG4 ) and proteasome ubiquitin-dependent protein regulation (associated genes: PRICKLE1, AXIN1, HSPBP1, PLK3, RFPL1, SUMO1, RNF14, PSMD10、UBQLN2 ) and the RHOH GTPase cycle in the Reactome. RHOH is a protein-coding gene whose related pathways include the GPCR pathway and Rho GTPase signaling. And the activity of RHOH is regulated by post-translational modifications downstream of the activated T cell receptor (TCR). In the Dead vs. Live group of patients, granulocyte chemotaxis-related genes were found to be significantly ( IL23A, S100A12, S100A8, S100A14, FCER1G, ITGA9, BST1, CXCL1, RARRES2、CXCL6 )difference( Figure 4 D in.

[0064] (3) Enrichment of exon regions adjacent to DMRs in sepsis diagnosis and patient mortality prediction

[0065] The present invention further examines the biological functions of differential methylation in the groups. Abdominal vs. Pulmonary exon region enrichment is mainly related to the regulation of signal transduction mediated by small GTPases in GO, the regulation of cell morphogenesis, immune response, etc. ( Figure 5 Reactome results showed that it may be related to the RAC1 GTPase cycle, Toll-like receptors, nuclear import of Rev protein, SARS-CoV infection, vascular wall cell surface interaction and CDC42 GTPase cycle ( Figure 5 B). The above pathway is plotted as cnetplot to find TLR1, TAB3, TAB2, NFKB1, PIK3C3, IKBKG, BTK, ATP1B1, PTPN6、ITGA4 Isogenic association, see Figure 5 C in Death vs. Live ( Figure 6) are mainly related to intracellular components such as GO lysosomes, endoplasmic reticulum-Golgi intermediate compartment membranes, and GTPase molecular functions. And they are mainly enriched in pathways such as neutrophil degranulation, CDC42 GTPase cycle, ion channel transport, RHO GTPase activation PAKs, collagen biosynthesis and modification enzymes, and RHO GTPase cycle in Reactome. Related genes: RAC1, CDC42, PAK3, SNAP23, RAB7A, LAMTOR1、IQGAP1 wait.

[0066] 3.2 GSEA of promoter and exon regions

[0067] In the enrichment score line graph, the highest peak score is the ES value of the gene set corresponding to methylation. The black line indicates the gene position in the sorted gene table. The leading edge subset is the gene portion corresponding to the peak ES origin of the blue curve, indicating the genes that contribute mainly to the enrichment. The red part of the heat map represents the methylation neighboring genes that are highly expressed in Sepsis; Abdominal; Death, and the blue part represents the genes that are highly expressed in Non_Sepsis; Pulmonary; Live. The gray area of ​​the figure represents the signal-to-noise ratio of each gene, and the normalized enrichment score (NES) is shown in the upper right corner of the figure.

[0068] (1) Sepsis vs. Non-Sepsis

[0069] In the promoter region, GSEA showed that these methylation-enriched differentially expressed genes were downregulated in the GO pathways of biological stimulation response, external biological stimulation response, other microbial response, and immune system pathways. However, they were upregulated in the endomembrane system, intracellular vesicles, and cytoplasmic vesicle pathways ( Figure 7 For Reactome analysis, these genes were enriched in biological metabolic signaling pathways. Figure 8 ), these methylation-enriched differentially expressed genes were enriched in the regulation of cellular component biogenesis, positive regulation of cell death, organic nitrogen compound metabolism, protein decomposition, and organic matter decomposition in GO and were upregulated overall.

[0070] (2) Abdominal vs. Pulmonary

[0071] Compared with the enrichment of Abdominal vs. Pulmonary promoter regions in the above text, GSEA related membrane protein complexes in GO, and the overall trend of the pathway was consistent; in Reactome, the GTPase cycle was highly related, and the overall trend of the pathway was consistent, see Fig. 9In the exon region, GSEA confirmed significant enrichment in GO related to immune response (such as regulation of B cell activation); and related to innate immunity and viral infection in Reactome ( Fig.10 ).

