WO2023138266A1 - Biomarker related to parkinson's disease and application thereof - Google Patents

Abstract

Provided are a biomarker related to Parkinson's disease (PD) and an application thereof. Specifically, fungi, in particular Saccharomyces cerevisiae, can be used as a biomarker for PD diagnosis. Also provided is a detection method for fungi, in particular Saccharomyces cerevisiae. The biomarker and the detection method therefor can be used for auxiliary display of biological information related to PD, in particular early PD, can be used for assisting in distinguishing PD from essential tremor (ET), and can also be used for identifying a PD patient in a prodromal phase, the PD patient in the prodromal phase being a sleep behavior disorder (RBD) patient having the possibility of progressing to PD. Also provided are a kit, a system and a related application. The method can be independently used as a clinical auxiliary diagnosis method, and can also be used as a joint composite index for clinical auxiliary diagnosis.

Description

A biomarker related to Parkinson's disease and its application technical field

The present invention relates to the field of biomedicine, in particular to biomarkers related to Parkinson's disease and their applications, related methods, corresponding kits, systems and the like.

Background technique

Parkinson's disease (PD) is characterized by selective and progressive degeneration and loss of dopaminergic (DA) neurons in the substantia nigra compacta of the midbrain. However, the specific pathogenic mechanism of PD is still unclear. Clinically, PD is currently diagnosed mainly based on clinical typical motor symptoms and signs, and there are no other accurate and effective auxiliary bioinformatics indicators for early display of Parkinson's disease-related risks.

According to the latest criteria of the International Movement Disorder Society, the course of PD includes preclinical PD and clinical PD (that is, PD is diagnosed based on typical clinical symptoms). Among them, preclinical PD includes risk phase PD and prodromal phase PD. At this time, motor symptoms have not yet appeared, and non-motor symptoms are the core manifestations, including constipation, hyposmia/loss, rapid eye movement sleep disorder (RBD), etc. According to the Hoehn-Yahr classification, PD in the clinical stage is divided into early, middle and late stages, among which grade 1-2 is early PD, grade 2.5-3 is middle-stage PD, and grade 4-5 is late PD. As mentioned above, the diagnosis of PD is mainly based on typical clinical symptoms. According to the current clinical diagnostic standards, in many cases, it may take 3-5 years of follow-up to make a clinical diagnosis of PD.

In addition, essential tremor (Essential Tremor, ET), as an idiopathic disease with tremor as the main manifestation, needs to be differentiated clinically from early PD. PD mostly occurs in the elderly. This period is also the age of ET, so many ETs are misdiagnosed as PD. Although typical PD is characterized by resting tremor, muscle rigidity, and bradykinesia, these characteristic manifestations are often lacking in the early stage of PD, especially when there is only tremor at the onset, which may easily lead to misdiagnosis. The differential diagnosis of ET and PD is very important.

Therefore, research on the early diagnosis of PD has become a research hotspot in related fields, and it is of great significance to find biomarkers closely related to the onset of PD in early patients or patients at risk of developing PD as an auxiliary method for disease diagnosis.

Therefore, when a patient begins to have motor symptoms, effective biomarkers should be found from a non-motor perspective to achieve rapid and accurate collection of biological information for early PD patients, and provide the possibility for screening early PD patients, so that effective measures can be taken to slow down the onset process, and then carry out early intervention on symptoms.

Contents of the invention

An object of the present invention is to provide a quick and simple biomarker for assisting in displaying biological information related to Parkinson's disease (PD), especially early PD.

Another object of the present invention is to provide a quick and simple biomarker for assisting in distinguishing Parkinson's disease (PD) from essential tremor (ET).

Yet another object of the present invention is to provide a quick and simple biomarker for identifying prodromal PD patients who are sleep behavior disorder (RBD) patients who may progress to PD.

The present inventors found that the abundance of a fungus (Saccharomyces cerevisiae) was increased in the intestines of PD patients, whereas the abundance of this fungus was lower in the intestines of age-matched healthy people or ET patients. Furthermore, the inventors developed a detection method for this intestinal fungus, which can effectively distinguish PD patients, ET patients and healthy people through the detection of this fungus, and can be used for the discovery of prodromal PD patients (RBD progresses to PD), thereby realizing the auxiliary diagnosis of PD.

The present application finds that there is a fungus in the intestinal tract of PD patients, which can be used as a biomarker of PD, and the detection method of the fungus can be combined with the evaluation results of clinical scales to assist doctors in diagnosis. In the application of the present invention, the detection method includes but not limited to serum anti-Saccharomyces cerevisiae antibody (ASCA) IgG and IgA assay, fecal fungal ITS sequencing, fecal fungal fluorescent quantitative PCR assay, and the like. The detection method of the present invention can be used independently as a clinical auxiliary diagnosis method, and can also be used as a combined composite index for clinical auxiliary diagnosis.

Therefore, in one aspect, the present invention provides a biomarker to detect the presence and/or abundance of the fungus in the intestinal tract in the body of the subject's body. PCR technology can detect a biomarker or the abundant biomarker of the selected winemaking yeast or the biomarker that can detect antibacterials in serum through ELISA to detect antifungal or selected wine yeast.

In one embodiment, the present invention provides a biomarker, which is used to assist in displaying biological information related to Parkinson's disease (PD), especially early PD, or to assist in distinguishing Parkinson's disease (PD) from essential tremor (ET), or to identify prodromal PD patients, and the prodromal PD patients are sleep behavior disorder (RBD) patients who may progress to PD.

In another aspect, the present invention provides the use of biomarkers in the preparation of kits, wherein the kits are used to assist in the display of biological information related to Parkinson's disease (PD), especially early PD, or the kits are used to assist in distinguishing 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 intestinal tract of a subject and/or the level of antibodies against the fungus in serum, optionally the fungus is Saccharomyces cerevisiae (Sac charomyces 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 anti-fungal or optionally Saccharomyces cerevisiae antibody levels in serum by ELISA.

