WO2021184412A1 - 基于肠道微生物的双相情感障碍生物标志物及其筛选应用 - Google Patents

基于肠道微生物的双相情感障碍生物标志物及其筛选应用 Download PDF

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WO2021184412A1
WO2021184412A1 PCT/CN2020/081944 CN2020081944W WO2021184412A1 WO 2021184412 A1 WO2021184412 A1 WO 2021184412A1 CN 2020081944 W CN2020081944 W CN 2020081944W WO 2021184412 A1 WO2021184412 A1 WO 2021184412A1
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bipolar disorder
biomarker
relative abundance
species
patients
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French (fr)
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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
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浙江大学
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  • the invention relates to biomarkers for bipolar disorder based on intestinal microorganisms and their screening applications.
  • Bipolar disorder is a group of severe and recurrent chronic mental illnesses. The global incidence rate is about 2 to 3%. The age of onset is mainly concentrated in late adolescence or early adulthood. It is the fourth leading cause of disability among young people worldwide. . Alternate or mixed depression, mania, or hypomania are the main clinical features, including bipolar disorder type I (at least one manic episode, no need for psychosis or major depressive episodes) and bipolar disorder type II ( Patients have at least one hypomanic episode and at least one severe depressive episode) and subtypes such as subliminal forms, which are manifested as dysfunctions in perception, emotional processing, and cognition. Even after treatment, residual emotional symptoms often still exist.
  • bipolar disorder type I at least one manic episode, no need for psychosis or major depressive episodes
  • bipolar disorder type II Patients have at least one hypomanic episode and at least one severe depressive episode
  • subtypes such as subliminal forms
  • bipolar disorder is the result of the interaction of genetic and environmental factors
  • diagnosis of bipolar disorder is still based on symptomatic evaluation, and there is still a lack of reliable biomarkers, which hinders bipolar disorder.
  • biomarkers which hinders bipolar disorder.
  • the risk identification of obstacles greatly increases the complexity and refractory of the treatment of the disease, and worsens the prognosis.
  • the purpose of the present invention is to provide biomarkers for bipolar disorder based on gut microbes and their screening applications, and provide guidance for the early identification of bipolar disorder, development trend prediction, and precise medication. To help research on the pathogenesis of bipolar disorder and targeted medication.
  • Bipolar disorder biomarkers based on intestinal microbes including many of the following 25 species: Gemella_morbillorum, Actinomyces_oris, Ruminococcus_gnavus, Barnesiella_intestinihominis, Coprobacillus_unclassified, Propionibacterium_propionicum, Fusobacterium_mortia_frequence, Corynebacterium_durum_frequens, Corynebacterium_durum_frequence, Corynebacterium_durum_frequence, Corynebacterium_durum_frequence Streptococcus_intermedius, Eubacterium_hallii, Paraprevotella_clara, Prevotella_copri, Alistipes_onderdonkii, Alistipes_sp_AP11, Faecalibacterium_prausnitzii, Coprococcus_sp_ART55_1, Prevotella_stercorea, Bacteroidal, Bacteroidal
  • the biomarkers are the following 6 types:
  • Biomarker 1 Ruminococcus-gnavus; importance 0.056007;
  • Biomarker 2 Eubacterium_hallii; importance 0.179768;
  • Biomarker 4 Faecalibacterium_prausnitzii; importance 0.098368;
  • Biomarker 5 Prevotella_stercorea; Importance 0.077305;
  • Biomarker 6 Bacteroidales_bacterium_ph8; Importance 0.069615;
  • the biomarkers are provided based on the calculation of their gene sequences.
  • the relative abundance information of the biomarkers is used for comparison with reference values.
  • An application of the biomarker as a detection target or a detection target in the preparation of a detection kit.
  • biomarker as a target in screening drugs for the treatment and/or prevention of bipolar disorder.
  • a method for screening said biomarkers the steps are as follows:
  • Sample collection Collect fecal samples of subjects including bipolar disorder patients and healthy controls, and store them in a refrigerator at -80°C for DNA sample extraction;
  • LDA linear discriminant analysis
  • the described screening method further uses the random forest model to predict and analyze, and the steps are as follows:
  • the sample subjects included 50 bipolar disorder patients and 50 healthy controls, and in the test set, the sample subjects included 12 bipolar disorder patients and 10 healthy controls.
  • the present invention analyzes the intestinal flora and gene sequences of patients with bipolar disorder and healthy people, thereby screening biomarkers with high correlation with bipolar disorder, and using the markers to diagnose or predict suffering from bipolar disorder The risk of emotional disorders.
  • Stool is the excrement of the body, in addition to the non-absorbed metabolites, it also contains intestinal microorganisms.
  • the study of stool samples reveals the difference in flora between patients with bipolar disorder and healthy people, and then accurately assesses the risk of patients with bipolar disorder, which is helpful for early diagnosis.
  • the present invention is based on the comparison and analysis of the intestinal microbes of patients with bipolar disorder and healthy controls, and obtains the difference flora between the two groups, and combines the relative abundance data of the difference flora between patients with bipolar disorder and healthy controls as Training set for risk assessment and early diagnosis of patients with bipolar disorder.
  • the biomarkers related to bipolar disorder proposed by the present invention have high value for early diagnosis of diseases.
  • Third, the markers of the present invention can also be used to monitor the response of patients with bipolar disorder to drug treatment.
  • Figure 1 shows the difference in the relative abundance of the flora between patients with bipolar disorder and healthy controls at the species level according to an embodiment of the present invention.
  • the diagram shows that there is a significant difference in the relative abundance of the flora between patients with bipolar disorder and healthy controls at different species levels.
  • Fig. 2 shows the error rate distribution of the classifier for 5 times of 10-fold cross-validation according to an embodiment of the present invention.
  • Figure 3 is a receiver operating characteristic (ROC) curve of a training set composed of bipolar disorder patients and healthy controls based on a random forest model (6 intestinal markers) according to an embodiment of the present invention and Area under Curve (AUC).
  • ROC receiver operating characteristic
  • Figure 4 is a receiver operating characteristic (ROC) curve of a test set composed of patients with bipolar disorder and healthy controls based on a random forest model (6 gut microbial markers) according to an embodiment of the present invention And the area under the curve (Area under Curve, AUC).
