Screening method of Lyme disease diagnostic marker and related application

Abstract

The invention belongs to the technical field of metabonomics, and particularly relates to a screening method and related application of lyme disease diagnostic markers. According to the diagnostic marker provided by the screening method, early detection and monitoring can be thoroughly changed, infection state evaluation which is more direct, dynamic and accurate than existing antibody-based detection is provided, and a potential target spot is provided for development of anti-LD drugs.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of metabolomics, and particularly relates to a screening method for a Lyme disease diagnosis marker and related applications. BACKGROUND

[0002] Lyme disease (LD) is a zoonosis transmitted by hard ticks, and its pathogen is Borrelia burgdorferi. Lyme arthritis has the highest incidence and disability rate among the various manifestations of LD, which is the most harmful. The disease is widely distributed, and more than 70 countries have reported the existence of LD. In recent years, the harmfulness of LD has attracted increasing attention, and the disease has become a quite important arbovirus, which should be highly valued by the international community.

[0003] The specific indicator of early acute infection of LD is erythema migrans, but the disease usually shows various non-specific clinical symptoms. Therefore, detection methods for LD have been derived, mainly including antigen detection, nucleic acid amplification, microscopy and in vitro culture, which mainly rely on serological detection. However, these methods have obvious limitations, including variable sensitivity in the early stage of the disease, and inability to distinguish active infection from past exposure. The dependence on serological detection can lead to insufficient or delayed diagnosis, especially in the absence of characteristic skin rash or symptoms overlapping with other diseases. In addition, the sensitivity, specificity and accuracy of direct antigen detection are low in ineffective clinical samples. Advanced diagnostic methods such as polymerase chain reaction (PCR) detection can help improve diagnostic accuracy, but due to their high specificity but low sensitivity in some stages of the disease, they are usually not used as first-line detection. Although these detection methods have made great progress after improvement, these detection strategies play a limited role in the clinical diagnosis of LD, especially in distinguishing current and past infections.

[0004] The existing technologies provide antigen detection, nucleic acid amplification, microscopy and in vitro culture, which mainly rely on serological detection. However, these methods have obvious limitations, such as variable sensitivity in the early stage of the disease, and inability to distinguish active infection from past exposure. The dependence on serological detection can lead to insufficient or delayed diagnosis, especially in the absence of characteristic skin rash or symptoms overlapping with other diseases. At the same time, in ineffective clinical samples, the sensitivity, specificity and accuracy of direct antigen detection are low. In addition, the current polymerase chain reaction (PCR) detection, although it helps to improve the accuracy of diagnosis, due to its high specificity but low sensitivity in some stages of the disease, it is usually not used as first-line detection.

[0005] Metabolomics is the comprehensive analysis of small molecules in biological systems, reflecting the downstream output of genomic, transcriptomic, and proteomic processes, and can provide the results of the physiological state of an organism. By analyzing the metabolomics of LD patients, unique insights into the biochemical disorders caused by Borrelia burgdorferi infection can be obtained. SUMMARY

[0006] Therefore, the present application aims to create a new method of using non-targeted metabolomics screening to identify potential biomarkers for LD. The present application uses high-resolution mass spectrometry for non-targeted metabolomics analysis, which can detect various metabolites with high sensitivity and specificity. At the same time, the present application focuses on multi-organ analysis, examining organs such as the heart, liver, spleen, lungs, kidneys, brain, and joints that are known to be affected by LD, to comprehensively understand the impact of infection on the whole body. Strict statistical screening criteria, fold change (FC), P value, and variable importance in the projection (VIP) score are used to determine the relevant differential metabolites of biomarkers. These biomarkers can completely change early detection and monitoring, provide a more direct, dynamic, and accurate assessment of the infection state than the current antibody-based detection, and provide potential targets for the development of anti-LD drugs.