In yet another aspect, the present invention provides a use of a biomarker in screening a candidate drug for treating Parkinson's disease (PD) or reducing the risk of early PD, wherein the biomarker is used to detect the presence and/or abundance of a fungus optionally in the intestinal tract of a subject and/or the antibody level against the fungus in serum, optionally the fungus is Saccharomyces cerevisiae, wherein the biomarker is selected from biomarkers capable of detecting the abundance of fungi or Saccharomyces cerevisiae through ITS next-generation sequencing, PCR technology or A biomarker capable of detecting the level of antibodies against the fungus or optionally Saccharomyces cerevisiae in serum by ELISA.

In another aspect, the present invention provides a kit comprising reagents for detecting the presence and/or abundance of a fungus optionally in the intestinal tract of a subject and/or the level of antibodies against the fungus in serum, optionally the fungus is Saccharomyces cerevisiae, wherein the kit is used to assist in displaying biological information related to Parkinson's disease (PD), especially early PD, or the kit is used to assist in distinguishing Parkinson's disease (PD) from essential tremor (ET), or the kit is used to identify Phase PD patients.

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

a. Obtain subject samples;

b. detecting the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antibodies against fungi, or optionally Saccharomyces cerevisiae, in said sample;

Wherein the abundance of the fungus or the level of antibodies against the fungus or optionally Saccharomyces cerevisiae is greater than the control indicates that the subject is at risk of developing PD or has early PD.

In one embodiment, obtaining the subject sample includes selective isolation and enrichment of intestinal fungi, or optionally the sample is selected from blood and serum.

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

a. Obtain subject samples;

b. detecting the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antibodies against fungi, or optionally Saccharomyces cerevisiae, in said sample;

Where the abundance of the fungus or the level of antibodies against the fungus or optionally Saccharomyces cerevisiae is greater than the control indicates that the subject exhibits a risk of developing PD or has biological information associated with early PD.

In one embodiment, obtaining the subject sample includes selective isolation and enrichment of intestinal fungi, or optionally the sample is selected from blood and serum.

In yet another aspect, the present invention provides a method for aiding in differentiating Parkinson's disease (PD) from essential tremor (ET), comprising:

a. Obtaining a sample from a subject comprising a first subject suspected of having PD and a second subject suspected of having ET;

b. detecting the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antibodies against fungi, or optionally Saccharomyces cerevisiae, in said sample;

wherein if the abundance of said fungus or the level of antibodies against fungi or optionally Saccharomyces cerevisiae is greater in said first subject than said second subject indicates that said first subject has PD and said second subject has ET.

In one embodiment, obtaining the subject sample includes selective isolation and enrichment of intestinal fungi, or optionally the sample is selected from blood and serum.

In another aspect, the present invention provides a method for identifying a prodromal PD patient who is a sleep behavior disorder (RBD) patient who may progress to PD, the method comprising:

a. Obtain subject samples;

b. detecting the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antibodies against fungi, or optionally Saccharomyces cerevisiae, in said sample;

Wherein the abundance of the fungus or the level of antibodies against the fungus or optionally Saccharomyces cerevisiae is greater than the control indicates that the subject is a prodromal PD patient.

In one embodiment, obtaining the subject sample includes selective isolation and enrichment of intestinal fungi, or optionally the sample is selected from blood and serum.

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

a. A device for detecting the level of a biomarker according to the invention in a sample from a subject;

b. A device for analyzing the level of said biomarker, wherein the relative abundance of said biomarker is obtained by said analysis;

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

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

In another embodiment, wherein said detection device is a PCR device, preferably a real-time PCR device.

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

In another embodiment, wherein said sample is selected from the group consisting of blood, serum, fluid from the digestive tract, and fecal extract.

In yet another embodiment, wherein the biomarkers are selected from polynucleotides or antibodies.

In another embodiment, wherein said polynucleotide is a primer.

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

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

a. A device for detecting the level of a biomarker according to the invention in a sample from a subject;

b. A device for analyzing the level of said biomarker, wherein the relative abundance of said biomarker is obtained by said analysis;

c. A device for outputting analysis results, wherein the analysis results can be used to assist in displaying biological information related to Parkinson's disease (PD), especially early PD.

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

In another embodiment, wherein said detection device is a PCR device, preferably a real-time PCR device.

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

In another embodiment, wherein said sample is selected from the group consisting of blood, serum, fluid from the digestive tract, and fecal extract.

In yet another embodiment, wherein the biomarkers are selected from polynucleotides or antibodies.

In another embodiment, wherein said polynucleotide is a primer.

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

In yet another aspect, the present invention provides a system for aiding in differentiating Parkinson's disease (PD) from essential tremor (ET), the system comprising:

a. A device for detecting the level of a biomarker according to the invention in a sample from a subject;

b. A device for analyzing the level of said biomarker, wherein the relative abundance of said biomarker is obtained by said analysis;

c. A device for outputting analysis results, wherein the analysis results can be used to assist in differentiating Parkinson's disease (PD) from essential tremor (ET).

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

In another embodiment, wherein said detection device is a PCR device, preferably a real-time PCR device.

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

In another embodiment, wherein said sample is selected from the group consisting of blood, serum, fluid from the digestive tract, and fecal extract.

In yet another embodiment, wherein the biomarkers are selected from polynucleotides or antibodies.

In another embodiment, wherein said polynucleotide is a primer.

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

definition

As used in the present invention, the term "abundance" refers to the percentage of the amount of a certain fungus among all detected fungi. For example, in the ITS next-generation sequencing method of the present invention, the "abundance" of Saccharomyces cerevisiae refers to the percentage of the amount of Saccharomyces cerevisiae in all detected fungi;

As used in the present invention, the term "Parkinson's disease patient" or "PD patient" refers to a Parkinson's disease patient who has been diagnosed by at least one experienced neurologist according to the standard diagnostic criteria of the British Brain Bank.

As used in the present invention, the term "essential tremor patient" or "ET patient" refers to a patient with essential tremor diagnosed by at least one experienced neurologist according to the tremor consensus developed by the International Movement Disorders Association.

As used herein, the term "healthy control" or "HC" refers to a healthy subject without positive neurological signs.

