Application of geotrichum candidum as biomarker in heart failure diagnosis

Abstract

The invention discloses application of geotrichum candidum as a biomarker in diagnosis of heart failure. The correlation between oral flora and heart failure is studied, and clinical sample detection shows that geotrichum candidum can be used as an independent biomarker for diagnosing heart failure. Meanwhile, the invention provides a biomarker combination containing geotrichum candidum, and the biomarker combination has relatively high heart failure diagnosis efficiency. According to the invention, early diagnosis of heart failure can be realized, and a clinician is guided to provide a prevention or treatment scheme for a subject, so that the disease progress of occurrence and development of heart failure can be better intervened.

Description

[0001] This application is a divisional application, and the information of the parent application is as follows: The invention name is the application of oral flora in the diagnosis of heart failure; the original application date is July 1, 2024, and the original application number is 2024108713528. Technical Field

[0002] The present invention relates to the field of clinical medicine, and particularly to the application of Geotrichum candidum as a biomarker in the diagnosis of heart failure. Background Art

[0003] Heart failure (HF, hereinafter referred to as heart failure) refers to a group of clinical syndromes in which the filling or ejection ability of the ventricle is impaired due to various cardiac structural or functional abnormalities, and it is the end stage of various heart diseases. With the increasing aging of the population, the prevalence of chronic cardiovascular diseases such as coronary heart disease and hypertension has gradually increased, and the prevalence of heart failure has been on the rise year by year in China and even worldwide. According to statistics, the prevalence of heart failure in the adult population is about 1-2%, among which the prevalence of heart failure in people over 35 years old in China is about 1.3%, while the prevalence of heart failure in the elderly over 70 years old is as high as more than 10%. Chronic heart failure leads to a decline in exercise tolerance, complications, etc., seriously affecting the quality of life of patients. In addition, heart failure has a high mortality rate. According to statistics, the one-year mortality rate of patients with congestive heart failure after discharge is about 16.5%, and the 5-year mortality rate of inpatients with heart failure exceeds 75%. Despite the continuous improvement of medical standards, the readmission rate and mortality rate of heart failure patients remain high. The increasing prevalence of heart failure and high mortality rate bring serious medical and economic burdens to patients and society, and have become a global public health problem. However, the current effective prevention and treatment measures for heart failure are still limited. Therefore, studying the pathogenesis of heart failure, achieving early diagnosis, and finding protection strategies are crucial for reducing the harm of heart failure.

[0004] In recent years, studies have found that there is a very close relationship between flora and inflammation. Oral flora has been repeatedly proven to be related to various systemic inflammatory diseases. However, the evidence regarding the relationship between oral flora and the risk of heart failure is very limited, and most of it is based on the risk between periodontitis and heart failure. Therefore, the present invention takes oral flora as the starting point for research to provide a new direction for the diagnosis and treatment of heart failure. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention provides Geotrichum candidum that can be used as an independent biomarker for the diagnosis of heart failure, as well as a diagnostic biomarker combination containing Geotrichum candidum.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] The first aspect of the present invention provides the use of a reagent for detecting the abundance of a biomarker in a sample in the preparation of a product for diagnosing heart failure, wherein the biomarker is Geotrichum candidum.

[0008] The second aspect of the present invention provides the use of a reagent for detecting the abundance of a biomarker in a sample in the preparation of a product for diagnosing heart failure, wherein the biomarker is a combination of Geotrichum candidum, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Streptococcus toyakuensis, Haemophilus influenzae, and Veillonella massiliensis.

[0009] In the present invention, the term "biomarker" refers to a specific strain in the oral microbiota. In some embodiments, the term also encompasses a measurable entity that has been identified as a target indicative of a target output such as one or more diagnostic, prognostic, predictive drug sensitivity, and / or treatment outputs, such as the abundance of the biomarker.

[0010] The biomarker can differentially exist at any level, but generally exists at a differential level that is increased by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more; or generally exists at a level that is decreased by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% (i.e., absent). Preferably, the biomarker has a statistical difference (P < 0.05).

