A biomarker associated with parkinson's disease and use thereof

Abstract

The present application relates to biomarkers associated with Parkinson's disease and their applications. Specifically, the present application relates to fungi, particularly Saccharomyces cerevisiae, as biomarkers for PD diagnosis. The present application also relates to methods for detecting fungi, particularly Saccharomyces cerevisiae. The biomarkers and their detection methods of the present application can be used to assist in displaying biological information associated with Parkinson's disease (PD), particularly early-stage PD, to assist in differentiating Parkinson's disease (PD) from essential tremor (ET), and to identify prodromal PD patients, i.e. patients with REM sleep behavior disorder (RBD) who can progress to PD. The present application also relates to corresponding kits, systems and related applications. The present application can be used as a clinical auxiliary diagnosis method independently, or as a combined composite indicator for clinical auxiliary diagnosis.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, specifically to biomarkers related to Parkinson's disease and their applications, related methods, and corresponding reagent kits and systems. Background Technology

[0002] Parkinson's disease (PD) is characterized by the selective and progressive degeneration and loss of dopaminergic (DA) neurons in the substantia nigra pars compacta of the midbrain. However, the specific pathogenesis of PD remains unclear. Clinically, PD is currently diagnosed primarily based on typical motor symptoms and signs, and there are no other precise and effective auxiliary bioindicators that can indicate early risk factors associated with Parkinson's disease.

[0003] According to the latest standards of the International Society for Movement Disorders (ISMD), the course of Parkinson's disease (PD) includes preclinical PD and clinical PD (i.e., PD diagnosed based on typical clinical symptoms). Preclinical PD includes risk period PD and prodromal period PD. At this stage, motor symptoms have not yet appeared, and non-motor symptoms are the core manifestations, including constipation, decreased / loss of smell, and rapid eye movement sleep disorder (RBD). According to the Hoehn-Yahr classification, clinical PD is divided into early, intermediate, and late stages, with grades 1-2 being early PD, 2.5-3 being intermediate PD, and 4-5 being late PD. As mentioned above, the diagnosis of PD is mainly based on typical clinical symptoms. According to current clinical diagnostic criteria, it often requires 3-5 years of follow-up to achieve a clinical diagnosis of PD.

[0004] Furthermore, essential tremor (ET), as an idiopathic disease characterized primarily by tremor, needs to be differentiated from early-stage psychogenic tremor (PD) in clinical practice. PD often develops in the elderly, which is also the age at which ET commonly occurs, leading to many cases of ET being misdiagnosed as PD. Although typical PD is characterized by resting tremor, rigidity, and bradykinesia, these characteristic manifestations are often lacking in the early stages of PD, especially when only tremor is present at onset, which easily leads to misdiagnosis. Differential diagnosis between ET and PD is therefore crucial.

[0005] Therefore, research on the early diagnosis of PD has become a hot topic in related fields. Discovering biomarkers closely related to the onset of PD in early-stage patients or patients at risk of progressing to PD is of great significance as an auxiliary method for disease diagnosis.

[0006] Therefore, when patients first develop motor symptoms, it is important to identify effective biomarkers from a non-motor perspective to achieve rapid and accurate collection of biological information for early-stage PD patients. This will enable screening for early-stage PD patients, allowing for effective measures to slow the disease progression and facilitate early intervention for symptoms. Summary of the Invention

[0007] One object of the present invention is to provide a rapid and simple biomarker for assisting in the display of biological information associated with Parkinson's disease (PD), particularly early PD.

[0008] Another object of the present invention is to provide a rapid and simple biomarker for assisting in the differentiation between Parkinson's disease (PD) and essential tremor (ET).

[0009] Another object of the present invention is to provide a rapid and simple biomarker for identifying patients with prodromal PD, wherein the prodromal PD patients are patients with sleep behavior disorder (RBD) that can progress to PD.

[0010] The inventors discovered that the abundance of a certain fungus (Saccharomyces cerevisiae) is increased in the intestines of patients with progressive disease (PD), while its abundance is lower in the intestines of age-matched healthy individuals or patients with endocrine disorders (ET). Furthermore, the inventors developed a method for detecting this intestinal fungus, which can effectively differentiate between PD patients, ET patients, and healthy individuals, and can be used to detect prodromal PD patients (RBD progressing to PD), thereby achieving auxiliary diagnosis of PD.

[0011] This application identifies a fungus present in the gut of patients with Parkinson's disease (PD), which can serve as a biomarker for PD. The detection method for this fungus can be combined with clinical scale assessment results to assist physicians in diagnosis. In the applications of this invention, the detection methods include, but are not limited to, the determination of serum anti-Saccharomyces cerevisiae antibody (ASCA) IgG and IgA, fecal fungal ITS sequencing, and fecal fungal quantitative real-time PCR determination. The detection method of this invention can be used independently as a clinical auxiliary diagnostic method, or as a combined composite indicator for clinical auxiliary diagnosis.

[0012] Therefore, in one aspect, the present invention provides a biomarker for detecting the presence and / or abundance of fungi in the gut of a subject and / or the level of antibodies against the fungi in serum, optionally said fungi being Saccharomyces cerevisiae, wherein said biomarker is selected from biomarkers capable of detecting the abundance of fungi or optionally Saccharomyces cerevisiae by ITS next-generation sequencing or PCR technology, or biomarkers capable of detecting the level of antibodies against fungi or optionally Saccharomyces cerevisiae in serum by ELISA.

[0013] In one embodiment, the present invention provides a biomarker for assisting in displaying biological information associated with Parkinson's disease (PD), particularly early PD, or for assisting in distinguishing between Parkinson's disease (PD) and essential tremor (ET), or for identifying patients with prodromal PD, said prodromal PD patients being patients with sleep behavior disorder (RBD) that can progress to PD.

[0014] In another aspect, the present invention provides the use of biomarkers in the preparation of kits, wherein the kits are used to assist in displaying biological information associated with Parkinson's disease (PD), particularly early PD, or the kits are used to assist in differentiating Parkinson's disease (PD) from essential tremor (ET), or the kits are used to identify patients with prodromal PD, wherein the biomarkers are used to detect the presence and / or abundance of fungi in the optional gut of the subject and / or the level of antibodies against the fungi in the serum, optionally the fungi being Saccharomyces cerevisiae, wherein the biomarkers are selected from biomarkers capable of detecting the abundance of fungi or optionally Saccharomyces cerevisiae by ITS next-generation sequencing, PCR technology, or biomarkers capable of detecting the level of antibodies against fungi or optionally Saccharomyces cerevisiae in the serum by ELISA.

[0015] In another aspect, the present invention provides the use of biomarkers in screening candidate drugs for the treatment of Parkinson's disease (PD) or to reduce the risk of early PD, wherein the biomarkers are used to detect the presence and / or abundance of fungi in the optional gut of a subject and / or the level of antibodies against the fungi in serum, optionally the fungi being Saccharomyces cerevisiae, wherein the biomarkers are selected from biomarkers capable of detecting the abundance of fungi or optionally Saccharomyces cerevisiae by ITS next-generation sequencing, PCR technology, or biomarkers capable of detecting the level of antibodies against fungi or optionally Saccharomyces cerevisiae in serum by ELISA.

[0016] In another aspect, the present invention provides a kit comprising reagents for detecting the presence and / or abundance of fungi in the gut of a subject and / or the level of antibodies against such fungi in serum, optionally said fungi being Saccharomyces cerevisiae, wherein said kit is used to assist in displaying biological information associated with Parkinson's disease (PD), particularly early PD, or said kit is used to assist in differentiating between Parkinson's disease (PD) and essential tremor (ET), or said kit is used to identify patients with prodromal PD.