  • ROC receiver operating characteristic
  • Bipolar disorder is a group of chronic mental illnesses of unknown etiology and recurrent episodes. The age of onset is mainly concentrated in late adolescence or early adulthood. The main clinical features are depression, mania or hypomania alternate or mixed. It is manifested as dysfunction in perception, emotional processing, and cognition.
  • Biomarker generally refers to a certain characteristic biochemical index that can be used to objectively measure and evaluate the biological state of an individual, and it can be any specific organisms at different biological levels (individuals, cells, molecules) that reflect the body The substance of the state (such as disease). Including protein markers, antigen antibody markers, gene markers, functional markers and other fields. Among them, the gene marker includes all nucleic acid fragments, and can be any DNA, RNA, or a collection composed of them, or any other gene capable of expressing biologically active proteins. In the present invention, “biomarkers” can also be expressed as "intestinal microbial markers” and "intestinal flora", because the biomarkers related to bipolar disorder discovered in the present invention are all derived from Stool sample after intestinal metabolism of the subject.
  • the said biomarkers use high-throughput sequencing technology to analyze the stool samples of healthy people and patients with bipolar disorder in batches. By comparing the sequencing results of patients with bipolar disorder and healthy controls, the relative abundance information of biomarkers related to the group of patients with bipolar disorder is determined.
  • patients with bipolar disorder included in the group are hospitalized and/or outpatient patients who meet the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR).
  • DSM-IV-TR Diagnostic and Statistical Manual of Mental Disorders
  • the following entry criteria were met: 1) No drugs or other psychiatric drugs were taken for at least 3 months; 2) No obvious suicidal thoughts or previous suicide attempts; 3) No comorbidity with other mental disorders. Healthy subjects were recruited from the local community and did not have any family history of mental disorders or mental illnesses. Age, gender, and body mass index match between the two groups.
  • the exclusion criteria for all subjects include: 1) Chronic infections, serious systemic diseases (such as diabetes) and autoimmune diseases; 2) Consumption of antibiotics, probiotics or probiotics within 4 weeks before screening; 3) Current pregnancy , Breastfeeding or women with irregular menstruation; 4) history of traumatic brain injury; 5) magnetic resonance imaging (MRI) contraindications, such as metal implants or claustrophobia. All enrolled subjects with bipolar disorder were given quetiapine monotherapy (maintaining a daily dose of 200-300 mg). Store the collected stool samples in a refrigerator at -80°C within half an hour and keep them for later use.
  • Extract DNA Use QIAGENT DNA kit to extract DNA to obtain nucleic acid samples. Detect the degree of DNA degradation and potential contamination on a 1% agarose gel. use Fluroeter (Life Technologies, CA, USA) The dsDNA analysis kit measures the concentration.
  • DNA library sequencing was performed with Illumina NovaSeq 6000, the sequencing strategy was paired-end 150bp sequencing, and the quality control (QC) used custom scripts.
  • the sequence will be discarded: 1) Contains three or more ambiguous bases; 2) Contains 20 or more bases of low Phred quality (the threshold is Q20); 3) By using The CutAdap version 1.8.1 of the "-u6" parameter is designated as the adaptor sequence; 4) By using Bowtie2 version 2.3.4.2 with the "-very sensitive” parameter, it can be compared with the human genome reference (HG19). For double-ended sequences, if a sequence is considered to be a linker contamination or human genome, its matched sequence will also be filtered out. Finally, high-quality sequencing fragments (reads) are obtained.
  • Metaphlan2 analysis software was published in Nature Methods (MetaPhlAn2 for enhanced metadata taxonomic profiling, Nature Methods 12,902-903 (2015)) in 2015. This document provides the steps for the Metaphlan2 analysis software to calculate the relative abundance information of the species, as follows: 1) Compare the high-quality sequenced fragments with the reference marker gene; 2) Count the number of inserted fragments based on the comparison results; 3) Insert the The number of fragments is normalized to the length of the marker gene to obtain the corresponding abundance.
  • LDA Effect Size (LEfSe) analysis technology is used to analyze the difference in flora between groups based on the relative abundance of the flora, and to screen the difference in the relative abundance of the flora between the two groups of patients with bipolar disorder and the control group.
  • Genome Biol, 2011, 12(6): R60. are mainly divided into three steps: 1) First, use non The parameter factor Kruskal-Wallis rank sum test detects the relative abundance difference between the two groups and obtains the significant difference species; 2) Secondly, the Wilcoxon rank sum test is used to detect all subspecies of the species with significant differences obtained in the previous step. Whether the species tend to be at the same classification level; 3) Finally, linear discriminant analysis (LDA) is used to reduce the dimensionality of the data and evaluate the influence of the significantly different flora (ie LDA score) to obtain the final different species.
  • LDA linear discriminant analysis
  • the relative abundance of the flora obtained by sequencing 122 samples is input into the LEfSe online analysis webpage, and the result shows the relative abundance of 25 species of flora, including "Gemella_morbillorum “,”Actinomyces_oris”,”Ruminococcus_gnavus”,”Barnesiella_intestinihominis”,”Coprobacillus_unclassified”,”Propionibacterium_propionicum”,”Fusobacterium_mortiferum”,”Corynebacterium_durum”,”Clostridium_perfringens”,”Clostridium_perfringens”,”Clostridium_perfringens”,”Clostridium_perfringens”,”Clostridium_perfringens”,”Clostridium_perfringens”,”Clostridium_perfringens”,”Clostridium_perfringens”,” Clostridium_perfringens”,” “Adlercreutzia_e
  • Figure 1 uses LEFSE and LDA analysis to compare the different flora. Use LDA ⁇ 2 as the threshold for the significance of the difference. The LDA score showed significant differences in bacteria between untreated BD patients (right part, before) and healthy controls (left part, HC).
  • the random forest classifier was used to screen potential biomarkers of the risk of bipolar disorder.
  • the bipolar disorder test was constructed.
  • the training set and test set of gut microbial markers of human subjects and healthy control subjects, and the content value of biomarkers in the test set samples to be tested is evaluated.
  • the training set refers to a certain number of samples, age, height, weight and other epidemiological data matching bipolar disorder and healthy control subjects to test the stool samples of each biomarker content
  • the data set, the remaining samples are used as the test set regardless of whether the epidemiological data match.
