WO2021184413A1 - 双相情感障碍疗效预测的肠道微生物标志物及其筛选应用 - Google Patents

双相情感障碍疗效预测的肠道微生物标志物及其筛选应用 Download PDF

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WO2021184413A1
WO2021184413A1 PCT/CN2020/081945 CN2020081945W WO2021184413A1 WO 2021184413 A1 WO2021184413 A1 WO 2021184413A1 CN 2020081945 W CN2020081945 W CN 2020081945W WO 2021184413 A1 WO2021184413 A1 WO 2021184413A1
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bipolar disorder
treatment
biomarker
patients
relative abundance
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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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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids

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  • the invention relates to an intestinal microbial marker for predicting the curative effect of bipolar disorder and its screening application.
  • Bipolar disorder is a type of chronic mental illness with alternating or mixed depression, mania, or hypomania as the main clinical features.
  • the etiology is unknown.
  • the age of onset is mainly concentrated in early adulthood or late adolescence.
  • the global incidence is high, about 2 to 3%, related to obvious damage to occupational, personal and social functions, and accompanied by physical diseases and early death.
  • the treatment of bipolar disorder is mainly based on drug therapy, supplemented by the comprehensive treatment principle of physical therapy and psychotherapy. Even after treatment, residual emotional symptoms often still exist. About 37% of patients relapse as depression or mania within 1 year, and 60% of patients relapse within 2 years. Repeated attacks for many times severely damage the brain function of patients, prolong the time of drug treatment, and reduce the effectiveness of treatment.
  • bipolar disorder is the result of the interaction of genetic factors and environmental factors, and changes in the nervous, immune, and endocrine systems are also shown to varying degrees, it is aimed at the early stage of the treatment of bipolar disorder. It is predicted that there is still a lack of specific biomarkers.
  • the purpose of the present invention is to provide gut microbial markers for predicting the curative effect of bipolar disorder and their screening applications.
  • Biomarkers for predicting the efficacy of bipolar disorder based on gut microbes including many of the following 6 types: “Bacteroides_clarus”, “Eubacterium_biforme”, “Weissella_confusa”, “Ruminococcus_torques”, “Bifidobacterium_dentium”, “Collinsella_unclassified” in bipolar
  • biomarkers for predicting the efficacy of bipolar disorder based on gut microbes including many of the following 6 types: “Bacteroides_clarus”, “Eubacterium_biforme”, “Weissella_confusa”, “Ruminococcus_torques”, “Bifidobacterium_dentium”, “Collinsella_unclassified” in bipolar
  • the biomarkers are the following three types:
  • Biomarker 1 Eubacterium_biforme; importance 0.15259;
  • Biomarker 2 Ruminococcus_torques; importance 0.339395;
  • the relative abundance of the above-mentioned biomarker combination in the treatment-effective group of patients with bipolar disorder is significantly increased.
  • 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.
  • a screening method based on the biomarker the steps are as follows:
  • Sample collection Collect samples of subjects including fecal samples from patients with bipolar disorder before treatment, and store them in the refrigerator at -80°C for later use;
  • the described screening method further uses the random forest model to predict and analyze, and the steps are as follows:
  • the treatment-effective group and the treatment-ineffective group of patients with bipolar disorder are the training set, and the remaining samples are used as the test set, and the relative abundance of the species in each sample in the training set is calculated;
  • the sample subjects included 27 patients with effective treatment of bipolar disorder and 14 patients with ineffective treatment, and in the test set, the sample subjects included 8 patients with effective treatment of bipolar disorder and 5 patients with ineffective treatment.
  • the present invention analyzes the intestinal flora and gene sequence of the effective and ineffective groups of patients with bipolar disorder to screen out biomarkers that are highly correlated with the curative effect of bipolar disorder, and use the markers It can accurately predict the curative effect of bipolar disorder and monitor the treatment effect.
