Fungal Biomarkers for the Diagnosis of Non-Obese Polycystic Ovary Syndrome Population

Abstract

The present invention relates to the diagnosis, prediction of disease risk or prognosis monitoring of patients with non-obese polycystic ovary syndrome. Based on sequencing and bioinformatics analysis, fungal biomarkers in the non-obese polycystic ovary syndrome population are provided, and a detection kit for screening patients with non-obese polycystic ovary syndrome is disclosed, providing a detection tool with high sensitivity and high specificity for clinical diagnosis and treatment.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedicine, and particularly relates to an intestinal flora marker and its application in the diagnosis, prediction of disease risk or prognosis monitoring of non-obese polycystic ovary syndrome. Background Art

[0002] Polycystic ovary syndrome (PCOS) is a common female reproductive endocrine and metabolic disorder disease, whose clinical manifestations are hyperandrogenism, chronic anovulation and polycystic ovaries, and the prevalence rate in women of childbearing age can reach 6% to 20%. PCOS is one of the most common endocrine diseases and can cause infertility in women of childbearing age. Many studies have shown that PCOS patients have a higher risk of metabolic-related diseases, such as insulin resistance (IR), type 2 diabetes mellitus (T2DM), dyslipidemia and cardiovascular diseases. So far, the cause of PCOS is still unclear, and researchers speculate that various factors, such as congenital inheritance, intrauterine environment, lifestyle and potential changes in intestinal flora, may promote the occurrence and development of the disease.

[0003] More and more evidence shows that intestinal bacteria play an important role in obesity, obesity-related inflammation and insulin resistance. For example, Professor Zhaoliping of Shanghai Jiao Tong University found that short-chain fatty acid (SCFA)-producing bacteria may improve the glycated hemoglobin (HbAlc) level in T2DM patients by increasing the production of glucagon-like peptide-1 (GLP-1). The symbiotic bacterium Bacteroides thetaiotaomicron can reduce the plasma glutamate concentration and alleviate diet-induced body weight gain and obesity in mice, and its abundance is related to the efficiency of bariatric surgery. In recent years, studies on PCOS and intestinal flora have shown that the bacterial alpha diversity, and the abundances of Bacteroidaceae, Clostridiaceae, Erysipelotrichidae, Lachnospiraceae, Lactobacillaceae, Porphyromonadaceae, Prevotellaceae, Ruminococcaceae and Actinobacteria in PCOS patients are reduced. Qi et al. found that the abundance of Bacteroides vulgatus in the intestines of PCOS patients increased significantly while the amounts of glycochenodeoxycholic acid and tauroursodeoxycholic acid decreased. Hyperandrogenism is associated with the dysregulation of intestinal microbiota, indicating that androgens may regulate the intestinal flora community, suggesting that regulating intestinal flora may be a potential method for treating PCOS. For example, transplanting the flora of PCOS female patients to mice can induce a PCOS-like phenotype in mice, while transplanting the intestinal flora of healthy women can relieve the PCOS symptoms in mice.

[0004] According to the Rotterdam ESHRE / ASRM (2003) criteria, two of the following three items must be met: oligomenorrhea; clinical or biochemical hyperandrogenism; polycystic-like changes and / or enlargement of the bilateral or unilateral ovaries. However, the PCOS diagnosed by the above method is highly heterogeneous, and the important clinical feature of the degree of metabolic abnormality has not been introduced to subtype the PCOS patients. Recent studies have shown that intestinal microbiota disorders play an important role in the occurrence and development of PCOS, but the research on the relationship between PCOS and obesity is still relatively scarce. For example, what are the differential characteristics of the intestinal microbiota between healthy women with high BMI and PCOS patients with high BMI, and whether it is possible to assess the PCOS risk of healthy women with high BMI from the perspective of intestinal fungi remains to be further explored. The change in the abundance of intestinal fungi in the body is expected to provide a new path for the characterization or diagnosis of polycystic ovary syndrome.