[0017] In another aspect, the present invention provides a method for assisting in the screening of Parkinson's disease (PD), particularly early PD, comprising:

[0018] a. Obtaining samples from test subjects;

[0019] b. Detect the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antifungal or optionally Saccharomyces cerevisiae antibodies in the sample;

[0020] The abundance of the fungus or the level of antifungal or optionally Saccharomyces cerevisiae antibody greater than that of the control indicates that the subject is at risk of developing PD or has early PD.

[0021] In one implementation, obtaining subject samples includes selective isolation and enrichment of intestinal fungi, or optionally, the samples are selected from blood or serum.

[0022] In another aspect, the present invention provides a method for assisting in displaying biological information related to Parkinson's disease (PD), particularly early PD, in subjects, comprising:

[0023] a. Obtaining samples from test subjects;

[0024] b. Detect the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antifungal or optionally Saccharomyces cerevisiae antibodies in the sample;

[0025] The abundance of the fungus or the level of antifungal or optionally Saccharomyces cerevisiae antibody greater than that of the control indicates that the subject shows biological information related to the risk of progressing to PD or having early PD.

[0026] In one implementation, obtaining subject samples includes selective isolation and enrichment of intestinal fungi, or optionally, the samples are selected from blood or serum.

[0027] In another aspect, the present invention provides a method for assisting in the differentiation between Parkinson's disease (PD) and essential tremor (ET), comprising:

[0028] a. Obtain subject samples, the subjects including a first subject and a second subject, the first subject being suspected of having PD and the second subject being suspected of having ET;

[0029] b. Detect the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antifungal or optionally Saccharomyces cerevisiae antibodies in the sample;

[0030] If the abundance of the fungus or the level of antifungal or optionally Saccharomyces cerevisiae antibody in the first subject is greater than that in the second subject, it indicates that the first subject has PD and the second subject has ET.

[0031] In one implementation, obtaining subject samples includes selective isolation and enrichment of intestinal fungi, or optionally, the samples are selected from blood or serum.

[0032] In another aspect, the present invention provides a method for identifying patients with prodromal PD, wherein the prodromal PD patient is a patient with sleep behavior disorder (RBD) that can progress to PD, the method comprising:

[0033] a. Obtaining samples from test subjects;

[0034] b. Detect the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antifungal or optionally Saccharomyces cerevisiae antibodies in the sample;

[0035] The presence of fungal abundance or antifungal or optionally Saccharomyces cerevisiae antibody levels greater than those of the control indicates that the subject is a pre-PD patient.

[0036] In one implementation, obtaining subject samples includes selective isolation and enrichment of intestinal fungi, or optionally, the samples are selected from blood or serum.

[0037] In another aspect, the present invention provides a system for assisting in the screening of Parkinson's disease (PD), particularly early PD, said system comprising:

[0038] a. An apparatus for detecting the level of a biomarker according to the invention in a sample from a subject;

[0039] b. An apparatus for analyzing the level of the biomarker, wherein the relative abundance of the biomarker is obtained by the analysis;

[0040] c. A device for outputting analytical results, wherein the analytical results can be used to assist in screening for Parkinson's disease (PD), especially early PD.

[0041] In one embodiment, the detection device is a sequencing device, preferably an ITS next-generation sequencing device.

[0042] In another embodiment, the detection device is a PCR device, preferably a real-time PCR device.

[0043] In yet another embodiment, the detection device is an antibody level detection device, preferably an ELISA assay device.

[0044] In another embodiment, the sample is selected from blood, serum, fluids from the digestive tract, and fecal extracts.

[0045] In yet another embodiment, the biomarker is selected from polynucleotides or antibodies.

[0046] In another embodiment, the polynucleotide is a primer.

[0047] In yet another embodiment, the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

[0048] In another aspect, the present invention provides a system for assisting in the display of biological information associated with Parkinson's disease (PD), particularly early PD, the system comprising:

[0049] a. An apparatus for detecting the level of a biomarker according to the invention in a sample from a subject;

[0050] b. An apparatus for analyzing the level of the biomarker, wherein the relative abundance of the biomarker is obtained by the analysis;

[0051] c. A device for outputting analytical results, wherein the analytical results are capable of assisting in the display of biological information related to Parkinson's disease (PD), particularly early PD.

[0052] In one embodiment, the detection device is a sequencing device, preferably an ITS next-generation sequencing device.

[0053] In another embodiment, the detection device is a PCR device, preferably a real-time PCR device.

[0054] In yet another embodiment, the detection device is an antibody level detection device, preferably an ELISA assay device.

[0055] In another embodiment, the sample is selected from blood, serum, fluids from the digestive tract, and fecal extracts.

[0056] In yet another embodiment, the biomarker is selected from polynucleotides or antibodies.

[0057] In another embodiment, the polynucleotide is a primer.

[0058] In yet another embodiment, the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

[0059] In another aspect, the present invention provides a system for assisting in the differentiation between Parkinson's disease (PD) and essential tremor (ET), the system comprising:

[0060] a. An apparatus for detecting the level of a biomarker according to the invention in a sample from a subject;

[0061] b. An apparatus for analyzing the level of the biomarker, wherein the relative abundance of the biomarker is obtained by the analysis;

[0062] c. A device for outputting analytical results, wherein the analytical results can be used to help differentiate between Parkinson's disease (PD) and essential tremor (ET).

[0063] In one embodiment, the detection device is a sequencing device, preferably an ITS next-generation sequencing device.

[0064] In another embodiment, the detection device is a PCR device, preferably a real-time PCR device.

[0065] In yet another embodiment, the detection device is an antibody level detection device, preferably an ELISA assay device.

[0066] In another embodiment, the sample is selected from blood, serum, fluids from the digestive tract, and fecal extracts.

[0067] In yet another embodiment, the biomarker is selected from polynucleotides or antibodies.

[0068] In another embodiment, the polynucleotide is a primer.

[0069] In yet another embodiment, the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

[0070] definition

[0071] As used in this invention, the term "abundance" refers to the percentage of a particular fungus among all detected fungi. For example, in the ITS next-generation sequencing method of this invention, the "abundance" of *Saccharomyces cerevisiae* refers to the percentage of the *Saccharomyces* genus among all detected fungi; for example, in the quantitative real-time PCR assay method of this invention, the "abundance" of *Saccharomyces cerevisiae* refers to the percentage of the *Saccharomyces* genus among all detected fungi.

[0072] As used in this invention, the term "Parkinson's disease patient" or "PD patient" refers to a patient diagnosed with Parkinson's disease by at least one experienced neurologist according to the UK Brain Bank Standard Diagnostic Criteria.

[0073] As used in this invention, the term "patient with essential tremor" or "ET patient" refers to a patient diagnosed with essential tremor by at least one experienced neurologist in accordance with the tremor consensus established by the International Movement Disorders Association.

[0074] As used in this invention, the term "health control" or "HC" refers to a healthy subject without positive neurological signs.

[0075] As used in this invention, the term "ITS" refers to the internal transcribed spacer, located between the 18S, 5.8S, and 28S rRNA genes in fungi, specifically ITS1 and ITS2. In fungi, the 5.8S, 18S, and 28S rRNA genes exhibit high conservation, while ITS, due to less natural selection pressure, can tolerate more variation during evolution, exhibiting extremely broad sequence polymorphism in most eukaryotes. Furthermore, the conservation of ITS shows relative consistency within a species, but significant differences between species, reflecting variations between genera and even strains. Moreover, ITS sequence fragments are relatively small (ITS1 and ITS2 are 350 bp and 400 bp in length, respectively), making them easy to analyze and currently widely used for phylogenetic analysis of different fungal species.