  • the response of the relevant biomarkers of patients with bipolar disorder proposed in the present invention to the therapeutic effect is valuable.
  • stool sample extraction is portable and non-invasive, which can increase patient compliance. at the same time.
  • stool samples are transportable, and sample analysis is accurate and safe.
  • the markers of the present invention have high specificity and sensitivity, and can be used for the prediction of therapeutic efficacy.
  • Fig. 1 shows the difference in the relative abundance of the flora between the treatment-effective group and the treatment-ineffective group of patients with bipolar disorder 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 bacterial flora in the treatment-effective group and the treatment-ineffective group in patients with bipolar disorder.
  • 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.
  • Fig. 3 is based on a random forest model (3 gut markers) according to an embodiment of the present invention, and the receiver operating characteristic (Receiver Operating Characteristic, ROC) curve and the area under the curve (Area under Curve, AUC).
  • ROC Receiveiver Operating Characteristic
  • Figure 4 shows the receiver operating characteristic ROC curve and curve of a test set consisting of a treatment-effective group and a treatment-ineffective group of patients with bipolar disorder based on a random forest model (3 gut microbial markers) according to an embodiment of the present invention Area under AUC.
  • Bipolar disorder is a type of chronic mental illness that has both depressive episodes and manic episodes. It is recurrent. The patient has obstacles in emotional processing and cognitive function, which seriously affects the patient's normal social life And other functions. About 1% of the world's comorbid population is one of the four major causes of adolescent disability, but the pathogenesis has not yet been fully elucidated. The current view is that it is closely related to the interaction between genetics and the environment.
  • Therapeutic effect that is, “therapeutic effect” refers to the effect of treatment of diseases by means of drugs or surgery. It is mainly based on the four-level evaluation criteria, including recovery, marked effect, progress, and ineffectiveness. The “curative effect” in this study mainly refers to "significantly effective” and “ineffective”. Because in the present invention, the treatment of bipolar disorder is a single drug treatment, and whether the change rate of the score of the 17 Hamilton Depression Scale (HDRS-17) scale is ⁇ 50% is used to evaluate the drug treatment for 1 month Is it valid afterwards?
  • HDRS-17 Hamilton Depression Scale
  • Biomarkers are indicators that can be used to determine the cause of a disease, diagnose early, and evaluate the occurrence and development of the disease, or to evaluate the efficacy or safety of drug treatment in the target population. It mainly includes any substances at different biological levels (individuals, cells, molecules) that reflect the specific biological state of the body (such as diseases), such as small molecular substances such as blood sugar for evaluating diabetes, and large molecular substances such as proteins, nucleic acids, and lipids.
  • diseases such as small molecular substances such as blood sugar for evaluating diabetes
  • large molecular substances such as proteins, nucleic acids, and lipids.
  • biomarkers can also be expressed as "intestinal microbes” and "intestinal flora", because the biomarkers related to bipolar disorder discovered in this study are all derived from subjects Stool sample after intestinal metabolism.
  • the biomarker of the present invention uses high-throughput sequencing to batch analyze stool samples of the treatment-effective group and the treatment-ineffective group of patients with bipolar disorder. Based on high-throughput sequencing data, the treatment-effective group and the treatment-ineffective group of patients with bipolar disorder are compared, so as to determine the specific flora related to the prediction of the curative effect of patients with bipolar disorder.
  • Sample collection and processing Collect the stool samples of 62 cases of bipolar disorder, collect the stool samples and distribute them into labeled cryotubes, transport them under freezing conditions and transfer them to -80°C refrigerator for storage for later use.
  • the subject of the present invention is based on inpatient and/or outpatient patients who meet the diagnostic criteria for bipolar disorder in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR).
  • DSM-IV-TR Diagnostic and Statistical Manual of Mental Disorders
  • the exclusion criteria are: 1) Chronic infection, 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 menstruation Irregular women; 4) History of traumatic brain injury; 5) Contraindications of magnetic resonance imaging (MRI), such as metal implants or claustrophobia.