[0005] Therefore, providing a set of fungal biomarkers that can be used to detect polycystic ovary syndrome can be used for the rapid and efficient diagnosis of patients with polycystic ovary syndrome. Summary of the Invention

[0006] The object of the present invention is to solve the problems existing in the prior art, and a gene detection kit for polycystic ovary syndrome in obese people, its preparation method and application are proposed.

[0007] On the one hand, the present invention provides a biomarker that can be used for the diagnosis, prediction of disease risk or prognosis monitoring of polycystic ovary syndrome in obese people. The biomarker is Candida and Malassezia fungi. The biomarker also includes Tetracladium, Exophiala, Knufia, lyonectria, Mortierella, Solicoccozyma, Cladophialophora, Pseudogymnoascus, Coniochaeta, Didymella, Trichoderma, Cladosporium, Epicoccum, Penicillium, Dichotomopilus, Neurospora, Alternaria, Paraphoma, Staphylotrichum, Purpureocillium, Thelonectria, Pyrenochaetopsis, Talaromyces, Acrocalymma, Bolbitius and Gibberella fungi.

[0008] On the one hand, the present invention provides a biomarker that can be used for the diagnosis, prediction of disease risk or prognosis monitoring of polycystic ovary syndrome in non-obese populations. The biomarker is fungi including Aspergillus, Kazachstania, Candida, Botryotrichum, Meyerozyma, Neurospora, Cylindrocarpon and Sarocladium. The biomarker also includes Humicola, Trichderma, Penicillium, Malassezia, Mortierella, Setophoma, Fusarium, Talaromyces, Preussia, Cephaliophora, Cladosporium, Myrothecium, Schizophyllum, Gibberella, Stachybotrys, Debaryomyces, Saitozyma, Bolbitius, Nectria and Schizothecium.

[0009] On the other hand, the present invention provides a kit for the diagnosis, prediction of disease risk or prognosis monitoring of polycystic ovary syndrome in obese or non-obese populations, and the kit contains reagents for detecting the fungal content of the biomarker. The detection reagents are selected from PCR method, microarray method, sequencing method or immunoassay reagents.

[0010] On the other hand, the present invention provides the use of reagents for detecting the biomarker in the preparation of a kit for the diagnosis, prediction of disease risk or prognosis monitoring of polycystic ovary syndrome in obese populations.

[0011] Compared with the prior art, the present invention has the following beneficial technical effects:

[0012] The present invention is made based on the discovery and understanding of the following facts and problems: Currently, there are still few studies at home and abroad on measuring the intestinal fungal flora structure of PCOS patients and healthy populations and comparing their differential characteristics. The main reasons are as follows: 1) Current researchers' research on the intestinal microecology still mainly focuses on intestinal bacteria; 2) The relative abundance of intestinal fungi in the human body is relatively low, and the relevant databases need to be further expanded; 3) The understanding of the relationship between fungi and diseases needs to be further expanded. Coupled with the fact that the cultivation of fungi is more difficult than that of bacteria, it is relatively difficult to conduct research on fungi. This study assesses the disease risk and conducts early diagnosis on high-BMI populations from the perspective of fungi, especially focusing on distinguishing healthy women with high BMI and PCOS patients with high BMI. Based on the characteristic analysis of intestinal fungi in PCOS patients and healthy populations, especially by comparing the fungal alpha and beta diversities, flora structures, and compositions of characteristic markers in healthy women with high BMI and PCOS patients with high BMI, and combining the characteristic species data of high-BMI PCOS populations and high-BMI healthy people as a training set, the present invention can accurately assess the disease risk and conduct early diagnosis on PCOS patients. Compared with the current conventional microbial culture methods and diagnostic means, this method has the characteristics of convenience and rapidity.