[0076] This invention is the first to explore the relationship between intestinal fungi, intestinal inflammation-related serological antibodies, and Parkinson's disease (PD). It discovered that the abundance of the intestinal fungus *Saccharomyces cerevisiae*, and the levels of serum anti-*Saccharomyces cerevisiae* antibody (ASCA) IgG and IgA were significantly elevated in PD patients. This invention is also the first to discover that intestinal inflammation-related markers can serve as biomarkers for the early diagnosis of PD. In assisting early diagnosis, the levels of *Saccharomyces cerevisiae*, measured by ITS next-generation sequencing or quantitative real-time PCR, and the combined levels of serum anti-*Saccharomyces cerevisiae* antibody IgG and IgA can be used to differentiate between PD patients, healthy controls (HC), and ET patients. Furthermore, *Saccharomyces cerevisiae* markers can also be used to detect prodromal PD patients (RBD progressing to PD). This invention can assist in the clinical diagnosis of PD patients, enabling rapid and accurate clinical diagnosis of early-stage PD and early disease control. Attached Figure Description

[0077] Figure 1 The diagram shows the gut fungal composition and key differentially expressed fungi of the PD and HC groups. The left figure A is a PLSDA diagram based on the ASV abundance matrix, and the right figure B shows the contribution of 25 differentially expressed ASVs to distinguishing the two groups of samples.

[0078] Figure 2 The figure shows the relative abundance of Saccharomyces cerevisiae in the PD and HC groups.

[0079] Figure 3 The diagram shows the gut fungal composition and key differentially expressed fungi in the PD and ET groups. The left figure A is a PLSDA diagram based on the ASV abundance matrix, and the right figure B shows the contribution of 25 differentially expressed ASVs to distinguishing the two groups of samples.

[0080] Figure 4 The figure shows the relative abundance of Saccharomyces cerevisiae in the PD and ET groups.

[0081] Figure 5 The diagram shown illustrates the principle of fungal magnetic separation, using mannan-modified magnetic microspheres to separate yeast.

[0082] Figure 6 The image shows the results of fluorescence detection demonstrating magnetic specific capture. The experiment involved magnetic microsphere specific capture (emission wavelength for fungal fluorescence detection was 420 nm, and for bacterial fluorescence detection, it was 620 nm). The results showed that only fungal fluorescence signals responded in the magnetically separated solution, with no bacterial fluorescence signals observed.

[0083] Figure 7 The image shown is a result of the magnetic separation and enrichment effect.

[0084] Figure 8 The results shown are real-time PCR quantitative results of Saccharomyces cerevisiae in PD patients and healthy controls (HC).

[0085] Figure 9 The results show the diagnostic accuracy of real-time PCR quantitative analysis of Saccharomyces cerevisiae in differentiating PD patients from healthy controls with HC.

[0086] Figure 10 The results shown are real-time PCR quantitative results of Saccharomyces cerevisiae from PD and ET patients.

[0087] Figure 11 This figure shows the diagnostic accuracy of real-time PCR quantitative analysis of *Saccharomyces cerevisiae* in differentiating between patients with progressive disease (PD) and endocrine disorders (ET). This indicator can significantly distinguish between PD and ET patients. ROC: Receiver operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0088] Figure 12 This figure shows the diagnostic accuracy of serum anti-Saccharomyces cerevisiae antibody IgG levels in differentiating PD patients from healthy controls with HC. Serum anti-Saccharomyces cerevisiae antibody IgG levels can significantly distinguish PD patients from HC. ROC: Recipient operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0089] Figure 13 This demonstrates the diagnostic accuracy of serum anti-Saccharomyces cerevisiae antibody (ISA) levels in differentiating PD patients from healthy controls with hepatitis C (HC). Serum IA levels significantly differentiate PD patients from HC. ROC: Recipient operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0090] Figure 14This figure shows the diagnostic accuracy of a composite index of serum anti-Saccharomyces cerevisiae antibody IgG and IgA levels in differentiating PD patients from healthy controls with HC. The composite index can significantly distinguish PD patients from healthy controls with HC. ROC: Receiver operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0091] Figure 15 This figure shows the diagnostic accuracy of serum anti-Saccharomyces cerevisiae antibody IgG levels in differentiating between PD and ET patients. Serum anti-Saccharomyces cerevisiae antibody IgG levels can significantly distinguish between PD and ET patients. ROC: Recipient operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0092] Figure 16 This demonstrates the diagnostic accuracy of serum anti-Saccharomyces cerevisiae antibody (ISA) levels in differentiating between patients with progressive disease (PD) and those with end-stage renal disease (ET). Serum IA levels can significantly differentiate between PD and ET patients. ROC: Recipient operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0093] Figure 17 This figure shows the diagnostic accuracy of a composite index of serum anti-Saccharomyces cerevisiae antibody IgG and IgA levels in distinguishing between PD and ET patients. The composite index can significantly differentiate between PD and ET patients. ROC: Receiver operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0094] Figure 18 The image shows the real-time PCR quantitative results of Saccharomyces cerevisiae from RBD patients.

[0095] Figure 19 This demonstrates the diagnostic accuracy of real-time PCR-quantitative analysis of *Saccharomyces cerevisiae* in patients with prodromal disease (PD). This indicator can significantly distinguish between RBD patients who have progressed to PD and those who have not. ROC: Receiver operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0096] Figure 20 This figure shows the diagnostic accuracy of serum anti-Saccharomyces cerevisiae antibody IgG levels in RBD patients who have progressed to PD. Serum anti-Saccharomyces cerevisiae antibody IgG levels can identify RBD patients who have progressed to PD. ROC: Receiver operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0097] Figure 21This figure shows the diagnostic accuracy of serum anti-Saccharomyces cerevisiae antibody IgA levels in RBD patients who have progressed to PD. Serum anti-Saccharomyces cerevisiae antibody IgA levels can identify RBD patients who have progressed to PD. ROC: Receiver operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval.

[0098] Figure 22 This figure shows the diagnostic accuracy of a composite index of serum anti-Saccharomyces cerevisiae antibody IgG and IgA levels in RBD patients who have progressed to PD. The composite index can identify RBD patients who have progressed to PD. ROC: Receiver operating characteristic curve; AUC: Area under the curve; 95% CI: 95% confidence interval. Detailed Implementation

[0099] The present invention will be specifically explained through the following embodiments, but the scope of the present invention is not limited thereto.

[0100] Unless otherwise explicitly stated, all reagents, instruments, etc. used in this invention are commercially available.

[0101] Example

[0102] Example 1: Determining the abundance of Saccharomyces cerevisiae using ITS next-generation sequencing to differentiate between PD patients and healthy controls.

[0103] We performed ITS next-generation sequencing on stool samples from 33 Parkinson's disease (PD) patients, 21 healthy controls (HC), and 46 etiologically impaired patients who visited the Department of Neurology at the Second Affiliated Hospital of Zhejiang University School of Medicine (Zhejiang University School of Medicine) between December 1, 2020, and February 28, 2021. The study protocol complied with the Declaration of Helsinki and was approved by the Ethics Committee of Zhejiang University School of Medicine. All participants were fully informed of the study procedures and protocols and voluntarily signed informed consent forms. All participants underwent detailed demographic data collection and clinical assessment. All PD patients underwent detailed clinical scale assessments to evaluate disease severity and cognitive and emotional states. In addition, all participants completed a nonmotor symptom questionnaire to assess the frequency of Parkinson's disease-related nonmotor symptoms.

[0104] In short, total DNA was extracted according to the instructions of the QIAamp PowerFecal Pro DNA Kit (Qiagen, Cat No. / ID: 51804). DNA concentration and purity were determined using a NanoDrop2000, and DNA extraction quality was assessed using 1% agarose gel electrophoresis. Then, universal primer pairs for all fungi were used:

[0105] ITS3F:GCATCGATGAAGAACGCAGC

[0106] ITS4R:TCCTCCGCTTATTGATATGC

[0107] PCR amplification was performed on the variable region of ITS2.

