CN111370116A

CN111370116A - Intestinal microbial marker for predicting curative effect of bipolar affective disorder and screening application thereof

Info

Publication number: CN111370116A
Application number: CN202010193593.3A
Authority: CN
Inventors: 胡少华; 张佩芬; 来建波; 蒋佳俊; 许毅; 奚彩曦; 杜彦莉; 吴玲玲; 路静; 牟婷婷
Original assignee: Zhejiang University ZJU
Current assignee: Zhejiang University ZJU
Priority date: 2020-03-18
Filing date: 2020-03-18
Publication date: 2020-07-03
Also published as: WO2021184413A1

Abstract

The invention discloses an intestinal microbial marker for predicting curative effect of bipolar affective disorder and screening application thereof. Wherein the biomarker 1) is Eubacterium _ biform; biomarker 2) Ruminococcus _ queries; biomarker 3) collinesila unclassified; the relative abundance of the biomarker combinations in the treatment effective group of the bipolar affective disorder patients is obviously increased. The invention also discloses application of the biomarker as a detection target or a detection target in preparation of a detection kit and application of the biomarker as the target in prediction of curative effect.

Description

Intestinal microbial marker for predicting curative effect of bipolar affective disorder and screening application thereof

Technical Field

The invention relates to an intestinal microbial marker for predicting curative effect of bipolar affective disorder and screening application thereof.

Background

Bipolar disorder is a chronic mental disease with depression, mania or hypomania as main clinical features, the cause of the disease is unknown, the onset age is mainly concentrated in early adulthood or later adolescence, the global morbidity is high and is about 2-3%, the bipolar disorder is related to obvious damages of occupational, personal and social functions and the like, and is accompanied with body diseases and early death. At present, the treatment aiming at the bipolar affective disorder is mainly based on drug treatment and assisted by a comprehensive treatment principle of physical treatment and psychological treatment. Even with treatment, residual mood symptoms often remain, with about 37% of patients relapsing to depression or mania within 1 year and 60% of patients relapsing within 2 years. Repeated attacks can seriously damage the brain function of a patient, prolong the treatment time of the medicine and reduce the treatment effectiveness.

Although the current consensus view suggests that the onset of bipolar disorder is the result of the interaction of genetic factors and environmental factors, and shows different degrees of changes in nervous, immune and endocrine systems, the early prediction of curative effect on bipolar disorder treatment lacks specific biomarkers.

Disclosure of Invention

In order to overcome the defects of the prior art, the invention aims to provide an intestinal tract microbial marker for predicting the curative effect of bipolar affective disorder and screening application thereof.

A predictive biomarker of bipolar affective disorder efficacy based on gut microbiota, comprising a plurality of 6 of:

"bacteria _ clauus", "Eubacterium _ biforme", "Weissella _ confuga", "Ruminococcus _ torques", "Bifidobacterium _ dense", "Collinsella _ unclassified" clearly differ between the treatment-effective group and the treatment-ineffective group of bipolar affective patients. Among them, "bacteria _ clauus", "Eubacterium _ biforme", "Weissella _ confosa", "Ruminococcus _ requests", "collissella _ unclassified" were significantly increased in relative abundance in the treatment effective group of bipolar affective patients, while "Bifidobacterium _ dense" was decreased.

The biomarkers are 3 of the following types:

biomarker 1) Eubacterium _ biform; importance 0.15259;

biomarker 2) Ruminococcus _ queries; importance 0.339395;

biomarker 3) collinesila unclassified; importance 0.258467;

the relative abundance of the biomarker combinations in the treatment effective group of the bipolar affective disorder patients is obviously increased.

The biomarkers are provided based on calculation of their gene sequences.

The relative abundance information of the biomarkers is used to compare to a reference value.

An application of the biomarker as a detection target or a detection target in preparing a detection kit.

The application of the biomarker as a target point in the prediction of curative effect.

A screening method based on the biomarkers comprises the following steps:

1) collecting samples: collecting fecal samples of sample subjects including patients with bipolar affective disorder before treatment, and storing at-80 deg.C in refrigerator for use;

2) extracting DNA of a nucleic acid sample from the collected and stored excrement sample, and inputting a high-quality sequencing fragment into Metapthlan 2 software after sequencing, assembling, comparing, screening and quality control of a metagenome, so as to calculate the relative abundance of the species;

3) the relative abundance information of the bipolar affective disorder patients and healthy control species obtained above was input into LDA effect size (LEfSe) system and differential flora between groups was analyzed.