  • MRI magnetic resonance imaging
  • HDRS-17 17-item Hamilton Depression Scale
  • MADRS Montgomery-Asperger Depression Scale
  • YMRS Youth Mania Scale
  • HDRS-17 score not less than 14 points is set as the threshold for the current depressive episode.
  • the LDA Effect Size (LEfSe) analysis technology was used to analyze the difference in the relative abundance of the flora between the treatment effective group and the treatment ineffective group.
  • the non-parametric factor Kruskal-Wallis rank sum test is used to detect the relative abundance difference between the two groups to obtain significantly different species; 2) Second, use the Wilcoxon rank sum test to detect whether all subspecies of the species with significant differences obtained in the previous step tend to the same classification level; 3) Finally, use linear discriminant analysis (LDA) to reduce the dimensionality and evaluate the data The significant difference in the influence of the flora (ie LDA score) results in the final difference species (refer to the literature: Segata N, et al. Metagenomic biomarker discovery and explanation [J]. Genome Biol, 2011, 12(6): R60.).
  • LDA linear discriminant analysis
  • 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 shows that there is a significant difference in bacteria between the BD patients in the treatment-ineffective group (right part, uneffective) and the treatment effective group (left, effective).
  • the random forest classifier was used to screen potential biomarkers of curative effect prediction during the treatment of bipolar disorder.
  • the treatment of bipolar disorder was constructed.
  • the training set and test set of the gut microbial markers of the effective group and the ineffective group, and the content value of the biomarker of the test set sample to be tested is evaluated.
  • the training set refers to a data set of the content of each biomarker in the samples to be tested for subjects in the effective and ineffective groups of bipolar disorder treatment with a certain number of samples.
  • the present invention selects 27 bipolar disorder patients and 14 healthy people as the training set from 54 samples (35 treatment-effective bipolar disorder patient groups and 19 treatment-ineffective bipolar disorder patient groups). The remaining 13 samples (8 patients with bipolar disorder and 5 healthy people) were used as the test set.
  • the present invention performs five 10-fold cross-validation on the RF classifier ( Figure 2 shows the error rate distribution of the 5-fold 10-fold cross-validation in the random forest classifier).
  • the present invention is based on 5 times of 10-fold cross-validation results, and the RF classifier finally selects 3 optimal biomarker combinations.
  • the detailed information of the relative abundance of microbial markers in the training set is shown in Tables 1-1 and 1-2. Test The detailed relative abundance information of microbial markers in the set is shown in Table 2 below. Table 3 shows the combination of three biomarkers to predict the curative effect probability of the training set).
  • Table 1-1 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 therapeutic effect
  • 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.
  • the results show that the combination of markers obtained from this model can be used as a potential biomarker for predicting the efficacy of treatment of bipolar disorder and that of treatment failure.
  • Table 4 shows the combination of 3 biomarkers to predict the probability of disease in the test set.
  • Table 5 shows the detailed information of the 3 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 composition and relative abundance of the fecal samples by sequencing; the Lefse analysis method is used to analyze the effective groups and the groups of patients with bipolar disorder. The difference in the relative abundance of the bacteria in the treatment ineffective group; the random forest discriminant model is used to distinguish between the effective and ineffective groups of bipolar disorder to obtain the disease probability, which can be used to predict and evaluate the efficacy of bipolar disorder or to find potential Drug targets.
  • metagenomic association analysis Methodagenome-Wide Association Study, MWAS
  • MWAS Metagenomic association analysis
  • the sequencing (second-generation 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 performed 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 (Drogan D, et al. Untargeted Metabolic Profiling Identifies Alter Serum Metabolites of Type 2 Diab etes Melitus in a Prospective, Nested Case Control Study. Clin Chem 2015, 61:487-497 ;) The method described in.