[0108] After amplification, PCR amplification products from the same sample were mixed and recovered using a 2% agarose gel. The recovered products were then purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). Detection was performed using 2% agarose gel electrophoresis and Quantus. TM The Fluorometer (Promega, USA) is used to detect and quantify the recovered products.

[0109] Next, library construction was performed using the NEXTFLEX Rapid DNA-Seq Kit, including the following steps: (1) adapter ligation; (2) screening with magnetic beads to remove adapter self-ligation fragments; (3) enrichment of library templates using PCR amplification; and (4) recovery of PCR amplification products with magnetic beads to obtain the final library.

[0110] Then, the library was sequenced using the Illumina Miseq PE300 platform.

[0111] Next, the raw sequencing sequences were analyzed using the QIIME 2 (Quantitative Insight Into Microbial Ecology2, v2018.11) platform (see, for example, Boylen, E., et al., Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol, 2019, 37(8): p.852-857). The cutadapter software was used to find and cut the adapter sequences. Then, DADA2 was used for denoising (see, for example, Callahan, BJ, et al., DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods, 2016, 13(7): p.581-583). The pruned sequences were filtered, denoised, and spliced ​​to obtain the ASV (Amplicon Sequence Variants) sequences and abundance information in each sample. Chimeras in the ASVs were removed, and the original ASV abundance matrix was finally obtained.

[0112] Then, the taxonomic status of ASV was defined based on the SILVA database. The sequence number of all samples was normalized to 24,781 (repeated 1,000 times) to eliminate the differences between samples due to different sequencing depths, and then an ASV abundance matrix table with the same sequence number for each sample was obtained.

[0113] Next, use the R package mixOmics (version v6.3.1) (see, for example, Kim-Anh Le Cao, FR, Ignacio Gonzalez, Sebastien Dejean with key contributors Benoit Gautier, Francois Bartolo, contributions from Pierre Monget, Jeff Coquery, FangZou Yao and Benoit Liquet.mixOmics:Omics Data Integration Project.R package version 6.1.1.2016; available at the following URL:) https: / / CRAN.R-project.org / package=mixOmics Sparse partial least squares discriminant analysis (sPLS-DA) was performed (see, for example, Le Cao, KA, S. Boitard, and P. Besse, Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems. BMC Bioinformatics, 2011.12: p.253) to identify key differential fungal compositional ASVs in the samples.

[0114] When using the sPLS-DA model, we employed a Centered Log Ratio transformation (CLR) to avoid spurious results. Based on the perf function, we used leave-one-out cross-validation to test the model's misclassification rate, and the sPLS-DA model with the lowest error rate was identified as the optimal classification model.

[0115] We compared the fungal composition of the PD and HC group samples. Using the PLSDA plot based on the ASV abundance matrix, we found that the gut fungi in the PD group were mainly separated from the HC group along the first principal component, which explained 3% of the overall gut microbiota structural variability. Figure 1 A). Subsequently, we identified the differential ASVs between the two groups on the first principal component. We selected 25 ASVs based on stability (stability = 0.8), and the contribution of these 25 ASVs to distinguishing the two groups is as follows: Figure 1 As shown in B. Of these 25 ASVs, 4 ASVs were significantly enriched in the PD group, with *Saccharomyces cerevisiae* contributing the most to distinguishing the two groups. Figure 1 B).

[0116] We found that the average relative abundance of *Saccharomyces cerevisiae* was 18.6% in the PD group and 9.4% in the HC group. Figure 2 Our analysis using ITS amplicon sequencing revealed significant differences in fungal composition between PD patients and healthy HC controls, identifying a key difference in *Saccharomyces cerevisiae*. Our results demonstrate that ITS next-generation sequencing can differentiate *Saccharomyces cerevisiae* between PD patients and healthy HC controls.

[0117] Example 2: Determining the abundance of Saccharomyces cerevisiae using ITS next-generation sequencing to differentiate between PD patients and ET patients.

[0118] We compared the gut fungal composition of the PD and ET groups using the same method as in Example 1. Among the 25 differentially expressed ASVs, Saccharomyces cerevisiae again contributed the most to distinguishing the two groups. Figure 3 ).

[0119] We found that the average relative abundance of *Saccharomyces cerevisiae* was 18.6% in the PD group and 11.6% in the ET group. Figure 4 Our analysis using ITS amplicon sequencing revealed significant differences in fungal composition between PD and ET patients, identifying a key difference in *Saccharomyces cerevisiae*. Our results demonstrate that ITS next-generation sequencing can differentiate between PD and ET patients.

[0120] Using the aforementioned ITS sequencing method, 33 patients with primary PD, 21 healthy controls with HC, and 46 patients with ET who visited the Department of Neurology at the Second Affiliated Hospital of Zhejiang University between December 1, 2020, and February 28, 2021, were clinically diagnosed.

[0121] Example 3: Detection of *Saccharomyces cerevisiae* by quantitative real-time PCR to differentiate between PD patients and healthy controls.

[0122] In this embodiment, we used real-time polymerase chain reaction (Real-time PCR) to quantitatively determine Saccharomyces cerevisiae.

[0123] Since fungi constitute a very small percentage (≤0.1%) of the gut microbiota, direct sequencing methods are insufficient to effectively obtain fungal information. Selective isolation and enrichment of fungi are necessary for subsequent research. Therefore, we first selectively isolated and enriched gut fungi, and then used primers for quantitative determination.

[0124] 3.1 Separation and Enrichment Methods

[0125] 3.1.1 Method Background

[0126] Microbial isolation and enrichment can improve the detection efficiency of target microorganisms. Research has shown that immunomagnetic separation is a commonly used method for detecting pathogenic microorganisms, applicable to bacteria, fungi, and other single-celled pathogens (see, for example, BMC Microbiol., 2008, 22(8):157-160, J.Clin.Microbiol., 2010, 48(4):1126-1131). Antibodies that specifically recognize the target microorganisms are attached to the surface of magnetic microspheres, serving as capture probes. After specific recognition and high-affinity binding, magnetic separation separates the target microorganisms from the complex matrix sample, removing impurities and contaminants from the sample and reducing their influence on detection. Simultaneously, the magnetic separation process reduces the volume of liquid samples, effectively enriching the target microorganisms.

[0127] Bacteria are the biggest interfering factor in the effective detection of fungi in samples. Since the cell wall composition of fungi differs greatly from that of bacteria, specific components of the fungal cell wall can be selected as capture sites to bind to capture probes. The main component of fungal cell walls is chitin, the main polysaccharide component of yeast cell walls is mannan, and the main polysaccharide component of bacterial cell walls is peptidoglycan. Therefore, chitin and mannan can be selected as fungus-specific capture sites. Recombinant chitinase can specifically recognize and bind to chitin. Therefore, recombinant chitinase that can specifically bind to fungi is used as a capture probe. On the other hand, the cell wall composition of yeast is quite unique, containing a large amount of mannan. Therefore, mannan-binding proteins can be selected as recognition proteins to specifically recognize and bind to fungi of the genus *Saccharomyces*, thereby isolating yeast from biological samples (see, for example, Proc. Jpn. Acad. Ser. B Phys. Biol. Sci., 2012, 88(6):250-265, J. Clin. Microbiol., 2020, 58(4):e00057-20).

[0128] 3.1.2 Experimental Objective

[0129] Using an affinity-based magnetic separation enrichment method, fungi can be effectively extracted from microbial samples, bacteria can be removed, fungal content can be increased, fungal counting can be easily performed, and the results can be used for subsequent research.