  • the present invention selects 50 patients with bipolar disorder and 50 healthy people with matching epidemiological data such as age, height, weight, etc. from 122 samples (62 patients with bipolar disorder and 60 healthy people) as training Set (Table 1-1, 1-2, 1-3), and the remaining 22 samples (12 bipolar disorder patients and 10 healthy people) were used as the test set (Table 2). Then input the relative abundance information of the differential flora in the training set into the random forest (RF) classifier, and perform 5 10-fold cross-validation on the classifier. According to the cross-validation result ( Figure 2), the RF classifier finally selects 6 Two kinds of biomarkers are used as the optimal combination of markers to predict the risk of bipolar disorder.
  • the abscissa represents a different number of species combinations, and the ordinate represents the probability of making mistakes in predicting bipolar disorder.
  • the risk of bipolar disorder is calculated based on its relative abundance, the ROC curve is drawn, and the area under the curve (AUC) is calculated.
  • AUC area under the curve
  • Table 1-1 The relative abundance information of different bacterial groups in the training set
  • Table 1-3 The relative abundance information of different bacterial groups in the training set
  • Table 3 The accuracy of the training set using the relative abundance information of flora markers to predict the prevalence rate
  • BP_3 0.818744 s1B1065 0.668756 HC028 0.247163 BP_6 0.665534 s1B1067 0.777025 HC029 0.364967 BP_9 0.667716 s1B1068 0.510033 HC030 0.258746 BP21 0.650515 H_4 0.290338 HC031 0.306325 BP23 0.585875 H_44 0.288271 HC032 0.308022 HSH_104 0.56104 H_45 0.12369 HC033 0.091807 HSH_106 0.778131 H_50 0.521267 HC034 0.307086 HSH_109 0.509922 H_53 0.627444 HC035 0.350065 HSH_112 0.7741 H_56 0.476707 HC036 0.162977 HSH_120 0.603087 H_57 0.229628 HC038 0.302604 HSH_127 0.561353 H_62 0.316555 HC039 0.
  • the RF model to screen the relative abundance of the bacterial population in the training set, calculate the risk of bipolar disorder for each individual, draw the ROC curve, and calculate the AUC as the efficiency evaluation parameter of the discriminant model.
  • the specificity characterizes the probability of judging the right without disease
  • the sensitivity refers to the probability of judging the right with the disease.
  • the results show that the metabolite combination obtained from this model can be used as a potential biomarker to distinguish bipolar disorder from non-bipolar disorder (Figure 3).
  • Figure 4 shows the ROC curve and AUC of a test set composed of patients with bipolar disorder and healthy controls based on the random forest model (25 biomarkers), where the specific characterization is the probability of correcting the disease. Sensitivity refers to the probability of correcting the disease.
  • Table 4 shows the combination of 6 biomarkers to predict the probability of disease in the test set.
  • Table 5 shows the detailed information of the 6 biomarkers.
  • Table 4 The accuracy of predicting the prevalence of the test set using the relative abundance of flora markers
  • the technical means used in the examples are conventional means well known to those skilled in the art, and can be carried out with reference to the third edition of the "Molecular Cloning Experiment Guide” or related products.
  • the reagents and products used are also available. Commercially acquired.
  • the various processes and methods that are not described in detail are conventional methods known in the art.
  • the source of the reagents, trade names, and those that need to list their components are all indicated when they appear for the first time, and the same reagents used thereafter, if there is no special The description is the same as the content indicated for the first time.
  • the present invention adopts the analysis method of metagenomic association analysis (Metagenome-Wide Association Study, MWAS), and analyzes the bacterial composition and relative abundance of the fecal samples by sequencing; the LEfse analysis method is used to analyze the group of patients with bipolar disorder and healthy controls The difference in the relative abundance of the group flora; the random forest discriminant model is used to distinguish between the bipolar disorder group and the non-bipolar disorder group, and obtain the disease probability, which is used for the risk assessment of bipolar disorder.
  • metagenomic association analysis Methodagenome-Wide Association Study, MWAS
  • the Illumina NovaSeq 6000 sequencing and MWAS are well-known in the art, and those skilled in the art can make adjustments according to specific conditions. According to the embodiment of the present invention, it can be carried out according to the method described in the literature (Jun Wang, and Huijue Jia. Metagenome-wide association studies: fine-mining the microbiome. Nature Reviews Microbiology 14.8 (2016): 508-522.).
  • the method of using the random forest model and the ROC curve is well known in the art, and those skilled in the art can set and adjust the parameters according to specific conditions. According to the embodiment of the present invention, it can be based on the literature (Stephanie J., et al. Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2 diabetes: evidence for enhanced, mitochondrial oxidation. Diabetes 35: 605 611.).
  • a training set of biomarkers for subjects with bipolar disorder and subjects with non-bipolar disorder is constructed, and on this basis, the biomarker content value of the sample to be tested is evaluated.
  • the normal content value range (absolute value) of each biomarker in the sample can be obtained by using sample detection and calculation methods known in the art.
  • the absolute value of the detected biomarker content can be compared with the normal content value.
  • statistical methods can be combined to obtain the risk assessment and diagnosis of bipolar disorder and for monitoring bipolar disorder The efficiency of the treatment effect of the patient with the disorder, etc.
  • biomarkers are the intestinal flora present in the human body.
  • the subject's intestinal flora is associated with the analysis, and it is obtained that the biomarkers of the bipolar disorder population show a certain content range value in the flora detection.
  • the biomarker disclosed in the present invention has high accuracy and specificity, and has a good prospect of being developed as a diagnostic method, thereby providing a basis for disease risk assessment, diagnosis, and early diagnosis of bipolar disorder.
  • the biomarker combination for bipolar disorder based on the intestinal flora is used as a detection target or a detection target in the preparation of a detection kit.

