CN111505288B

CN111505288B - Novel depression biomarker and application thereof

Info

Publication number: CN111505288B
Application number: CN202010414711.9A
Authority: CN
Inventors: 王刚; 谢鹏; 杨健; 郑鹏; 孙作厘
Original assignee: Beijing Anding Hospital; Chongqing Medical University
Current assignee: Beijing Anding Hospital; Chongqing Medical University
Priority date: 2020-05-15
Filing date: 2020-05-15
Publication date: 2022-03-01
Anticipated expiration: 2040-05-15
Also published as: CN111505288A

Abstract

The present disclosure provides a depression intestinal biomarker selected from one or more of intestinal microorganisms and fecal metabolites, and uses thereof. The present disclosure also provides a kit for diagnosing depression, and a screening method for screening biomarkers for depression.

Description

Novel depression biomarker and application thereof

Technical Field

The disclosure belongs to the technical field of biology, and particularly relates to application of intestinal microorganisms, feces metabolites and/or combinations thereof in depression diagnosis, and a method for screening intestinal biomarkers of depression patients and/or intestinal biomarker groups of depression patients.

Background

Depression is a common mental disorder in which genes interact with the environment. The major clinical features are marked and persistent mood depression and decreased interest, and the major ones are severe and even suicide. Epidemiological data indicate that the prevalence of global depression is about 4.4%, for a total of 3.22 million depressed patients. In china, depression has become second to heart disease only. Therefore, intensive studies on the prevention and treatment of depression and the pathogenesis of depression are urgently needed. The research on the pathogenesis of the past depression mainly focuses on various abnormal theories in the brain. These abnormal theories in brain provide important theoretical basis for revealing the pathophysiological mechanism of depression, but most of them can only explain some reasons. Therefore, the research on the pathogenesis of the depression is not limited to enriching the existing theories, and the research thought needs to be expanded to discover new potential mechanisms.

There are a large number of symbiotic microorganisms in human intestinal tract, and the total amount of gene information carried by the symbiotic microorganisms is 50-100 times of that of human self genome information, namely "Gut microbiome", also known as "second genome" of human. The intestinal microbiome is the largest and most direct external environment of the human body and plays an indispensable role in maintaining the health of the human body. From the proposal of the National Institute of Health (NIH) in 2007, the human Microbiome program was launched by the U.S. government repudiation of 1.21 hundred million dollars in 2016, and the U.S. led to the study of the gut Microbiome in nutrition disorders, metabolic disorders, and complex diseases (such as obesity, diabetes, inflammatory bowel disease, and tumors, etc.).

Because of the blood brain barrier between the central nervous system and the periphery, there is a debate on whether early gut microbiome affects brain function and disease, resulting in a lag in its study with other systemic diseases. In 2012, Cryan, J.F written text states that the gut microbiome can adversely affect brain function and behavior, i.e., "microbiota-gut-brain axis", via metabolites of microbial origin, cytokines and immune, vagal pathways, etc. The proposal of the concept not only provides a brand-new entry point for enriching and developing the traditional brain-intestine-microorganism axis, but also discloses the pathogenesis of mental diseases and screens new diagnosis and treatment targets. The clinical value is that once key intestinal microorganisms (such as intestinal bacterial strains, viruses and fungi) causing mental diseases are locked, new early-stage and targeted diagnosis and treatment strategies can be developed aiming at the microorganisms and effector molecules, and the method is expected to become a watershed for preventing and treating the mental diseases.

Disclosure of Invention

The technical problem to be solved by the present disclosure is how to provide a biomarker for depression to predict the onset and development trend of depression, and to apply the biomarker to the preparation and application of a kit for disease pathological typing.

In order to solve the above technical problems, it is necessary to screen biomarkers related to depression, thereby helping to reveal the underlying pathophysiological mechanism and develop early and targeted diagnosis and treatment methods.

The present disclosure demonstrates a novel biomarker for the diagnosis of depression and the manner of application of this marker. The present disclosure discovers a series of specific microorganisms (including bacteria and viruses) and metabolites in the stools of depression patients and normal people by performing high-throughput (whole genome) metagenomic sequencing and non-targeted metabonomics detection on the stools of depression patients and healthy controls, and verifies that the novel depression biomarker has a considerable potential for differential diagnosis of depression by means of a random forest algorithm.

Based on the research findings, the present disclosure provides the following technical solutions:

the present disclosure provides a intestinal biomarker for depression, the intestinal biomarker comprising intestinal microorganisms and/or fecal metabolites.

Preferably, the gut microbes comprise gut bacteria and/or gut phages.

Preferably, the intestinal biomarker comprises one or more of intestinal bacteria, intestinal phages, fecal metabolites, more preferably, the intestinal biomarker comprises intestinal bacteria, intestinal phages and fecal metabolites;

preferably, the enteric bacteria belong to one or more of the families of Lachnospiraceae (Lachnospiraceae), Clostridiaceae (Clostridiaceae), Bacteroidaceae (Bacteroidaceae), bifidobacterium (bifidobacterium), Veillonellaceae (Veillonellaceae), Eubacteriaceae (eubacteraceae), oncobacteriaceae (Ruminococcaceae), Bacteroidaceae (Bacteroidaceae), Enterobacteriaceae (Enterobacteriaceae), aminocaceae (Enterobacteriaceae), aminococculaceae (acidaminococculaceae), oscillariaceae (Oscillospiraceae), Enterobacteriaceae (Enterobacteriaceae), porphyraceae (porphyrinomonas);