[0130] 3.1.3 Basic Principles

[0131] Two functionalized magnetic microspheres were designed and synthesized, coated with recombinant chitinase and mannan-binding lectin (mannan lectin), respectively. By binding to chitin and mannan on the surface of fungi, the functionalized magnetic microspheres can be attached to the fungal cell wall. Magnetic separation can effectively isolate the fungi connected to the magnetic microspheres from complex systems, removing other substances and achieving the effect of fungal isolation and enrichment.

[0132] Figure 5Using the separation of yeast from magnetic microspheres modified with mannan-lectin as an example, the principle of fungal magnetic separation is demonstrated. Recombinant mannan-lectin can be linked to the surface of TED-Ni-coated magnetic microspheres via His-tag, or to the surface of streptavidin (SA)-coated magnetic microspheres via biotinylation modification. Both reactions can be used to prepare magnetic microspheres coated with recombinant chitinase. The mannan component unique to the yeast cell wall specifically binds to the recombinant mannan-lectin on the surface of the magnetic microspheres, thus immobilizing them. Bacterial cell walls, lacking mannan components, do not specifically bind to the magnetic microspheres. Therefore, during subsequent magnetic separation, bacteria not attached to the magnetic microsphere surface and other free components contained in the supernatant are removed during washing, while the fungi immobilized on the magnetic microsphere surface are retained. Furthermore, the magnetic separation process can reduce the volume of the final solution, thereby achieving the effect of fungal isolation and enrichment. The magnetic separation method using recombinant chitinase as a capture probe is similar to... Figure 5 Similarly, fungi can be captured by recognizing chitinases on the fungal cell wall.

[0133] 3.1.4 Experimental Materials

[0134] 1. Biotinylation Kit (Abcam): Biotinylation Kit / Biotin Conjugation

[0135] 2. Abcam: Recombinant human mannan-binding lectin / MBL protein;

[0136] 3. Recombinant chitinase (Abcam): Recombinant mouse chitinase 3-like protein 3;

[0137] 4. Magnetic microspheres (Invitrogen): Dynabeads TM M-280Streptavidin;

[0138] 5. Magnetic rack (Invitrogen): DynaMag TM -2 magnetic rack;

[0139] 6. PBS (1×), PBST.

[0140] 3.1.5 Experimental Procedure

[0141] I. Preparation of Functionalized Magnetic Microspheres

[0142] Functionalized magnetic microspheres can be prepared by using His-tag with TED-Ni, or by the high affinity reaction of biotin with streptavidin.

[0143] This scheme uses a biotin-streptavidin reaction system, and the reaction process is as follows:

[0144] 1. Use a biotinylation kit to biotinylate mannan lectins, following the kit's instructions for use.

[0145] 2. React biotinylated mannan lectin with streptavidin-coated magnetic microspheres at a ratio of 10 μg protein to 100 μL of magnetic microspheres. An appropriate amount of PBS buffer can be added to the reaction system. After reacting at room temperature for 30 minutes, wash the magnetic microspheres three times with PBST (operated on a magnetic rack). Finally, add PBS to restore the magnetic microspheres to their initial concentration.

[0146] 3. Recombinant chitinase-coated magnetic beads were prepared by repeating steps 1 and 2 above.

[0147] If it is necessary to dissociate the fungi from the magnetic microspheres after magnetic separation, a His-tag reaction with TED-Ni can be used. Purchase recombinant chitinase and mannan lectin with a His-tag label and react them with the TED-Ni-coated magnetic microspheres. Refer to the TED-Ni magnetic microsphere product instructions for reaction and dissociation conditions.

[0148] II. Magnetic Separation of Fungi

[0149] 1) Remove the fecal sample to be tested from the -80℃ freezer and place it on ice for about five minutes to allow the sample to partially thaw;

[0150] 2) Weigh approximately 0.5g-1.0g of fecal sample and add it to a 2mL lysis homogenization tube. Add 1.0mL of PBS buffer that has been pre-cooled to 4℃ to the tube.

[0151] 3) Place it on a vortex oscillator and oscillate at maximum speed twice for 2 minutes each time, with a 1-minute interval in between, to avoid excessively high temperature inside the tube caused by prolonged high-speed oscillation;

[0152] 4) Use a cell disruptor to lyse the sample, setting 5, for 30 seconds, with a 30-second interval between each lysing step, repeating three times.

[0153] 5) Place the sample on ice and transfer it to a centrifuge. Centrifuge at 4°C and 15,000 rpm for 10 minutes.

[0154] 6) After centrifugation, take the supernatant, which is the fecal extract. Store the sample at 4°C immediately for later use.

[0155] Mix the liquid sample to be separated with functionalized magnetic microspheres, add 5-10 μL of functionalized magnetic microspheres to each 1 mL sample, gently shake and react for 30 minutes at room temperature, wash three times with PBST (operate on a magnetic rack), remove the supernatant, and add 100 μL of PBS to obtain the fungal isolation solution.

[0156] If DNA extraction is required, lysis buffer and extraction buffer can be added directly after washing, and the operation can be performed on a magnetic rack. The supernatant can then be used for DNA extraction.

[0157] 3.1.6 Method Validation

[0158] I. Specificity of Fungal Capture

[0159] The above method was used to perform magnetic separation on a mixed sample of fungi and bacteria. Fluorescent staining reagents for fungi and bacteria were then used to stain and detect fluorescence in the separated sample. The results are as follows: Figure 6 As shown, the separated samples exhibited a significant fluorescent signal response from fungi, while samples containing high concentrations of bacteria showed no significant bacterial fluorescent signal response after magnetic separation. This demonstrates that the recombinant chitinase and mannan lectin coated on the magnetic beads can efficiently recognize and bind to fungi, without specifically binding to bacteria.

[0160] II. Verification of the enrichment effect of magnetic separation

[0161] The same sample to be separated was stained with a fungal fluorescent dye before and after magnetic separation, and the fluorescence intensity was measured. The results showed that the fluorescence signal intensity of the sample after magnetic separation was significantly increased, proving that magnetic separation can effectively concentrate the sample volume and enrich fungi in liquid samples. Figure 7 ).

[0162] 3.2 Primer Quantitative Determination

[0163] The primer SCoH sequence we designed for Saccharomyces cerevisiae is as follows:

[0164] Primer sequence F: 5'-GTTAGATCCCAGGCGTAGAACAG-3'

[0165] Primer sequence R: 5'-GCGAGTACTGGACCAAATCTTATG-3'

[0166] The annealing temperature is 58℃

[0167] The amplification product is 400 bp in length.

[0168] We performed quantitative PCR validation on fecal DNA samples from 10 PD patients and 10 healthy controls (HC). The Ct value of *Saccharomyces cerevisiae* in the fecal DNA samples of PD patients was significantly lower than that in the healthy controls (HC). Figure 8 This means that the abundance of Saccharomyces cerevisiae in PD patient samples was significantly higher than that in healthy control HC samples.

[0169] Therefore, quantitative real-time PCR can be used to distinguish between PD patients and healthy controls with HC.

[0170] ROC analysis was used to evaluate the accuracy of this index in the diagnosis of PD. This index could significantly distinguish PD patients from HC patients (area under the curve = 0.90, p = 0.0025). Figure 9 ), and the quantitative determination of Saccharomyces cerevisiae by PCR after isolating and enriching intestinal fungi can distinguish between PD patients and healthy people.

[0171] Example 4: Differentiating between PD and ET patients by quantitative real-time PCR assay of Saccharomyces cerevisiae.

[0172] We performed quantitative PCR validation on fecal DNA samples from 10 PD patients and 10 ET patients. The Ct value of *Saccharomyces cerevisiae* was significantly lower in the fecal DNA samples of PD patients than in those of ET patients. Figure 10 This means that the abundance of Saccharomyces cerevisiae in PD patient samples was significantly higher than that in ET patient samples.