The screening method further uses a random forest model for predictive analysis, and comprises the following steps:

4.1) taking the treatment effective group and the treatment ineffective group of the bipolar affective disorder patient as training sets, taking the rest samples as test sets, and calculating the relative abundance of the species in each sample in the training sets;

4.2) inputting the relative abundance information of the species in the training set into a Random Forest (RF) classifier, carrying out 5 times of 10-fold cross validation on the classifier, calculating the risk of developing bipolar affective disorder according to the relative abundance information of each species screened by using an RF model, drawing an ROC curve, calculating the area under the curve (AUC), taking the AUC as a parameter for judging the efficiency evaluation of the model, and outputting the importance index of each species in the model, wherein the higher the importance index is, the higher the importance index represents that the marker is used for judging the importance of the bipolar affective disorder and the non-bipolar affective disorder.

In the training set, the sample subjects included 27 patients with effective bipolar disorder treatment and 14 patients with ineffective bipolar disorder treatment, and in the test set, the sample subjects included 8 patients with effective bipolar disorder treatment and 5 patients with ineffective bipolar disorder treatment.

The invention has the beneficial technical effects that:

the intestinal microorganisms are microbial communities existing in human intestinal tracts, interact with the brain through a two-way information transfer system of nerves, immunity, endocrine and the like of a brain-intestine axis, participate in influencing the functions of emotion, behavior, cognition and the like of people, and the imbalance of the intestinal flora is related to the occurrence of various neuropsychiatric diseases such as Alzheimer disease, autism, depression, bipolar disorder and the like. The function and composition of gut microbiota is influenced by a number of factors, such as diet, exercise, probiotics, antibiotics, and fecal transplantation. At present, research has been carried out to further discuss the mechanism of action of improving the functional state of the brain by intestinal flora through building animal models and regulating the change of the intestinal flora. Patients with bipolar disorder may also experience changes in the intestinal flora while using second generation antipsychotics such as quetiapine to stabilize mood and improve symptoms.

Therefore, the invention screens out the biomarker with high correlation with the curative effect of the bipolar affective disorder treatment by analyzing the intestinal flora and the gene sequence of the effective and ineffective treatment groups of the bipolar affective disorder patient, and can accurately predict the curative effect of the bipolar affective disorder and monitor the curative effect by utilizing the biomarker.

The biomarkers related to bipolar affective patients proposed by the present invention are valuable in response to therapeutic efficacy. First, stool sample extraction is portable and non-invasive, increasing patient compliance. At the same time. Secondly, the fecal sample is transportable and the sample analysis is accurate and safe. Third, the markers of the invention have high specificity and sensitivity and can be used for predicting therapeutic efficacy.

Drawings

FIG. 1 is a graph showing the difference in relative abundance of bacterial populations in a therapeutically effective group and a therapeutically ineffective group of patients with bipolar disorder at the species level according to one embodiment of the present invention. The graphs show that the relative abundance of the flora at different levels in the treatment-effective group and the treatment-ineffective group of patients with bipolar affective disorder are significantly different.

FIG. 2 is an error rate distribution for a classifier with 5-fold 10-fold cross validation according to one embodiment of the present invention.

Fig. 3 is a receiver operating characteristics (R e i v e rO p e R a t i n g characteristics, ROC) Curve and Area under the Curve (Area under dark, AUC) of a training set consisting of a treatment-effective group and a treatment-ineffective group of bipolar affective patients based on a random forest model (3 gut markers) according to an embodiment of the present invention.

Fig. 4 is a receiver operating characteristic ROC curve and area under the curve AUC for a test set consisting of a treatment-effective group and a treatment-ineffective group of bipolar affective patients based on a random forest model (3 gut microbial markers) according to an embodiment of the present invention.