preferably, the enteric bacteria belong to one or more of Blautia, Clostridium (Clostridium), Bacteroides (Bacteroides), Bifidobacterium (Bifidobacterium), Veillonella (Veillonella), Eubacterium (Eubacterium), faecal bacteria (Faecalibacterium), Coprococcus (Coprococcus), anaerobe (anaerobiostatis), unclassified _ o _ Bacteroides, adlercutzia, scleritis (unclassified _ p _ firmutes), bacillus (unclassified _ c _ bacillus), Ruminococcus (Ruminococcus), Dorea, Enterococcus (Enterococcus), bacteriobacterium, Faecalibacterium, subperiogranulum, oscillinbacter, Citrobacter (Citrobacter), Klebsiella (Klebsiella), Parabacteroides (Parabacteroides); preferably, the enteric bacteria are selected from the group consisting of Blattia _ OBeum, Clostridium CAG:217(Clostridium _ sp. _ CAG:217), Bacteroides tympani (bacteria _ thetaiotaomicron), Bifidobacterium longum (Bifidobacterium _ longum), Vellonella _ sp. _ CAG:933, Eubacterium CAG:202(Eubacterium _ sp. _ CAG:202), Escherichia coli CAG:74(Faecalibacterium _ sp. _ CAG:74), enterococcus veratus (Coprococcus _ eutacterius), anaerobe (anaerobe _ harderius), unclassified _ o _ Bacteroides, enterococcus faecalis (coprecium), enterococcus _ archae), enterococcus faecalis _ o _ Bacteroides, enterococcus faecalis (coprecium _ coprecium), Bacillus faecalis _ coprecilomyces _ coprecidum, Clostridium _ faecalis _ 120 (bacillus _ sp.: 120, Clostridium _ faecalis _ 12, Bacillus fragilis _ sp.: 39, Bacillus _ fungal wall, Bacillus _ fungal strain AA _ 156, Bacillus subtilis SP.156, Bacillus subtilis, Clostridium _ 23, Bacillus fragilis _ G.83, Bacillus _ fungal strain 3, Bacillus _ fungal strain No. 7, Bacillus _ fungal strain G.23, Bacillus _ fungal strain G.sp.: 39, Bacillus _ fungal strain No. 23, Bacillus _ fungal strain G.sp.: 39, Bacillus _ fungal strain, Bacillus sp.: 39, Bacillus _ fungal strain G.sp.: 39, Bacillus _ fungal strain No. 4, Bacillus sp.: 8, Bacillus _ fungal strain, Bacillus sp.: 8. coli strain, Bacillus sp.: 8. coli, Bacillus sp.: C.c, Bacillus sp.: fungal strain, Bacillus sp.: 8. coli, Bacillus sp.: fungal strain, Bacillus sp.: 8. coli, Bacillus sp.: Bacillus sp.sp.sp.: Bacillus sp.sp.: Bacillus sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp., unclassified _ g _ Bifidobacterium, Eubacterium (Eubacterium _ villi), Enterococcus faecalis (Enterococcus _ faecalis), Phascolatobacterium _ sp.CAG: 207, Eubacterium CAG:12 (Eubacterium _ villi _ CAG:12), Bacteroides _ Masilisensis, Bacteroides _ dorferi (Bacteroides _ dorei), Blautia _ wexlerae, Bacillus faecalis (Faecalixensis), Subdellus _ var. vachelli, Blautia _ Marseille-P2398, Bacillus austenitic ER4 (Oscillatobacter _ sp.E. 4), Citrobacter (Citrobacter _ fusobacteri), Bacteroides (Bacillus vulus _ sp.sp.E.P 2398), Clostridium sp.faecalis (Clostridium sp.146), Clostridium sp.E.coli (C.146), Clostridium sp.coli (C.146), Clostridium sp.sp.E.E.E.146, Clostridium sp.coli (C.146, Clostridium sp.C.146, Clostridium sp.E.G.146, Clostridium sp.coli (C.146, Clostridium sp.coli G.coli, Clostridium sp.coli (C.146), Clostridium sp.coli, Clostridium sp.sp.coli, Clostridium sp.coli (C.sp.146, Clostridium sp.coli) C.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.146, Clostridium sp.sp.sp.sp.sp.sp.146, Clostridium sp.sp.sp.sp.E.E.sp.sp.146, Clostridium sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.146, Clostridium sp.sp.sp.sp.sp.sp.sp.sp.146, Clostridium sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.146, Clostridium sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.146, Clostridium sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.D.146, Clostridium sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.146, Clostridium sp.sp.146, Clostridium sp.sp.sp.sp.sp.146, Clostridium sp.sp.sp.sp.sp.sp.sp.sp.sp.146, Clostridium sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp, One or more of Geobacillus durans CAG:41(Firmicutes _ bacteria _ CAG:41), Blautia _ sp.CAG: 237, and Bacteroides _ eggerthii strains;

more preferably, the enteric bacteria are preferably selected from one or more of Klebsiella (Klebsiella) bacteria, eubacteria CAG:146(Eubacterium _ sp. _ CAG:146) bacteria;

preferably, the bacteriophage belongs to a bacteriophage of one or more of the virus families of the long tail virus (sipoviridae), short tail virus (Podoviridae);

preferably, the gut phage is selected from one or more of the bacteriophages Clostridium phi8074-B1(Clostridium _ phage _ phi8074-B1), Escherichia coli phage (Escherichia _ phage _ ECBP5), Klebsiella phage (Klebsiella _ phage _ vB _ KpnP _ SU 552A);

preferably, the fecal metabolites are Pipecolic Acid (Pipecolic Acid), Leucine (Leucine), Phosphate (Phosphonate), Erythronic Acid Lactone (Erythronic Acid latex), Triacetin (Triacetin), Homoserine (L-Homoseriine), aminoethyl Phosphate (Cilitatine), Norvaline (Norvaline), Thymine (Thymine), Hexose (Z Hexose), Ibuprofen (Ibutrofen), N-Acetylornithine (N-Acetylornithine), Proline (Proline), Altrose (Altrose), sulfamic Acid (Amidosylfonic Acid), Edetic Acid (Edetic Acid), 2-Dodecyl-1, 4-dihydroxybenzene (2-Dodecacyl-1, 4-dihydroxy-Benyne), Quinolinic Acid (Quinnolic Acid), Beta-mannitol glyceride (Beta-cysteine), cysteine (Phosphonate (O-phospho), phospho-O (Phosphonate), phospho (Phosphorine), norphine (Cithosine), Norvaline (Norvaline), Norvaline (Thymine), norgestimatine (Thymine), norgestimatinib (Evone), and Glycine (Beta-1, 4-dihydroxyl) and mixtures thereof, Oxyproline (Oxoproline), Sebacic Acid (Sebacic Acid), Isocitric Acid (Isocitric Acid), Palmitic Acid (Palmitic Acid), Glutathione (Glutathione), D-Fructose (D-Fructtose), 2-piperidinecarbonitrile (2-Piperidinonitrile), Alpha-D-Glucose (Alpha-D-Glutose), Trans-4-Hydroxy-L-Proline (Trans-4-Hydroxy-L-Proline), Gamma-Aminobutyric Acid (Gamma-Aminobutyric Acid), bisphenol A (bisphenol A), Inosine-5 '-Acid (Inosine-5' -Monophosphate), 4-Methylbenzenesulfonamide (4-Methylphenylzeylsulfonamide), Trans-4-Hydroxyproline (Trann-4-Hydroxyproline), N- {2-hydroxyethyl } tetradecane 1-tetradecane } 1-2-tetradecyl-1-hydroxyethyl } amine, Tryptophan (Tryptophol), Terephthalic Acid (Terephthalic Acid), Homovanillic Acid (Homovanillic Acid), Biphenyl (biphenol), 4-Methyl-5-thiazoleethane (4-Methyl-5-Thiazoleethanol), Itaconic Acid (Itaconic Acid), Arbutin (Arbutin), Adenosine 5-Monophosphate (Adenosine-5-Monophosphate), 2-Indolecarboxylic Acid (2-Indolecarboxylic Acid), Hydrocinnamic Acid (Hydrocinnamic Acid), 2'-Biphenyldiol (2,2' -Biphenyldiol), 1,3, 5-benzenetriol (1,3, 5-benzotriol) 5,7-Dimethyl [1,2,4] triazolo [1,5-a ] pyrimidin-2-diamine (5, 7-monomethyldiol [1,2,4] triazolo [1,5-a ] pyrimidine-2-diamine (5, 7-triazine [1,2,4] pyri-2-diamine [ 5, 5-a ] pyri-2-diamine (5, 7-methy [1,2,4] pyri [1, 5-thiazol [ 5-a ] pyri-2-diol ] pyri-2-diamine (5, 7-Methyl [1, 4] pyri-5-a ] pyri-2-D, 5-D, 4-a ] pyri-d, 2-D, 2-di-D, 2-a, 2-di (5, 7-one, 4-one, 4, 2,4, 2,4, 2, 1,2, one or more of Benzylamine (Benzylamine);