[0173] Therefore, quantitative real-time PCR can be used to distinguish between patients with PD and ET.

[0174] ROC analysis was used to evaluate the accuracy of this index in diagnosing PD. This index significantly distinguished between PD patients and ET patients (area under the curve = 0.77, p = 0.0413). Figure 11 ) After isolating and enriching intestinal fungi, PCR quantitative determination of Saccharomyces cerevisiae can differentiate between PD patients and ET patients.

[0175] Using quantitative real-time PCR to detect Saccharomyces cerevisiae, we aided in the clinical diagnosis of 18 patients with primary PD, 10 patients with ET, and 18 healthy controls with HC who visited the Department of Neurology at the Second Affiliated Hospital of Zhejiang University between February 1, 2021 and October 1, 2021.

[0176] Example 5: Using serum anti-Saccharomyces cerevisiae antibody IgG or IgA and its composite index to differentiate PD patients and healthy controls.

[0177] Serum anti-Saccharomyces cerevisiae antibody levels were measured in blood samples from volunteers.

[0178] Serum anti-Saccharomyces cerevisiae antibodies IgG and IgA were detected using enzyme-linked immunosorbent assay (ELISA). The cutoff value for both serum anti-Saccharomyces cerevisiae antibodies IgG and IgA was 25 AU / ml.

[0179] ROC analysis showed that serum anti-Saccharomyces cerevisiae antibody IgG levels could significantly distinguish PD patients from HC patients (Area under the curve = 0.712, p < 0.001). Figure 12 Serum anti-Saccharomyces cerevisiae antibody IgA levels can also significantly differentiate PD patients from HC patients (area under the curve = 0.671, p < 0.001). Figure 13 ).

[0180] Furthermore, using a logistic regression model with PD and HC groups as classification criteria, a composite index was generated by combining the two variables of serum anti-Saccharomyces cerevisiae antibody IgG and IgA. The calculation method for this index is as follows: Composite Index = 1.067 × IgG + 1.081 × IgA + 0.156. ROC analysis was used to evaluate the accuracy of the composite index in PD diagnosis. The composite index significantly distinguished PD patients from HC patients (Area under the curve = 0.730, p < 0.001). Figure 14 ).

[0181] Example 6: Differentiating PD patients and ET patients by serum anti-Saccharomyces cerevisiae antibody IgG or IgA and their combined indicators.

[0182] Serum anti-Saccharomyces cerevisiae antibody levels in PD and ET patient samples were detected using the same method as in Example 5.

[0183] ROC analysis showed that serum anti-Saccharomyces cerevisiae antibody IgG levels could significantly distinguish between PD patients and ET patients (area under the curve = 0.753, p < 0.001). Figure 15 Serum anti-Saccharomyces cerevisiae antibody IgA levels can also clearly distinguish between PD patients and ET patients (area under the curve = 0.725, p < 0.001). Figure 16 ).

[0184] Furthermore, using a logistic regression model with PD and HC groups as classification criteria, a composite index was generated by combining the two variables of serum anti-Saccharomyces cerevisiae antibody IgG and IgA. This index was calculated as follows: Composite Index = 1.067 × IgG + 1.081 × IgA + 0.156. ROC analysis was used to evaluate the accuracy of the composite index in PD diagnosis. The composite index significantly distinguished between PD patients and ET patients (Area under the curve = 0.778, p < 0.001). Figure 17 ).

[0185] Serum anti-Saccharomyces cerevisiae antibody assays were used to assist in the clinical diagnosis of 140 PD patients, 105 ET patients, and 130 healthy controls who visited the Department of Neurology at the Second Affiliated Hospital of Zhejiang University between December 1, 2018 and October 1, 2020.

[0186] Example 7: Identifying RBD patients who have progressed to PD by detecting Saccharomyces cerevisiae using quantitative real-time PCR.

[0187] We performed quantitative PCR validation on early fecal DNA samples from 18 RBD patients who progressed to PD and 18 RBD patients who did not. The Ct value of *Saccharomyces cerevisiae* in the fecal DNA samples of RBD patients who progressed to PD was significantly lower than that in RBD patients who did not. Figure 18 The abundance of Saccharomyces cerevisiae was high in RBD patient samples that progressed to PD.

[0188] Therefore, quantitative real-time PCR can be used to identify RBD patients who may progress to PD.

[0189] ROC analysis was used to evaluate the accuracy of this index in diagnosing RBD patients who may progress to PD. This index significantly distinguished between RBD patients who may progress to PD and those who will not (Area under the curve = 0.7330, p = 0.0169). Figure 19 After isolating and enriching intestinal fungi, quantitative PCR assays of Saccharomyces cerevisiae can identify RBD patients who have progressed to PD.

[0190] Example 8: Serum anti-Saccharomyces cerevisiae antibody IgG or IgA and their combined indicators can identify RBD patients who have progressed to PD.

[0191] ROC analysis showed that serum anti-Saccharomyces cerevisiae antibody IgG levels could identify RBD patients who had progressed to PD (area under the curve = 0.75, p = 0.0129). Figure 20 Serum anti-Saccharomyces cerevisiae antibody IgA levels can also identify RBD patients who have progressed to PD (Area under curve = 0.7191, p = 0.0054). Figure 21 ).

[0192] Furthermore, using a logistic regression model with the PD group and HC group as classification criteria, a composite index was generated by combining the two variables of serum anti-Saccharomyces cerevisiae antibody IgG and IgA. The calculation method for this index is as follows: Composite index = 1.067 × IgG + 1.081 × IgA + 0.156. ROC analysis was used to evaluate the accuracy of the composite index in diagnosing PD. The composite index was able to identify RBD patients who progressed to PD (Area under the curve = 0.8704, p = 0.0002). Figure 22 ).

[0193] We tracked 36 patients with sleep behavior disorder (RBD) who visited the Department of Neurology at the Second Affiliated Hospital of Zhejiang University School of Medicine between December 1, 2015, and December 1, 2020. During the five-year follow-up, 18 RBD patients progressed to PD, while 18 RBD patients did not. Early samples from these patients were analyzed using quantitative real-time analysis of *Saccharomyces cerevisiae* and detection of serum anti-*Saccharomyces cerevisiae* antibodies. This example demonstrates that this biomarker can be used to detect prodromal PD patients (RBD progressing to PD).

[0194] In this invention, we explored for the first time the relationship between intestinal fungi, intestinal inflammation-related serological antibodies, and Parkinson's disease (PD), discovering that the abundance of the intestinal fungus *Saccharomyces cerevisiae*, and serum anti-*Saccharomyces cerevisiae* antibody (ASCA) IgG and IgA levels were significantly elevated in PD patients. We also discovered for the first time that intestinal inflammation-related indicators can serve as biomarkers for the auxiliary early diagnosis of PD. Our embodiments for auxiliary early diagnosis include: 1. Detection of the intestinal fungus *Saccharomyces cerevisiae* using ITS next-generation sequencing or quantitative real-time PCR; 2. The combined serum anti-*Saccharomyces cerevisiae* antibody IgG and IgA indicators can be used to differentiate between PD patients, healthy controls (HC), and ET patients; 3. *Saccharomyces cerevisiae* indicators can also be used to detect prodromal PD patients (RBD progressing to PD). These embodiments can assist in the clinical diagnosis of PD patients, enabling rapid and accurate clinical diagnosis of early-stage PD and early disease control.

