阅读说明：本技术 多发性骨髓瘤肠道微生物标志物及应用和检测制剂 (Multiple myeloma intestinal tract microbial marker, application and detection preparation ) 是由 周文 简星星 朱应红 武明花 向娟娟 于 2019-10-30 设计创作，主要内容包括：本发明首次公开了多发性骨髓瘤疾病检测的肠道微生物标志物及其应用和检测制剂,即：弗氏柠檬酸杆菌、阴沟肠杆菌、解鸟氨酸拉乌尔菌、产气克雷伯菌、变栖克雷伯菌、肺炎克雷伯菌、唾液链球菌、口腔链球菌、格登链球菌、缓症链球菌、肺炎链球菌<I>、</I>丁酸盐产生菌、丁酸梭菌、糖丁基梭菌。这些肠道微生物标志物可一种单独或多种组合应用。本发明公开了以这些微生物标志物为特征构建的支持向量机(SVM)分类模型,应用于多发性骨髓瘤疾病的临床诊断。本发明还公开了该微生物标志物应用于制备多发性骨髓瘤疾病的诊断制剂。(The invention discloses an intestinal microbial marker for multiple myeloma disease detection, application and a detection preparation thereof for the first time, wherein the intestinal microbial marker comprises the following components in parts by weight: citrobacter freundii, enterobacter cloacae, Raoultella ornithinolytica, Klebsiella aerogenes, and Klebsiella variicolaBacteria, Klebsiella pneumoniae, Streptococcus salivarius, Streptococcus oralis, Streptococcus gordonii, Streptococcus mitis, and Streptococcus pneumoniae 、 Butyrate producing bacteria, clostridium butyricum and clostridium saccharolyticum. These intestinal microbial markers may be used alone or in combination of two or more. The invention discloses a Support Vector Machine (SVM) classification model constructed by taking the microbial markers as characteristics, which is applied to clinical diagnosis of multiple myeloma diseases. The invention also discloses application of the microbial marker in preparation of a diagnostic preparation for multiple myeloma diseases.)

1. Specific intestinal microbial markers for multiple myeloma diseases, characterized by being derived from enterobacteriaceae (A)Enterobacteriaceae) And Streptococcus (Streptococcus) And one or more of short chain fatty acid-producing bacteria.

2. The multiple myeloma specific gut microbial marker according to claim 1, wherein the markers comprise: from the family Enterobacteriaceae (A)Enterobacteriaceae) Citrobacter freundii (C.), (Citrobacter freundii) Enterobacter cloacae: (A), (B), (C)Enterobacter cloacae) Raoultella ornithinolytica (A), (B), (CRaoultella ornithinolytica) Klebsiella aerogenes (B) ((R))Klebsiella aerogenes) Klebsiella pneumoniae (K.pneumoniae) ((B))Klebsiella pneumonia) Klebsiella variicola (B), (C)Klebsiella variicola) (ii) a From the genus Streptococcus (Streptococcus) Of Streptococcus gordonii (A), (B) and (C)Streptococcus gordonii) Oral streptococci (A)Streptococcus oralis）、Chronic hammerBacteria (A), (B)Streptococcus mitis) Streptococcus pneumoniae (I)Streptococcus pneumoniae) Streptococcus salivarius (S.salivarius)Streptococcus salivarius) (ii) a And short chain fatty acid producing bacteria: butyrate-producing bacterium (A), (B)Anaerostipes hadrus) Clostridium butyricum (Clostridium butyricum) (II)Clostridium butyricum) Clostridium saccharobutylicum (II)Clostridium saccharobutylicum) One or more of (a).

3. The multiple myeloma specific gut microbial marker according to claim 1, wherein the markers comprise: from the family Enterobacteriaceae (A)Enterobacteriaceae) Citrobacter freundii (C.), (Citrobacter freundii) Enterobacter cloacae: (Enterobacter cloacae) Klebsiella aerogenes (C.) (Klebsiella aerogenes) Klebsiella pneumoniae (C.) (Klebsiella pneumoniae) Raoultella ornithinolytica (A), (B), (C)Raoultella ornithinolytica) (ii) a And from the genus Streptococcus (Streptococcus) Of Streptococcus gordonii (A), (B) and (C)Streptococcus gordonii) Streptococcus mitis (S. mitis)Streptococcus mitis) One or more of (a).