As used in the present invention, the term "ITS" refers to the internal transcribed spacer (Internal Transcribed Spacer), located between fungal 18S, 5.8S and 28S rRNA genes, respectively ITS1 and ITS2. In fungi, 5.8S, 18S, and 28S rRNA genes are highly conserved, while ITS can tolerate more variation during evolution due to less natural selection pressure, and exhibits extremely extensive sequence polymorphism in most eukaryotes. At the same time, the conservative type of ITS is relatively consistent within the species, and the differences between the species are obvious, which can reflect the differences between species and even strains. Moreover, the ITS sequence fragments are relatively small (the lengths of ITS1 and ITS2 are 350bp and 400bp, respectively), which are easy to analyze and have been widely used in the phylogenetic analysis of different fungal species.

The present invention explored the relationship between intestinal fungi, intestinal inflammation-related serological antibodies and PD for the first time, and found that the abundance of intestinal fungi Saccharomyces cerevisiae, serum anti-Saccharomyces cerevisiae antibody (ASCA) IgG and IgA levels were significantly increased in PD patients. The present invention finds for the first time that the relevant indicators of intestinal inflammation can be used as biomarkers for assisting early diagnosis of PD. In terms of auxiliary early diagnosis, the intestinal fungus Saccharomyces cerevisiae can be detected by ITS next-generation sequencing or fluorescent quantitative PCR, and the serum anti-Saccharomyces antibody IgG and IgA composite indicators can be used to distinguish PD patients, healthy controls, HC and ET patients, and Saccharomyces indicators can also be used for the discovery of prodromal PD patients (RBD progresses to PD). The present invention can assist in clinically diagnosing PD patients, realize rapid and accurate clinical diagnosis of early PD patients, and carry out early control of the disease.

Description of drawings

Figure 1 shows the intestinal fungal composition and key differential fungi in the PD group and the HC group. The left panel A is the PLSDA map based on the ASV abundance matrix, and the right panel B is the contribution of 25 differential ASVs to distinguish the two groups of samples.

Figure 2 shows the relative abundance of Saccharomyces cerevisiae Saccharomyces cerevisiae in PD group and HC group.

Figure 3 shows the intestinal fungal composition and key differential fungi in the PD group and the ET group. The left panel A is the PLSDA map based on the ASV abundance matrix, and the right panel B is the contribution of 25 differential ASVs to distinguish the two groups of samples.

Figure 4 shows the relative abundance of Saccharomyces cerevisiae Saccharomyces cerevisiae in PD group and ET group.

Figure 5 shows the separation of yeast using mannan lectin-modified magnetic microspheres as an example, showing the schematic diagram of the magnetic separation of fungi.

Figure 6 shows the results of fluorescence detection showing magnetically specific capture. The magnetic microsphere specific capture experiment was carried out (the emission wavelength of fungal fluorescence detection is 420nm, and the wavelength of bacterial fluorescence detection is 620nm). The results showed that only the fluorescent signal of fungi responded in the solution after magnetic separation, and there was no fluorescent signal of bacteria.

Figure 7 shows the results of magnetic separation and enrichment effects.

Figure 8 shows the real-time PCR quantitative results of Saccharomyces cerevisiae in PD patients and healthy controls HC.

Figure 9 shows the diagnostic accuracy of real-time PCR quantification of S. cerevisiae in distinguishing PD patients from healthy controls HC.

Figure 10 shows the real-time PCR quantitative results of Saccharomyces cerevisiae in PD patients and ET patients.

Figure 11 shows the diagnostic accuracy of real-time PCR quantification of S. cerevisiae in distinguishing PD patients from ET patients. This indicator can significantly distinguish PD patients from ET patients. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 12 shows the diagnostic accuracy of serum anti-S. cerevisiae antibody IgG levels in differentiating PD patients from healthy controls HC. Serum anti-S. cerevisiae IgG levels could significantly distinguish PD patients from HC. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 13 shows the diagnostic accuracy of serum anti-S. cerevisiae antibody IgA levels in differentiating PD patients from healthy controls HC. Serum anti-S. cerevisiae antibody IgA levels can significantly distinguish PD patients from HC. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 14 shows the diagnostic accuracy of the composite index of serum anti-Saccharomyces cerevisiae antibody IgG and IgA levels to distinguish PD patients from healthy controls HC. The composite index could significantly distinguish PD patients from healthy controls HC. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 15 shows the diagnostic accuracy of serum anti-S. cerevisiae IgG levels in differentiating PD patients from ET patients. Serum anti-S. cerevisiae IgG levels can significantly distinguish PD patients from ET patients. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 16 shows the diagnostic accuracy of serum anti-S. cerevisiae antibody IgA levels in differentiating PD patients from ET patients. Serum anti-Saccharomyces cerevisiae antibody IgA levels can significantly distinguish PD patients from ET patients. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 17 shows the diagnostic accuracy of the composite index of serum anti-Saccharomyces cerevisiae antibody IgG and IgA levels to distinguish PD patients from ET patients. The composite index can significantly distinguish PD patients from ET patients. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 18 shows the real-time PCR quantitative results of Saccharomyces cerevisiae in RBD patients.

Figure 19 shows the diagnostic accuracy of real-time PCR quantification of Saccharomyces cerevisiae in prodromal PD patients. This index can significantly distinguish RBD patients who progressed to PD from RBD patients who did not progress to PD. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 20 shows the diagnostic accuracy of serum anti-S. cerevisiae IgG levels in RBD patients who progressed to PD. Serum anti-Saccharomyces cerevisiae IgG levels can identify patients with RBD who progress to PD. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 21 shows the diagnostic accuracy of serum anti-S. cerevisiae antibody IgA levels in RBD patients who progressed to PD. Serum anti-Saccharomyces cerevisiae antibody IgA levels can identify patients with RBD who progress to PD. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Figure 22 shows the diagnostic accuracy of the composite index of serum anti-Saccharomyces cerevisiae antibody IgG and IgA levels for RBD patients who progressed to PD. The composite index identified RBD patients who progressed to PD. ROC: receiver operating characteristic curve; AUC: area under the curve; 95% CI: 95% confidence interval.

Detailed ways

The present invention is specifically explained by the following examples, but the scope of the present invention is not limited thereto.

Unless otherwise specified, the reagents, instruments, etc. used in the present invention are commercially available.