[0072] (3) Death vs. Live

[0073] In the promoter region, GSEA confirmed that this group was significantly enriched in the GO granulocyte chemotaxis pathway; it was found to be associated with the KEGG neutrophil degranulation pathway, see Fig.11 In the exon region, it was confirmed that this grouping was related to the membrane of the endoplasmic reticulum-Golgi intermediate compartment in GO; it was found to be related to multiple MAPK signal transduction pathways and immune cell receptor signaling pathways in Reactome ( Fig.12 ).

[0074] 3.3 Sepsis diagnosis and mortality prediction based on plasma cfDNA host methylation

[0075] (1) Diagnostic efficacy of sepsis and non-sepsis based on plasma cfDNA host methylation

[0076] Through forward feature selection, RF and LR and other machine learning, we get chr5: 176596401-176596900 ( GPRIN1 ), chr20: 63537201-63537700 ( PTK6 ), chr7: 67292401-67292900 ( PMS2P4 ), chr22: 42071201-42071700 ( havana ), chr17: 74612201-74612700 ( CD300E ), chr22: 49723801-49724300 ( havana ), chr3: 48566001-48566500 ( COL7A1 ), chr12: 6772401-6772900 ( LAG3 ), chr10: 42333001-42333500 (havana), chr9: 15015601-15016100 (havana), chrX: 149525801-149526300 ( havana ), chr8: 100474801-100475300 ( havana ), chr9: 13223601-13224100 ( DXF ), chr12: 96786801-96787300 ( CFAP54), chr1: 207908801-207909300 ( havana ) and other optimal DMRs as classifiers. The aforementioned 186 samples (finally included in the analysis, the same below) were divided into training set and validation set, training set: validation set = 7 / 3. The aforementioned classifier was used to classify sepsis and non-sepsis, and the results showed that the sensitivity was 85.91%, the specificity was 92.86%, the area under the curve (AUC) of the training set was 0.92 (95% CI: 0.899-0.944), and the area under the curve of the validation set was 0.90 ( Fig.13 ).

[0077] (2) Efficacy of classifying the abdominal and pulmonary infection sites of sepsis based on plasma cfDNA host methylation

[0078] The present invention obtains chr11:65492201-65492700 ( havana_tagene ), chr13: 25095601-25096100 ( PABPC3 ), chr17: 10683601-10684100 ( havana ), chr17: 10683801-10684300 ( havana ), chr17: 81862801-81863300 ( havana ), chr1: 203172001-203172500 ( MYBPH ), chr4: 23516001-23516500 ( RFPL4AP3 ), chr8: 144052201-144052700 ( OPLAH ) and other optimal DMRs as classifiers. The aforementioned 186 samples were divided into training set and validation set, with training set: validation set = 7 / 3. The aforementioned classifier was used to classify the abdominal and pulmonary infection sites of sepsis, with a sensitivity of 91.50%, a specificity of 74.81%, an area under the curve (AUC) of 0.902 (95% CI: 0.881-0.923) for the training set, and an area under the curve of 0.833 ( Fig.14 ).

[0079] (3) Application of plasma cfDNA host methylation in predicting patient mortality

[0080] Through forward feature selection, RF and LR and other machine learning, we get chr2: 127566601-127567100 ( MYO7B ), chr12: 10052601-10053100 ( CLEC9A ), chr1: 25240201-25240700 ( havana ), chr7: 149866801-149867300 ( ZNF862 ), chr17: 44364001-44364500 ( FAM171A2 )、chr14:39113601-39114100( GEMIN2 ), chrX: 70289401-70289900 ( KIF4A ), chr22: 29882601-29883100 ( MTMR3 ) and other optimal DMRs as classifiers. The above 186 samples were divided into training set and validation set, training set: validation set = 7 / 3. The above classifier was used to classify patient mortality, with a sensitivity of 92.50%, a specificity of 80.68%, an area under the curve (AUC) of 0.905 (95% CI: 0.886-0.923) for the training set, and an area under the curve of 0.819 ( Fig.15 ).