Detailed Description

The terms used herein have meanings commonly understood by those of ordinary skill in the relevant art. However, for a better understanding of the present invention, some definitions and related terms are explained as follows:

the bipolar affective disorder is a chronic mental disease with depressive episode and manic episode, has recurrent episodes, and has disorders in aspects of mood processing, cognitive function and the like of patients, thereby seriously influencing the functions of normal social life and the like of the patients. The common suffering of patients around 1% all over the world is one of four causes of the disability of teenagers, but the pathogenesis is not completely clarified, and the current view is that the common suffering is closely related to the interaction between genetics and environment.

The term "therapeutic effect", which means the effect of a drug or an operation on a disease, is mainly based on four evaluation criteria, including recovery, significant effect, progress, and ineffectiveness. The term "therapeutic effect" in this study mainly means "significant effect" and "ineffective effect". Because in the present invention the treatment for bipolar affective disorder is performed as monotherapy and the effectiveness of the drug treatment after 1 month is assessed as to whether the rate of change on the 17-item Hamilton Depression Scale (HDRS-17) scale score is greater than or equal to 50%.

Biomarkers are indicators that can be used to determine the cause of a disease, to diagnose early, to evaluate the progression of a disease, or to evaluate the efficacy or safety of a drug treatment in a target population. Mainly comprises any substance reflecting a specific biological state (such as diseases) of a body on different biological levels (individuals, cells and molecules), small molecular substances such as blood sugar for evaluating diabetes mellitus and macromolecular substances such as proteins, nucleic acids and lipids. In the present invention, "biomarker" may also be referred to as "intestinal microorganism" or "intestinal flora", since the biomarkers found to be associated with bipolar disorder in this study are derived from stool samples after being metabolized by the intestinal tract of the subject.

The biomarker of the invention analyzes fecal samples of treatment effective groups and treatment ineffective groups of bipolar affective disorder patients in batches by using high-throughput sequencing. And comparing the treatment effective group and the treatment ineffective group of the bipolar affective disorder patient based on high-throughput sequencing data, so as to determine a specific flora related to prediction of curative effect of the bipolar affective disorder patient.

Examples

Collecting and processing samples: collecting 62 samples of the stool with bipolar affective disorder, collecting the stool samples, subpackaging the stool samples into a freezing storage tube which is marked, transporting the stool samples under a freezing condition, and transporting the stool samples to a refrigerator at the temperature of 80 ℃ below zero for storage and standby. In particular, the subject of the invention is based on hospitalized and/or outpatient patients who comply with the diagnostic guidelines for bipolar affective disorder (DSM-IV-TR) in the handbook of diagnosis and statistics of psychiatric disorders. Subjects were not taking medication or had taken medication but had discontinued at least 3 months at the time of enrollment. While satisfying patients without obvious suicidal thoughts or previous suicidal failure, without co-morbid with other mental disorders. Exclusion criteria were: : 1) chronic infections, severe systemic diseases (such as diabetes) and autoimmune diseases; 2) edible antibiotics, probiotics or probiotics are screened within 4 weeks before the screening; 3) women who are currently pregnant, lactating, or irregular menstruation; 4) history of craniocerebral trauma; 5) contraindications for Magnetic Resonance Imaging (MRI), such as metal implants or claustrophobia. In the cohort, the severity of depression was assessed using the 17-item Hamilton Depression Scale (HDRS-17) and the Montgomery-Arberg Depression Scale (MADRS), and the mania was assessed using the youth mania Scale (YMRS). Wherein a HDRS-17 score of not less than 14 is set as the threshold for the current depressive episode. After 1 month of standard treatment with quetiapine single dose after patient enrollment, the HDRS-17 scale was again evaluated and a score was calculated, which was compared to the HDRS-17 scale score at enrollment to calculate the rate of change. According to whether the HDRS-17 score change rate is more than or equal to 50% or not when a bipolar affective disorder patient takes the quetiapine single-drug treatment for 1 month and is compared with the treatment before, the bipolar affective disorder is divided into a treatment effective group (HDRS-17 is more than or equal to 50%) and a treatment ineffective group (HDRS-17 is less than 50%). The final subjects were divided into a treatment-effective group (n = 35) and a treatment-ineffective group (n = 19) for patients with bipolar affective disorder. 8 bipolar affective patients lack the follow-up scale score and are not classified.

And (3) extracting DNA: DNA was extracted using QIAGENT DNA kit to obtain a nucleic acid sample.