Example

Example 1: Determination of the abundance of Saccharomyces cerevisiae by ITS next-generation sequencing to distinguish PD patients from healthy controls HC

We performed ITS next-generation sequencing on the stool samples of 33 PD patients, 21 healthy controls HC and 46 ET patients who visited the Department of Neurology of Zhejiang Second Hospital from December 1, 2020 to February 28, 2021. The research protocol of this study complies with the Declaration of Helsinki and has been reviewed and approved by the Ethics Committee of Zhejiang Second Hospital. All subjects were fully aware of the procedures and protocols of this study, and had voluntarily signed informed consent. All subjects received detailed demographic data collection and clinical evaluation. All PD patients received detailed clinical scales to assess disease severity and cognitive and emotional states. In addition, all subjects underwent a nonmotor symptom questionnaire to assess the frequency of Parkinson's disease-related nonmotor symptoms.

In short, the total DNA was extracted according to the instructions of QIAamp PowerFecal Pro DNA Kit (Qiagen, Cat No./ID: 51804), the DNA concentration and purity were detected by NanoDrop2000, and the quality of DNA extraction was detected by 1% agarose gel electrophoresis. Then use a universal primer pair for all fungi:

ITS3F:GCATCGATGAAGAACGCAGC

ITS4R:TCCTCCGCTTATTGATATGC

PCR amplification of the ITS2 variable region was performed.

After amplification, the PCR amplification products of the same sample were mixed and the PCR amplification products were recovered using 2% agarose gel, and the recovered products were purified using AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). The recovered product was detected and quantified by 2% agarose gel electrophoresis and Quantus ^TM Fluorometer (Promega, USA).

Next, use the NEXTFLEX Rapid DNA-Seq Kit to build a library, including the following steps: (1) adapter ligation; (2) use magnetic beads to screen and remove adapter self-ligating fragments; (3) use PCR amplification to enrich library templates; (4) magnetic beads to recover PCR amplification products to obtain the final library.

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

Next, based on the QIIME 2 (Quantitative Insight Into Microbial Ecology2, v2018.11) platform (for example, Bolyen, E., et al., Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol, 2019.37(8): p.85 2-857) Analyze raw sequencing sequences. Use the cutadapter software to find and excise the adapter sequence. Then, use DADA2 for denoising (refer to e.g. Callahan, B.J., et al., DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods, 2016.13(7): p.581-583), filter, denoise, splice the pruned sequence, and obtain the ASV (Amplicon Sequence Variants) sequence and abundance information, and remove chimeras in ASV, and finally get the original ASV abundance matrix.

Then, the taxonomic status of ASV was defined based on the SILVA database. The number of sequences of all samples was normalized to 24781 (repeated 1000 times) to eliminate the differences between samples due to different sequencing depths, and then the ASV abundance matrix table of samples with the same number of sequences for each sample was obtained.

Next, using the R package mixOmics (version v6.3.1) (see e.g. Kim-Anh Le Cao, F.R., Ignacio Gonzalez, Sebastien Dejean with key contributors Benoit Gautier, Francois Bartolo, contributions from Pierre Monget, Jeff Coquery, FangZou Yao and Ben oit Liquet.mixOmics:Omics Data Integration Project.R package version 6.1.1.2016; available at: https://CRAN.R-project.org/package=mixOmics) for sparse Partial Least Squares Discriminant Analysis (sPLS-DA) (refer to for example Le Cao, K.A., S.Boitard, and P.Besse, Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multi class problems. BMC Bioinformatics, 2011.12: p.253) to identify key differences in fungal composition in samples ASV.

When using the sPLS-DA model, we adopted the Centered Log Ratio transformation (CLR) to avoid spurious results. Based on the perf function and using leave-one-out cross-validation to test the misclassification rate of the model, the sPLS-DA model with the smallest error rate is the best classification model.

We compared the fungal composition of samples from PD and HC groups. Through the PLSDA plot based on the ASV abundance matrix, we found that the intestinal fungi in the PD group were mainly separated from the HC group along the first principal component, which explained the overall structural variability of the intestinal flora (3%) (Fig. 1A). Then, we found the difference ASV between the two groups on the first principal component, and selected 25 ASVs according to the stability (stability=0.8). The contribution of these 25 ASVs to distinguish the two groups of samples is shown in Figure 1B. Among these 25 ASVs, 4 ASVs were significantly enriched in the PD group, among which Saccharomyces cerevisiae contributed the most to distinguish the two groups (Fig. 1B).

We found that the average relative abundance of Saccharomyces cerevisiae in the PD group was 18.6%, and that in the HC group was 9.4% (Fig. 2). We demonstrated significant differences in fungal composition between PD patients and HC healthy controls through sample ITS amplicon sequencing analysis and identified key differences in Saccharomyces cerevisiae. Our results demonstrate that S. cerevisiae, as determined by ITS next-generation sequencing, can distinguish PD patients from HC healthy controls.

Example 2: Determination of the abundance of Saccharomyces cerevisiae by ITS next-generation sequencing to distinguish PD patients and ET patients

We used the same method as in Example 1 to compare the intestinal fungal composition of the PD group and the ET group. Among the 25 differential ASVs, Saccharomyces cerevisiae Saccharomyces cerevisiae was again found to have the greatest contribution to distinguishing the two groups ( FIG. 3 ).

We found that the average relative abundance of Saccharomyces cerevisiae in the PD group was 18.6%, and that in the ET group was 11.6% (Fig. 4). We demonstrated significant differences in the fungal composition between PD patients and ET patients by ITS amplicon sequencing analysis of samples and identified key differences in Saccharomyces cerevisiae S. cerevisiae. Our results showed that Saccharomyces cerevisiae could be distinguished between PD patients and ET patients by ITS next-generation sequencing.

Through the above-mentioned ITS sequencing method, 33 primary PD patients, 21 HC healthy controls and 46 ET patients were diagnosed in the Neurology Department of Zhejiang Second Hospital from December 1, 2020 to February 28, 2021.

Example 3: Determination of Saccharomyces cerevisiae by fluorescent quantitative PCR to distinguish PD patients from healthy controls HC

In this example, we use the technique of fluorescent real-time polymerase chain reaction (Real-time polymerase chain reaction, Real-time PCR) to quantitatively measure Saccharomyces cerevisiae Saccharomyces cerevisiae.