[0081] 3.4 Sepsis diagnosis and mortality prediction based on microbial metagenomics

[0082] The present invention further takes the relative abundance of various bacteria, fungi and bacteriophages as variables, and constructs an optimal classifier through machine learning methods such as forward feature selection, RF and LR.

[0083] (1) Diagnostic efficacy of bacterial and fungal metagenomes in sepsis and non-sepsis patients

[0084] Obtained through machine learning such as forward feature selection, RF and LR Enterobacteriaceae-endosymbiont- of-Donacia-fulgens, Citrobacter-werkmanii, Paenibacillus-psychroresistens, Encephalitozoon-hellem, Flexistipes-sinusarabici, Neurospora-crassa, Tetrapisispora-blattae, Mycoplasma-hyorhinis, Salmonella-enterica, Pseudomonas- tolaasii, Orientia-tsutsugamushi, Bacillus-safensis, Streptococcus-oralis, Escherichia-fergusonii The relative abundance of the best bacterial and fungal species was used as the classifier. The 186 samples were divided into a training set and a validation set, with a training set: validation set = 7 / 3. The above classifier was used to classify sepsis and non-sepsis patients, with a sensitivity of 87.85%, a specificity of 80.00%, an area under the curve (AUC) of 0.913 (95% CI: 0.891-0.935) for the training set, and an area under the curve of 0.898 ( Figure 16 ).

[0085] (2) Efficacy of classifying patients with sepsis based on the site of infection in the abdominal cavity and lungs based on bacterial and fungal metagenomes

[0086] Obtained through machine learning such as forward feature selection, RF and LR Brevundimonas-sp.-Bb-A, Bradyrhizobium-sp.-PSBB068, Fusobacterium-varium, Bacillus-pseudomycoides, Acinetobacter-haemolyticus, Enterobacter-hormaecheiThe relative abundance of the best bacterial and fungal species was used as the classifier. The 186 samples were divided into a training set and a validation set, with a training set: validation set = 7 / 3. The above classifier was used to classify the abdominal and pulmonary infection sites of sepsis, with a sensitivity of 78.00%, a specificity of 84.61%, an area under the curve (AUC) of 0.875 (95% CI: 0.848-0.901) for the training set, and an area under the curve of 0.874 ( Figure 17 ).

[0087] (3) Application of bacterial and fungal metagenomes in predicting patient mortality

[0088] Obtained through machine learning such as forward feature selection, RF and LR Lutibacter-profundi, Winogradskyella-forsetii, Lachnoclostridium-phytofermentans The relative abundance of the best bacterial and fungal species was used as a classifier. The 186 samples were divided into a training set and a validation set, with a training set: validation set = 7 / 3. The above classifier was used to classify patient mortality, with a sensitivity of 84.00%, a specificity of 71.51%, an area under the curve (AUC) of 0.804 (95% CI: 0.775-0.833) for the training set, and an area under the curve of 0.793 (95% CI: 0.794-0.833) for the validation set. Figure 18 ).

[0089] (4) Diagnostic efficacy of phages and their host bacterial metagenomes in sepsis and non-sepsis patients

[0090] Obtained through machine learning such as forward feature selection, RF and LR Escherichia, Enterobacter, Escherichia-virus-HK630, Escherichia-virus-Lambda, Escherichia-phage-YDC107-1, Vibrio, Campylobacter-phage-A11a, Klebsiella-phage-020009, Aeromonas, Phage-P1 The relative abundance of the best phage and its host bacteria was used as a classifier. The 186 samples were divided into a training set and a validation set, with a training set: validation set = 7 / 3. The above classifier was used to classify sepsis and non-sepsis patients, with a sensitivity of 82.37%, a specificity of 85.71%, an area under the curve (AUC) of 0.912 (95% CI: 0.89-0.934) for the training set, and an area under the curve of 0.87 (95% CI: 0.89-0.934) for the validation set. Figure 19 ).