[0011] In the present invention, the term "oral microbiota" refers to the collection of microorganisms colonized in the human oral cavity, and its components include bacteria, fungi, viruses, etc. Many of these species have low detection rates and abundances in the healthy human oral cavity. Therefore, specific species can be used as biomarkers to distinguish patients from healthy people.

[0012] In the present invention, the term "abundance" refers to the ratio of the number of biomarker species in a sample to the total number of all microbial species in the sample. This ratio is usually expressed as a percentage.

[0013] Furthermore, the method for detecting the abundance of the biomarker includes any one or more of metagenomic sequencing, 16S-rRNA sequencing, ITS sequencing, qRT-PCR method, Southern blotting, in situ hybridization.

[0014] Furthermore, the abundance of the biomarker is determined by amplifying the ASV sequence of the biomarker in the subject sample and then according to its proportion in the overall sample.

[0015] Furthermore, the reagent for detecting the abundance of the biomarker is a primer or probe capable of specifically amplifying the ASV sequence of the biomarker, and the ASV sequence is the sequence shown in SEQ ID NO: 1-10.

[0016] In the present invention, ASV is the abbreviation of Amplicon Seauence Varant, which refers to the determination and analysis of DNA sequences in a microbial community by high-throughput sequencing technology, so as to obtain the ASV number of each microorganism. The sequence corresponding to the ASV number has sufficient nucleotide diversity to distinguish the genus and species of the biomarker.

[0017] In the context of the present invention, the term "sample" used refers to a composition obtained from or derived from a subject (such as an individual of interest), which contains cells and / or other molecular entities to be characterized and / or identified according to, for example, physical, biochemical, chemical, and / or physiological characteristics. For example, a sample refers to any sample derived from a subject of interest, which is expected or known to contain cells and / or molecular entities to be characterized. Samples include but are not limited to tissue samples, primary or cultured cells or cell lines, cell cultures, cell supernatants, cell lysates, platelets, serum, plasma, vitreous humor, lymph fluid, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, sputum, tears, sweat, mucus, saliva, dental plaque, carious plaque, supragingival plaque, subgingival plaque, peri-implant submucosal plaque, endodontic plaque, dorsal tongue plaque, and other mucosal surface plaque samples, tissue culture media, tissue extracts, homogenized tissues, cell extracts, and combinations thereof.

[0018] In the present invention, a subject refers to an individual suffering from or suspected of suffering from heart failure, preferably a mammal such as a mouse, rat, rabbit, sheep, monkey, human, and particularly preferably a human.

[0019] Furthermore, the sample is a sample containing the oral microbiota of the subject to be tested.

[0020] Furthermore, the sample containing the oral microbiota of the subject to be tested includes saliva, dental plaque, carious lesion plaque, supragingival plaque, subgingival plaque, peri-implant submucosal plaque, endodontic plaque, dorsal tongue plaque, and other mucosal surface plaque samples.

[0021] Furthermore, the sample containing the oral microbiota of the subject to be tested is supragingival plaque.

[0022] Furthermore, the product further contains reagents for sample processing.

[0023] Furthermore, the reagents for sample processing include cryoprotectants, buffer solutions, and storage media.

[0024] The third aspect of the present invention provides a method for constructing a heart failure diagnosis model. The steps of the method include: obtaining the abundance data of the biomarker described in the second aspect of the present invention in the sample and the clinical characteristics corresponding to the sample, and constructing a diagnosis model based on the abundance data and the clinical characteristics.

[0025] The clinical characteristics are heart failure patients and healthy controls.

[0026] The construction of the diagnosis model is to construct the diagnosis model through an algorithm.