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Abstract

提供基于肠道微生物的双相情感障碍生物标志物及其筛选应用。其中，生物标志物1)Ruminococcus‐gnavus；生物标志物2)Eubacterium_hallii；生物标志物3)Alistipes_onderdonkii；生物标志物4)Faecalibacterium_prausnitzii；生物标志物5)Prevotella_stercorea；生物标志物6)Bacteroidales_bacterium_ph8；上述6种中，Ruminococcus‐gnavus在双相情感障碍患者组中相对丰度升高，其余在双相情感障碍患者组中相对丰度降低。还提供所述的生物标志物作为检测靶点或检测目标在制备检测试剂盒中的应用，作为靶点在筛选治疗和/或者预防双相情感障碍的药物中的应用。

Description

基于肠道微生物的双相情感障碍生物标志物及其筛选应用 技术领域
本发明涉及基于肠道微生物的双相情感障碍生物标志物及其筛选应用。
背景技术
双相情感障碍是一组严重的、反复发作的慢性精神疾病，全球发病率约为2～3％，发病年龄主要集中在青春期后期或者成年早期，是引起全世界年轻人残疾的第四大原因。以抑郁、躁狂或者轻躁狂交替或混合发作为主要临床特征，包括双相情感障碍I型(至少有一个躁狂发作、不需要有精神病或重大抑郁发作)和双相情感障碍II型(患者至少有一次轻躁发作和至少一次严重抑郁发作)以及阈下形式等亚型，表现为感知觉、情绪处理以及认知等方面的功能障碍。即使经过治疗，残留的情绪症状往往仍然存在，大约37％的患者在1年内复发为抑郁症或躁狂症，60％的患者在2年内复发。致残率较高，平均寿命较一般人减少10‐20年，具有较高的致残率和死亡风险，其中因自杀而死亡的约有15％，因心血管疾病死亡的约为35％‐40％，严重损害患者正常的社会生活功能。
尽管目前的研究认为双相情感障碍的发生是遗传因素和环境因素相互作用的结果，但针对双相情感障碍的诊断仍基于症状学的评估，尚缺乏可靠的生物标志物，阻碍了双相情感障碍的风险识别，大大增加了该病治疗的复杂性和难治性，恶化了预后。
发明内容
为了克服现有技术的不足，本发明的目的是提供基于肠道微生物的双相情感障碍生物标志物及其筛选应用，为双相情感障碍的早期识别、发展趋势预测以及精准用药提供指导，进一步帮助双相情感障碍发病机理、靶向用药等方面的研究。
本发明通过以下技术方案实现：
基于肠道微生物的双相情感障碍生物标志物，包括以下25种中的多种：Gemella_morbillorum，Actinomyces_oris，Ruminococcus_gnavus，Barnesiella_intestinihominis，Coprobacillus_unclassified，Propionibacterium_propionicum，Fusobacterium_mortiferum，Corynebacterium_durum，Clostridium_perfringens，Rothia_aeria，Streptococcus_cristatus，Rothia_dentocariosa，Eubacterium_infirmum，Adlercreutzia_equolifaciens， Streptococcus_intermedius，Eubacterium_hallii，Paraprevotella_clara，Prevotella_copri，Alistipes_onderdonkii，Alistipes_sp_AP11，Faecalibacterium_prausnitzii，Coprococcus_sp_ART55_1，Prevotella_stercorea，Bacteroidales_bacterium_ph8，Bacteroides_plebeius；
其中，Propionibacterium_propionicum，Ruminococcus_gnavus，Corynebacterium_durum，Fusobacterium_mortiferum，Barnesiella_intestinihominis，Gemella_morbillorum，Eubacterium_infirmum，Rothia_dentocariosa，Rothia_aeria Streptococcus_cristatus，Streptococcus_intermedius，Adlercreutzia_equolifaciens，Coprobacillus_unclassified，Clostridium_perfringens，Actinomyces_oris菌群的相对丰度在双相情感障碍患者组中显示升高，剩余菌群的相对丰度在双相情感障碍患者组中则下降。
所述的生物标志物，为以下6种：
生物标志物1)Ruminococcus‐gnavus；重要性0.056007；
生物标志物2)Eubacterium_hallii；重要性0.179768；
生物标志物3)Alistipes_onderdonkii；重要性0.082099；
生物标志物4)Faecalibacterium_prausnitzii；重要性0.098368；
生物标志物5)Prevotella_stercorea；重要性0.077305；
生物标志物6)Bacteroidales_bacterium_ph8；重要性0.069615；
上述6种中，Ruminococcus‐gnavus在双相情感障碍患者组中相对丰度升高，其余在双相情感障碍患者组中相对丰度降低。
所述的生物标志物是基于对其基因序列的计算所提供的。
所述的生物标志物的相对丰度信息用于和参考值进行比较。
一种所述的生物标志物作为检测靶点或检测目标在制备检测试剂盒中的应用。
一种所述的生物标志物作为靶点在筛选治疗和/或者预防双相情感障碍的药物中的应用。
一种所述的生物标志物的筛选方法，步骤如下：
1)样本收集：收集样本受试者包括双相情感障碍患者和健康对照者的粪便样品，于‐80℃条件下在冰箱内保存，以便对其进行DNA样本的提取；
2)对提取的DNA样本进行宏基因组测序与组装，之后将高质量的测序片段输入到Metaphlan2软件，计算出物种的相对丰度，依据以下步骤：
2.1)将高质量的测序片段与参考基因进行比对；
2.2)根据比对后的结果统计插入片段的数量；
2.3)将插入片段的数量与参考基因的长度进行标准化，最终得到对应的丰度；
3)将上述所得双相情感障碍患者与健康对照物种的相对丰度信息输入到LDA Effect Size(LEfSe)系统，分析组间差异菌群，包括以下三个步骤：
3.1)首先，利用非参数因子Kruskal‐Wallis秩和检验检测两组之间的物种相对丰度差异，获得显著性差异物种；
3.2)其次，利用Wilcoxon秩和检验检测上一步所获得的具有显著性差异的物种的所有亚种是否趋向于同一分类级别；
3.3)最后用线性判别分析(LDA)，对数据进行降维、评估差异显著的菌群影响力(即LDA score)得到最终的差异物种。
所述的筛选方法，进一步使用随机森林模型预测分析，步骤如下：