[0007] The present application provides a screening method for Lyme disease diagnostic markers, comprising the following steps:

[0008] (1) Establishing an infection model and collecting samples;

[0009] Setting up an experimental group and a control group, using Borrelia garinii SZ strain to infect experimental animals in the experimental group;

[0010] Collecting samples from multiple organs at different time points after infection;

[0011] The samples include heart, liver, spleen, lungs, kidneys, brain, and joints;

[0012] (2) Non-targeted metabolomics analysis;

[0013] Using gas chromatography-time-of-flight mass spectrometry to detect metabolites in the samples;

[0014] Analyzing and screening the metabolites to obtain differential metabolites;

[0015] (3) Screening to obtain diagnostic markers for Lyme disease;

[0016] Analyzing the differential metabolites obtained in step (2) by receiver operating characteristic curve, and selecting metabolites with an area under the curve > 0.8 as diagnostic markers.

[0017] The present application also provides a Lyme disease diagnostic kit, which comprises a reagent for detecting the diagnostic marker according to claim 1.

[0018] In the present application, the reagent comprises a detection probe or an antibody for the following metabolites:

[0019] At least one of hypoxanthine, ornithine, glucose, L-allothreonine, phenylalanine, histidine, guanine, 5'-methylthioadenosine, lauric acid, oxoproline, lactic acid, 1,5-anhydroglucitol, licanic acid, L-cysteine, hydroxylamine, 2,3-dihydroxypyridine, galactose, monostearate, beta-mannosyl glyceric acid, and maltose.

[0020] The present application also provides the use of the above-mentioned Lyme disease diagnostic marker in the preparation of a Lyme disease early diagnostic.

[0021] In the present application, when the detection organ for early diagnosis is the heart, the Lyme disease diagnostic marker comprises hypoxanthine, ornithine, and glucose.

[0022] When the detection organ for early diagnosis is the liver, the Lyme disease diagnostic marker comprises L-allothreonine, phenylalanine, histidine, and guanine.

[0023] When the detection organ for early diagnosis is the spleen, the Lyme disease diagnostic marker comprises 5'-methylthioadenosine, lauric acid, and oxoproline.

[0024] When the detection organ for early diagnosis is the lung, the Lyme disease diagnostic marker comprises lactic acid and 1,5-anhydroglucitol.

[0025] When the detection organ for early diagnosis is the kidney, the Lyme disease diagnostic marker comprises licanic acid and L-cysteine.

[0026] When the detection organ for early diagnosis is the brain, the Lyme disease diagnostic marker comprises hydroxylamine, 2,3-dihydroxypyridine, galactose, and monostearate.

[0027] When the detection organ for early diagnosis is the joint, the Lyme disease diagnostic marker comprises beta-mannosyl glyceric acid and maltose.

[0028] The present application also provides the use of the above-mentioned Lyme disease diagnostic marker in the preparation of a Lyme disease drug target.

[0029] The present application provides comprehensive metabolomics analysis, which respectively identifies 44, 43, 48, 28, 45, 31 and 29 metabolites in heart, liver, spleen, lung, kidney, brain and joint respectively, and the metabolic characteristics of each organ at different stages of infection are significantly different. The number of differential metabolites in heart and kidney is the most at 7 days after infection (DPI); the metabolic changes in liver and joint are the most significant at 15 DPI; and in the middle and late stages of infection (21-30 DPI), the spleen and liver become the main affected organs.

[0030] The present application provides organ-specific biomarkers with high ROC curve area (AUC>0.8), such as hypoxanthine (1) and ornithine (1) in heart, and lignoceric acid and L-cysteine in kidney. The research data prove the potential of metabolomics analysis in exploring LD diagnostic targets and drug development. These biomarkers can completely change the traditional method of early Lyme disease detection and monitoring, provide more direct, dynamic and accurate evaluation of infection status than the current antibody-based detection, and provide potential targets for the development of anti-LD drugs. BRIEF DESCRIPTION OF DRAWINGS

[0031] Figure 1 Serological ELISA detection and pathological tissue results;

[0032] Figure 2 Serological ELISA detection and pathological tissue results;

[0033] Figure 3 PCA total plot of seven organs;