[0013] The fungal biomarkers related to PCOS proposed by the present invention are valuable for early diagnosis. First, there are currently no fungal markers for PCOS diseases, and the markers in the present invention have high specificity and sensitivity; second, fungi are relatively difficult to culture. Based on the screened characteristic fungi, primer design is carried out, and subsequently, the characteristic fungi can be identified by high-throughput sequencing means or qPCR methods; third, fecal analysis is non-invasive and convenient to operate, which can increase the compliance of patients. Fourth, the fungal indicators involved in the present invention can not only be used for treatment monitoring of PCOS patients, but also serve as important indicators for PCOS treatment intervention and clinical follow-up, guiding the treatment plan by tracking the abundance of characteristic fungi. Brief Description of the Drawings

[0014] From the following description of the specific embodiments of the present invention, the above content and other purposes, features, and advantages will become clear, as shown in the drawings. The drawings are not necessarily to scale, but emphasize showing the principles of various embodiments of the present invention. Figure 1 For the comparison of fungal diversity among the four groups of LC, OC, LP, and OP. (A) Alpha diversity; (B) Beta diversity.

[0015] Figure 2 For the comparison of the fungal flora composition and structure among the four groups of LC, OC, LP, and OP. (A) Fungal phylum level; (B) Fungal genus level.

[0016] Figure 3 For the LEfSe analysis of fungal taxa between the OC and OP groups.

[0017] Figure 4 The importance ranking of the species groups of characteristic substances of the disease model (A) constructed for the OC group and the OP group based on the random forest algorithm; (B)(C) The ROC curves constructed for the training set and the test set based on the characteristic values.

[0018] Figure 5 Analysis of characteristic markers between the LC group and the LP group.

[0019] Figure 6 The importance ranking of the species groups of characteristic substances of the disease model (A) constructed for the OC group and the OP group based on the random forest algorithm; (B)(C) The ROC curves constructed for the training set and the test set based on the characteristic values. Specific implementation manners

[0020] The present invention will be further described below in conjunction with specific embodiments, so that those skilled in the art can better understand the present invention and be able to implement it, but the specific embodiments cited are not intended to limit the present invention.

[0021] Embodiment 1

[0022] 1.1 Sample collection

[0023] Healthy female volunteers with regular menstrual cycles and normal ovarian morphology were recruited from the general community. Their hormone levels, BMI, glucose tolerance status, blood pressure and blood lipids were all within the normal range. Women who had breastfed or been pregnant in the past year were excluded from the study. According to the Rotterdam criteria in 2003, women diagnosed with PCOS should meet: (1) Oligo-ovulation and / or anovulation; (2) Clinical and / or biochemical signs of hyperandrogenism; (3) Polycystic ovaries were found by ultrasound in one or both ovaries, ≥12 follicles with a diameter of 2-9 mm, and / or an ovarian volume ≥10 mL. All PCOS patients were newly diagnosed patients and had not received PCOS-related treatment. A total of 41 healthy women and 47 PCOS female patients were included. The population was divided into a normal BMI (BMI < 24) group and a high BMI (BMI ≥ 24) group. Finally, the numbers of healthy women with normal BMI (LC), healthy women with high BMI (OC), PCOS patients with normal BMI (LP), and PCOS patients with high BMI (OP) were 21, 20, 22, and 25 respectively.

[0024] 1.2 DNA extraction and fungal ITS2 gene sequencing

[0025] Fecal samples from healthy individuals and PCOS patients were collected on the day of physical examination and immediately frozen at -80°C. According to the kit instructions, use The StoolDNA Kit (Omega Bio-tek, Norcross, GA, U.S.) extracts DNA from approximately 200 mg of feces. The purity and concentration of the isolated DNA are detected using a Qubit 3.0 fluorometer (Invitrogen, Carlsbad, CA, USA). The ITS2 gene of the DNA template is amplified with the forward amplification primer: 5’-CTTGGTCATTTAGAGGAAGTAA-3’ and the reverse amplification primer: 5’-GCTGCGTTCTTCATCGATGC-3’. The quality of the DNA is inspected using the QuantiFluor TM -ST blue fluorescence quantification system (Promega). After constructing the Illumina PE250 library, sequencing is performed on an Illumina MiSeq instrument (Illumina, San Diego, CA, USA). The Unite database (Release 8.2 http: / / unite.ut.ee / index.php) is used for fungal taxonomic alignment.