preferably, the fecal metabolite is preferably selected from one or more of sebacic acid, 2-indolecarboxylic acid;

more preferably, the gut biomarker comprises one or more of Klebsiella (Klebsiella) bacteria, eubacteria CAG:146(Eubacterium _ sp. CAG:146) bacteria, Clostridium phage phi8074-B1(Clostridium _ phage _ phi8074-B1), Escherichia coli phage ECBP5(Escherichia _ phage _ ECBP5), sebacic acid, 2-indolecarboxylic acid,

more preferably, the gut biomarker comprises Klebsiella (Klebsiella) bacteria and Eubacterium CAG:146(Eubacterium _ sp. _ CAG:146) bacteria,

more preferably, the gut biomarker comprises Clostridium phage phi8074-B1(Clostridium _ phage _ phi8074-B1) and Escherichia coli phage ECBP5(Escherichia _ phage _ ECBP5), more preferably, the gut biomarker comprises sebacic acid and 2-indolecarboxylic acid,

more preferably, the gut biomarkers include Klebsiella (Klebsiella) bacteria, eubacteria CAG:146(Eubacterium _ sp. CAG:146) bacteria, Clostridium phage phi8074-B1(Clostridium _ phage _ phi8074-B1), Escherichia coli phage ECBP5(Escherichia _ phage _ ECBP5), sebacic acid, and 2-indolecarboxylic acid.

In one aspect, the present disclosure also provides a kit for diagnosing depression, the kit comprising the intestinal biomarker and/or the detection reagent for the intestinal biomarker, preferably, the kit further comprises a depression diagnosis evaluation table, preferably, the depression diagnosis evaluation table comprises one or more of various american mental disease diagnosis and statistics manual, hamilton depression scale-17 item (HAMD-17); optionally, the kit is one or more of a biochemical diagnostic kit, an immunodiagnostic kit, or a molecular diagnostic kit, preferably an immunodiagnostic kit, preferably the kit is one or more of a Western blot kit, an enzyme-linked immunosorbent assay (ELISA) kit, a Radioimmunoassay (RIA) kit, a radioimmunodiffusion kit, an ouchterlony immunodiffusion kit, a rocket immunoelectrophoresis kit, an immunohistochemical staining kit, an immunoprecipitation assay kit, a complement fixation assay kit, a Fluorescence Activated Cell Sorting (FACS) kit, an aptamer chip kit, a microarray kit, or a protein chip kit.

In one aspect, the present disclosure also provides the use of the intestinal biomarker, and/or a detection reagent for the intestinal biomarker, and/or the kit, in the diagnosis of depression.

In one aspect, the present disclosure also provides a use of the intestinal biomarker in the preparation of a detection reagent for depression, or a use of the detection reagent for intestinal biomarker in the preparation of a kit for diagnosing depression.

In another aspect, the present disclosure also provides a method for screening intestinal biomarkers of a patient with depression, the method comprising the steps of:

dividing the subject into a depression patient group and a healthy control group;

detecting a level of a candidate gut biomarker in the subject, said gut biomarker being derived from gut microbes, fecal metabolites and/or combinations thereof in a fecal sample from the subject;

and judging the difference degree and/or similarity of the candidate intestinal biomarkers between the depression patients and healthy controls to obtain different intestinal biomarkers, namely the intestinal biomarkers of the depression patients.

Preferably, the detecting the level of the candidate intestinal biomarker in the subject is performed by methods such as genomic sequencing, preferably high-throughput Whole-gene metagenomic sequencing (Whole-genome shotgun metagenomics analysis), and/or non-targeted metabolomics, preferably mass spectrometric metabolomics detection, such as liquid chromatography/gas chromatography-mass spectrometry (LC/GC-MS);

preferably, the determining the difference and/or similarity of the candidate intestinal biomarker between the depressive disorder patient and the healthy control comprises a step of predicting the difference and/or similarity of the candidate intestinal biomarker between the depressive disorder patient and the healthy control by a method such as partial least squares discriminant analysis (PLS-DA) to obtain a difference candidate intestinal biomarker, and more preferably, a step of confirming the difference intestinal biomarker from the difference candidate intestinal biomarker by a method such as LEfse (linear discriminant analysis);

optionally, the screening method may further comprise the steps of:

verifying the diagnosis rate of the intestinal biomarkers of the depression patients, preferably, the diagnosis rate of the intestinal biomarkers of the depression patients is verified by an analysis method of a machine learning model, preferably a random forest analysis method.

In one aspect, the present disclosure also provides the use of the aforementioned screening method in determining intestinal biomarkers in patients with depression, or in the preparation of a reagent and/or kit for diagnosing depression.

In one aspect, the present disclosure also provides intestinal biomarkers of depression patients obtained by the aforementioned screening method, which intestinal biomarkers of depression patients are used for diagnosing depression.

The beneficial effects of this disclosure include:

the present disclosure provides a corresponding diagnosis and reagent by detecting intestinal microorganisms, fecal metabolites and/or combinations thereof as the basis for the diagnosis of depression, which has higher sensitivity and specificity for the diagnosis of depression compared with the traditional diagnosis method that can be judged by clinical experience after symptom description and duration and elimination of other diseases, and can also predict depression at the early stage of onset to achieve the purpose of early and targeted diagnosis of depression.

Drawings

In order to more clearly illustrate the technical solutions of the present disclosure, the drawings needed to be used are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.

FIG. 1A is a graph of Receiver Operating Characteristic (ROC) curves and Area under the Curve (AUC) of training and validation sets consisting of depression patients and healthy controls, with intestinal bacteria, intestinal phages, fecal metabolites as biomarkers, respectively

The discrimination efficiency of 2 intestinal bacteria (Klebsiella and eubacterium CAG:14) on the training set and the verification set samples is respectively AUC 0.89 and AUC 0.81;

the discrimination potency of 2 intestinal phages (clostridium phage phi8074-B1 and escherichia coli phage ECBP5) on the training set and validation set samples was AUC ═ 0.77 and AUC ═ 0.65, respectively;

the discrimination efficiency of 2 fecal metabolites (sebacic acid and 2-indolecarboxylic acid) on the training set and the validation set samples is 0.93 AUC and 0.83 AUC, respectively.

FIG. 1B is a graph of Receiver Operating Characteristic (ROC) and Area under the Curve (AUC) of training and validation sets consisting of depression patients and healthy controls, using intestinal bacteria, intestinal phages, fecal metabolites as combined biomarkers

The discrimination efficiency of the training set and the validation set samples by combining 2 intestinal bacteria (Klebsiella and eubacterium CAG:14), 2 intestinal phages (Clostridium phage phi8074-B1 and Escherichia coli phage ECBP5) and 2 fecal metabolites (sebacic acid and 2-indolecarboxylic acid) is AUC 0.98 and AUC 0.90 respectively.