Since fungi account for very little (≤0.1%) in the intestinal flora, it is difficult to effectively obtain fungal information by direct sequencing methods, and it is necessary to selectively isolate and enrich fungi to facilitate subsequent research. Therefore, we first selectively isolate and enrich intestinal fungi, and then use primers for quantitative determination.

3.1 Separation and enrichment method

3.1.1 Method background

The separation and enrichment of microorganisms can improve the detection effect of target microorganisms. After investigation, it was found that the immunomagnetic separation method is a commonly used separation method in the detection of pathogenic microorganisms, and can be used for the detection of bacteria, fungi and other single-cell pathogenic sources (refer to, for example, BMC Microbiol., 2008, 22(8): 157-160, J. Clin. Microbiol., 2010, 48(4): 1126-1131). The antibody that can specifically recognize the microorganism to be tested is connected to the surface of the magnetic microsphere as a capture probe for the microorganism to be tested. After specific recognition and high-affinity reaction binding, the microorganism to be tested attached to the surface of the magnetic microsphere can be separated from the complex matrix sample by magnetic separation, and the influence of impurities and bacteria in the sample on the detection can be removed. At the same time, the magnetic separation process can reduce the volume of the liquid sample, which has the effect of enriching the microorganisms to be tested.

Bacteria are the biggest interference factor in the effective detection of fungi in samples. Since fungal cell wall components are very different from bacteria, specific components of fungal cell walls can be selected as capture sites to bind to capture probes. The main component of the fungal cell wall is chitin, the main polysaccharide component of the yeast cell wall is mannan, and the main polysaccharide component of the bacterial cell wall is peptidoglycan. Therefore, chitin and mannan can be selected as fungal-specific capture sites. Recombinant chitinase can specifically recognize and bind to chitin. Therefore, a recombinant chitinase that can specifically bind to fungi was used as a capture probe. On the other hand, the cell wall composition of yeast is relatively special, containing a large amount of mannan components. Therefore, mannan-binding protein can be selected as the recognition protein, specifically recognizes and binds to fungi of the genus Saccharomyces, and isolates yeasts in biological samples (refer to for example Proc.

3.1.2 Purpose of experiment

Use the magnetic separation and enrichment method based on affinity reaction to effectively extract fungi from microbial samples, remove bacteria, increase fungal content, facilitate fungal counting, and be used for follow-up research.

3.1.3 Basic Principles

Two kinds of functionalized magnetic microspheres are connected by designing and synthesizing, respectively coated with recombinant chitinase and mannan-binding protein (mannan lectin). Functionalized magnetic microspheres can be attached to the fungal cell wall surface by binding to chitin and mannan on the fungal surface. Through magnetic separation, the fungi connected to the magnetic microspheres can be effectively separated from the complex system, and other substances in the system can be removed to achieve the effect of fungal separation and enrichment.

Figure 5 uses mannan lectin-modified magnetic microspheres to separate yeast as an example, demonstrating the principle of fungal magnetic separation. Recombinant mannanboundin can be connected to the surface of TED-Ni-coated magnetic microspheres through His-tag, or can be modified by biotinylation (Biotin) to connect to the surface of streptavidin (SA)-coated magnetic microspheres. Both reactions can be used to make recombinant chitinase-coated magnetic microspheres. The unique mannan component on the yeast cell wall will specifically bind to the recombinant mannan-boundin on the surface of the magnetic microspheres, thereby immobilizing on the surface of the magnetic microspheres, while the bacterial cell wall does not contain mannan components, so it will not specifically bind to the magnetic microspheres. Therefore, in the subsequent magnetic separation process, the bacteria and other free components not connected to the surface of the magnetic microspheres contained in the supernatant will be removed during the washing process, while the fungi fixed on the surface of the magnetic microspheres can be retained. And the magnetic separation process can reduce the volume of the final solution, so as to achieve the effect of fungal separation and enrichment. The magnetic separation method using recombinant chitinase as a capture probe is similar to Figure 5, and fungi are captured by recognizing chitinase on the fungal cell wall.

3.1.4 Experimental materials

1. Biotinylation kit (Abcam): Biotinylation Kit/Biotin Conjugation Kit (Fast, Type B)-Lightning-

2. Mannan-binding protein (Abcam): Recombinant Human Mannan Binding Lectin/MBL protein;

3. Recombinant chitinase (Abcam): recombinant mouse chitinase 3-like protein 3;

4. Magnetic microspheres (Invitrogen): Dynabeads ^TM M-280 Streptavidin;

5. Magnetic stand (Invitrogen): DynaMag ^TM -2 magnetic stand;

6. PBS (1×), PBST.

3.1.5 Experimental process

1. Preparation of functionalized magnetic microspheres

To prepare functionalized magnetic microspheres, you can choose the high-affinity reaction between His-tag and TED-Ni, or biotin and streptavidin.

This scheme adopts the biotin-streptavidin reaction system, and the reaction process is as follows:

1. Use a biotinylation kit to biotinylate mannan lectin, and follow the instructions of the kit for specific operations;

2. React biotinylated mannan lectin with streptavidin-coated magnetic microspheres, and react with 100 μL of magnetic microspheres for every 10 μg of protein, and an appropriate amount of PBS buffer can be added to the reaction system. After reacting at room temperature for 30 minutes, the magnetic microspheres were washed 3 times with PBST (operated on a magnetic stand), and finally PBS was added to make the magnetic microspheres return to the initial concentration.

3. Recombinant chitinase-coated magnetic balls, the preparation method repeats the above steps 1 and 2.

If it is necessary to dissociate the fungi from the magnetic microspheres after magnetic separation, the His-tag can be used to react with TED-Ni. Purchase recombinant chitinase and mannan lectin with His-tag tags, and react them with TED-Ni-coated magnetic microspheres. The reaction conditions and dissociation conditions refer to the product instructions of TED-Ni magnetic microspheres.