[0091] (5) Classification of patients with sepsis based on the location of abdominal and pulmonary infection by bacteriophages and their host bacteria

[0092] Obtained through machine learning such as forward feature selection, RF and LR Enterobacteria-virus-mEp213, Uncultured-Phage, Klebsiella, Stx2a-converting-phage-Stx2-KIH15-140, Bacillus- phage-Maceta, Acinetobacter-phage-TAC1, Escherichia-virus-HK022, Synechococcus, Escherichia-virus-DE3, Klebsiella-phage-Mineola, Klebsiella-phage-KP179The relative abundance of the best phage and its host bacteria was used as a classifier. The 186 samples were divided into a training set and a validation set, with a training set: validation set = 7 / 3. The above classifier was used to classify the abdominal and pulmonary infection sites of sepsis, with a sensitivity of 73.00%, a specificity of 79.81%, an area under the curve (AUC) of 0.837 (95% CI: 0.806-0.869) for the training set, and an area under the curve of 0.821 (95% CI: 0.821-0.869) for the validation set. Figure 20 ).

[0093] (6) Application of bacteriophages and their host bacteria in predicting patient mortality

[0094] Obtained through machine learning such as forward feature selection, RF and LR Klebsiella-phage-vB-KleS-HSE3, Mycobacterium-phage-Hail, Mycobacterium-phage-Xula, Tenacibaculum-virus-PTm1, Streptococcus-phage-142, Komagataeibacter-phage-phiKM1 The relative abundance of the best phage and its host bacteria was used as a classifier. The 186 samples were divided into a training set and a validation set, with a training set: validation set = 7 / 3. The above classifier was used to classify patient mortality, with a sensitivity of 87.00%, a specificity of 61.37%, an area under the curve (AUC) of 0.715 (95% CI: 0.676-0.754) for the training set, and an area under the curve of 0.651 (95% CI: 0.651-0.654) for the validation set. Figure 21 ).

[0095] 3.5 Improving sepsis diagnosis and patient mortality prediction capabilities based on MAMMOT

[0096] (1) Diagnostic efficacy of sepsis and non-sepsis based on MAMMOT

[0097] The present invention further obtains the following by forward feature selection, RF and LR machine learning. Escherichia-virus- Lambda, Escherichia-virus-HK630, Citrobacter-werkmanii, chr7:67292401-6729290 0 ( PMS2P4 ) 、 chrX:149525801-14952630 0 ( IDSP1 ) , Streptococcus-oralis, chr20:63537201-63537700 ( PTK6 ) , Enterobacteriaceae-endosymbiont-of-Donacia-fulgens, chr10:42333001-42333500 ( LOC441666 ) , Paenibacillus-psychroresistens, chr3:48566001-48566500 ( COL7A1 ), chr12: 6772401-6772900 ( LAG3 )、 Campylobacter-phage-A11a, chr9:13223601-13224100 ( MPDZ) and other optimal DMSs and relative abundance of microorganisms as classifiers. The above 186 samples were divided into training set and validation set, training set: validation set = 7 / 3. The above classifier was used to classify sepsis and non-sepsis patients, with a sensitivity of 86.77%, a specificity of 88.21%, an area under the curve (AUC) of 0.954 (95% CI: 0.944-0.965) for the training set, and an area under the curve of 0.948 ( Figure 22 ).

[0098] Specifically, people who meet any of the following criteria have a higher likelihood of developing sepsis: (i) For chr7:67292401-67292900, the methylation level is lower; (ii) For chrX:149525801-149526300, the methylation level is lower; (iii) For chr20:63537201-63537700, the methylation level is even lower; (iv) For chr10:42333001-42333500, the methylation level was lower; (v) For chr3:48566001-48566500, the methylation level is even lower; (vi) For chr12:6772401-6772900, the methylation level is even lower; (vii) For chr9:13223601-13224100, the methylation level is even lower; (viii) For Citrobacter werkmanii The abundance is lower. (ix) For Streptococcus oralis The abundance is lower. (x) for Paenibacillus psychroresistens The abundance is lower. (xi) For Enterobacteriaceae endosymbiont of Donacia fulgens In terms of abundance, it is higher; (xii) For Escherichia virus Lambda The abundance is lower. (xiii) For Escherichia virus HK630 The abundance is lower. (xiv) For Campylobacter phage A11a The abundance is lower.