Detailed Description

In order to make the technical solutions of the present disclosure better understood by those skilled in the art, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the present disclosure, and it is apparent that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

I. Definition of

The term "depression" also known as depressive disorder, is characterized primarily clinically by a marked and persistent depression in the mood, the main type of mood disorder.

The term "diagnosis" refers to a method of detecting and/or identifying the pathological state of a subject, identifying and/or determining the course of whether a subject has a given disease or disorder, estimating or determining the future clinical progression of a subject, following the onset of symptoms.

The term "gut microorganisms" refers to the large number of microorganisms present in the human gut that depend on the intestinal life of an animal while helping the host perform a variety of physiological and biochemical functions. The intestine is not only an important part of human digestion and absorption, but also the largest immune organ, and plays an extremely important role in maintaining normal immune defense functions. The "intestinal microorganisms" include the intestinal flora, enteroviruses such as bacteriophage, and the like.

The term "intestinal microbial metabolites", "intestinal microbial metabolites" or "fecal metabolites" refers to a plurality of metabolites produced by intestinal microbes during their metabolism.

The term "biomarker", also referred to as "biological marker", refers to a measurable indicator of a biological state of an individual. Such biomarkers may be any substance in the individual, which may be one marker or a group of markers, as long as they are related to a particular biological state (e.g., disease) of the subject being examined. Biomarkers in the present disclosure are denoted "intestinal biomarkers" because the biomarkers found to be associated with depression are all present in the intestinal tract of the subject. Biomarkers are measured and evaluated, often to examine normal biological processes, pathogenic processes, or therapeutic intervention drug responses, and are useful in many scientific fields.

The "training set", "training data set", "verification set" and "verification data set" refer to that in the machine learning model establishment process of the present disclosure, a sample can be divided into an independent training set and a verification set. The training set is used for training the model, and the verification set is used for verifying the performance of the model.

Detailed description of the preferred embodiments

The present disclosure provides intestinal biomarkers including one or more of intestinal microbiota, fecal metabolites for diagnosing depression.

The present disclosure also provides a detection reagent and/or a kit for detecting the biomarker and/or the intestinal biomarker for diagnosing depression, for detecting the biomarker and/or diagnosing depression.

In one aspect, the present disclosure provides the use of the intestinal biomarker in diagnosing depression.

In one aspect, the present disclosure provides use of a detection reagent for the intestinal biomarker in diagnosing depression.

In one aspect, the present disclosure also provides use of the intestinal biomarker in the preparation of a reagent and/or kit for the diagnostic detection of depression.

In one aspect, the present disclosure also provides a use of the detection reagent for intestinal biomarkers in preparing a kit for diagnosing depression.

In one embodiment of the present disclosure, the kit may be a biochemical diagnostic kit, an immunodiagnostic kit, or a molecular diagnostic kit, etc., and is preferably prepared as the immunodiagnostic kit.

The kit is prepared by adopting the principles or methods of immunology, microbiology, molecular biology and the like, and is used for diagnosing and detecting human diseases, investigating epidemiology and the like in vitro.

The kit may be of a type well known in the art, including, but not limited to, Western blot kits, enzyme linked immunosorbent assay (ELISA) kits, Radioimmunoassay (RIA) kits, radioimmunodiffusion kits, ouchterlony immunodiffusion kits, rocket immunoelectrophoresis kits, immunohistochemical staining kits, immunoprecipitation assays, complement fixation assays, Fluorescence Activated Cell Sorting (FACS) kits, aptamer chip kits, microarray kits, protein chip kits, and the like.

In one embodiment of the present disclosure, the kit comprises a detection reagent for the intestinal biomarker, wherein the detection reagent refers to a nucleic acid molecule, protein, compound, etc. capable of specifically determining the intestinal biomarker, including but not limited to, the nucleic acid molecule, protein, compound, etc. can specifically bind to the intestinal microorganism, the fecal metabolite, and/or the combination thereof.

In one embodiment of the present disclosure, a protein capable of specifically binding to the intestinal biomarker may be selected as the detection reagent, and in another embodiment of the present disclosure, an antibody, which may be an antibody specifically binding to a marker protein, including but not limited to a polyclonal antibody, a monoclonal antibody, a recombinant antibody, and an antigen binding fragment thereof, is preferred as the detection reagent for the intestinal biomarker, as long as it retains an antigen binding function.

In the present disclosure, a detectable marker molecule may be linked to the detection reagent of the intestinal biomarker.

In one embodiment of the present disclosure, the intestinal biomarkers of the present disclosure for diagnosing depression include intestinal microorganisms or fecal metabolites.

In one embodiment of the present disclosure, the intestinal biomarkers of the present disclosure for diagnosing depression include intestinal microorganisms and fecal metabolites.

In one embodiment of the present disclosure, the enteric microorganism comprises an enteric bacterium or an enteric phage.

In one embodiment of the present disclosure, the enteric microbes comprise enteric bacteria and enteric bacteriophages.

In a specific embodiment of the present disclosure, the intestinal biomarker comprises one of intestinal bacteria, intestinal phages, fecal metabolites.

In a specific embodiment of the present disclosure, the intestinal biomarker includes two of intestinal bacteria, intestinal phages, fecal metabolites.

In a specific embodiment of the present disclosure, the gut biomarkers include both gut bacteria, gut phages and fecal metabolites.