[0026] 1.3 Sequencing data analysis

[0027] The Fastp software (version 0.19.6) is used to trim the quality and length of the raw sequencing data and remove the Illumina adapters. The QIIME2 (version 2019.4) is used to process and analyze the ITS2 sequencing data. Cutadapt is used to remove the sequencing primers, and Dada2 is used for noise reduction. The Dada2 software is used to obtain the representative sequences and the species abundance table based on the representative sequences. The q2-feature-classifier classify-sklearn is used to annotate the species of the sequences.

[0028] 1.4 Statistical analysis

[0029] Calculate the Shannon index of fungal α-diversity based on the OTUs table. For the OTUs table, calculate the distance matrix based on the Bray Curtis distance and draw the PCoA plot. Use the Kruskal-Wallis method for statistical testing and the Benjamini-Hochberg (B-H) FDR method for p-value correction. The LDA cutoff value of linear discriminant analysis effect size (LEfSE) is 2, which is used to identify the differential fungal taxa that distinguish between groups, so as to find out the statistically significant biomarkers. Use the Randomforest algorithm in the randomForest R package to train with fungal classification or characteristic OTU features. The entire dataset is assigned into training (80%) and test (20%) datasets. In the test stage, evaluate the prediction performance of each machine learning model through performance parameters such as the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity.

[0030] Example 2

[0031] 2.1 Fungal α and β diversity analysis

[0032] As Figure 1 shown in A, the Shannon index of fungal α-diversity in the four groups of LC, OC, LP, and OP was significantly different among the four groups (p < 0.05). Among them, the Shannon index value of the high-BMI population was higher than that of the population with normal BMI. PCoA analysis based on the Bray-Curtis distance found that the microbial community structures of the four groups were quite different, and PERMANOVA analysis showed significant differences (p < 0.05)( Figure 1 B), and the difference between the OC group and the other three groups was obvious.

[0033] 2.2 Fungal microbiota structure analysis

[0034] As Figure 2As shown in Figure 2 B

[0035] , at the phylum level of fungi, the abundances of Ascomycota, Basidiomycota, and Mortierellomycota fungi in the bodies of healthy people and PCOS patients are dominant. Among them, the average abundance of Ascomycota in the LC group can reach up to 80%, but the abundance of Mortierellomycota is the lowest. The average abundance of Ascomycota in the OC group is the lowest, while the abundances of Basidiomycota and Mortierellomycota are the lowest. The abundances of the fungal phyla in the LP and OP groups are similar. At the genus level of fungi, the abundances of Candida and Botryotrichum in the LC group are the highest. The OC group does not contain Candida and Malassezia, but the abundances of Fusarium, Mortierella, and Humicola are the highest. The abundances of Aspergillis and Cladosporium in the LP group are the highest (2.3 Using LEfSe for biomarker analysis between the OC group and the OP group

[0036] Comparative analysis of the characteristic indicator biomarkers of the OC and OP groups by LEfSe found that different groups have their respective indicator biomarkers. Among them, the main characteristic fungal biomarkers (LDA > 3.6) in the OP group are fungi such as Candida, Saccharomycetales fam Incertae sedis, Ascomycota, Candida albicans, Eurotiales, Aspergillaceae, Malassezia, etc. The main characteristic fungal biomarkers in the OC group are Mortierella, Agaricomycetes, Basidiomycota, Piskurozymaceae, Tremellomycetes, Solicoccozyma, Filobasidiales, and Mortierella elongate fungi. The above results indicate that these fungal types may be important indicator microorganisms for PCOS disease( Figure 3 )

[0037] 2.4 Potential of the characteristic biomarkers between the OC group and the OP group in the early diagnosis of the disease

[0038] ​To screen out characteristic fungal taxa that can distinguish between the OC and OP groups, thus contributing to the early diagnosis of PCOS for early intervention, we ranked the importance of features for the entire fungal OTU taxa in the OC and OP groups using the random forest algorithm. Based on the mean decrease in Gini index, Candida, Tetracladium, Malassezia, Exophiala, Knufia, IIynonectria, Mortierella, Solicoccozyma, Cladophialophara, Pseudogymnoascus, Coniochaeta, Didymella, Trichoderma, Cladosporium, Epicoccum, Penicillium, Dichotomopilus, Neurospora, Alternaria, Paraphoma, Staphylotrichum, Purpureocillium, Thelonectria, Pyrenochaetopsis, Talaromyces, Acrocalymma, Bolbitius, and Gibberella are the top 30 important fungal taxa in the model ( Figure 4 A). The AUC for distinguishing between the OC and OP groups using the training set model constructed with the top 30 important characteristic fungi was 1.0 ( Figure 4 B), while the AUC for this model for the OC and OP groups in the test set was 0.94 ( Figure 4 C).