[0034] Figure 4 Differential metabolites in different organs;

[0035] Figure 5 Upregulation and downregulation of differential metabolites in different organs;

[0036] Figure 5 Upregulation and downregulation of differential metabolites in different organs;

[0037] Figure 6 Upregulation and downregulation of differential metabolites in different organs;

[0038] Figure 7 Upregulation and downregulation of differential metabolites in different organs;

[0039] Figure 8 Upregulation and downregulation of differential metabolites in different organs;

[0040] Figure 9 Upregulation and downregulation of differential metabolites in different organs;

[0041] Figure 10Up- and down-regulated metabolites for different organs;

[0042] Figure 11 Up- and down-regulated metabolites for different organs;

[0043] Figure 12 Commonly different metabolites for different organs at 4 time points;

[0044] Figure 13 Commonly different metabolites for different organs at 4 time points;

[0045] Figure 14 Commonly different metabolites for different organs at 4 time points;

[0046] Figure 15 Commonly different metabolites for different organs at 4 time points;

[0047] Figure 16 Commonly different metabolites for different organs at 4 time points;

[0048] Figure 17 Commonly different metabolites for different organs at 4 time points;

[0049] Figure 18 Commonly different metabolites for different organs at 4 time points;

[0050] Figure 18 Commonly different metabolites for different organs at 4 time points;

[0051] Figure 19 Detailed information for potential biomarkers in heart;

[0052] Figure 20 Detailed information for potential biomarkers in liver;

[0053] Figure 21 Detailed information for potential biomarkers in spleen;

[0054] Figure 22 Detailed information for potential biomarkers in lung;

[0055] Figure 23 Detailed information for potential biomarkers in kidney;

[0056] Figure 24 Detailed information for potential biomarkers in brain;

[0057] Figure 25 Detailed information for potential biomarkers in joints. DETAILED DESCRIPTION

[0058] The reagents or apparatuses used in the following are not specified in the specific technology or condition, which is carried out according to the conventional experimental condition, and the reagent company instruction is not specified, which is carried out according to the instruction recommended condition. The reagents or apparatuses used are not specified by the manufacturer, which are conventional products that can be obtained by purchase.

[0059] Examples

[0060] 1. Establishment of mouse model and collection of samples

[0061] Borreliagarinii SZ strain from China Microbial Culture Collection (ATCC, ATCC 51383) was inoculated into a culture bottle in an incubator at 33°C until the strain grew to the logarithmic phase, centrifuged at 12000 rpm for 10 minutes at 4°C, discarded the supernatant, washed twice with PBS, and diluted to 10 6 / mL.

[0062] 120 SPF BALB / c mice (BALB / c, female, 3 weeks old) were purchased from the Experimental Animal Center of Lanzhou Veterinary Research Institute. The original serum of each mouse was collected before purchase. Then, they were randomly divided into four control groups (CK-7DPI, CK-15DPI, CK-21DPI, CK-30DPI) and four experimental groups (ogans-7DPI, ogans-15DPI, ogans-21DPI, ogans-30DPI), with 15 mice in each group. After feeding in the animal room for one week, the same amount of SZ strain or PBS was inoculated subcutaneously through the abdomen of mice, respectively. The food and drinking water of mice were disinfected, and they were fed freely, with the bedding changed every 3 days. After inoculation, the physiological status of mice such as food intake, fur color and movement were observed every day.

[0063] Each mouse in the experiment was inoculated with 0.2 μL (10 6 / mL) of SZ strain, while the control group was inoculated with 0.2 μL dose of sterile PBS. 80 samples were collected at 7 days after inoculation (DPI, acute infection), 15-DPI and 21-DPI (moderate infection) and 30-DPI (chronic infection), respectively, from 7 tissues (heart, liver, spleen, lung, kidney, brain and joint) of each mouse in the experimental and control groups. Similarly, at four time points, 10 organ samples of mice were randomly selected, and their serum was collected from the eyes of the experimental and control groups, respectively.