[0027] As is known to those skilled in the art, the step of associating the biomarker abundance data of a subject with a certain probability or risk can be implemented and realized in different ways. Mathematically combine the abundance measurements of the biomarker and one or more other markers, and associate the combined value with the underlying diagnostic problem. The determination of the biomarker abundance data can be combined by any suitable existing mathematical method, such as at least one of logarithmic regression, linear discriminant analysis, feature gene linear discriminant analysis, support vector machine, random forest, cross-validation, receiver operating characteristic curve, recursive partitioning tree, XGBoost, ShrunkenCentroids, StepAIC, Kth-Nearest Neighbor, Boosting, neural network, Bayesian network, and hidden Markov model.

[0028] Further, the algorithm includes at least one of logistic regression, linear discriminant analysis, linear discriminant analysis of characteristic genes, support vector machine, random forest, cross-validation, receiver operating characteristic curve, recursive partitioning tree, XGBoost, ShrunkenCentroids, StepAIC, Kth-Nearest Neighbor, Boosting, neural network, Bayesian network, and hidden Markov model.

[0029] In a fourth aspect of the present invention, there is provided a computer-implemented heart failure diagnosis system, the diagnosis system including a classification unit that classifies using the diagnosis model obtained by the construction method described in the third aspect of the present invention to obtain a classification result that the subject has heart failure or is at risk of having heart failure, or to obtain a classification result that the subject does not have heart failure.

[0030] Further, the diagnosis system further includes an input unit and an output unit.

[0031] The input unit is used to input the abundance data of the biomarker described in claim 2.

[0032] The output unit is used to output the classification result of the classification unit.

[0033] Both the input unit and the output unit are connected to the classification unit in some way.

[0034] The present invention provides a system programmed to implement the methods of the present invention. The system can regulate various aspects of the oral microbiota analysis of the present invention, such as, for example, matching data against known sequences. The system can be the user's electronic device or a computer system remotely located relative to the electronic device. The electronic device can be a mobile electronic device.

[0035] Advantages and beneficial effects of the present invention: The present invention provides Geotrichum candidum and a biomarker combination including Geotrichum candidum, which has a high heart failure diagnosis efficacy. The present invention can achieve early diagnosis of heart failure, guide clinicians to provide prevention or treatment plans for the subject, and thus better intervene in the disease process of the occurrence and development of heart failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 It is a random forest prediction result diagram of the experimental set samples, where A is a visualization image of the random forest prediction model, and B is the importance ranking result of the 10 selected species.

[0037] Figure 2 It is a classification result diagram of the experimental set samples, where A is the abundance difference level between heart failure patients and healthy controls, and B is the ROC curve diagram of the experimental set sample combination.

[0038] Figure 3 ROC curve of a single species in the experimental set samples.

[0039] Figure 4 Classification result graph of the validation set samples, where A is the abundance difference level between heart failure patients and healthy controls, and B is the ROC curve graph of the validation set sample combination.

[0040] Figure 5 ROC curve of a single species in the validation set samples. Detailed implementation manner

[0041] All kinds of reagents involved in the technical solutions and experimental processes described in the present invention are common reagents or commercial reagents that those skilled in the art can clearly know and easily obtain based on their professional knowledge and routine practices. The description of the reagents in the present invention aims to clearly explain the material basis involved in the technical solutions, and those skilled in the art can smoothly obtain and correctly use these reagents based on their professional qualities and industry common sense to achieve the technical purposes of the present invention.

[0042] The present invention conducts a differential test on the abundances of all species in the oral flora of heart failure patients and healthy controls, screens out the species with significant differences between the two groups through an algorithm, and performs classification modeling on the preliminarily screened differential species to select the biomarkers that can be used for inter-group classification. After analysis, 10 key strains are found, namely ASV10 (Streptococcus toyakuensis, Streptococcus), ASV24 (Neisseria perflava, Neisseria), ASV37 (Neisseria subflava, Subflavine Neisseria), ASV7 (Veillonella parvula, Veillonella parvula), ASV215 (Neisseria cinerea, Neisseria cinerea), ASV1 (Haemophilus parainfluenzaes, Haemophilus parainfluenzae), ASV127 (Neisseria sicca, Neisseria sicca), ASV443 (Geotrichum candidum, Geotrichum candidum), ASV25 (Haemophilus influenzae, Haemophilus influenzae), ASV86 (Veillonellamassiliensis, Veillonella massiliensis), and the ASV sequences are the sequences shown in SEQ ID NO: 1-10.