2. Magnetic separation of fungi

1) Take out the feces sample to be tested from the -80°C refrigerator, and place it on ice for about five minutes to partially melt the sample;

2) Weigh about 0.5g-1.0g of feces sample into a 2mL lysis homogeneous tube, and add 1.0mL of pre-cooled PBS buffer at 4°C to the tube;

3) Put it on a vortex shaker, oscillate and mix twice at the maximum speed, each time for 2 minutes, with an interval of 1 minute in between, to avoid excessive temperature in the tube caused by long-term high-speed oscillation;

4) Use a tissue cell disruptor to lyse the sample, setting 5, crush for 30 seconds, with an interval of 30 seconds, and repeat three times.

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

6) After centrifugation, take the supernatant, that is, the feces extract, and store the sample immediately at 4°C for future use.

Mix the liquid sample to be separated with functionalized magnetic microspheres, add 5-10 μL functionalized magnetic microspheres to each 1 mL sample, slightly oscillate at room temperature for 30 minutes, wash with PBST three times (operate on a magnetic stand), remove the supernatant, and add 100 μL PBS to obtain the fungal separation solution.

If you need to extract DNA, you can directly add lysate and extraction solution after washing, operate on a magnetic stand, and take the supernatant for DNA extraction.

3.1.6 Method validation

1. Specificity of fungal capture

Using the method described above, a mixed sample of fungi and bacteria was subjected to a magnetic separation operation. Fluorescent staining reagents for fungi and bacteria were used to carry out staining and fluorescence detection with the separated samples. As shown in Figure 6, the separated samples had obvious fungal fluorescent signal responses, while samples containing high concentrations of bacteria had no obvious bacterial fluorescent signal responses after magnetic separation. It is proved that the recombinant chitinase and mannan lectin coated on the magnetic ball can efficiently recognize and bind to fungus, but will not specifically bind to bacteria.

2. Verification of enrichment effect of magnetic separation

The same sample to be separated was stained with fungal fluorescent dye before and after magnetic separation, and the fluorescence intensity was detected. As a result, it was found that the fluorescence signal intensity of the sample after magnetic separation increased significantly, which proved that magnetic separation can effectively concentrate the sample volume and enrich the fungi in the liquid sample ( FIG. 7 ).

3.2 Quantitative determination of primers

The SCoH sequence of the primer we designed for Saccharomyces cerevisiae is as follows:

Primer sequence F: 5'-GTTAGATCCCAGGCGTAGAACAG-3'

Primer sequence R: 5'-GCGAGTACTGGACCAAATCTTATG-3'

Annealing temperature is 58°C

The length of the amplified product is 400bp.

We performed quantitative PCR on the stool DNA samples of 10 PD patients and 10 healthy control HCs. The Ct value of Saccharomyces cerevisiae in the stool DNA samples of PD patients was significantly lower than that of healthy control HCs (Figure 8), that is, the abundance of Saccharomyces cerevisiae in PD patient samples was significantly higher than that in healthy control HC samples.

Therefore, detection of Saccharomyces cerevisiae by real-time quantitative PCR can distinguish PD patients from healthy controls HC.

ROC analysis was used to evaluate the accuracy of this indicator for the diagnosis of PD. This indicator could significantly distinguish PD patients from HC (area under the curve = 0.90, p = 0.0025, Figure 9). Saccharomyces cerevisiae PCR quantitative assay after isolation and enrichment of intestinal fungi could distinguish PD patients from healthy people.

Example 4: Determination of Saccharomyces cerevisiae by fluorescent quantitative PCR to distinguish PD patients and ET patients

We performed quantitative PCR verification on fecal DNA samples of 10 PD patients and 10 ET patients. The Ct value of Saccharomyces cerevisiae in fecal DNA samples of PD patients was significantly lower than that of ET patients (Figure 10), that is, the abundance of Saccharomyces cerevisiae in samples from PD patients was significantly higher than that in samples from ET patients.

Therefore, measuring Saccharomyces cerevisiae by fluorescent quantitative PCR can distinguish PD patients from ET patients.

ROC analysis was used to evaluate the diagnostic accuracy of this index for PD, and this index could significantly distinguish PD patients from ET patients (area under the curve=0.77, p=0.0413, FIG. 11 ). Saccharomyces cerevisiae quantification by PCR after isolation and enrichment of intestinal fungi can distinguish PD patients from ET patients.

Saccharomyces cerevisiae was detected by real-time quantitative PCR, and 18 primary PD patients, 10 ET patients and 18 healthy controls HC were diagnosed in the Neurology Department of Zhejiang Second Hospital from February 01, 2021 to October 1, 2021.

Example 5: Serum anti-Saccharomyces cerevisiae antibody IgG or IgA and its composite index are used to distinguish PD patients and healthy controls HC

Serum anti-Saccharomyces cerevisiae antibody levels were measured on blood samples from volunteers.

Serum anti-Saccharomyces cerevisiae antibodies IgG and IgA were detected by enzyme-linked immunosorbent assay (ELISA). Serum anti-Saccharomyces cerevisiae IgG and IgA cut-off values were both 25 AU/ml.

ROC analysis results showed that serum anti-Saccharomyces cerevisiae antibody IgG level could significantly distinguish PD patients from HC (area under the curve=0.712, p<0.001, Figure 12); serum anti-Saccharomyces cerevisiae antibody IgA level could also significantly distinguish PD patients from HC (area under the curve=0.671, p<0.001, Figure 13).

Furthermore, the logistic regression model was used to classify the PD group and the HC group, and the two variables of serum anti-Saccharomyces cerevisiae antibodies, IgG and IgA, were combined to generate a composite index, which was calculated as follows: composite index=1.067×IgG+1.081×IgA+0.156. ROC analysis was used to evaluate the diagnostic accuracy of the composite index for PD, and the composite index could significantly distinguish PD patients from HC (area under the curve=0.730, p<0.001, FIG. 14 ).

Example 6: Serum anti-Saccharomyces cerevisiae antibody IgG or IgA and its composite index are used to distinguish PD patients from ET patients

The same method as in Example 5 was used to detect the serum anti-Saccharomyces cerevisiae antibody levels of samples from PD patients and ET patients.

The results of ROC analysis showed that serum anti-Saccharomyces cerevisiae antibody IgG level could significantly distinguish PD patients from ET patients (area under the curve=0.753, p<0.001, Figure 15); serum anti-Saccharomyces cerevisiae antibody IgA level could also significantly distinguish PD patients from ET patients (area under the curve=0.725, p<0.001, Figure 16).