[0099] (2) Efficacy of MAMMOT-based classification of abdominal and pulmonary infection sites in sepsis

[0100] get Bradyrhizobium-sp.-PSBB068, Uncultured-Phage (Phages that have been reported but not yet in pure culture) 、chr11-65492201-65492700( LINC02736 ), chr8: 144052201-144052700 ( OPLAH ), chr13: 25095601-25096100 ( PABPC3 ), chr17: 81862801-81863300 ( LOC124904086 ), chr17: 10683601-10684100 ( SCO1 ), chr17: 10683801-10684300 ( SCO1 ) and other optimal DMSs and relative abundance of microorganisms as classifiers. The aforementioned 186 samples were divided into training set and validation set, training set: validation set = 7 / 3. The aforementioned classifier was used to classify the abdominal and pulmonary infection sites of sepsis, with a sensitivity of 91.00%, a specificity of 80.39%, an area under the curve (AUC) of 0.939 (95% CI: 0.924-0.955) for the training set, and an area under the curve of 0.913 (95% CI: 0.914-0.916) for the validation set. Figure 23 ).

[0101] The infection site is more likely to be in the abdomen than in the lungs if any of the following conditions are met: (i) For chr11-65492201-65492700, the methylation level is higher; (ii) For chr8:144052201-144052700, the methylation level is higher; (iii) For chr13:25095601-25096100, the methylation level is even lower; (iv) For chr17:81862801-81863300, the methylation level is even lower; (v) For chr17:10683601-10684100, the methylation level is higher; (vi) For chr17:10683801-10684300, the methylation level is higher; (vii) For Bradyrhizobium-sp.-PSBB068 The abundance is lower. (viii) For Uncultured-Phage The abundance is higher.

[0102] (3) Application of MAMMOT in predicting patient mortality

[0103] Get chr12:10052601-10053100 ( CLEC9A) , chr2: 127566601-127567100 (MYO7B ), chr1: 25240201-25240700 ( LOC124903881 ), chr7: 149866801-149867300 ( ZNF862 ) 、 Mycobacterium-phage-Hail, Winogradskyella-forsetii, chr17:44364001-44364500 ( FAM171A2 ) , Lachnoclostridium-phytofermentans, Streptococcus-phage-142, chr14:3911360139114100 ( GEMIN2 ), chr22: 29882601-29883100 ( MTMR3 ) , Mycobacterium-phage- Xula, Tenacibaculum-virus-PTm1, Komagataeibacter-phage-phiKM1 The best DMSs and relative abundance of microorganisms were used as classifiers. The above 186 samples were divided into training set and validation set, training set: validation set = 7 / 3. The above classifier was used to classify patient mortality, with a sensitivity of 83.50%, a specificity of 85.34%, an area under the curve (AUC) of 0.921 (95% CI: 0.904-0.938) for the training set, and an area under the curve of 0.915 (95% CI: 0.904-0.938) for the validation set. Figure 24 ).