4. A Support Vector Machine (SVM) classification model for clinical diagnosis of multiple myeloma characterized by being constructed based on the absolute or relative abundance of characteristic microorganisms in a fecal sample, characterized by the intestinal biomarkers according to any of claims 1-3.

5. Use of a test preparation for detecting an intestinal biomarker according to any of claims 1 to 3 in the manufacture of a multiple myeloma diagnostic preparation.

6. The use according to claim 5, wherein the preparation for detecting the gut microbial marker of any one of claims 1-3 comprises: obtaining a stool sample to be detected, extracting all microbial DNA in the sample, and performing metagenome sequencing on the extracted DNA sequence to obtain a preparation of microbial marker absolute abundance or performing qPCR on 16S rDNA gene of the microbial marker to detect the relative abundance of the preparation.

7. The multiple myeloma diagnosis preparation comprises the clinical diagnosis SVM classification model applied to multiple myeloma in claim 4 and the preparation for detecting the absolute abundance or relative abundance of a microbial marker in a fecal sample in claim 6.

8. A food, probiotic or medicament for the adjunctive treatment of multiple myeloma, wherein the food, probiotic or medicament is capable of reducing the level of Citrobacter freundii (R) ((R))Citrobacter freundii) Enterobacter cloacae: (A), (B), (C)Enterobacter cloacae) Raoultella ornithinolytica (A), (B), (CRaoultella ornithinolytica) Klebsiella aerogenes (B) ((R))Klebsiella aerogenes) Klebsiella pneumoniae (K.pneumoniae) ((B))Klebsiella pneumonia) Klebsiella variicola (C.) (Klebsiella variicola) Streptococcus gordonii (S.gordonii)Streptococcusgordonii), streptococcus oralis (Streptococcus oralis) Streptococcus mitis (S.mitilis)Streptococcus mitis) Streptococcus pneumoniae (I)Streptococcus pneumoniae) Streptococcus salivarius (S.salivarius)Streptococcus salivarius) And/or is capable of increasing the abundance or amount of one or more of (a), (b), (c), (d), (Anaerostipes hadrus) Clostridium butyricum (Clostridium butyricum) (II)Clostridium butyricum) Clostridium saccharobutylicum (II)Clostridium saccharobutylicum) Abundance or content of one or more of (a).

9. A method for screening a food, probiotic or drug for adjuvant treatment of multiple myeloma, comprising detecting Citrobacter freundii (Citrobacter freundii) before and after an intervention in a candidate food, probiotic or drugCitrobacter freundii) Enterobacter cloacae: (A), (B), (C)Enterobacter cloacae) Raoultella ornithinolytica (A), (B), (CRaoultella ornithinolytica) Klebsiella aerogenes (B) ((R))Klebsiella aerogenes) Klebsiella pneumoniae (K.pneumoniae) ((B))Klebsiella pneumonia) Klebsiella variicola (C.) (Klebsiella variicola) Streptococcus gordonii (S.gordonii)Streptococcus gordonii) Oral streptococci (A)Streptococcus oralis) Streptococcus mitis (S.mitilis)Streptococcus mitis) Streptococcus pneumoniae (I)Streptococcus pneumoniae) Streptococcus salivarius (S.salivarius)Streptococcussalivariaus), butyrate-producing bacteria (see (1)Anaerostipes hadrus) Clostridium butyricum (Clostridium butyricum) (II)Clostridium butyricum) Clostridium saccharobutylicum (II)Clostridium saccharobutylicum) Abundance or content of one or more of (a).

Technical Field

The invention relates to the fields of biotechnology, disease diagnosis and biomedicine, in particular to an intestinal microbial marker of multiple myeloma and application and a detection preparation thereof.

Background

Multiple Myeloma (MM) is a hematological malignancy that is characterized by the abnormal proliferation of bone marrow plasma cells and the overproduction of monoclonal immunoglobulins or light chains (M protein). The incidence rate is about 2-3 persons/10 ten thousand persons, and is mostly seen in the elderly over 60 years old; with the aging of people in recent years in China, the number of MM diseases is on the rise. Clinically, MM patients are often associated with multiple osteolytic lesions, hypercalcemia, anemia, renal damage; and MM patients are susceptible to various bacterial infections due to the inhibition of normal immunoglobulin production. However, MM has a slow onset and no obvious symptoms in the early stage, and is easily misdiagnosed. Currently, clinical diagnostic criteria for MM are mainly based on: 1) the presence of M-protein in blood or urine; 2) clonal plasma cells or plasmacytomas are present in the bone marrow; 3) associated organ or tissue damage. The basis is that the traditional Chinese medicine composition is also required to be identified with certain chronic diseases (such as rheumatism system diseases, chronic tuberculosis infection, nephropathy, chronic liver diseases and the like), lymphoma and the like which can cause reactive plasmacytosis and monoclonal globulin blood disease (MGUS) with unknown significance; some severe osteoporosis or low-phosphate bone disease or metastatic cancer also require identification of bone destruction with MM; in addition, there are also very few asymptomatic MM patients. In addition, a direct method of assessing the therapeutic efficacy of MM patients is to measure the proportion of cancer cells in the patient's bone marrow, and bone punctures during this process can increase patient pain and can cause secondary damage to the patient.