[0043] Further research found that both the individual diagnostic AUC and the combined diagnostic AUC of 10 species in the validation sample set were greater than 0.7, showing significant effectiveness in differentiating heart failure patients from healthy controls. Among them, the AUC of Geotrichum candidum in the validation set was as high as 0.912, indicating that it can serve as an independent biomarker for heart failure.

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0045] Example 1 Screening and validating biomarkers related to heart failure in oral microbiota

[0046] I. Experimental materials and experimental procedures

[0047] 1. Experimental samples

[0048] 94 samples. There were 40 samples in the experimental group (17 HC and 23 HF), and 54 samples in the validation group (14 HC and 40 HF).

[0049] The samples were selected from 63 patients hospitalized at Peking Union Medical College Hospital from April 2022 to March 2023. These patients were diagnosed with HF by at least two experienced cardiologists according to the 2021 ESC heart failure guidelines. Detailed demographic, medical history, laboratory test results, and echocardiogram data were obtained through the electronic medical record system of PUMCH. The New York Heart Association (NYHA) functional classification was used to evaluate exercise tolerance.

[0050] According to the clinical manifestations of the patients, we divided them into: 1) Chronic HF (CHF): patients who had received appropriate anti-HF treatment and had stable clinical manifestations for at least 1 month; 2) Acute HF (AHF): patients with new-onset or acute decompensated heart failure; NYHA class II to IV at the time of enrollment; with ≥1 symptom (such as worsening dyspnea, orthopnea) or ≥1 sign (such as rales, peripheral edema, serous effusion) at admission; and NT-proBNP (N-terminal of brain natriuretic peptide) > 300 pg / ml or B-type natriuretic peptide (BNP) > 100 pg / ml.

[0051] According to the left ventricular ejection fraction (LVEF) evaluated by echocardiogram, it is divided into: 1) Heart failure with reduced ejection fraction (HFrEF): LVEF ≤ 40%; 2) Heart failure with mid-range ejection fraction (HFmrEF): LVEF is 41% - 49%; (3) Heart failure with preserved ejection fraction (HFpEF): LVEF ≥ 50%; 4) Heart failure with improved ejection fraction (HFimpEF): LVEF ≤ 40% at baseline, improved by up to 40% from 1 month to 1 year after discharge, and increased by at least ≥ 10%.

[0052] A total of 31 subjects without heart disease or HF-related symptoms or signs were included in the control group. Subjects with one or more of the following conditions were excluded: 1) Evidence of acute myocardial infarction; 2) Received cardiac surgery within the previous 6 months; 3) Systemic diseases such as systemic lupus erythematosus and malignancies; 4) End-stage renal disease (eGFR < 15 ml / kg / 1.73m2, CKD-EPI); 5) Used antibiotics for more than 3 days within the previous 3 months.

[0053] This research protocol was approved by the Ethics Committee of Peking Union Medical College Hospital (K23C0687) and was carried out in accordance with the principles of the Declaration of Helsinki. All subjects signed a written informed consent form agreeing to participate in this study.

[0054] 2. Sample collection

[0055] Sample type: Supragingival plaque.

[0056] Specific steps: The supragingival plaque of all subjects was collected using sterile swabs. All subjects rinsed their mouths with 10 ml of 0.9% sodium chloride solution for 15 seconds before sampling to remove residual food residues in the oral cavity, and none of them had eaten, drunk, brushed their teeth, or used any oral cleaning solution within two hours before sampling. The collected samples were stored frozen at -80°C.

[0057] 3. DNA extraction

[0058] The genomic DNA of the flora in the samples was extracted using the CTAB extraction method.