  • a training set of biomarkers of subjects in the treatment-effective group and the treatment-ineffective group of patients with bipolar disorder is constructed, and on this basis, the biomarker content value of the sample to be tested is evaluated.
  • 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.
  • biomarker disclosed in the present invention has high accuracy and specificity, and provides a basis for predicting the therapeutic effect of bipolar disorder and searching for potential drug targets.
  • 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.
  • biomarker combination based on the intestinal flora-based treatment efficacy of bipolar disorder as a target in predictive treatment.

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Abstract

提供了双相情感障碍疗效预测的肠道微生物标志物及其筛选应用。其中，生物标志物1)Eubacterium_biforme；生物标志物2)Ruminococcus_torques；生物标志物3)Collinsella_unclassified；所述生物标志物组合在双相情感障碍患者治疗有效组中相对丰度均显著升高。还提供了根据所述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和作为靶点在疗效预测中的应用。

Description

双相情感障碍疗效预测的肠道微生物标志物及其筛选应用 技术领域
本发明涉及双相情感障碍疗效预测的肠道微生物标志物及其筛选应用。
背景技术
双相情感障碍是一类以抑郁、躁狂或者轻躁狂交替或混合发作为主要临床特征的慢性精神疾病，病因未明，发病年龄主要集中在成年早期或者青春期后期，全球发病率高，约为2～3％，与明显的职业、个人和社会功能等损害有关，并伴随着躯体疾病和早期死亡。目前针对双相情感障碍的治疗，主要是以药物治疗为基础，辅以物理治疗和心理治疗的综合治疗原则。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。多次反复发作，严重损害患者大脑功能，延长药物治疗时间，降低治疗有效性。
尽管目前较为一致的观点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，在神经、免疫以及内分泌等系统也表现出不同程度的变化，但针对双相情感障碍治疗疗效的早期预测，尚缺乏特异性的生物标志物。
发明内容
为了克服现有技术的不足，本发明的目的是提供双相情感障碍疗效预测的肠道微生物标志物及其筛选应用。
基于肠道微生物的双相情感障碍疗效预测生物标志物，包括以下6种中的多种："Bacteroides_clarus","Eubacterium_biforme","Weissella_confusa","Ruminococcus_torques","Bifidobacterium_dentium","Collinsella_unclassified"在双相情感障碍患者治疗有效组与无效组中存在明显的差异。其中，"Bacteroides_clarus","Eubacterium_biforme","Weissella_confusa""Ruminococcus_torques","Collinsella_unclassified"在双相情感障碍患者治疗有效组相对丰度显著升高，而"Bifidobacterium_dentium"则下降。
所述的生物标志物，为以下3种：
生物标志物1)Eubacterium_biforme；重要性0.15259；
生物标志物2)Ruminococcus_torques；重要性0.339395；
生物标志物3)Collinsella_unclassified；重要性0.258467；
上述所述生物标志物组合在双相情感障碍患者治疗有效组组中相对丰度均显著升高。
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。
一种根据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。
一种根据所述的生物标志物作为靶点在疗效预测中的应用。
一种根据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治疗前的粪便样品，在冰箱内‐80℃条件下保存备用；
2)对收集后储存的粪便样本进行核酸样本DNA的提取，宏基因组测序与组装、比对、筛选与质控之后，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；