Preferably, the enteric bacteria belong to one or more of the families of Lachnospiraceae (Lachnospiraceae), Clostridiaceae (Clostridiaceae), Bacteroidaceae (Bacteroidaceae), bifidobacterium (bifidobacterium), Veillonellaceae (Veillonellaceae), Eubacteriaceae (eubacteraceae), oncobacteriaceae (Ruminococcaceae), Bacteroidaceae (Bacteroidaceae), Enterobacteriaceae (Enterobacteriaceae), aminocaceae (acidococcaceae), oscillariaceae (Oscillospiraceae), Enterobacteriaceae (Enterobacteriaceae), porphyraceae (porphyrinomonas); preferably, the enteric bacteria belong to one or more of Blautia, Clostridium (Clostridium), Bacteroides (Bacteroides), Bifidobacterium (Bifidobacterium), Veillonella (Veillonella), Eubacterium (Eubacterium), faecal bacteria (Faecalibacterium), Coprococcus (Coprococcus), anaerobes (anaerobiostatis), Bacteroides (unclassified _ o _ Bacteroides), adlercutzia, Firmicutes (unclassified _ p _ firmutes), bacillus (unclassified _ c _ bacillus), Ruminococcus (Ruminococcus), Dorea, Enterococcus (Enterococcus), phaseolus, faecalobacterium, Faecalibacterium, sudolilellum, oscillinbacter, Citrobacter (Clostridium), Klebsiella (Klebsiella), paracoccus (Klebsiella); preferably, the enteric bacteria are selected from the group consisting of Blattia _ OBeum, Clostridium CAG:217(Clostridium _ sp. _ CAG:217), Bacteroides tympani (bacteria _ thetaiotaomicron), Bifidobacterium longum (Bifidobacterium _ longum), Vellonella _ sp. _ CAG:933, Eubacterium CAG:202(Eubacterium _ sp. _ CAG:202), Escherichia coli CAG:74(Faecalibacterium _ sp. _ CAG:74), enterococcus veratus (Coprococcus _ Eubacterium), anaerobium (anaerobium _ hardwire), unclassified _ o _ Bacillus, enterococcus faecalis (Coprococcus), enterococcus _ atus, enterococcus _ o _ bacillus, enterococcus faecalis (Coprococcus _ bacteria), enterococcus _ faecalis _ o _ bacillus, Bacteroides _ coprinus, Bacteroides _ bacillus fragilis, Clostridium _ bacillus sp.120 (Clostridium _ 120, enterococcus _ faecalis _ sp.: 39, Escherichia _ fungal strain AA _ fungal strain, Clostridium _ fungal strain # 156, Clostridium _ fungal strain # 35, Clostridium _ bacillus sp.: 39, Clostridium _ fungal strain # CAG.23, Clostridium _ fungal strain # 3, Clostridium _ fungal strain # C.23, Clostridium _ fungal strain # 3, Clostridium _ fungal strain # C, Clostridium _ fungal strain # 3, Clostridium _ fungal strain # C, Clostridium _ fungal strain # 3, Clostridium _ fungal strain # 3, Clostridium _ fungal strain # 3, Clostridium _ fungal strain # C, Clostridium _ fungal strain # 3, Clostridium _ fungal strain # C, Clostridium _ fungal strain # 2, Clostridium _ fungal strain # C, Clostridium _ fungal strain # 2, Clostridium _ fungal strain # C, Clostridium _ fungal strain # 3, Clostridium _ fungal strain # C, Clostridium _ fungal strain # C _ fungal strain, Clostridium _ fungal strain # C, Clostridium _ fungal strain # 2, Clostridium _ fungal strain # 3, Clostridium _ fungal strain, Clostridium, Dorema _ sp. _ CAG 105, unclassified Bifidobacterium (unclassified _ g _ Bifidobacterium), Eubacterium (Eubacterium _ villii), Enterococcus faecalis (Enterococcus _ faecalis), Phascolatobacterium _ sp. _ CAG 207, Eubacterium CAG:12 (Eubacterium _ villi _ CAG:12), Bacteroides _ Malsiliensis, Bacteroides (Bacteroides _ Dorem.), Blautia (Bacteroides _ dorei), Blautia _ xlerae, Bacillus faecalis (Faecalis _ praerucibacter), Subduligramulum _ variella, Blautia _ sp.Marseille-P2398, Austenitis _ Occidum-ER 4 (Oscilobacter _ 4), Clostridium citri _ citrate (Clostridium sp.146), Clostridium sporogenes (Clostridium sp.146), Clostridium sp.coli (Clostridium sp.146), Clostridium (Clostridium 146. coli) C.510, Clostridium sporogenes (Clostridium 146. sp., One or more species of Eubacterium sp CAG (180), Geobacillus harderii CAG (41), Blautia sp CAG (237), Bacteroides eggerthii) species; more preferably, the enteric bacteria are preferably selected from one or more of Klebsiella (Klebsiella) bacteria, eubacteria CAG:146(Eubacterium _ sp. _ CAG:146) bacteria.

Preferably, the bacteriophage belongs to one or more of the families of the long tail virus (sipoviridae) and short tail virus (Podoviridae), preferably the bacteriophage is selected from one or more of the bacteriophages Clostridium 8074-B1(Clostridium _ phage _ phi8074-B1), Escherichia coli bacteriophage (Escherichia _ phage _ ECBP5), Klebsiella phage (Klebsiella _ phage _ vB _ kpn _ SU 552A).

Preferably, the fecal metabolites are Pipecolic Acid (Pipecolic Acid), Leucine (Leucine), Phosphate (Phosphonate), Erythronic Acid Lactone (Erythronic Acid latex), Triacetin (Triacetin), Homoserine (L-Homoseriine), aminoethyl Phosphate (Cilitatine), Norvaline (Norvaline), Thymine (Thymine), Hexose (Z Hexose), Ibuprofen (Ibutrofen), N-Acetylornithine (N-Acetylornithine), Proline (Proline), Altrose (Altrose), sulfamic Acid (Amidosylfonic Acid), Edetic Acid (Edetic Acid), 2-Dodecyl-1, 4-dihydroxybenzene (2-Dodecacyl-1, 4-dihydroxy-Benyne), Quinolinic Acid (Quinnolic Acid), Beta-mannitol glyceride (Beta-cysteine), cysteine (Phosphonate (O-phospho), phospho-O (Phosphonate), phospho (Phosphorine), norphine (Cithosine), Norvaline (Norvaline), Norvaline (Thymine), norgestimatine (Thymine), norgestimatinib (Evone), and Glycine (Beta-1, 4-dihydroxyl) and mixtures thereof, Oxyproline (Oxoproline), Sebacic Acid (Sebacic Acid), Isocitric Acid (Isocitric Acid), Palmitic Acid (Palmitic Acid), Glutathione (Glutathione), D-Fructose (D-Fructtose), 2-piperidinecarbonitrile (2-Piperidinonitrile), Alpha-D-Glucose (Alpha-D-Glutose), Trans-4-Hydroxy-L-Proline (Trans-4-Hydroxy-L-Proline), Gamma-Aminobutyric Acid (Gamma-Aminobutyric Acid), bisphenol A (bisphenol A), Inosine-5 '-Acid (Inosine-5' -Monophosphate), 4-Methylbenzenesulfonamide (4-Methylphenylzeylsulfonamide), Trans-4-Hydroxyproline (Trann-4-Hydroxyproline), N- {2-hydroxyethyl } tetradecane 1-tetradecane } 1-2-tetradecyl-1-hydroxyethyl } amine, Tryptophan (Tryptophol), Terephthalic Acid (Terephthalic Acid), Homovanillic Acid (Homovanillic Acid), Biphenyl (biphenol), 4-Methyl-5-thiazoleethane (4-Methyl-5-Thiazoleethanol), Itaconic Acid (Itaconic Acid), Arbutin (Arbutin), Adenosine 5-Monophosphate (Adenosine-5-Monophosphate), 2-Indolecarboxylic Acid (2-Indolecarboxylic Acid), Hydrocinnamic Acid (Hydrocinnamic Acid), 2'-Biphenyldiol (2,2' -Biphenyldiol), 1,3, 5-benzenetriol (1,3, 5-benzotriol) 5,7-Dimethyl [1,2,4] triazolo [1,5-a ] pyrimidin-2-diamine (5, 7-monomethyldiol [1,2,4] triazolo [1,5-a ] pyrimidine-2-diamine (5, 7-triazine [1,2,4] pyri-2-diamine [ 5, 5-a ] pyri-2-diamine (5, 7-methy [1,2,4] pyri [1, 5-thiazol [ 5-a ] pyri-2-diol ] pyri-2-diamine (5, 7-Methyl [1, 4] pyri-5-a ] pyri-2-D, 5-D, 4-a ] pyri-d, 2-D, 2-di-D, 2-a, 2-di (5, 7-one, 4-one, 4, 2,4, 2,4, 2, 1,2, one or more of Benzylamine (Benzylamine); more preferably, the fecal metabolite is preferably selected from one or more of sebacic acid, 2-indolecarboxylic acid.