4.1)从受试者样本集中选取年龄、身高、体重等流行病学资料相匹配的双相情感障碍患者和健康对照者作为训练集，其余样品作为测试集，计算训练集内每个样本中物种的相对丰度；
4.2)将训练集中物种的相对丰度信息输入随机森林(RF)分类器中，并对分类器进行5次10折的交叉验证，对利用RF模型筛选出的每一个物种，依据其相对丰度信息计算双相情感障碍的患病风险、绘制ROC曲线，并计算其曲线下面积(AUC)，将AUC作为判别模型效能评价的参数，在模型中输出每个物种的重要性指数，重要性指数越高，代表该标志物用来判别双相情感障碍和非双相情感障碍的重要性就越高。
所述训练集中，样本受试者包括50个双相情感障碍患者和50个健康对照，测试集中，样本受试者包括12个双相情感障碍患者和10个健康对照。
本发明的有益技术效果：
本发明通过对双相情感障碍患者和健康人群的肠道菌群以及基因序列进行分析，从而筛选出与双相情感障碍相关性高的生物标志物，并且利用该标志物诊断或者预测罹患双相情感障碍的风险。
粪便是机体的排泄物，其内除了包含不被吸收的代谢产物外，还包括有肠道微生物。对粪便样本进行研究，发现双相情感障碍患者和健康人群的差异菌群，进而准确地对双相情感障碍患者进行患病风险评估，有助于早期诊断。本发明基于对双相情感障碍患者和健康对照肠道微生物的比较和分析，得到两组之间的差异菌群，结合高质量的双相情感障碍患者和健康对照差异菌群相对丰度数据作为训练集，对双相情感障碍患者进行患病风险评估与早期 诊断。
本发明提出的双相情感障碍相关生物标记物对疾病的早期诊断具有较高价值。首先，粪便样本的可获得性，可操作性以及安全性和可负担性保证了患者的依从性。其次，粪便样本的检测基于测序技术实现，由此得到的标志物灵敏性和特异性均较高。最后，本发明所述的标记物还可以用于监测双相情感障碍患者对药物治疗的反应。
附图说明
图1为根据本发明一个实施例物种种水平上双相情感障碍患者和健康对照者的菌群相对丰度差异情况。图示表明，双相情感障碍患者和健康对照在不同种水平菌群相对丰度存在显著差异。
图2为根据本发明的一个实施例对分类器进行5次10折交叉验证的错误率分布情况。
图3为根据本发明的一个实施例基于随机森林模型(6个肠道标志物)，由双相情感障碍患者和健康对照组成的训练集的接收者操作特征(Receiver Operating Characteristic，ROC)曲线和曲线下面积(Area under Curve，AUC)。
图4为根据本发明的一个实施例基于随机森林模型(6个肠道微生物标志物)，由双相情感障碍患者和健康对照组成的测试集的接收者操作特征(Receiver Operating Characteristic，ROC)曲线和曲线下面积(Area under Curve，AUC)。
具体实施方式
本发明所用术语具有相关领域普通技术人员通常理解的含义。然而，为了更好地理解本发明，对一些定义和相关术语的解释如下：
“双相情感障碍”，是一组病因未明的、反复发作的慢性精神疾病，发病年龄主要集中在青春期后期或者成年早期，以抑郁、躁狂或者轻躁狂交替或混合发作为主要临床特征，表现为感知觉、情绪处理以及认知等方面的功能障碍。
“生物标志物”，一般是指可供客观测定和评价个体生物状态的某种具有特征性的生物化学指标，可以是处于不同生物学水平(个体、细胞、分子)上的任何反映机体特定生物状态(如疾病)的物质。包含蛋白质标志物、抗原抗体标志物、基因标志物、功能标志物等多种领域。其中基因标志物，包含所有的核酸片段，可以是任何经过或者未经过修饰的DNA、RNA或者是由它们组成的集合，以及其他任何能够表达具有生物活性蛋白质的基因。在本发明中，“生物标志物”也可以用“肠道微生物标志物”、“肠道菌群”来表示，因为本发明所发现的与双相情感障碍相关的生物标志物均来自于经受试者肠道代谢后的粪便样本。
所述的生物标志物，通过运用高通量测序的技术，批量分析健康人群和双相情感障碍患者的粪便样本。通过将双相情感障碍患者与健康对照的测序结果进行比对，进而确定与双相情感障碍患者群相关的生物标志物的行相对丰度信息。
实施例
样品的收集与处理：本发明共收集受试者包括双相情感障碍患者(n＝62)与健康对照(n＝60)的粪便样本。其中，在本发明中，入组的双相情感障碍患者为符合《精神疾病的诊断和统计手册》(DSM‐IV‐TR)诊断依据的住院和/或门诊患者。同时符合以下入组标准：1)至少3个月内未服用药物或其他精神科药物；2)没有明显的自杀想法或既往自杀未遂；3)没有与其他精神障碍共病。健康受试者从当地社区招募，没有任何精神障碍和精神疾病家族史。两组间年龄、性别以及体重指数相匹配。所有受试者的排除标准包括：1)慢性感染、严重的系统性疾病(如糖尿病)和自身免疫性疾病；2)筛查前4周内食用抗生素、益生菌或益生菌；3)目前怀孕、哺乳或月经不调的女性；4)颅脑外伤史；5)磁共振成像(MRI)的禁忌症，如金属植入物或幽闭恐惧症。所有入组的双相情感障碍受试者给予喹硫平单药治疗(维持每日200～300mg剂量)。将收集到的粪便样本于半个小时之内在‐80℃冰箱冷藏，保存备用。
提取DNA：使用QIAGENT DNA试剂盒提取DNA，得到核酸样本。在1％琼脂糖凝胶上检测DNA降解程度和潜在污染。使用
Figure PCTCN2020081944-appb-000001
Fluroeter(Life Technologies，CA，USA)中的
Figure PCTCN2020081944-appb-000002
dsDNA分析试剂盒测量浓度。
文库构建和测序：用Covaris随机剪切DNA，然后使用NEBNextUltra TM DNA Library Prep Kit for Illumina(NEB，美国)构建DNA文库。用Illumina NovaSeq 6000进行DNA文库测序，测序策略为双端150bp测序，质量控制(QC)采用自定义脚本。如果满足以下任何标准，则序列将被丢弃：1)包含三个或更多不明确碱基；2)包含20个或更多低Phred质量碱基(阈值是Q20)；3)通过使用带有“‐u6”参数的CutAdap1.8.1版指定为适配序列；4)通过使用带有“‐非常敏感”参数的Bowtie2版本2.3.4.2，可以与人类基因组参考(HG19)进行比对。对于双端序列，如果一个序列被认为是接头污染或人类基因组，那么它的配对序列也会被过滤掉。最后，得到高质量的测序片段(reads)。
将上述所述的高质量测序片段(reads)输入到软件Metaphlan2(http://segatalab.cibio.unitn.it/tools/metaphlan2/)执行完成相应的命令即可计算出微生物物种的相对丰度信息。
Metaphlan2分析软件于2015年发表于Nature Methods(MetaPhlAn2  for enhanced metagenomic taxonomic profiling，Nature Methods 12,902‐903(2015))。该文献提供了Metaphlan2分析软件计算物种相对丰度信息的步骤，具体如下：1)将高质量测序片段与参考的标记基因比对；2)根据比对结果统计插入片段的数量；3)将插入片段的数量对标记基因的长度进行标准化，得到对应的丰度。
利用LDA Effect Size(LEfSe)分析技术基于菌群相对丰度分析组间差异菌群，筛选双相情感障碍患者与对照组两组之间菌群相对丰度的差异情况。