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。
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)双相情感障碍患者治疗有效组和治疗无效组为训练集，剩余样本作为测试集，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27个双相情感障碍治疗有效患者和14个治疗无效患者，测试集中，样本受试者包括8个双相情感障碍治疗有效患者和5个治疗无效患者。
本发明的有益技术效果：
肠道微生物是存在于人体肠道中的微生物群落，通过“脑‐肠”轴的神经、免疫以及内分泌等双向信息传递系统与大脑相互作用，参与影响人的情绪、行为和认知等功能，肠道菌群失调与多种神经精神疾病如阿尔茨海默病、孤独症、抑郁症以及双相情感障碍等的发生有关。肠道微生物的功能和组成受多种因素的影响，如饮食、运动、益生菌、抗生素以及粪菌移植等。目前已有研究通过建造动物模型、调节肠道菌群变化的方法进一步探讨肠道菌群对大脑功能 状态的改善作用机制。双相情感障碍患者在使用第二代抗精神病药物如喹硫平稳定情绪、改善症状的同时，同样可使肠道菌群发生变化。
因此，本发明通过对双相情感障碍患者治疗有效和治疗无效组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治疗疗效相关性高的生物标志物，并且利用该标志物能够准确地预测双相情感障碍疗效，监测治疗效果。
本发明提出的双相情感障碍患者相关生物标记物对治疗疗效的反应是有价值的。第一，粪便样本提取便携且无创，可增加患者依从性。同时。第二，粪便样本具有可运输性，样本分析具有准确性和安全性。第三，本发明的标记物具有较高的特异性和灵敏性，可用于治疗疗效的预测。
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治疗有效组和治疗无效组的菌群相对丰度差异情况。图示表明，双相情感障碍患者治疗有效组和治疗无效组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(3个肠道标志物)，由双相情感障碍患者治疗有效组和治疗无效组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(3个肠道微生物标志物)，由双相情感障碍患者治疗有效组和治疗无效组组成的测试集的接收者操作特征ROC曲线和曲线下面积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”，是一类既有抑郁发作又有躁狂发作的慢性精神疾病，呈反复发作性，患者在情绪处理以及认知功能方面等方面存在障碍，严重影响患者正常的社会生活等功能。全世界共患病人口约1％，是导致青少年残疾的四大原因之一，但发病机制尚未完全阐明，目前的观点认为与遗传和环境的交互作用关系密切。
“疗效”，也即“治疗效果”，是指药物或者手术等手段治疗疾病后的效果，主要为四级评价标准，包括痊愈、显效、进步、无效。本研究中的“疗效”主要指“显效”和“无效”。 因为在本发明中，针对双相情感障碍的治疗采取的手段是单一药物治疗，且以17项汉密尔顿抑郁量表(HDRS‐17)量表得分的变化率是否≥50％来评定药物治疗1月后是否有效。
“生物标志物”，是一类可用于判断疾病的病因、早期诊断以及评估疾病发生发展过程或者评价药物治疗在目标人群中药物疗效或者安全性的指示物。主要包括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，如评估糖尿病的血糖等小分子物质以及蛋白质、核酸、脂质等大分子物质。在本发明中，“生物标志物”也可以用“肠道微生物”、“肠道菌群”来表示，因为本研究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。
本发明所述的生物标志物，通过运用高通量测序，批量分析双相情感障碍患者治疗有效组和治疗无效组的粪便样本。基于高通量测序数据，对双相情感障碍患者治疗有效组和治疗无效组进行比对，从而确定与双相情感障碍患者群疗效预测相关的特异性菌群。
实施例
样品的收集与处理：收集62例双相情感障碍的粪便样本，采集粪便样本后分装至做好标记的冻存管内，冷冻条件下运输并转运至‐80℃冰箱内保存备用。具体的，本发明受试者是基于符合《精神疾病的诊断和统计手册》(DSM‐IV‐TR)双相情感障碍诊断依据的住院和/或门诊患者。入组时受试者未服用药物治疗或者曾服用药物治疗但已停药至少3个月。同时满足无明显的自杀想法或既往自杀未遂、没有与其他精神障碍共病的患者。排除标准为：：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。在入组时，使用17项汉密尔顿抑郁量表(HDRS‐17)和蒙哥马利‐阿斯伯格抑郁量表(MADRS)评定抑郁严重程度，用青年躁狂量表(YMRS)评定躁狂程度。其中，HDRS‐17得分不低于14分被设定为当前抑郁发作的阈值。在患者入组后给予喹硫平单药标准化治疗1个月后，再次评估HDRS‐17量表并计算得分，与入组时HDRS‐17量表得分进行比较，计算变化率。根据双相情感障碍患者服用喹硫平单药治疗1个月与治疗前比较，HDRS‐17评分变化率是否≥50％，将双相情感障碍分为治疗有效组(HDRS‐17≥50％)和治疗无效组(HDRS‐17＜50％)。最终受试者分为双相情感障碍患者治疗有效组(n＝35)和治疗无效组(n＝19)。8名双相情感障碍患者缺乏随访量表评分而未被分类。