[0104] The mortality rate is higher if any of the following conditions are met: (i) For chr12:10052601-10053100, the methylation level is higher; (ii) For chr2:127566601-127567100, the methylation level is even lower; (iii) For chr1:25240201-25240700, the methylation level is higher; (iv) For chr7:149866801-149867300, the methylation level was higher; (v) For chr17:44364001-44364500, the methylation level is higher; (vi) For chr14:39113601-39114100, the methylation level was higher; (vii) For chr22: 29882601-29883100, the methylation level was higher; (viii) For Winogradskyella forsetii The abundance is lower. (ix) For Lachnoclostridium phytofermentans The abundance is lower. (x) for Mycobacterium phage Hail In terms of abundance, it is higher; (xi) For Streptococcus phage 142 In terms of abundance, it is higher; (xii) For Mycobacterium phage Xula The abundance is lower. (xiii) For Tenacibaculum virus PTm1 The abundance is lower. (xiv) For Komagataeibacter phage phiKM1 The abundance is higher.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. Application of biological factors in the preparation of a kit for sepsis identification; The biological factors include differentially methylated regions and the microbiome; The differentially methylated regions include: chr7: 67292401-67292900, chrX: 149525801-149526300, chr20: 63537201-63537700, chr10: 42333001-42333500, chr3: 48566001-48566500, ch r12: 6772401-6772900, chr9: 13223601-13224100, chr11-65492201-65492700, chr8: 144052201-144052700, chr13: 25095601-25096100, chr17 :81862801-81863300、chr17:10683601-10684100、chr17:10683801-10684300、chr12:10052601-10053100、chr2:127566601-127567100、chr1:25240201-25240700、chr7:149866801-149867300、chr17:44364001-44364500、chr14:3911360139114100 or chr22:29882601-29883100; The microbiome includes: bacteria and / or bacteriophages; The bacteria include: Citrobacter werkmanii, Streptococcus oralis, Paenibacillus psychroresistens , Enterobacteriaceae endosymbiont of Donacia fulgens, Bradyrhizobium-sp.-PSBB068, Winogradskyella forsetii or Lachnoclostridium Phytofermentans One or more of; The bacteriophage comprises: Escherichia virus Lambda, Escherichia virus HK630, Campylobacter phage A11a、Uncultured-Phage、 One or more of Mycobacterium phage Hail, Streptococcus phage 142, Mycobacterium phage Xula, Tenacibaculum virus PTm1 or Komagataeibacter phagephiKM1.

2. The use according to claim 1, characterized in that: The sepsis identification includes: i) Identify whether it is sepsis; ii) Identify whether the site of sepsis infection is the abdomen or the lungs; iii) Identify mortality in patients with sepsis.

3. The use according to claim 2, characterized in that: The differentially methylated regions for application i) include one or more of: chr7: 67292401-67292900, chrX: 149525801-149526300, chr20: 63537201-63537700, chr10: 42333001-42333500, chr3: 48566001-48566500, chr12: 6772401-6772900, or chr9: 13223601-13224100; Bacteria used for application i) include: Citrobacter werkmanii, Streptococcus oralis, Paenibacillus psychroresistens or Enterobacteriaceae endosymbiont of Donacia fulgens One or more of; Bacteriophages used for application i) include: Escherichia virus Lambda、Escherichia virus HK630 or Campylobacter phage A11a One or more of .

4. The use according to claim 3, characterized in that: People who meet any of the following criteria have a higher risk of sepsis: (1) For chr7: 67292401-67292900, the methylation level is lower; (2) For chrX:149525801-149526300, the methylation level is lower; (3) For chr20:63537201-63537700, the methylation level is even lower; (4) For chr10:42333001-42333500, the methylation level is even lower; (5) For chr3:48566001-48566500, the methylation level is even lower; (6) For chr12: 6772401-6772900, the methylation level is even lower; (7) For chr9:13223601-13224100, the methylation level is even lower; (8) For Citrobacter werkmanii The relative abundance is lower. (9) For Streptococcus oralis The relative abundance is lower. (10) For Paenibacillus psychroresistens The relative abundance is lower. (11) For Enterobacteriaceae endosymbiont of Donacia fulgens The relative abundance is higher. (12) For Escherichia virus Lambda The relative abundance is lower. (13) For Escherichia virus HK630 The relative abundance is lower. (14) For Campylobacter phage A11a The relative abundance is lower.

5. The use according to claim 2, characterized in that: The differentially methylated regions for application ii) include one or more of: chr11-65492201-65492700, chr8: 144052201-144052700, chr13: 25095601-25096100, chr17: 81862801-81863300, chr17: 10683601-10684100 or chr17: 10683801-10684300; The microbial group used for application ii) includes: bacteria and / or bacteriophages; The bacteria are Bradyrhizobium-sp.-PSBB068 The bacteriophage is Uncultured-Phage .