Constructing a library and sequencing: a DNA Library was constructed using NEBNextultra TM DNA Library Prep Kit for Illumina (NEB, USA). Sequencing of the paired-end 150bp DNA library was performed with Illumina NovaSeq 6000. And removing the polluted or low-quality sequencing fragments (reads) through the steps of comparison, screening, quality control and the like, and finally obtaining the high-quality sequencing fragments.

Inputting the high-quality sequencing fragment obtained in the above into Metaplan 2 analytical software (http:// segatalab. cibio. unit. it/tools/metaplan 2/), and comparing 1) with a reference marker gene; 2) counting the number of the inserted fragments; 3) and (3) normalizing the length of the marker gene and the like to obtain the relative abundance information of the corresponding flora.

According to the obtained relative abundance of the flora, the difference of the relative abundance of the flora between the treatment effective group and the treatment ineffective group is analyzed by utilizing an LDA Effect Size (LEfSe) analysis technology.

The relative abundance of the flora of the treatment effective group and the treatment ineffective group of the bipolar affective disorder patients is input into an online LEfSe analysis webpage (http:// huttenhouwer. sph. harvard. edu/galaxy) to carry out the difference comparison of the information of the relative abundance of the flora between the two groups. Specifically, the method mainly comprises the following three steps: 1) firstly, detecting the relative abundance difference of species between two groups by using non-parametric factor Kruskal-Wallis rank sum test to obtain a significant difference species; 2) secondly, detecting whether all subspecies of the species with significant differences obtained in the last step tend to the same classification level by using Wilcoxon rank sum test; 3) finally, Linear Discriminant Analysis (LDA) was performed to reduce dimensions of the data and evaluate the influence of significantly different flora (i.e., LDA score) to obtain the final species of difference (see literature: segata N, et al, Metagenomicarmed discovery and expression [ J ]. Genome Biol, 2011, 12(6): R60.).

In the present invention, the relative abundance information of the flora obtained by sequencing and sequence alignment of 54 samples (35 treatment-effective bipolar disorder patient groups and 19 treatment-ineffective bipolar disorder patient groups) was input into an LEfSe online analysis web page, and the results showed that the relative abundance of 6 flora groups including "bacteria _ genus", "Eubacterium _ biform", "Weissella _ conjuga", "Ruminococcus _ requests", "Bifidobacterium _ dense", "collissella _ unclassified" was significantly different between the treatment-effective group and the treatment-ineffective group of bipolar disorder patients. Among them, "bacteria _ clauus", "Eubacterium _ biforme", "Weissella _ confosa", "Ruminococcus _ torques", "collinesla _ unclassified" were significantly increased in relative abundance in the treatment effective group of bipolar affective patients, while "Bifidobacterium _ dense" was decreased (fig. 1). FIG. 1 compares differential flora phases using LEFSE and LDA analysis. LDA is more than or equal to 2 as a threshold value with significance of difference. The LDA score showed a significant difference in bacteria between BD patients in the treatment null group (right panel) and the treatment active group (left).

And screening potential biomarkers of curative effect prediction in the treatment process of the bipolar affective disorder by using a random forest classifier.

In order to further screen the intestinal microbial markers of the bipolar affective disorder, a training set and a test set of the intestinal microbial markers of the effective treatment group and the ineffective treatment group of the bipolar affective disorder are constructed on the basis of the information condition of the relative abundance of differential flora between the effective treatment group and the ineffective treatment group of the bipolar affective disorder patients screened by the LEfSe analysis technology, and the content value of the biological markers of the sample of the test set to be tested is evaluated. In the embodiment of the invention, the training set refers to a data set of the content of each biomarker in a test sample of a bipolar affective disorder treatment effective group subject and a bipolar affective disorder treatment ineffective group subject with a certain sample number.

The method specifically comprises the following steps:

the present invention selected 27 bipolar affective patients and 14 healthy persons as a training set and the remaining 13 samples (8 bipolar affective patients and 5 healthy persons) as a test set from 54 samples (35 treatment-effective bipolar affective patient group and 19 treatment-ineffective bipolar affective patient group).