提取DNA：使用QIAGENT DNA试剂盒提取DNA，得到核酸样本。
构建文库并测序：使用NEBNextUltra TM DNA Library Prep Kit for Illumina(NEB，美国)构 建DNA文库。用Illumina NovaSeq 6000进行双端150bp DNA文库测序。再通过比对、筛选、质控等步骤去除污染或者低质量的测序片段(reads)，最终得到高质量的测序片段。
将上述所得高质量测序片段输入到Metaphlan2分析软件(http://segatalab.cibio.unitn.it/tools/metaphlan2/)，经1)与参考的标记基因比对；2)统计插入片段的数量；3)对标记基因的长度进行标准化等步骤得到对应菌群的相对丰度信息。
根据上述得到的菌群相对丰度情况，利用LDA Effect Size(LEfSe)分析技术分析治疗有效组和治疗无效组两组间的菌群相对丰度差异情况。
将上述“双相情感障碍患者治疗有效组和治疗无效组菌群的相对丰度输入到LEfSe在线分析网页(http://huttenhower.sph.harvard.edu/galaxy)进行两组之间菌群相对丰度信息的差异性比较。具体地，主要包括以下三个步骤：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(参照文献：Segata N,et al.Metagenomic biomarker discovery and explanation[J].Genome Biol,2011,12(6):R60.)。
在本发明中，将54个样本(35个治疗有效的双相情感障碍患者组和19个治疗无效的双相情感障碍患者组)经测序、序列比对后得到的菌群相对丰度信息输入LEfSe在线分析网页，结果显示6种菌群的相对丰度，包括"Bacteroides_clarus","Eubacterium_biforme","Weissella_confusa","Ruminococcus_torques","Bifidobacterium_dentium","Collinsella_unclassified"在双相情感障碍患者治疗有效组与无效组中存在明显的差异。其中，"Bacteroides_clarus","Eubacterium_biforme","Weissella_confusa","Ruminococcus_torques","Collinsella_unclassified"在双相情感障碍患者治疗有效组相对丰度显著升高，而"Bifidobacterium_dentium"则下降(图1)。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评分显示治疗无效组的BD患者(右边部分，uneffective)和治疗有效组(左边，effective)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治疗过程中疗效预测的潜在生物标志物。
为进一步筛选双相情感障碍肠道微生物标志物，以上述LEfSe分析技术筛选的双相情感障碍患者治疗有效组和无效组之间差异菌群相对丰度信息情况为基础，构建双相情感障碍治疗有效组和无效组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。其中，在本发明的实施方案中，训练集是指具有一定样本数的双相情感障碍治疗有效组和无效组受试者待测样本中各生物标志物含量的数据集合。
具体包括如下步骤：
本发明从54个样品(35个治疗有效的双相情感障碍患者组和19个治疗无效的双相情感障碍患者组)中，选取27个双相情感障碍患者和14个健康人作为训练集，剩余的13个样品(8个双相情感障碍患者和5个健康人)作为测试集。
利用Python3软件版本调用随机森林分类器(Random Forest Classifier，RF)。本发明对RF分类器进行5次10折交叉验证(图2示出了随机森林分类器中5次10折交叉验证的错误率分布情况)。
本发明基于5次10折交叉验证结果，RF分类器最终选择3个最优生物标记物组合，(训练集详细微生物标记物相对丰度信息见下表1‐1、1‐2所示，测试集详细微生物标记物相对丰度信息见下表2所示，表3显示出了3种生物标记物结合来预测训练集的疗效概率)。
表1-1训练集差异菌群相对丰度信息
Figure PCTCN2020081945-appb-000001
注释：B、BP、S1B、HSH：双相情感障碍
表1-2训练集差异菌群相对丰度信息
Figure PCTCN2020081945-appb-000002
注释：B、BP、S1B、HSH：双相情感障碍
表2测试集差异菌群相对丰度信息
Figure PCTCN2020081945-appb-000003
注释：B、BP、S1B、HSH：双相情感障碍
表3训练集利用菌群标志物相对丰度信息预测治疗疗效的准确性
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    
注释：B、BP、S1B、HSH：双相情感障碍。
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疗效预测风险，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无效判对的概率，敏感性指的是对于有效判对的概率，对训练集样本的判别效能为：AUC＝91％，95％置信区间CI＝82.4％～99.6％。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)。