[0195] The present invention includes the following embodiments:

[0196] 1. A biomarker for detecting the presence and / or abundance of a fungus in the gut of a subject and / or the level of antibodies against the fungus in serum, optionally said fungus being Saccharomyces cerevisiae, wherein said biomarker is selected from biomarkers capable of detecting the abundance of the fungus or optionally Saccharomyces cerevisiae by ITS next-generation sequencing or PCR technology, or biomarkers capable of detecting the level of antibodies against the fungus or optionally Saccharomyces cerevisiae in serum by ELISA.

[0197] 2. The biomarker according to Implementation Scheme 1 is used to assist in displaying biological information related to Parkinson's disease (PD), especially early PD, or to assist in distinguishing between Parkinson's disease (PD) and essential tremor (ET), or to identify patients with prodromal PD, wherein the prodromal PD patients are patients with sleep behavior disorder (RBD) that can progress to PD.

[0198] 3. Use of biomarkers in the preparation of a kit, wherein the kit is used to assist in displaying biological information associated with Parkinson's disease (PD), particularly early PD, or wherein the kit is used to assist in differentiating Parkinson's disease (PD) from essential tremor (ET), or wherein the kit is used to identify patients with prodromal PD, wherein the biomarkers are used to detect the presence and / or abundance of fungi in the subject's gut and / or the level of antibodies against the fungi in serum, optionally the fungi being Saccharomyces cerevisiae, wherein the biomarkers are selected from biomarkers that can detect the abundance of fungi or optionally Saccharomyces cerevisiae by ITS next-generation sequencing or PCR technology, or biomarkers that can detect the level of antibodies against fungi or optionally Saccharomyces cerevisiae in serum by ELISA.

[0199] 4. Use of biomarkers in screening candidate drugs for the treatment of Parkinson's disease (PD) or to reduce the risk of early PD, wherein the biomarkers are used to detect the presence and / or abundance of fungi in the gut of a subject and / or the level of antibodies against the fungi in serum, optionally the fungi being Saccharomyces cerevisiae, wherein the biomarkers are selected from biomarkers capable of detecting the abundance of fungi or optionally Saccharomyces cerevisiae by ITS next-generation sequencing or PCR technology, or biomarkers capable of detecting the level of antibodies against fungi or optionally Saccharomyces cerevisiae in serum by ELISA.

[0200] 5. A kit comprising reagents for detecting the presence and / or abundance of a fungus in the gut of a subject and / or the level of antibodies against the fungus in serum, optionally said fungus being *Saccharomyces cerevisiae*, wherein said kit is used to assist in displaying biological information associated with Parkinson's disease (PD), particularly early PD, or said kit is used to assist in differentiating Parkinson's disease (PD) from essential tremor (ET), or said kit is used to identify patients with prodromal PD.

[0201] 6. Methods used to assist in screening for Parkinson's disease (PD), especially early PD, include:

[0202] a. Obtaining samples from test subjects;

[0203] b. Detect the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antifungal or optionally Saccharomyces cerevisiae antibodies in the sample;

[0204] The abundance of the fungus or the level of antifungal or optionally Saccharomyces cerevisiae antibody greater than that of the control indicates that the subject is at risk of developing PD or has early PD.

[0205] 7. The method according to embodiment 6, wherein obtaining the subject sample includes selective isolation and enrichment of intestinal fungi, or optionally the sample is selected from blood or serum.

[0206] 8. Methods for assisting in displaying biological information related to Parkinson's disease (PD), particularly early PD, in subjects, including:

[0207] a. Obtaining samples from test subjects;

[0208] b. Detect the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antifungal or optionally Saccharomyces cerevisiae antibodies in the sample;

[0209] The abundance of the fungus or the level of antifungal or optionally Saccharomyces cerevisiae antibody greater than that of the control indicates that the subject shows biological information related to the risk of progressing to PD or having early PD.

[0210] 9. The method according to embodiment 8, wherein obtaining the subject sample includes selective isolation and enrichment of intestinal fungi, or optionally the sample is selected from blood or serum.

[0211] 10. Methods used to help differentiate between Parkinson's disease (PD) and essential tremor (ET) include:

[0212] a. Obtain subject samples, the subjects including a first subject and a second subject, the first subject being suspected of having PD and the second subject being suspected of having ET;

[0213] b. Detect the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antifungal or optionally Saccharomyces cerevisiae antibodies in the sample;

[0214] If the abundance of the fungus or the level of antifungal or optionally Saccharomyces cerevisiae antibody in the first subject is greater than that in the second subject, it indicates that the first subject has PD and the second subject has ET.

[0215] 11. The method according to embodiment 10, wherein obtaining the subject sample includes selective isolation and enrichment of intestinal fungi, or optionally the sample is selected from blood or serum.

[0216] 12. A method for identifying patients with prodromal PD, wherein the prodromal PD patient is a patient with sleep behavior disorder (RBD) that can progress to PD, the method comprising:

[0217] a. Obtaining samples from test subjects;

[0218] b. Detect the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antifungal or optionally Saccharomyces cerevisiae antibodies in the sample;

[0219] The presence of fungal abundance or antifungal or optionally Saccharomyces cerevisiae antibody levels greater than those of the control indicates that the subject is a pre-PD patient.

[0220] 13. The method according to embodiment 12, wherein obtaining the subject sample includes selective isolation and enrichment of intestinal fungi, or optionally the sample is selected from blood or serum.

[0221] 14. A system for assisting in the screening of Parkinson's disease (PD), particularly early PD, said system comprising:

[0222] a. An apparatus for detecting the level of a biomarker according to embodiment 1 in a sample from a subject;

[0223] b. An apparatus for analyzing the level of the biomarker, wherein the relative abundance of the biomarker is obtained by the analysis;

[0224] c. A device for outputting analytical results, wherein the analytical results can be used to assist in screening for Parkinson's disease (PD), especially early PD.

[0225] 15. The system according to embodiment 14, wherein the detection device is a sequencing device, preferably an ITS next-generation sequencing device.

[0226] 16. The system according to embodiment 14, wherein the detection device is a PCR device, preferably a real-time PCR device.

[0227] 17. The system according to embodiment 14, wherein the detection device is an antibody level detection device, preferably an ELISA assay device.

[0228] 18. The system according to embodiment 14, wherein the sample is selected from blood, serum, fluids from the digestive tract, and fecal extracts.

[0229] 19. The system according to embodiment 14, wherein the biomarker is selected from polynucleotides or antibodies.

[0230] 20. The system according to embodiment 19, wherein the polynucleotide is a primer.

[0231] 21. The system according to embodiment 19, wherein the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

[0232] 22. A system for assisting in the display of biological information associated with Parkinson's disease (PD), particularly early PD, said system comprising:

[0233] a. An apparatus for detecting the level of a biomarker according to embodiment 1 in a sample from a subject;

[0234] b. An apparatus for analyzing the level of the biomarker, wherein the relative abundance of the biomarker is obtained by the analysis;

[0235] c. A device for outputting analytical results, wherein the analytical results are capable of assisting in the display of biological information related to Parkinson's disease (PD), particularly early PD.

[0236] 23. The system according to embodiment 22, wherein the detection device is a sequencing device, preferably an ITS next-generation sequencing device.

[0237] 24. The system according to embodiment 22, wherein the detection device is a PCR device, preferably a real-time PCR device.

[0238] 25. The system according to embodiment 22, wherein the detection device is an antibody level detection device, preferably an ELISA assay device.

[0239] 26. The system according to embodiment 22, wherein the sample is selected from blood, serum, liquids from the digestive tract, and fecal extracts.

[0240] 27. The system according to embodiment 22, wherein the biomarker is selected from polynucleotides or antibodies.

[0241] 28. The system according to embodiment 27, wherein the polynucleotide is a primer.