It will be appreciated that the intestinal markers for diagnosing depression described in the present disclosure may include any of the above intestinal bacteria, intestinal phages and/or fecal metabolites; any two or more of the above intestinal bacteria, intestinal phages and/or fecal metabolites intestinal bacteria, intestinal phages, fecal metabolites, such as two, three, four, five, six, seven, eight, etc., may also be included.

Optionally, the intestinal marker for diagnosing depression may include only one intestinal bacterium, one intestinal bacteriophage, or one fecal metabolite, or may include only two or more intestinal bacteria, two or more intestinal bacteriophages, or two or more fecal metabolites.

Optionally, the intestinal marker for diagnosing depression may include one or more of the intestinal bacteria and one or more of the intestinal phages; or may comprise one or more of said intestinal bacteria and one or more of said fecal metabolites; or may comprise one or more of said intestinal phages and one or more of said fecal metabolites.

In a particular embodiment of the disclosure, the gut biomarker comprises one or more of Klebsiella (Klebsiella), Eubacterium CAG:146(Eubacterium _ sp. CAG:146), Clostridium phage phi8074-B1(Clostridium _ phage _ phi8074-B1), Escherichia coli phage ECBP5(Escherichia _ phage _ ECBP5), sebacic acid, 2-indolecarboxylic acid, preferably the gut biomarker comprises Klebsiella (Klebsiella) bacteria, Eubacterium CAG:146(Eubacterium _ sp. CAG:146), Clostridium phage phi8074-B1(Clostridium _ phage _ phi _ B1), Escherichia coli phage 5(Escherichia coli bp _ chop _ bp _ 5), indole-dicarboxylic acid, and indole-carboxylic acid.

In a specific embodiment of the present disclosure, the diagnosing depression comprises the steps of:

detecting the level of the intestinal biomarker in the subject, and determining the risk of the subject suffering from depression based on the level of the intestinal biomarker.

The present disclosure also provides a kit for diagnosing depression, comprising the intestinal biomarker and/or a detection reagent for the intestinal biomarker.

Preferably, the kit for diagnosing depression further comprises a depression diagnosis evaluation table including, but not limited to, one or more of depression disorder diagnosis criteria of the fourth edition (DSM-IV) or the fifth edition (DSM-5) of the American psychiatric and statistics Manual, Hamilton Depression Scale-17 item (HAMD-17).

The present disclosure also provides a method for screening intestinal biomarkers of a patient with depression, the method comprising the steps of:

detecting a level of a candidate intestinal biomarker in the subject;

Preferably, the candidate gut biomarker is from gut microbes, faecal metabolites and/or combinations thereof in a faecal sample of the subject.

Preferably, the division of the subject into depression patient groups and healthy control groups is according to conventional diagnostic methods well known in the art, including, but not limited to, diagnostic evaluation charts such as the diagnostic criteria for depressive disorders in the American diagnostic and statistical handbook for mental disorders, fourth edition (DSM-IV) or fifth edition (DSM-5), Hamilton Depression Scale-17 (HAMD-17), and the like.

Preferably, the detecting the level of the candidate intestinal biomarker in the subject is performed by methods such as genomic sequencing, particularly high-throughput Whole gene metagenomic sequencing (white-genome shotgun metagenomics analysis), and/or non-targeted metabolomics, preferably mass spectrometric metabolomics detection, such as liquid chromatography/gas chromatography-mass spectrometry (LC/GC-MS).

Preferably, the determining the difference and/or similarity of the candidate intestinal biomarker between the depressive disorder patient and the healthy control comprises the step of predicting the difference and/or similarity of the candidate intestinal biomarker between the depressive disorder patient and the healthy control by partial least squares discriminant analysis (PLS-DA), etc.; more preferably, the determining the difference and/or similarity of the candidate intestinal biomarkers between the depression patients and the healthy controls further comprises confirming the differential intestinal biomarkers from the differential candidate intestinal biomarkers by a method such as LEfse (linear discriminant analysis).

In one embodiment of the present disclosure, the screening method further comprises the steps of:

verifying the diagnostic rate of intestinal biomarkers of said depressive disorder patient.

Preferably, the verifying the diagnosis rate of the intestinal biomarkers of the depressive disorder patient is performed by an analysis method of a machine learning model, preferably a random forest analysis method.

In one embodiment of the present disclosure, the screening method comprises the steps of:

the subjects were tested by the American psychiatric manual for diagnosis and statistics, and used as the basis for grouping depression patients and healthy controls;

detecting the level of a candidate intestinal biomarker in the subject by using a method of high-throughput Whole-gene metagenomic sequencing (white-genome shotgun biomarkers analysis) and non-targeted metabolomics (LC/GC-MS), wherein the candidate intestinal biomarker is derived from a fecal sample of the subject and is subjected to intestinal microorganisms, fecal metabolites and/or a combination thereof;

predicting the difference degree and similarity of the candidate intestinal biomarkers between the depression patients and healthy controls through partial least squares discriminant analysis (PLS-DA) to obtain different candidate intestinal biomarkers, and determining different intestinal microorganisms from the different candidate intestinal biomarkers through LEfse (linear discriminant analysis) to obtain the intestinal biomarkers of the depression patients.

Preferably, the screening method further comprises the steps of:

and obtaining the diagnosis score of the intestinal biomarker of the depression patient by using a random forest analysis method, and verifying the diagnosis rate.

The present disclosure also relates to the use of any one of the above screening methods for determining intestinal biomarkers in a patient with depression.

The present disclosure also relates to the use of any one of the above screening methods in diagnosing depression.

The disclosure also relates to the application of any one of the screening methods in preparing a kit for diagnosing depression and/or a kit.

The disclosure also relates to intestinal biomarkers of depression patients obtained by screening by any one of the screening methods, wherein the intestinal biomarkers of depression patients are used for diagnosing depression.

Example III

Example 1 screening of intestinal biomarkers for diagnosing depression

Method

1. Sample source

The out-patient and community healthy volunteers who recruited the Beijing diazepam hospital to see a doctor collected stool samples, and the inclusion criteria were as follows:

1.1 Depression patients inclusion criteria:

(1) patients aged 18-65 years, with unlimited gender, who meet the diagnostic criteria for depression-suppressing disorder of the fourth edition of the diagnostic and statistical manual for mental disorders (DSM-IV);

(2) the Hamilton depression scale-17 item (HAMD-17) score is more than or equal to 14 points;

(3) the system has a certain cultural degree, and can complete evaluation and follow-up visit in a matching way;

(4) the patient signs an informed consent.

1.2 healthy controls inclusion criteria:

(1) sex, age, education, place of residence, etc. are matched with the case group, and the diagnosis evaluation does not conform to the diagnosis of depressive disorder;

(2) no mental illness was assessed by the DSM-IV clinical interview.

(3) Signing the informed consent.

1.3 exclusion criteria:

(1) patients co-suffering from other psychiatric disorders;

(2) patients who are comorbid of severe somatic disease and currently treated for instability;

(3) pregnant and lactating women;

(4) the subjects were participating in other clinical trials.