将上述所述双相情感障碍患者与健康对照组菌群相对丰度信息输入到LEfSe在线分析网页(http://huttenhower.sph.harvard.edu/galaxy)进行双相情感障碍患者组与健康对照组两组之间的菌群相对丰度的差异性比较分析。参照文献(Segata N,Izard J,Waldron L,et al.Metagenomic biomarker discovery and explanation[J].Genome Biol,2011,12(6):R60.)主要分为三个步骤：1)首先，利用非参数因子Kruskal‐Wallis秩和检验检测两组之间的物种相对丰度差异，获得显著性差异物种；2)其次，利用Wilcoxon秩和检验检测上一步所获得的具有显著性差异的物种的所有亚种是否趋向于同一分类级别；3)最后用线性判别分析(LDA)，对数据进行降维、评估差异显著的菌群影响力(即LDA score)得到最终的差异物种。
具体包括如下步骤：
本发明将122个样本(62个双相情感障碍患者和60个健康对照)经测序后得到的菌群相对丰度输入LEfSe在线分析网页，结果显示25种菌群的相对丰度，包括"Gemella_morbillorum","Actinomyces_oris","Ruminococcus_gnavus","Barnesiella_intestinihominis","Coprobacillus_unclassified","Propionibacterium_propionicum","Fusobacterium_mortiferum","Corynebacterium_durum","Clostridium_perfringens","Rothia_aeria","Streptococcus_cristatus","Rothia_dentocariosa","Eubacterium_infirmum","Adlercreutzia_equolifaciens","Streptococcus_intermedius","Eubacterium_hallii","Paraprevotella_clara","Prevotella_copri","Alistipes_onderdonkii","Alistipes_sp_AP11","Faecalibacterium_prausnitzii","Coprococcus_sp_ART55_1","Prevotella_stercorea","Bacteroidales_bacterium_ph8","Bacteroides_plebeius"在双相情感障碍患者组与健康对照组中存在明显的差异，其中"Propionibacterium_propionicum","Ruminococcus_gnavus","Corynebacterium_durum","Fusobacterium_mortiferum","Barnesiella_intestinihominis","Gemella_morbillorum","Eubacterium_infirmum","Rothia_dentocariosa",Rothia_aeria"Streptococcus_cristatus","Streptococcus_intermedius","Adlercreutzia_equolifaciens","Coprobacill us_unclassified","Clostridium_perfringens","Actinomyces_oris"菌群的相对丰度在双相情感障碍患者组中显示升高，剩余菌群的相对丰度在双相情感障碍患者组中则下降。图1采用LEFSE和LDA分析比较差异菌群相。以LDA≥2作为差异有显著性的阈值。LDA评分显示未经治疗的BD患者(右部分，before)和健康对照组(左部分，HC)之间的细菌差异显著。
利用随机森林分类器筛选双相情感障碍发生风险的潜在生物标志物。
为进一步筛选双相情感障碍肠道微生物标志物，以“LEfSe分析技术筛选双相情感障碍患者与对照组两组之间菌群相对丰度的差异情况”为基础，构建双相情感障碍受试者和健康对照受试者肠道微生物标志物的训练集和测试集，并评估待测的测试集样本生物标志物的含量值。在本发明中，训练集是指具有一定样本数量的、年龄、身高、体重等流行病学资料相匹配的双相情感障碍和健康对照受试者待测粪便样本中的各生物标志物含量的数据集合，剩余样品无论流行病学资料是否相匹配均作为测试集。
具体包括如下步骤：
本发明从122个样品(62个双相情感障碍病人和60个健康人)中，选取年龄、身高、体重等流行病学资料相匹配的50个双相情感障碍患者和50个健康人作为训练集(表1‐1、1‐2、1‐3)，剩余的22个样品(12个双相情感障碍患者和10个健康人)作为测试集(表2)。然后将训练集中差异菌群的相对丰度信息输入随机森林(RF)分类器中，并对分类器进行5次10折的交叉验证，依据交叉验证结果(图2)，RF分类器最终选取6种生物标记物作为最优标志物组合数来进行预测双相情感障碍疾病的风险。图2中，横坐标代表不同数量的物种组合，纵坐标代表预测双相情感障碍疾病时犯错误的概率。黑色的竖线(n＝6)代表5次10折交叉验证后所取物种组合的平均值。利用RF模型筛选出的每一个物种，依据其相对丰度计算双相情感障碍的患病风险、绘制ROC曲线，并计算其曲线下面积(AUC)。(预测双相情感障碍训练集差异菌群的相对丰度信息如下表1‐1、1‐2、1‐3，测试集差异菌群的相对丰度信息如下表2，表3显示出了6种生物标记物结合来预测训练集的患病概率)。
表1-1训练集差异菌群相对丰度信息
Figure PCTCN2020081944-appb-000003
注释：B、BP、S1B、HSH：双相情感障碍；H、HC：健康人
表1-2训练集差异菌群相对丰度信息
Figure PCTCN2020081944-appb-000004
注释：B、BP、S1B、HSH：双相情感障碍；H、HC：健康人
表1-3训练集差异菌群相对丰度信息
Figure PCTCN2020081944-appb-000005
注释：B、BP、S1B、HSH：双相情感障碍；H、HC：健康人
表2测试集差异菌群相对丰度信息
Figure PCTCN2020081944-appb-000006
注释：s1B：双相情感障碍；H：健康人
表3训练集利用菌群标志物相对丰度信息预测患病率的准确性
ID Accuracy ID Accuracy ID Accuracy
s1B1050 0.812849 HSH_44 0.567046 HC011 0.346077
s1B1055 0.557355 HSH_5 0.796871 HC012 0.277077
B1004 0.767324 HSH_54 0.808823 HC013 0.379269
B1038 0.585916 HSH_62 0.405607 HC014 0.501242
B1047 0.797622 HSH_76 0.843215 HC015 0.226487
B1066 0.637646 HSH_79 0.595853 HC016 0.133485
B1072 0.729712 HSH_88 0.513533 HC017 0.277374