At present, few research reports about the correlation between the pathogenesis of MM patients and their intestinal flora exist, and the mechanism is not clear. In addition, researchers have found that the host gut is able to preferentially absorb dietary nutrients, which allows a gradient nitrogen limitation of the microflora in the gut, indicating that the host can secrete nitrogenous metabolites to achieve regulation of the gut microflora. Furthermore, the large amount of ineffective monoclonal protein produced during the development of MM patients is deposited in renal tubules, which leads to renal injury of patients, and directly causes the accumulation of nitrogenous metabolites such as urea and creatinine in the serum of patients. Through research, the urea accumulated in the serum of a patient enters the intestinal tract and is used as a nitrogen source substance to regulate the change of the composition and abundance of the microbial flora in the intestinal tract, so that the microorganisms with high urea utilization efficiency in the intestinal tract are proliferated in a large quantity, and the growth of short-chain fatty acid producing bacteria is inhibited. Therefore, the study suggests that certain specific microorganisms can be used as markers, and the changes of the abundance of the specific microorganisms in the fecal samples can be detected and compared, so that the specific microorganisms can be used as one of the reference standards for clinical diagnosis of MM on the one hand, and can also be used as one of the evaluation factors of the treatment effect of MM patients on the other hand.

Disclosure of Invention

The invention aims to provide a specific intestinal tract microbial marker aiming at MM, and application and a detection preparation thereof, lays a foundation for early clinical diagnosis of MM and evaluation of MM treatment effect, provides a non-invasive and non-invasive detection method, and fills the gap of the application of the existing MM clinical detection technology.

In a first aspect of the invention there is provided a MM specific gut microbial marker, a combination of gut microbes from the genera Enterobacteriaceae (Enterobacteriaceae) and Streptococcus (Streptococcus), and one or more short chain fatty acid producing bacteria.

The experimental results of the invention show that the microorganisms of the Enterobacteriaceae and Streptococcus are significantly enriched in the MM sample, while the short-chain fatty acid-producing bacteria are significantly deficient in the MM sample.

Specifically included are Citrobacter freundii (Citrobacter freundii), Enterobacter cloacae (Enterobacter cloacae), Raoultella ornithinovyticus (ralstonia ornithica), Klebsiella aerogenenes (Klebsiella aerogenes), Klebsiella pneumonia (Klebsiella pneumoniae), Klebsiella variicola (Klebsiella mutabilis), and Streptococcus gordonii (Streptococcus mutans) from Streptococcus (Streptococcus), Streptococcus oralis (Streptococcus oralis), Streptococcus mitis (Streptococcus mitis), Streptococcus pneoniae (Streptococcus pneumoniae), Streptococcus salivarius (Streptococcus salivarius); and short chain fatty acid producing bacteria: butyrate producing bacteria (Anaerobiosis hadrus), Clostridium butyricum (Clostridium butyricum), Clostridium saccharolyticum (Clostridium saccharolyticum).

The results of the present invention showed that 11 bacteria from the Enterobacteriaceae or Streptococcus genera were significantly enriched in MM samples, and 3 short-chain fatty acid-producing bacteria were significantly deleted in MM samples.

According to a second aspect of the invention, a Support Vector Machine (SVM) classification model for clinical diagnosis of multiple myeloma is constructed based on the absolute abundance or relative abundance of a microbial marker in a fecal sample, based on the characteristics of the intestinal microbial marker. Can be applied to clinical diagnosis of multiple myeloma diseases.

The results of the invention show that: the SVM classification model which is constructed by taking the 14 microorganisms which are obviously different in MM sample as characteristics has better effect on diagnosing the multiple myeloma diseases; however, the SVM model which is constructed by only 7 microorganisms has better effect on diagnosing the multiple myeloma diseases.