[0059] 4. Sequencing

[0060] The 16S rRNA and ITS rRNA genes were amplified using the 16S V3-4 region and ITS 1-1F region, respectively. According to the manufacturer's recommendations, the NEB Next® Ultra™ II FS DNA PCR-Free Library Preparation Kit (New England Biolabs, USA, catalog #: E7430L) was used to generate sequencing libraries. The libraries were quantified using Qubit and real-time PCR, and the size distribution was detected using a bioanalyzer. Based on the PE250 strategy, the quantified libraries were pooled and sequenced on an Illumina NovaSeq 6000. All of the above procedures were completed at Novogene Bioinformatics Technology Co., Ltd. The ASV sequences of the biomarkers described in the present invention are shown in Table 1.

[0061] Table 1. SEQ ID corresponding ASV sequence list

[0062] 5. Species diversity analysis

[0063] Species diversity analysis was performed using the Wilcoxon rank-sum test. The specific steps are as follows: Through ranking, the original data information was replaced with rank order for testing. The data of the two populations were mixed and sorted, and values were assigned in order and the averages of the two populations were calculated. If the average ranks of the two populations are not very different, it indicates that there is no significant difference between the two populations; otherwise, the statistic P value was further calculated, and the species with a P value less than 0.05 were selected, that is, the species with significant differences.

[0064] 6. Construction of a classification model

[0065] The present invention constructed a random forest classifier using microbial genera and clinical index variables. Five times of 10-fold cross-validation was used to select microbial indicators to construct the final classifier, and a receiver operating characteristic curve (AUC) was established to evaluate the performance of the classifier.

[0066] 1) Random Forest analysis (RF)

[0067] Belongs to machine learning algorithms and is a classifier containing multiple decision trees, which can efficiently and quickly select the most important species categories for sample classification.

[0068] 2) Cross-validation analysis

[0069] Using 5-fold 10-fold cross-validation (K-folder Cross-validation, K-folder CV), traverse the combinations of key species selected by the random forest method, in order to construct an efficient classifier with the lowest error rate using the optimal species combination.

[0070] 3) Receiver Operating Characteristic curve (ROC) analysis

[0071] Used to verify the classification performance of the classification model. According to the classification test results, determine the upper and lower limits, group interval, and cut-off point of the measured values, and calculate the sensitivity and specificity of all cut-off points respectively. The larger the Area Under the Curve (AUC), the better the performance of the classification model.

[0072] II. Experimental results

[0073] 1. Screening results

[0074] In the experimental group, 270 species with significant differences between the two groups were selected through the results of the difference test, and a random forest classification model was trained with 270 species, as shown in Figure 1 Figure A.

[0075] After feature selection based on 5-fold 10-fold cross-validation, 10 signature species were retained with the best performance, namely: ASV10 (Streptococcus toyakuensis), ASV24 (Neisseria perflava), ASV37 (Neisseria subflava), ASV7 (Veillonella parvula), ASV215 (Neisseria cinerea), ASV1 (Haemophilus parainfluenzaes), ASV127 (Neisseria sicca), ASV443 (Geotrichum candidum), ASV25 (Haemophilus influenzae), ASV86 (Veillonella massiliensis). The ranking of these 10 species according to the importance is shown in Figure 1 Figure B.

[0076] The ROC curve further verified the classification performance of the classification model. As Figure 2 shown, the AUC obtained by the combined calculation of 10 species was as high as 0.96, indicating that the combination of 10 species had good classification performance in the diagnosis of heart failure.

[0077] 2. Verification Results

[0078] The validation group was classified and verified using the combination of 10 species screened from the experimental sample set. As Figure 4 shown, according to the ROC curve, the AUC was as high as 0.96, indicating that the combination of 10 species had good classification performance in the diagnosis of heart failure and could be used as a biomarker for heart failure. The ROC curves were separately plotted for each of the 10 screened species. As Figure 5 shown, it can be seen that for ASV443, namely Geotrichum candidum, the diagnostic AUC was as high as 0.912, indicating that Geotrichum candidum could be used as an independent biomarker for heart failure with excellent predictive classification performance.