The present disclosure finally recruited 156 patients with depression, 56 of them, 100 of them; the healthy controls were 155, 64 men and 91 women.

2. Fecal sample collection and DNA extraction

And (3) collecting a fecal sample, freezing and transporting the fecal sample, quickly transferring the fecal sample to-80 ℃ for storage, and performing DNA extraction to obtain an extracted DNA sample. All fecal specimens were collected and used in the United states

Extraction of DNA was performed with the Soil DNA Kit (Omega Bio-tek, Norcross, GA, U.S.) Kit. After extraction, the concentration and purity of the extracted DNA were determined using TBS-380 and Nano Drop 2000, respectively, and the quality was checked using 1% agarose gel.

3. High throughput Whole gene metagenomic sequencing (white-genome shotgun metagenomic analysis):

using the extracted DNA sample

Rapid DNA-Seq (Bio Scientific, USA) constructs a sequencing library, and metagenomic sequencing uses Illumina NovaSeq (Illumina, USA) sequencing platform. After sequencing was complete, the sequencing fragment was linker-removed using the software Seqprep (https:// github. com/jstjohn/Seqprep) (S.C.)adapter) and adopting fastp and BWA (http:// bio-bw. source. net) software to carry out mass shearing to remove a pollution sequence, a low-quality sequence and a host genome pollution sequence, thereby obtaining a high-quality sequencing fragment.

4. Determining the difference degree and similarity of intestinal microorganisms and fecal metabolites between depression patients and healthy controls, and verifying that: and comparing the genome and calculating abundance, and performing gene prediction and constructing a non-redundant gene set.

The high quality sequencing data were assigned to the microbial taxonomy using BLASTP (BLAST Version 2.2.28+, http:// blast.ncbi.nlm.nih.gov/blast.cgi) and the abundance was calculated as follows:

1) aligning the high-quality sequencing fragments to a reference marker gene;

2) counting the number of the inserted fragments according to the comparison result;

3) normalizing the length of the marker gene by the number of inserts (normalizing by the average gene length and rounding down to obtain the abundance of the corresponding species) yields the corresponding abundance. The degree of difference and similarity of intestinal microbes between depression patients and healthy controls was predicted by partial least squares discriminant analysis (PLS-DA), and differential intestinal microbes were determined by LEfse (linear discriminant analysis) setting a threshold (LDA >2.5, p < 0.05).

5. Stool metabolite profile

Extracting fecal metabolites, collecting a full-material spectrogram based on a research platform of non-targeted metabonomics (LC/GC-MS), introducing the normalized spectrogram into Simca-P12.0 (Umetrics, Sweden) to perform partial least squares discriminant analysis (PLS-DA) and LEfse (linear discriminant analysis), and screening out differential metabolites (LDA >2.5, and P < 0.05).

Results

1. Species difference of intestinal flora in two groups of depression patients and healthy people

In order to find out bacteria with obvious differences, firstly, strain sequencing data are normalized, and then a Lefse (linear discriminant analysis) method is used for obtaining the different flora of depression patients and healthy controls, wherein LDA is greater than 2.5 and p value is less than 0.05. The results showed that there was a significant difference between depression and the control group for the total 47 bacteria (table 1).

Table 1 fecal differential bacterial species between depression patients and healthy controls.

2. Fecal bacteriophage differential in both depression patients and healthy populations

Using LEfSe analysis, we found that 3 phages expressed differently between the two groups, including the phage Clostridium phi8074-B1, E.coli phage, and Klebsiella phage (Table 2).

TABLE 2 fecal phage differences between Depression patients and healthy controls

3. Fecal metabolite differences in both depression and healthy populations

Metabolic analysis was used to compare the differences in fecal metabolites between the depression and control groups. The depression group showed 16 metabolite enrichments and 34 metabolite reductions compared to the control group (table 3). These altered metabolites are primarily involved in amino acid metabolism (pipecolic acid, homoserine, N-acetylornithine, proline, quinolinic acid, cystine, oxypropylene, γ -aminobutyric acid, tryptophan, homovanillic acid, hydrocinnamic acid, leucine, trans-4-hydroxy-l-proline), nucleotide metabolism, carbohydrate metabolism, and lipid metabolism.

TABLE 3 fecal metabolite differences between Depression patients and healthy controls

Example 2 validation of intestinal biomarkers for Depression Using Random Forest (RF)

In order to further verify the disease intestinal biomarkers, the embodiment constructs a training set of biomarkers of depression subjects and healthy controls, and based on the training set, the biomarker content values of the samples to be tested are evaluated. Wherein, in the present disclosure, the training set and the validation set have meanings known in the art. In embodiments of the present disclosure, an exercise set refers to a data set comprising a number of samples of the content of each biomarker in a sample of a depressive subject and a healthy control. The validation set is an independent data set used to test the performance of the training set. The present disclosure selects 118 depression patients and 118 matched healthy persons who did not use antidepressants within the last month as a training set from 311 samples (156 depression patients and 155 healthy persons), and the remaining samples as a validation set (38 depression patients and 37 healthy persons).

Method

1. Screening to obtain biomarkers by using training set data

First, the relative abundance of species in each sample in the training set was calculated as described in 4; the intestinal microbial metabolite profiles were obtained according to the method described in 5. The training set's species are then input into a Random Forest (Random Forest analysis, Python's scimit-lean package) classifier. And 5-fold cross validation is carried out on the classifier for 5 times, the relative abundance or metabolite of the species screened by the RF model is used for calculating the suffering risk of depression of each individual, an ROC curve is drawn, and AUC is calculated to be used as the efficiency evaluation parameter of the discrimination model. The combination with the number of marker combinations less than 30 and the best discrimination performance is selected as the combination disclosed in the present invention. An importance index for each species is output in the model, with higher importance indices representing higher importance of the marker to discriminate between depression and non-depression.

2. Verifying the screened biomarkers using the validation set data

The present disclosure then validates the model using an independent population with a prevalence probability of greater than or equal to 0.5 to predict that the individual is at risk for or suffering from depression. First, the relative abundance of each biomarker in each sample in the training set was calculated as described in 4 and 5. The verification set data was then verified using a random forest model according to the method of 6.1.

As a result:value of phage, bacterial species and fecal metabolite combination markers in depression diagnosis

1. And (3) drawing an ROC curve by using the bacteriophage, the bacterial species and the fecal metabolites screened by the RF model according to the method in the step 1, and calculating AUC as a discrimination model efficiency evaluation parameter so as to evaluate the potential value of the intestinal metagenome and metabonomics markers in depression diagnosis. Given that diagnostic tools based on small amounts of parametric quantification are more feasible and economical in clinical practice, the five-fold cross-validation method was used to determine representative variations that can describe the most significant deviation between depressive patients and healthy controls. Figure 1 shows ROC curves and AUC for a training set consisting of depression patients and healthy controls based on a random forest model, where specificity is characterized by the probability for not-affected and sensitivity refers to the probability for affected, to assess the diagnostic value of the biomarker for depression.