The third aspect of the invention provides an application of the preparation for detecting the microbial markers in preparation of a multiple myeloma diagnosis preparation.

The preparation for detecting the intestinal microbial markers comprises: obtaining a stool sample to be detected, extracting all microbial DNA in the sample, performing metagenome sequencing on the extracted DNA sequence and obtaining a preparation of microbial marker absolute abundance or performing qPCR on 16S rDNA gene of the microbial marker to detect the relative abundance of the gene.

The fourth aspect of the invention provides a multiple myeloma diagnosis preparation, which comprises the SVM classification model applied to clinical diagnosis of multiple myeloma and the preparation for detecting absolute abundance or relative abundance of a microbial marker in a fecal sample.

The method specifically comprises the steps of detecting the absolute abundance or the relative abundance of the intestinal microbial markers in the fecal sample to be detected through metagenome sequencing or qPCR (quantitative polymerase chain reaction), and predicting the possibility of whether the detected sample has MM or not by utilizing a constructed SVM classification model.

In a fifth aspect of the invention there is provided a food, probiotic or medicament for use in the adjunctive treatment of multiple myeloma, said food, probiotic or medicament being capable of reducing the content of one or more of Citrobacter freundii (Citrobacter freundii), Enterobacter cloacae (Enterobacter cloacae), ralstonia ornithii (Raoultella ornithinovaculata), Klebsiella aerogenes (Klebsiella aerogenes), Klebsiella pneumoniae (Klebsiella pneoniana), Klebsiella variegata (Klebsiella variicola), Streptococcus gordonii (Streptococcus gordonii), Streptococcus oralis (Streptococcus oralis), Streptococcus mitis (Streptococcus mitis), Streptococcus salivarius (Streptococcus salivarius), and increasing the content of one or more of Clostridium butyricum, Clostridium butyricum (Clostridium butyricum), Clostridium butyricum or Clostridium butyricum, and said food, probiotic or medicament being capable of reducing the content of one or more of Clostridium butyricum, and/or said bacterium.

In a sixth aspect of the present invention, there is provided a method for screening food, probiotic bacteria or drugs for adjuvant treatment of multiple myeloma, the method comprising detecting the content of one or more species of Citrobacter freundii, Enterobacter cloacae, ralstonia aminolytica (Raoultella ornithinolytica), Klebsiella aerogenes (klebsigenes), Klebsiella pneumoniae (Klebsiella pneumoniae), Klebsiella variegata (varia variegata), Streptococcus gordonii (Streptococcus mutans), Streptococcus oralis (Streptococcus oralis), Streptococcus mitis (Streptococcus mitis), Streptococcus mitis (Streptococcus salivarius), Clostridium butyricum (Clostridium butyricum), Clostridium butyricum or Clostridium butyricum, before and after candidate food, probiotic or drug intervention.

The invention has the advantages that:

(1) the intestinal microbial marker of MM provided by the invention can accurately reflect clinical diagnosis of MM and evaluation of MM treatment effect through a large amount of screening and verification.

(2) The invention provides a non-invasive and non-invasive detection method for early MM clinical diagnosis and curative effect evaluation for the first time, and fills the gap of the application of the existing MM clinical detection technology.

Embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The nomenclature used herein and the laboratory procedures are well known and commonly employed in the art. All operations performed using standard techniques are generally performed according to the product specifications and general technical requirements of the manufacturer of the instrument consumables and the references provided herein. It should be noted that the following drawings and examples are only for illustrating the present invention, and are not to be construed as limiting the scope of the present invention. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.

Drawings

Figure 1. significantly different gut microbiota in the two groups of samples.

FIG. 2 microbial abundance enriched in several MM samples was compared in ISS-2 and ISS-3 stage patients.

FIG. 3 shows an ROC curve when 14 microbial markers are used as features and applied to absolute abundance data of metagenome sequencing to construct an SVM classification model.

FIG. 4 shows the ROC curve when the 7 microbial markers with the best classification effect are used as features and applied to the absolute abundance data of metagenome sequencing to construct an SVM classification model.

FIG. 5 ROC curves for the construction of SVM classification models using relative abundance data from qPCR assays, characterized by 14 microbial markers.

FIG. 6 ROC curves for the construction of SVM classification models using the relative abundance data of qPCR detection with 7 microbial markers as features.

Detailed Description