基于随机森林模型(3个生物标志物)由双相情感障碍有效和无效组组成的测试集的ROC曲线和AUC，其中特异性表征的是对于无效判对的概率，敏感性指的是对于有效判对的概率，对训练集样本的判别效能为：AUC＝82.5％，95％置信区间CI＝51.2～100％(图4)。结果表明该模型所得标志物组合可作为区分双相情感障碍治疗有效和治疗无效疗效预测的潜在生物标志物。
表4示出了3种生物标记物结合来预测测试集的患病概率。
表5示出了3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
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
注释：B、BP、S1B、HSH：双相情感障碍
表5
Figure PCTCN2020081945-appb-000004
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治疗有效组和治疗无效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治疗有效组和治疗无效组群体，获得患病概率，用于双相情感障碍的廖疗效预测评估或者寻找潜在药物靶点。
在本发明中，所述的测序(二代测序)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 Microbiology14.8(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(Drogan D,et al.Untargeted Metabolic Profiling Identifies Altered Serum Metabolites of Type 2‐Diab etes Mellitus in a Prospective,Nested Case Control Study.Clin Chem 2015,61:487‐497.；)中记载的方法进行。
在本发明中，构建了双相情感障碍患者治疗有效组和治疗无效组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。
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，为双相情感障碍患病治疗疗效的预测，寻找潜在药物靶点提供依据。
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治疗疗效生物标志物组合作为靶点在预测治疗中的应用。
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疗效预测生物标志物，其特征在于，包括以下6种中的多种：
    Bacteroides_clarus,Eubacterium_biforme,Weissella_confusa,Ruminococcus_torques,Bifidobacterium_dentium,Collinsella_unclassified在双相情感障碍患者治疗有效组与无效组中存在明显的差异；
    其中，Bacteroides_clarus,Eubacterium_biforme,Weissella_confusa，Ruminococcus_torques,Collinsella_unclassified在双相情感障碍患者治疗有效组相对丰度显著升高，而Bifidobacterium_dentium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3种：
    生物标志物1)Eubacterium_biforme；
    生物标志物2)Ruminococcus_torques；
    生物标志物3)Collinsella_unclassified；
    上述所述生物标志物组合在双相情感障碍患者治疗有效组组中相对丰度均显著升高。
  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疗效预测中的应用。
  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治疗前的粪便样品，在冰箱内‐80℃条件下保存备用；
    2)对收集后储存的粪便样本进行核酸样本DNA的提取，宏基因组测序与组装、比对、筛选与质控之后，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；
    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。
  8. 根据权利要求7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)双相情感障碍患者治疗有效组和治疗无效组为训练集，剩余样本作为测试集，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27个双相情感障碍治疗有效患者和14个治疗无效患者，测试集中，样本受试者包括8个双相情感障碍治疗有效患者和5个治疗无效患者。
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