[0079] The present invention has been described in detail above. For those skilled in the art, without departing from the gist and scope of the present invention and without unnecessary experiments, the present invention can be implemented within a relatively wide range under equivalent parameters, concentrations and conditions. Although embodiments of the present invention are given, it should be understood that the present invention can be further improved. In short, according to the principle of the present invention, this application intends to cover any modifications, uses or improvements of the present invention, including those that depart from the scope disclosed in this application but are made by conventional techniques known in the art.

Claims

1. Use of a reagent for detecting the abundance of a biomarker in a sample in the preparation of a product for diagnosing heart failure, characterized in that, The biomarker is Geotrichum candidum.

2. Use of a reagent for detecting the abundance of a biomarker in a sample in the preparation of a product for diagnosing heart failure, characterized in that, The biomarker is a combination of Geotrichum candidum, Neisseria perflava, Neisseria subflava, Veillonella parvula, Neisseria cinerea, Haemophilus parainfluenzaes, Neisseria sicca, Streptococcus toyakuensis, Haemophilus influenzae, and Veillonella massiliensis.

3. The application according to claim 1 or 2, characterized in that, The method for detecting the abundance of the biomarker includes any one or more of metagenomic sequencing, 16S-rRNA sequencing, ITS sequencing, qRT-PCR method, Southern blotting, and in situ hybridization.

4. The application according to claim 1 or 2, characterized in that, The abundance of the biomarker is determined by amplifying the ASV sequence of the biomarker in the sample of the subject and then based on its proportion in the total sample. Preferably, the reagent for detecting the abundance of the biomarker is a primer or probe capable of specifically amplifying the ASV sequence of the biomarker, and the ASV sequence is the sequence shown in SEQ ID NO: 1-10.

5. The application according to claim 1 or 2, characterized in that, The sample is a sample containing the oral flora of the subject to be tested. Preferably, the sample containing the oral flora of the subject to be tested includes samples of saliva, dental plaque, carious lesion plaque, supragingival plaque, subgingival plaque, subperi-implant mucosal plaque, endodontic plaque, dorsal tongue plaque, and other mucosal surface plaque. Preferably, the sample containing the oral flora of the subject to be tested is supragingival plaque.

6. The application according to claim 1 or 2, characterized in that, The product further contains reagents for sample processing. Preferably, the reagents for sample processing include cryoprotectant, buffer solution, and storage medium.

7. A method for constructing a heart failure diagnosis model, characterized in that The steps of the method include: obtaining the abundance data of the biomarker described in claim 2 in the sample and the corresponding clinical characteristics of the sample, and constructing a diagnostic model based on the abundance data and the clinical characteristics. The clinical characteristics are heart failure patients and healthy controls. The construction of the diagnostic model is by constructing a diagnostic model through an algorithm.

8. The construction method according to claim 7, wherein The algorithm includes at least one of logistic regression, linear discriminant analysis, linear discriminant analysis of characteristic genes, support vector machine, random forest, cross-validation, receiver operating characteristic curve, recursive partitioning tree, XGBoost, Shrunken Centroids, StepAIC, Kth-Nearest Neighbor, Boosting, neural network, Bayesian network, and hidden Markov model.

9. A computer-implemented heart failure diagnosis system, characterized in that, The diagnostic system includes a classification unit, which uses the diagnostic model obtained by the construction method according to any one of claims 7-8 to perform classification, and obtains a classification result that the subject has heart failure or is at risk of having heart failure, or obtains a classification result that the subject does not have heart failure.

10. The diagnostic system according to claim 9, wherein, The diagnostic system further includes an input unit and an output unit; The input unit is used for inputting the abundance data of the biomarker according to claim 2; The output unit is used for outputting the classification result of the classification unit; Both the input unit and the output unit are connected to the classification unit in a certain way.

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