B1076 0.865263 HSH_92 0.415546 HC019 0.585155
B1103 0.424417 HSH_96 0.828901 HC020 0.288213
B1111 0.804256 HSH_98 0.818419 HC021 0.327576
B2001 0.838626 s1B1007 0.46501 HC022 0.640315
BP_13 0.724254 s1B1008 0.608599 HC023 0.207575
BP_2 0.493857 s1B1063 0.501529 HC027 0.200377
BP_3 0.818744 s1B1065 0.668756 HC028 0.247163
BP_6 0.665534 s1B1067 0.777025 HC029 0.364967
BP_9 0.667716 s1B1068 0.510033 HC030 0.258746
BP21 0.650515 H_4 0.290338 HC031 0.306325
BP23 0.585875 H_44 0.288271 HC032 0.308022
HSH_104 0.56104 H_45 0.12369 HC033 0.091807
HSH_106 0.778131 H_50 0.521267 HC034 0.307086
HSH_109 0.509922 H_53 0.627444 HC035 0.350065
HSH_112 0.7741 H_56 0.476707 HC036 0.162977
HSH_120 0.603087 H_57 0.229628 HC038 0.302604
HSH_127 0.561353 H_62 0.316555 HC039 0.16876
HSH_128 0.777423 H_7 0.250399 HC040 0.340343
HSH_133 0.692059 H_8 0.153299 HC041 0.161086
HSH_138 0.689465 H_9 0.158859 HC042 0.302094
HSH_157 0.828337 H18 0.529342 HC044 0.391107
HSH_160 0.664228 HC001 0.506001 HC045 0.379987
HSH_21 0.533279 HC002 0.390909 HC046 0.318514
HSH_27 0.614566 HC005 0.114416 HC047 0.178105
HSH_31 0.705096 HC008 0.811865 HC048 0.190459
HSH_33 0.802478 HC009 0.330069    
HSH_39 0.830639 HC010 0.44499    
注释：B、BP、S1B、HSH：双相情感障碍；H、HC：健康人
利用RF模型筛选训练集中菌群相对丰度对每一个体计算其双相情感障碍患病风险，绘制ROC曲线，并计算出AUC，作为判别模型效能评价参数。其中特异性表征的是对于不患病判对的概率，敏感性指的是对于患病判对的概率，对训练集样本的判别效能为：AUC＝94.7％，95％置信区间CI＝90.4％～99.1％。结果表明该模型所得代谢物组合可作为区分双相情感障碍与非双相情感障碍的潜在生物标志物(图3)。
图4示出了基于随机森林模型(25个生物标志物)由双相情感障碍患者和健康对照组成的测试集的ROC曲线和AUC，其中特异性表征的是对于不患病判对的概率，敏感性指的是对于患病判对的概率，对训练集样本的判别效能为：AUC＝74.2％，95％置信区间CI＝51.9～96.5％。结果表明该模型所得代谢物组合可作为区分双相情感障碍与非双相情感障碍的潜在生物标志物。
表4示出了6种生物标记物结合来预测测试集的患病概率。
表5示出了6种生物标记物的详细信息。
表4测试集利用菌群标志物相对丰度预测患病率的准确性
ID Accuracy ID Accuracy
s1B1069 0.690738 s1B2026 0.160341
s1B1094 0.857345 H_14 0.418581
s1B1098 0.346751 H_15 0.795526
s1B2002 0.298095 H_16 0.266394
s1B2005 0.546693 H_17 0.296719
s1B2006 0.54932 H_20 0.281484
s1B2007 0.717752 H_23 0.203055
s1B2008 0.266804 H_27 0.266204
s1B2010 0.682385 H_3 0.218626
s1B2018 0.524695 H_30 0.354145
s1B2020 0.711019 H_31 0.495924
注释：s1B：双相情感障碍；H：健康人
表5
Figure PCTCN2020081944-appb-000007
若未特别指明，实施例中所采用的技术手段为本领域技术人员所熟知的常规手段，可以参照《分子克隆实验指南》第三版或者相关产品进行，所采用的试剂和产品也均为可商业获得的。未详细描述的各种过程和方法是本领域中公知的常规方法，所用试剂的来源、商品名以及有必要列出其组成成分者，均在首次出现时标明，其后所用相同试剂如无特殊说明，均与首次标明的内容相同。
本发明采用宏基因组关联分析(Metagenome‐Wide Association Study,MWAS)的分析方法，经测序分析粪便样本的菌群组成及菌群相对丰度；用LEfse分析方法分析双相情感障碍患者组和健康对照组菌群相对丰度的差异情况；用随机森林判别模型判别双相情感障碍群体和非双相情感障碍群体，获得患病概率，用于双相情感障碍的患病风险评估。
在本发明中，所述的Illumina NovaSeq 6000测序和MWAS具有本领域所公知，本领域技术人员可以根据具体情况进行调整。根据本发明的实施例，可以依据文献(Jun Wang,and Huijue Jia.Metagenome‐wide association studies:fine‐mining the  microbiome.Nature Reviews Microbiology 14.8(2016):508‐522.)中记载的方法进行。
在本发明中，随机森林模型和ROC曲线的使用方法为本领域所公知，本领域技术人员可以根据具体情况进行参数设置和调整。根据本发明的实施例，可以根据文献(Stephanie J.,et al.Metabolomic profiling of fatty acid and amino acid metabolism in youth with obesity and type 2 diabetes:evidence for enhanced mitochondrial oxidation.Diabetes Care 2012,35:605‐611.)中记载的方法进行。
在本发明中，构建了双相情感障碍受试者和非双相情感障碍受试者生物标志物的训练集，在此基础上，对待测样本的生物标志物含量值进行评估。