A Random Forest Classifier (RF) was called using the Python3 software version. The invention performs 5 times of 10-fold cross validation on the RF classifier (FIG. 2 shows the error rate distribution of 5 times of 10-fold cross validation in the random forest classifier).

According to the invention, based on 5-fold and 10-fold cross validation results, the RF classifier finally selects 3 optimal biomarker combinations (the detailed microbial marker relative abundance information of the training set is shown in tables 1-1 and 1-2, the detailed microbial marker relative abundance information of the testing set is shown in table 2, and table 3 shows the curative effect probability of the training set predicted by the combination of 3 biomarkers).

TABLE 3 accuracy of the training set to predict therapeutic efficacy using information on relative abundance of flora markers

ID	Accuracy	ID	Accuracy
				B1038	0.76579	HSH_5	0.609374
s1B1069	0.892749	HSH_54	0.745503
				BP23	0.481324	HSH_62	0.712095
HSH_104	0.546565	HSH_92	0.714679
				HSH_31	0.795832	HSH_96	0.761402
HSH_79	0.922007	s1B1065	0.714679
				s1B1008	0.908255	B1072	0.292687
s1B2002	0.893349	B2001	0.306945
				s1B1055	0.482749	BP_3	0.295577
B1004	0.609374	BP_6	0.53206
				B1066	0.725082	HSH_157	0.292865
B1103	0.910725	HSH_21	0.627038
				B1111	0.609374	HSH_27	0.567371
BP_2	0.757439	HSH_76	0.520719
				BP_9	0.65124	HSH_88	0.598444
HSH_106	0.76572	HSH_98	0.649793
				HSH_109	0.729001	s1B1063	0.707049
HSH_112	0.922245	s1B1094	0.420791
				HSH_33	0.65124	s1B2018	0.53206
HSH_39	0.76579	s1B2008	0.514091
				HSH_44	0.906386

Note that: B. BP, S1B, HSH: bipolar disorder.

And (3) calculating the prediction risk of the curative effect of the bipolar affective disorder of each individual by screening the relative abundance of the flora in the training set by using the RF model, drawing an ROC curve, and calculating AUC (AUC) as an efficiency evaluation parameter of the discrimination model. The specificity is characterized by the probability of invalid judgment pairs, the sensitivity refers to the probability of valid judgment pairs, and the judgment efficiency of the training set samples is as follows: AUC 91%, 95% confidence interval CI 82.4% ~ 99.6%. The results indicate that the resulting metabolite combination of this model can be used as a potential biomarker to distinguish bipolar from non-bipolar affective disorders (fig. 3).

ROC curves and AUC for a test set consisting of a valid and invalid set of bipolar affective disorder based on a random forest model (3 biomarkers), where specificity characterizes the probability for invalid decision pairs, sensitivity refers to the probability for valid decision pairs, and the decision potency for the training set samples is: AUC 82.5%, 95% confidence interval CI 51.2~ 100% (figure 4). The results show that the marker combination obtained by the model can be used as a potential biomarker for distinguishing the prediction of the curative effect of the bipolar affective disorder and the curative effect of the bipolar affective disorder.

Table 4 shows the 3 biomarker binding to predict the prevalence probability of the test set.

Table 5 shows the details of the 3 biomarkers.

Table 4 accuracy of test set prediction of prevalence using relative abundance of colony markers

ID	Accuracy
		s1B1067	0.707049
s1B1098	0.659275
		s1B2005	0.688713
s1B2020	0.688713
		s1B2006	0.76572
s1B2007	0.864901
		s1B2010	0.74717
HSH_120	0.713914
		B1076	0.51936
BP21	0.53206
		HSH_160	0.520719
s1B1050	0.51756
		B1047	0.766125

Note that: B. BP, S1B, HSH: bipolar disorder of emotion

TABLE 5

Name of marker	Importance in model prediction	Direction of enrichment	Test set AUC (%)	95% confidence interval
					Eubacterium_biforme	0.15259	effective	56.3	2.38~8.87
Ruminococcus_torques	0.339395	effective	80	4.49~10
					Collinsella_unclassified	0.258467	effective	50	1.65~8.35

Unless otherwise indicated, the techniques used in the examples are conventional and well known to those skilled in the art, and may be performed according to the third edition of the molecular cloning, laboratory Manual, or related products, and the reagents and products used are also commercially available. Various procedures and methods not described in detail are conventional methods well known in the art, and the sources, trade names, and components of the reagents used are indicated at the time of first appearance, and the same reagents used thereafter are the same as those indicated at the first appearance, unless otherwise specified.