[0039] By integrating the results of intersecting the features screened by LEfSe and random forest, it was found that the characteristic fungi Candida and Malassezia can be used as key indicator fungi for distinguishing between OP and OC.

[0040] 2.5 Feature marker analysis between the LC and LP groups using LEfSe

[0041] Comparative analysis of the characteristic indicative markers between the LC and LP groups by LEfSe revealed that different groups had their respective indicative markers. Among them, the main characteristic fungal markers in the LP group were Aspergillus, Kazachstaniaslooffiae, Funneliformis sp., and Plectosphaerella oratosquillae fungi. While the main characteristic fungal markers in the LC group were Saccharomycetales fam Incertae sedis, Candida, Ascomycota, Candida tropicalls, Botryotrichum atrogriseum, Hypocreales fam Incertar sedis, and Pezizomycetes fungi. These fungal types may be important indicative microorganisms for PCOS disease ( Figure 5 ).

[0042] 2.6 Potential of characteristic markers between the LC and LP groups in early disease diagnosis

[0043] We further conducted disease diagnosis on the LC and LP groups from the perspective of fungi through random forest modeling. Based on the mean decrease in Gini index, we screened out important characteristic fungal taxa such as Meyerozyma, Neurospora, Cylindrocarpon, Humicola, Trichderma, Candida, Penicillium, Botryotrichum, Sarocladium, Malassezia, Mortierella, Setophoma, Fusarium, Aspergillus, Talaromyces, Preussia, Cephaliophora, Cladosporium, Myrothecium, Schizophyllum, Kazachstania, Gibberella, Stachybotrys, Debaryomyces, Saitozyma, Bolbitius, Nectria, and Schizothecium ( Figure 6 A). The AUC values of the training set model constructed with the top 30 important fungal taxa characteristics for the LC and LP groups were 0.68 ( Figure 6 B), and the AUC of this model for the LC and LP groups in the test set was 0.70 ( Figure 6C), Overfitting of data occurred, and it is necessary to expand the specimens for model optimization in the future. By synthesizing the results of intersecting the features screened by LEfSe and random forest, it was found that the characteristic fungi Aspergillus, Kazachstania, Candida, Botryotrichum, Meyerozyma, Neurospora, Cylindrocarpon, and Saro cladium can be used as key indicator fungi for differentiating LP and LC.

[0044] Therefore, fungal characteristics have a good differentiating effect on the OC and OP groups, but a general differentiating effect on the LC and LP groups.

[0045] It should be understood that although the present invention has been specifically shown and described with reference to its exemplary embodiments, those of ordinary skill in the art should understand that various changes in form and detail can be made therein, and any combination of various embodiments can be made without departing from the spirit and scope of the present invention as defined by the appended claims.

Claims

1. A biomarker for the diagnosis of polycystic ovary syndrome in non-obese populations, characterized in that, The biomarker consists of fungi Aspergillus, Kazachstania, Candida, Botryotrichum, Meyerozyma, Neurospora, Cylindrocarpon, Sarocladium, Humicola, Trichderma, Penicillium, Malassezia, Mortierella, Setophoma, Fusarium, Talaromyces, Preussia, Cephaliophora, Cladosporium, Myrothecium, Schizophyllum, Gibberella, Stachybotrys, Debaryomyces, Saitozyma, Bolbitius, Nectria and Schizothecium.

2. The biomarker according to claim 1, wherein The biomarker is intestinal fungi.

3. Use of a reagent for detecting the biomarker according to any one of claims 1-2 in the preparation of a diagnostic kit for polycystic ovary syndrome in non-obese populations.

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