6. The use according to claim 5, characterized in that: The infection site is more likely to be in the abdomen than in the lungs if any of the following conditions are met: (1) For chr11-65492201-65492700, the methylation level is higher; (2) For chr8:144052201-144052700, the methylation level is higher; (3) For chr13:25095601-25096100, the methylation level is even lower; (4) For chr17:81862801-81863300, the methylation level is even lower; (5) For chr17:10683601-10684100, the methylation level is higher; (6) For chr17:10683801-10684300, the methylation level is higher; (7) For Bradyrhizobium-sp.-PSBB068 The relative abundance is lower. (8) For Uncultured-Phage The relative abundance is higher.

7. The use according to claim 2, characterized in that: The differentially methylated regions for application iii) include one or more of: chr12: 10052601-10053100, chr2: 127566601-127567100, chr1: 25240201-25240700, chr7: 149866801-149867300, chr17: 44364001-44364500, chr14: 39113601-39114100, or chr22: 29882601-29883100; Bacteria used for application iii) include: Winogradskyella forsetii or Lachnoclostridium Phytofermentans One or more of; Bacteriophages used in application iii) include: Mycobacterium phage Hail , Streptococcus phage 142 , Mycobacterium phage Xula , Tenacibaculum virus PTm1 or Komagataeibacter phage phiKM1 One or more of .

8. The use according to claim 7, characterized in that: The mortality rate is higher if any of the following conditions are met: (1) For chr12:10052601-10053100, the methylation level is higher; (2) For chr2:127566601-127567100, the methylation level is even lower; (3) For chr1:25240201-25240700, the methylation level is higher; (4) For chr7:149866801-149867300, the methylation level is higher; (5) For chr17:44364001-44364500, the methylation level is higher; (6) For chr14:39113601-39114100, the methylation level is higher; (7) For chr22: 29882601-29883100, the methylation level is higher; (8) For Winogradskyella forsetii The relative abundance is lower. (9) For Lachnoclostridium phytofermentans The relative abundance is lower. (10) For Mycobacterium phage Hail The relative abundance is higher. (11) For Streptococcus phage 142 The relative abundance is higher. (12) For Mycobacterium phage Xula The relative abundance is lower. (13) For Tenacibaculum virus PTm1 The relative abundance is lower. (14) For Komagataeibacter phage phiKM1 The relative abundance is higher.

9. A biological factor combination, characterized in that: including differentially methylated regions and the microbiome; The differentially methylated regions include: chr7: 67292401-67292900, chrX: 149525801-149526300, chr20: 63537201-63537700, chr10: 42333001-42333500, chr3: 48566001-48566500, chr12: 6772401-6772900, chr9: 13223601-13224100, chr11-65492201-65492700, chr8: 144052201-144052700, chr13: 25095601-25096100 、chr17:81862801-81863300、chr17:10683601-10684100、chr17:10683801-10684300、chr12:10052601-10053100、chr2:127566601-127567100、chr1:25240201-25240700、chr7:149866801-149867300、chr17:44364001-44364500、chr14:3911360139114100 or chr22:29882601-29883100; The microbiome includes: bacteria and / or bacteriophages; The bacteria include: Citrobacter werkmanii, Streptococcus oralis, Paenibacillus psychroresistens , Enterobacteriaceae endosymbiont of Donacia fulgens, Bradyrhizobium - sp. - PSBB068, Winogradskyella forsetii, Lachnoclostridium phytofermentans or Komagataeibacter One or more of; The phages include: Escherichia virus Lambda, Escherichia virus HK630, Campylobacter phage A11a, Uncultured - Phage, One or more of Mycobacterium phage Hail, Streptococcus phage 142, Mycobacterium phage Xula, Tenacibaculum virus PTm1 or Komagataeibacter phagephiKM1.

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