The invention adopts a Metagenome-Wide Association Study (MWAS) analysis method, and analyzes the flora composition and relative abundance of flora of the fecal sample through sequencing; analyzing the difference of relative abundance of the flora of the treatment effective group and the treatment ineffective group of the bipolar affective disorder patient by using a Lefse analysis method; and distinguishing the groups of the treatment effective group and the treatment ineffective group of the bipolar affective disorder by using a random forest distinguishing model to obtain the disease probability, and using the disease probability to Liao curative effect prediction evaluation of the bipolar affective disorder or search for potential drug targets.

In the present invention, the sequencing (next generation sequencing) and MWAS are well known in the art, and can be adjusted by those skilled in the art according to the specific situation. According to the embodiments of the present invention, the method can be performed according to the method described in the literature (Jun Wang, and HuijueJea. Metagenome-side association students: fine-mining the microbiome. Nature reviews microbiology 14.8 (2016): 508-522.).

In the present invention, the use methods of the random forest model and the ROC curve are well known in the art, and those skilled in the art can set and adjust parameters according to specific situations. According to an embodiment of the present invention, the method may be performed according to the method described in the literature (Drogand, et al. Untargeted Metabolic Profiling identities Type 2-Diabetes mellitis in a reactive, Nested Case Control Study. Clinchem 2015, 61: 487-.

In the invention, a training set of biomarkers of subjects in a treatment effective group and a treatment ineffective group of bipolar affective disorder patients is constructed, and the biomarker content value of a sample to be tested is evaluated on the basis.

The inventors indicate that these biomarkers are intestinal flora present in humans. The method of the invention is used for carrying out correlation analysis on intestinal flora of a subject to obtain a certain content range value of the biomarker of the bipolar affective disorder population in flora detection.

The results show that the biomarker disclosed by the invention has higher accuracy and specificity, and provides a basis for predicting the curative effect of the bipolar affective disorder disease and searching potential drug targets.

The invention therefore proposes the following applications:

the intestinal flora-based bipolar affective disorder biomarker combination is used as a detection target or an application of a detection target in preparation of a detection kit.

The intestinal flora-based bipolar disorder treatment effect biomarker combination is used as an application of a target point in prediction treatment.

Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims

1. A bipolar affective disorder efficacy prediction biomarker based on gut microbiota, comprising a plurality of 6 of:

bacilli _ cladus, Eubacterium _ biform, Weissella _ confosa, Ruminococcus _ torques, Bifidobacterium _ denium, collisella _ unclassified significant differences between the treatment-effective group and the treatment-ineffective group of patients with bipolar affective disorder;

among them, Bacteroides _ clauus, Eubacterium _ biforme, Weissella _ confosa, Ruminococcus _ torques, Collinsella _ unclassified were significantly increased in relative abundance in the treatment effective group of bipolar affective patients, while Bifidobacterium _ dentium was decreased.

2. The biomarker of claim 1, which is 3 of the following:

biomarker 1) Eubacterium _ biform;

biomarker 2) Ruminococcus _ queries;

biomarker 3) collinesila unclassified;

3. The biomarker of claim 1, wherein the biomarker is provided based on calculation of its gene sequence.

4. The biomarker of claim 1 or 2, wherein the relative abundance information of the biomarker is used to compare to a reference value.

5. Use of the biomarker according to claim 1 or 2 as a target or target for detection in the preparation of a detection kit.

6. Use of a biomarker according to claim 1 or 2 as a target in the prediction of therapeutic efficacy.

7. The method for screening biomarkers according to claim 1, comprising the following steps:

8. The screening method of claim 7, further using a random forest model predictive analysis, comprising the steps of:

9. The screening method according to claim 8, wherein the sample subjects in the training set comprise 27 patients with effective bipolar disorder treatment and 14 patients with ineffective bipolar disorder treatment, and the sample subjects in the testing set comprise 8 patients with effective bipolar disorder treatment and 5 patients with ineffective bipolar disorder treatment.