Furthermore, the logistic regression model was used to classify the PD group and the HC group, and the two variables of serum anti-Saccharomyces cerevisiae antibodies, IgG and IgA, were combined to generate a composite index, which was calculated as follows: composite index=1.067×IgG+1.081×IgA+0.156. ROC analysis was used to evaluate the diagnostic accuracy of the composite index for PD, and the composite index could significantly distinguish PD patients from ET patients (area under the curve=0.778, p<0.001, FIG. 17 ).

Through the determination of serum anti-Saccharomyces cerevisiae antibodies, 140 PD patients, 105 ET patients and 130 healthy controls HC were diagnosed in the Neurology Department of Zhejiang Second Hospital from December 1, 2018 to October 1, 2020.

Example 7: Detection of Saccharomyces cerevisiae by fluorescent quantitative PCR to identify RBD patients who progressed to PD

We performed quantitative PCR verification on the early stool DNA samples of 18 RBD patients who progressed to PD and 18 RBD patients who did not progress to PD. The Ct value of Saccharomyces cerevisiae Saccharomyces cerevisiae in the stool DNA samples of RBD patients who progressed to PD was significantly lower than that of RBD patients who did not progress to PD (Figure 18), and the abundance of Saccharomyces cerevisiae in samples from RBD patients who progressed to PD was high.

Therefore, measuring Saccharomyces cerevisiae by real-time PCR can identify patients with RBD who can progress to PD.

ROC analysis was used to evaluate the diagnostic accuracy of this indicator for RBD patients who could progress to PD. This indicator could significantly distinguish RBD patients who could progress to PD from RBD patients who would not progress to PD (area under the curve = 0.7330, p = 0.0169, Figure 19). Saccharomyces cerevisiae PCR quantitative assay after isolation and enrichment of intestinal fungi could identify RBD patients who progressed to PD.

Example 8: Serum anti-Saccharomyces cerevisiae antibody IgG or IgA and its composite index can identify RBD patients who progressed to PD

ROC analysis results showed that serum anti-Saccharomyces cerevisiae antibody IgG levels could identify RBD patients progressing to PD (area under the curve=0.75, p=0.0129, Figure 20); serum anti-Saccharomyces cerevisiae antibody IgA levels could also identify RBD patients progressing to PD (area under the curve=0.7191, p=0.0054, Figure 21).

Furthermore, the logistic regression model was used to classify the PD group and the HC group, and the two variables of serum anti-Saccharomyces cerevisiae antibodies, IgG and IgA, were combined to generate a composite index, which was calculated as follows: composite index=1.067×IgG+1.081×IgA+0.156. ROC analysis was used to evaluate the diagnostic accuracy of the composite index for PD, and the composite index could identify RBD patients who progressed to PD (area under the curve=0.8704, p=0.0002, FIG. 22 ).

We tracked 36 patients with sleep behavior disorder (RBD) who visited the Department of Neurology of Zhejiang Second Hospital from December 1, 2015 to December 1, 2020. During the five-year follow-up, 18 RBD patients developed into PD patients, and 18 RBD patients did not progress to PD patients. Fluorometric quantification of S. cerevisiae and serum anti-S. cerevisiae antibodies were performed on early samples from these patients. This example proves that this marker can be used for the discovery of prodromal PD patients (RBD progressing to PD).

In the present invention, we explored the relationship between intestinal fungi, intestinal inflammation-related serological antibodies and PD for the first time, and found that the abundance of intestinal fungi Saccharomyces cerevisiae, serum anti-Saccharomyces cerevisiae antibody (ASCA) IgG and IgA levels were significantly increased in PD patients. For the first time, we found that intestinal inflammation-related indicators can be used as biomarkers for assisting early diagnosis of PD. In terms of auxiliary early diagnosis, our examples: 1. The intestinal fungus Saccharomyces cerevisiae is detected by ITS next-generation sequencing or fluorescent quantitative PCR; 2. Serum anti-Saccharomyces cerevisiae antibody IgG and IgA composite indicators can be used to distinguish PD patients, healthy controls HC and ET patients; 3. Saccharomyces cerevisiae indicators can also be used for the discovery of prodromal PD patients (RBD progresses to PD). The above-mentioned embodiments can assist in the clinical diagnosis of PD patients, realize rapid and accurate clinical diagnosis of early PD patients, and carry out early control of the disease.

Claims (37)