[0242] 29. The system according to embodiment 27, wherein the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

[0243] 30. A system for assisting in the differentiation between Parkinson's disease (PD) and essential tremor (ET), said system comprising:

[0244] a. An apparatus for detecting the level of a biomarker according to embodiment 1 in a sample from a subject;

[0245] b. An apparatus for analyzing the level of the biomarker, wherein the relative abundance of the biomarker is obtained by the analysis;

[0246] c. A device for outputting analytical results, wherein the analytical results can be used to help differentiate between Parkinson's disease (PD) and essential tremor (ET).

[0247] 31. The system according to embodiment 30, wherein the detection device is a sequencing device, preferably an ITS next-generation sequencing device.

[0248] 32. The system according to embodiment 30, wherein the detection device is a PCR device, preferably a real-time PCR device.

[0249] 33. The system according to embodiment 30, wherein the detection device is an antibody level detection device, preferably an ELISA assay device.

[0250] 34. The system according to embodiment 30, wherein the sample is selected from blood, serum, fluids from the digestive tract, and fecal extracts.

[0251] 35. The system according to embodiment 30, wherein the biomarker is selected from polynucleotides or antibodies.

[0252] 36. The system according to embodiment 35, wherein the polynucleotide is a primer.

[0253] 37. The system according to embodiment 35, wherein the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

Claims

1. The use of reagents for detecting biomarkers in the preparation of a kit, wherein the kit is used to assist in displaying biological information related to Parkinson's disease (PD), or the kit is used to assist in differentiating between Parkinson's disease (PD) and essential tremor (ET), or the kit is used to identify patients with prodromal PD, wherein the biomarkers are used to detect the abundance of Saccharomyces cerevisiae in the subject's feces and / or the level of anti-Saccharomyces cerevisiae antibodies in serum, wherein the biomarkers are selected from biomarkers that can detect the abundance of Saccharomyces cerevisiae by ITS next-generation sequencing or PCR technology, or biomarkers that can detect the level of anti-Saccharomyces cerevisiae antibodies in serum by ELISA, wherein the antibodies are IgG antibodies, IgA antibodies, or combinations thereof.

2. The use according to claim 1, wherein the kit is used to assist in displaying biological information related to early PD.

3. The use of reagents for detecting the abundance of Saccharomyces cerevisiae in fecal samples or the level of anti-Saccharomyces cerevisiae antibodies in serum in the preparation of kits for assisting screening of Parkinson's disease (PD), wherein the antibodies are IgG antibodies, IgA antibodies, or combinations thereof.

4. Use of reagents for detecting the abundance of Saccharomyces cerevisiae in fecal samples or the level of anti-Saccharomyces cerevisiae antibodies in serum in the preparation of kits for assisting in displaying biological information related to Parkinson's disease (PD) in subjects, wherein the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

5. Use of reagents for detecting the abundance of Saccharomyces cerevisiae in fecal samples or the level of anti-Saccharomyces cerevisiae antibodies in serum in the preparation of kits for assisting in the differentiation of Parkinson's disease (PD) and essential tremor (ET), wherein the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

6. The use of reagents for detecting the abundance of Saccharomyces cerevisiae in fecal samples or the level of anti-Saccharomyces cerevisiae antibodies in serum in the preparation of a kit for identifying patients with prodromal PD, wherein the prodromal PD patient is a patient with sleep behavior disorder RBD that can progress to PD, and wherein the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

7. Use of reagents for detecting the abundance of *Saccharomyces cerevisiae* nucleic acid in fecal samples from subjects or the level of anti-*Saccharomyces cerevisiae* antibodies in serum in the preparation of a system for assisting in the screening of Parkinson's disease (PD), wherein said antibody is an IgG antibody, an IgA antibody, or a combination thereof, and said system comprises: a. A device for detecting the levels of nucleic acid or anti-Saccharomyces cerevisiae in samples from subjects; b. An apparatus for analyzing the levels of nucleic acids or antibodies against *Saccharomyces cerevisiae*, wherein the relative abundance of *Saccharomyces cerevisiae* is obtained by said analysis; c. A device for outputting analytical results, wherein the analytical results can be used to assist in screening for Parkinson's disease (PD).

8. The use according to claim 7, wherein the analysis results can be used to assist in screening for early PD.

9. The use according to claim 7 or 8, wherein the detection device is a sequencing device.

10. The use according to claim 9, wherein the detection device is an ITS next-generation sequencing device.

11. The use according to claim 7 or 8, wherein the detection device is a PCR device.

12. The use according to claim 11, wherein the detection device is a real-time PCR device.

13. The use according to claim 7 or 8, wherein the detection device is an antibody level detection device.

14. The use according to claim 13, wherein the detection device is an ELISA assay device.

15. The use according to claim 7 or 8, wherein the nucleic acid abundance of the brewer's yeast is detected by polynucleotide assay.

16. The use according to claim 15, wherein the polynucleotide is a primer.

17. Use of reagents for detecting the abundance of *Saccharomyces cerevisiae* nucleic acids in fecal samples from subjects or the level of anti-*Saccharomyces cerevisiae* antibodies in serum in the preparation of a system for assisting in the display of biological information associated with Parkinson's disease (PD), wherein said antibody is an IgG antibody, an IgA antibody, or a combination thereof, and said system comprises: a. A device for detecting the levels of nucleic acid or anti-Saccharomyces cerevisiae in samples from subjects; b. An apparatus for analyzing the levels of nucleic acids or antibodies against *Saccharomyces cerevisiae*, wherein the relative abundance of *Saccharomyces cerevisiae* is obtained by said analysis; c. A device for outputting analytical results, wherein the analytical results can be used to assist in displaying biological information related to Parkinson's disease (PD).

18. The use according to claim 17, wherein the analytical results can be used to assist in displaying biological information related to early PD.

19. The use according to claim 17 or 18, wherein the detection device is a sequencing device.

20. The use according to claim 19, wherein the detection device is an ITS next-generation sequencing device.

21. The use according to claim 17 or 18, wherein the detection device is a PCR device.

22. The use according to claim 21, wherein the detection device is a real-time PCR device.

23. The use according to claim 17 or 18, wherein the detection device is an antibody level detection device.

24. The use according to claim 23, wherein the detection device is an ELISA assay device.

25. The use according to claim 17 or 18, wherein the nucleic acid abundance of the brewer's yeast is detected by polynucleotide assay.

26. The use according to claim 25, wherein the polynucleotide is a primer.

27. Use of reagents for detecting the abundance of *Saccharomyces cerevisiae* nucleic acids in fecal samples from subjects or the level of anti-*Saccharomyces cerevisiae* antibodies in serum in the preparation of a system for assisting in the differentiation of Parkinson's disease (PD) and essential tremor (ET), wherein said antibody is an IgG antibody, an IgA antibody, or a combination thereof, and said system comprises: a. A device for detecting the levels of nucleic acid or anti-Saccharomyces cerevisiae in samples from subjects; b. An apparatus for analyzing the levels of nucleic acids or antibodies against *Saccharomyces cerevisiae*, wherein the relative abundance of *Saccharomyces cerevisiae* is obtained by said analysis; c. A device for outputting analytical results, wherein the analytical results can be used to help differentiate between Parkinson's disease (PD) and essential tremor (ET).

28. The use according to claim 27, wherein the detection device is a sequencing device.

29. The use according to claim 28, wherein the detection device is an ITS next-generation sequencing device.

30. The use according to claim 27, wherein the detection device is a PCR device.

31. The use according to claim 30, wherein the detection device is a real-time PCR device.

32. The use according to claim 27, wherein the detection device is an antibody level detection device.

33. The use according to claim 32, wherein the detection device is an ELISA assay device.

34. The use according to claim 27, wherein the nucleic acid abundance of the brewer's yeast is detected by polynucleotide assay.

35. The use according to claim 34, wherein the polynucleotide is a primer.

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