The RF classifier obtained in the present disclosure contains 2 bacteria, 2 types of phage and 2 fecal metabolites, and the discriminatory potency of these 6 intestinal biomarkers on the training set samples is shown in table 4. Wherein, the discrimination effect of the 2 screened bacteria, Klebsiella (Klebsiella) and Eubacterium CAG:146(Eubacterium _ sp. _ CAG:146) on the training set sample is as follows: AUC 0.89 (95% CI: 0.86-0.93); 2 types of phages, Clostridium phage phi8074-B1(Clostridium _ phage _ phi8074-B1) and Escherichia coli phage ECBP5(Escherichia _ phage _ ECBP5) were screened, and the discrimination efficiency of the training set samples was: AUC 0.77 (95% CI: 0.72-0.84); screening 2 fecal metabolites, namely Sebacic acid (Sebacic acid) and 2-Indolecarboxylic acid (2-indole carboxylic acid), and judging the effectiveness of the training set sample as follows: AUC 0.93 (95% CI:0.90-0.96) (fig. 1A).

Taking the above six indexes as combined markers, the discrimination efficiency of the training set sample is: AUC 0.98 (95% CI:0.97-0.99) (fig. 1B), indicating that this combination of six indicators can be a potential biomarker to distinguish depression from healthy people.

2. And (3) verifying the six indexes or combinations (the Klebsiella and the eubacterium CAG:14, the Clostridium phage phi8074-B1 and the Escherichia coli phage ECBP5, the sebacic acid and the 2-indolecarboxylic acid) by using an RF model according to the method of the step 2, drawing an ROC curve, and calculating AUC (AUC) as a judgment model efficiency evaluation parameter so as to evaluate the potential value of the intestinal metagenome and metabonomic markers in depression diagnosis.

The discrimination efficiency of the six intestinal microbial markers on the test set samples is shown in table 4. Figure 1 shows ROC curves and AUC for a validation set consisting of depression patients and healthy controls based on a random forest model (6 biomarker combinations). As shown in FIG. 1, the discrimination effect of 2 kinds of bacteria (Klebsiella and eubacterium CAG:14) on the sample of the test set is as follows: AUC 0.81 (95% CI: 0.71-0.91); discriminatory potency of 2 phages (clostridial phage phi8074-B1 and escherichia coli phage ECBP5) on validation set samples: AUC 0.65 (95% CI: 0.52-0.78); discriminatory potency of 2 metabolites (sebacic acid and 2-indolecarboxylic acid) on the validation set of samples: AUC 0.83 (95% CI:0.73-0.93) (fig. 1A). Taking the six indexes as combined markers, and judging the effectiveness of the verification set samples: AUC 0.90 (95% CI:0.82-0.98) (fig. 1B), indicating that these six stool markers in combination have comparable potential for diagnosing depression.

TABLE 4 evaluation of the efficacy of the screened biomarkers for depression discrimination using the subject working characteristic curves

The results show that the biomarker disclosed by the disclosure has higher accuracy and specificity, and has good prospect of developing a diagnostic method, thereby providing basis for evaluating and diagnosing the risk of the depression and searching potential drug targets.

Claims

1. A kit for diagnosing depression, the kit comprising detection reagents for gut biomarkers, wherein the gut biomarkers comprise gut phages including the phages clostridia phi8074-B1(Clostridium _ phage _ phi8074-B1) and/or Escherichia coli phage ECBP5(Escherichia _ phage _ ECBP 5).

2. A kit for diagnosing depression, the kit comprising detection reagents for intestinal biomarkers, wherein the intestinal biomarkers comprise fecal metabolites comprising sebacic acid and/or 2-indolecarboxylic acid.

3. The kit according to claim 1 or 2, wherein the gut biomarker further comprises gut bacteria comprising bacteria of the genus Klebsiella (Klebsiella) and/or the genus eubacteria CAG:146(Eubacterium _ sp. _ CAG: 146).

4. A kit according to claim 1 or 2, wherein the gut biomarker comprises a combination of gut bacteria, gut phages and fecal metabolites.

5. A kit according to claim 3, wherein the gut biomarker comprises a combination of gut bacteria, gut phages and fecal metabolites.

6. The kit according to claim 4, wherein the enteric bacteria comprise Klebsiella (Klebsiella) bacteria and Eubacterium CAG:146(Eubacterium _ sp. _ CAG:146) bacteria, the enteric phages comprise Clostridium phage phi8074-B1(Clostridium _ phage _ phi8074-B1) and Escherichia coli phage ECBP5(Escherichia _ phage _ ECBP5), and the fecal metabolites comprise sebacic acid and 2-indolecarboxylic acid.

7. The kit according to claim 1 or 2, wherein the kit further comprises a diagnostic evaluation table for depression.

8. The kit according to claim 7, wherein the diagnostic evaluation table for depression comprises one or more of the American handbook of diagnosis and statistics of mental disorders, Hamilton Depression Scale-17 item (HAMD-17).

9. The kit according to claim 1 or 2, wherein the kit is one or more of a biochemical diagnostic kit, an immunodiagnostic kit, or a molecular diagnostic kit.

10. The kit according to claim 9, wherein the kit is an immunodiagnostic kit.

11. The kit of claim 9, wherein the kit is one or more of a Western blot kit, an enzyme-linked immunosorbent assay (ELISA) kit, a Radioimmunoassay (RIA) kit, a radioimmunodiffusion kit, an ouchterlony immunodiffusion kit, a rocket immunoelectrophoresis kit, an immunohistochemical staining kit, an immunoprecipitation assay kit, a complement fixation assay kit, a Fluorescence Activated Cell Sorting (FACS) kit, an aptamer chip kit, a microarray kit, and a protein chip kit.

12. Use of the intestinal biomarker in the kit according to any one of claims 1 to 11 for the preparation of a detection reagent for depression, or use of the intestinal biomarker in the kit according to any one of claims 1 to 11 for the preparation of a kit for the diagnosis of depression.

13. A screening method for screening intestinal biomarkers in a kit according to any one of claims 1 to 11, said method comprising the steps of:

14. The screening method according to claim 13, wherein the detecting the level of the candidate intestinal biomarker in the subject is by a method of genomic sequencing which is high-throughput Whole gene metagenomic sequencing (wheel-genome shotgun metagenomics analysis) and/or by non-targeted metabolomics which is mass spectrometric metabolomics detection.

15. The screening method according to claim 13, wherein the judging the difference and/or similarity of the candidate intestinal biomarker between the depressive disorder patient and the healthy control comprises a step of predicting the difference and/or similarity of the candidate intestinal biomarker between the depressive disorder patient and the healthy control by a partial least squares discriminant analysis (PLS-DA) method to obtain a differential candidate intestinal biomarker.

16. The screening method according to claim 14 or 15, further comprising the step of identifying a differential gut biomarker from the differential candidate gut biomarkers by the LEfse (linear discriminant analysis) method.

17. Screening method according to claim 14 or 15, further comprising the steps of:

18. The screening method according to claim 17, wherein said verifying the diagnosis rate of intestinal biomarkers of said depressed patients is performed by an analysis method of a machine learning model.

19. A screening method according to claim 18 wherein the analysis method is a random forest analysis method.

20. Use of a screening method according to any one of claims 1 to 19 in the manufacture of a reagent or kit for the diagnosis of depression.