A biomarker for detecting the presence and/or abundance of a fungus optionally in the intestinal tract of a subject and/or the level of antibodies against the fungus in serum, optionally the fungus is Saccharomyces cerevisiae, wherein the biomarker is selected from biomarkers capable of detecting the abundance of fungi or optionally Saccharomyces by ITS next-generation sequencing, PCR technology or biomarkers capable of detecting anti-fungal or optionally Saccharomyces antibody levels in serum by ELISA. The biomarker according to claim 1, which is used to assist in displaying biological information related to Parkinson's disease (PD), especially early PD, or to assist in distinguishing Parkinson's disease (PD) from essential tremor (ET), or to identify prodromal PD patients, and the prodromal PD patients are sleep behavior disorder (RBD) patients who can progress to PD. The use of a biomarker in the preparation of a kit, wherein the kit is used to assist in displaying biological information related to Parkinson's disease (PD), especially early PD, or the kit is used to assist in distinguishing Parkinson's disease (PD) from essential tremor (ET), or the kit is used to identify patients with prodromal PD, wherein the biomarker is used to detect the presence and/or abundance of fungi in a subject's optional intestinal tract and/or the level of antibodies against the fungus in serum, and optionally the fungus is 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 anti-fungal or optionally Saccharomyces cerevisiae antibody levels in serum by ELISA. Use of a biomarker in screening a drug candidate for treating Parkinson's disease (PD) or reducing the risk of early PD, wherein the biomarker is used to detect the presence and/or abundance of fungi in the subject's body and/or the level of antibodies against the fungus in the serum, optionally the fungus is Saccharomyces cerevisiae, wherein the biomarker is selected from biomarkers that can detect the abundance of fungi or Saccharomyces cerevisiae through ITS next-generation sequencing, PCR technology, or can be detected by ELISA A biomarker for the level of antibodies against the fungus or optionally Saccharomyces cerevisiae in the serum. A kit comprising reagents for detecting the presence and/or abundance of fungi in the optional intestinal tract of a subject and/or the level of antibodies against the fungus in serum, optionally the fungus is Saccharomyces cerevisiae, wherein the kit is used to assist in displaying biological information related to Parkinson's disease (PD), especially early PD, or the kit is used to assist in distinguishing Parkinson's disease (PD) from essential tremor (ET), or the kit is used to identify prodromal PD patients . A method for assisting screening of Parkinson's disease (PD), especially early PD, comprising: a. Obtain subject samples; b. detecting the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antibodies against fungi, or optionally Saccharomyces cerevisiae, in said sample; Wherein the abundance of the fungus or the level of antibodies against the fungus or optionally Saccharomyces cerevisiae is greater than the control indicates that the subject is at risk of developing PD or has early PD. The method according to claim 6, wherein obtaining the subject sample comprises selectively isolating and enriching intestinal fungi, or optionally the sample is selected from blood and serum. A method for assisting in displaying biological information related to Parkinson's disease (PD), especially early PD in a subject, comprising: a. Obtain subject samples; b. detecting the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antibodies against fungi, or optionally Saccharomyces cerevisiae, in said sample; Where the abundance of the fungus or the level of antibodies against the fungus or optionally Saccharomyces cerevisiae is greater than the control indicates that the subject exhibits a risk of developing PD or has biological information associated with early PD. The method according to claim 8, wherein obtaining the subject sample comprises selectively isolating and enriching intestinal fungi, or optionally the sample is selected from blood and serum. A method to aid in differentiating Parkinson's disease (PD) from essential tremor (ET), comprising: a. Obtaining a sample from a subject comprising a first subject suspected of having PD and a second subject suspected of having ET; b. detecting the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antibodies against fungi, or optionally Saccharomyces cerevisiae, in said sample; wherein if the abundance of said fungus or the level of antibodies against fungi or optionally Saccharomyces cerevisiae is greater in said first subject than said second subject indicates that said first subject has PD and said second subject has ET. The method according to claim 10, wherein obtaining the subject sample comprises selective isolation and enrichment of intestinal fungi, or optionally the sample is selected from blood and serum. A method for identifying a prodromal PD patient who is a sleep behavior disorder (RBD) patient who may progress to PD, the method comprising: a. Obtain subject samples; b. detecting the abundance of fungi, optionally Saccharomyces cerevisiae, or the level of antibodies against fungi, or optionally Saccharomyces cerevisiae, in said sample; Wherein the abundance of the fungus or the level of antibodies against the fungus or optionally Saccharomyces cerevisiae is greater than the control indicates that the subject is a prodromal PD patient. The method according to claim 12, wherein obtaining the subject sample comprises selectively isolating and enriching intestinal fungi, or optionally the sample is selected from blood and serum. A system for assisting screening of Parkinson's disease (PD), particularly early PD, the system comprising: a. A device for detecting the level of a biomarker according to claim 1 in a sample from a subject; b. A device for analyzing the level of said biomarker, wherein the relative abundance of said biomarker is obtained by said analysis; c. A device for outputting analysis results, wherein the analysis results can be used to assist in the screening of Parkinson's disease (PD), especially early PD. The system according to claim 14, wherein the detection device is a sequencing device, preferably an ITS next-generation sequencing device. The system according to claim 14, wherein the detection device is a PCR device, preferably a real-time PCR device. The system according to claim 14, wherein said detection device is an antibody level detection device, preferably an ELISA assay device. The system of claim 14, wherein the sample is selected from the group consisting of blood, serum, fluid from the digestive tract, and fecal extract. The system of claim 14, wherein the biomarkers are selected from polynucleotides or antibodies. The system of claim 19, wherein the polynucleotide is a primer. The system of claim 19, wherein the antibody is an IgG antibody, an IgA antibody, or a combination thereof. A system for assisting in displaying biological information related to Parkinson's disease (PD), particularly early PD, the system comprising: a. A device for detecting the level of a biomarker according to claim 1 in a sample from a subject; b. A device for analyzing the level of said biomarker, wherein the relative abundance of said biomarker is obtained by said analysis; c. A device for outputting analysis results, wherein the analysis results can be used to assist in displaying biological information related to Parkinson's disease (PD), especially early PD. The system according to claim 22, wherein the detection device is a sequencing device, preferably an ITS next-generation sequencing device. The system according to claim 22, wherein said detection device is a PCR device, preferably a real-time PCR device. The system according to claim 22, wherein said detection device is an antibody level detection device, preferably an ELISA assay device. The system of claim 22, wherein said sample is selected from the group consisting of blood, serum, fluid from the digestive tract, and fecal extract. The system of claim 22, wherein the biomarkers are selected from polynucleotides or antibodies. The system of claim 27, wherein the polynucleotide is a primer. The system of claim 27, wherein the antibody is an IgG antibody, an IgA antibody, or a combination thereof. A system for aiding in differentiating Parkinson's disease (PD) from essential tremor (ET), comprising: a. A device for detecting the level of a biomarker according to claim 1 in a sample from a subject; b. A device for analyzing the level of said biomarker, wherein the relative abundance of said biomarker is obtained by said analysis; c. A device for outputting analysis results, wherein the analysis results can be used to assist in differentiating Parkinson's disease (PD) from essential tremor (ET). The system according to claim 30, wherein the detection device is a sequencing device, preferably an ITS next-generation sequencing device. The system according to claim 30, wherein said detection device is a PCR device, preferably a real-time PCR device. The system according to claim 30, wherein said detection device is an antibody level detection device, preferably an ELISA assay device. The system of claim 30, wherein the sample is selected from the group consisting of blood, serum, fluid from the digestive tract, and fecal extract. The system of claim 30, wherein the biomarkers are selected from polynucleotides or antibodies. The system of claim 35, wherein the polynucleotide is a primer. The system of claim 35, wherein the antibody is an IgG antibody, an IgA antibody, or a combination thereof.

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