本领域技术人员知晓，当进一步扩大样本量时，利用本领域公知的样本检测和计算方法，可以得出每种生物标志物在样本中的正常含量值区间(绝对数值)。可以将检测得到的生物标志物含量的绝对值与正常含量值进行比较，任选地，还可以结合统计学方法，以得出双相情感障碍患病风险评价、诊断以及用于监控双相情感障碍患者的治疗效果的效率等。
发明人指出这些生物标志物是存在于人体中的肠道菌群。通过本发明所述的方法对受试者肠道菌群进行关联分析，得到双相情感障碍群体的所述生物标志物在菌群检测中表现出一定的含量范围值。
以上结果表明，本发明公开的生物标志物具有较高的准确度和特异性，具有良好的开发为诊断方法的前景，从而为双相情感障碍的患病风险评估、诊断、早期诊断提供依据。
因此，本发明提出以下应用：
所述的基于肠道菌群的双相情感障碍生物标志物组合作为检测靶点或检测目标在制备检测试剂盒中的应用。
所述的基于肠道菌群的双相情感障碍生物标志物组合作为靶点在筛选双相情感障碍预测风险中的应用。
尽管上面已经示出和描述了本发明的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本发明的限制，本领域的普通技术人员在本发明的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (9)

  1. 基于肠道微生物的双相情感障碍生物标志物，其特征在于，包括以下25种中的多种：
    Gemella_morbillorum，Actinomyces_oris，Ruminococcus_gnavus，Barnesiella_intestinihominis，Coprobacillus_unclassified，Propionibacterium_propionicum，Fusobacterium_mortiferum，Corynebacterium_durum，Clostridium_perfringens，Rothia_aeria，Streptococcus_cristatus，Rothia_dentocariosa，Eubacterium_infirmum，Adlercreutzia_equolifaciens，Streptococcus_intermedius，Eubacterium_hallii，Paraprevotella_clara，Prevotella_copri，Alistipes_onderdonkii，Alistipes_sp_AP11，Faecalibacterium_prausnitzii，Coprococcus_sp_ART55_1，Prevotella_stercorea，Bacteroidales_bacterium_ph8，Bacteroides_plebeius；
    其中，Propionibacterium_propionicum，Ruminococcus_gnavus，Corynebacterium_durum，Fusobacterium_mortiferum，Barnesiella_intestinihominis，Gemella_morbillorum，Eubacterium_infirmum，Rothia_dentocariosa，Rothia_aeria Streptococcus_cristatus，Streptococcus_intermedius，Adlercreutzia_equolifaciens，Coprobacillus_unclassified，Clostridium_perfringens，Actinomyces_oris菌群的相对丰度在双相情感障碍患者组中显示升高，剩余菌群的相对丰度在双相情感障碍患者组中则下降。
  2. 根据权利要求1所述的生物标志物，其特征在于，为以下6种：
    生物标志物1)Ruminococcus‐gnavus；
    生物标志物2)Eubacterium_hallii；
    生物标志物3)Alistipes_onderdonkii；
    生物标志物4)Faecalibacterium_prausnitzii；
    生物标志物5)Prevotella_stercorea；
    生物标志物6)Bacteroidales_bacterium_ph8；
    上述6种中，Ruminococcus‐gnavus在双相情感障碍患者组中相对丰度升高，其余在双相情感障碍患者组中相对丰度降低。
  3. 根据权利要求1所述的生物标志物，其特征在于，所述的生物标志物是基于对其基因序列的计算所提供的。
  4. 根据权利要求1或者2所述的生物标志物，其特征在于，所述的生物标志物的相对丰度信息用于和参考值进行比较。
  5. 一种根据权利要求1或者2所述的生物标志物作为检测靶点或检测目标在制备检测试剂盒中的应用。
  6. 一种根据权利要求1或者2所述的生物标志物作为靶点在筛选治疗和/或者预防双相情感障碍的药物中的应用。
  7. 一种根据权利要求1所述的生物标志物的筛选方法，其特征在于，步骤如下：
    1)样本收集：收集样本受试者包括双相情感障碍患者和健康对照者的粪便样品，于‐80℃条件下在冰箱内保存，以便对其进行DNA样本的提取；
    2)对提取的DNA样本进行宏基因组测序与组装，之后将高质量的测序片段输入到Metaphlan2软件，计算出物种的相对丰度，依据以下步骤：
    2.1)将高质量的测序片段与参考基因进行比对；
    2.2)根据比对后的结果统计插入片段的数量；
    2.3)将插入片段的数量与参考基因的长度进行标准化，最终得到对应的丰度；
    3)将上述所得双相情感障碍患者与健康对照物种的相对丰度信息输入到LDA Effect Size(LEfSe)系统，分析组间差异菌群，包括以下三个步骤：
    3.1)首先，利用非参数因子Kruskal‐Wallis秩和检验检测两组之间的物种相对丰度差异，获得显著性差异物种；
    3.2)其次，利用Wilcoxon秩和检验检测上一步所获得的具有显著性差异的物种的所有亚种是否趋向于同一分类级别；
    3.3)最后用线性判别分析(LDA)，对数据进行降维、评估差异显著的菌群影响力(即LDA score)得到最终的差异物种。
  8. 根据权利要求7所述的筛选方法，其特征在于，进一步使用随机森林模型预测分析，步骤如下：
    4.1)从受试者样本集中选取年龄、身高、体重等流行病学资料相匹配的双相情感障碍患者和健康对照者作为训练集，其余样品作为测试集，计算训练集内每个样本中物种的相对丰度；
    4.2)将训练集中物种的相对丰度信息输入随机森林(RF)分类器中，并对分类器进行5次10折的交叉验证，对利用RF模型筛选出的每一个物种，依据其相对丰度信息计算双相情感障碍的患病风险、绘制ROC曲线，并计算其曲线下面积(AUC)，将AUC作为判别模型效能评价的参数，在模型中输出每个物种的重要性指数，重要性指数越高，代表该标志物用来判别双相情感障碍和非双相情感障碍的重要性就越高。
  9. 根据权利要求8所述的筛选方法，其特征在于，所述训练集中，样本受试者包括50个双相情感障碍患者和50个健康对照，测试集中，样本受试者包括12个双相情感障碍患者和10个健康对照。
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