[0064] To comply with the welfare requirements of experimental animals, the mice were euthanized when organs were taken, with blood collection at the same time. In addition, tissues from 7 organs were extracted from 80 samples, and 200 milligrams of organ samples were sent to BIOTREE Company (https: / / www.biotree.cn / China Shanghai) for metabolomics sequencing. At the same time, part of the organs were preserved, fixed in 4% paraformaldehyde for a period of time, and then sent to Servicebio Company for hematoxylin and eosin (H&E) pathological tissue sections. The remaining organ samples were stored at -80°C. All experimental by-products, including mouse carcasses, fur, and organ fragments, were disposed of in an environmentally friendly manner.

[0065] 2. Metabolite extraction

[0066] Take 50 mg of sample per tube, add 0.4 mL of extraction solution (methanol chloroform volume ratio: 3:1) and 20 μL of L-2-chlorophenylalanine to a 2 mL EP tube, add a steel ball, vortex to mix, and treat with a 45 Hz 6 min grinder;

[0067] Centrifuge at 4°C, 12000 rpm for 15 minutes;

[0068] Carefully take 0.32 mL of supernatant in a 2 mL sample bottle (methane silicon-based), mix 10 μL of each sample to make a QC sample.

[0069] 3. Metabolite derivatization

[0070] Dry the extract in a vacuum concentrator;

[0071] Add 80 μL of methoxylamine salt reagent (methoxylamine hydrochloride, dissolved in pyridine 20 mg / mL) to the dried metabolites, mix gently, and then incubate in an oven at 80°C for 20 minutes;

[0072] Rapidly add 100 μL of BSTFA (containing 1% TCMS, v / v) to each sample and incubate the mixture at 70°C for 1 hour;

[0073] Cool to room temperature, add 10 uL of FAMEs (saturated fatty acid methyl ester standard mixture, dissolved in chloroform) to the mixture;

[0074] Mix well and run on the instrument.

[0075] 4. Instrument detection

[0076] Agilent 7890 gas chromatography-time of flight mass spectrometry instrument with Agilent DB-5MS capillary column (30 m x 250 μm x 0.25 μm, J&W Scientific, Folsom, CA, USA), GC-TOF-MS specific analysis conditions as follows:

[0077] Injection volume: 1 μL, splitless mode;

[0078] Carrier gas: helium;

[0079] Forward injection purge flow: 3 mL / min;

[0080] Column flow: 1 mL / min;

[0081] Column temperature: 50 °C for 1 min, ramped at 10 °C per min to 320 °C, hold for 5 min;

[0082] Forward injection temperature: 280 °C;

[0083] Transfer line temperature: 280 °C;

[0084] Ion source temperature: 220 °C;

[0085] Ionization voltage: -70 eV;

[0086] Scan range: 85-600 m / z;

[0087] Scan rate: 20 spectra / sec;

[0088] Solvent delay: 366 s.

[0089] 5. Analysis content

[0090] The basic data analysis includes: data pre-processing, principal component analysis (PCA), orthogonal partial least squares-discriminant analysis (OPLS-DA), differential compound screening and identification.

[0091] 6. Data analysis results

[0092] 6.1 Data pre-processing

[0093] From the original file, the obtained samples and QC samples, and the Peak of the samples.

[0094] 1) In order to better analyze the downstream data, the data was filtered to remove noise data. The method used was: interquartile range or interquartile range, retaining single group of null values less than or equal to 50% or all groups of null values less than or equal to 50% of peak area data.

[0095] 2) The missing values in the original data were simulated, and the numerical simulation method was half of the minimum value method for filling.

[0096] 3) Standardization of the data after filling in, method is internal standard normalization method.

[0097] 6.2 Principal Component Analysis (PCA)

[0098] Multivariate pattern recognition analysis was performed on the normalized data using SIMCA software (V14, Umetrics AB, Umea, Sweden). Principal component analysis was performed on the data using LOG transformation + CTR formatting. Automatic modeling was performed on the data. A represents the number of principal components, N represents the number of observation objects (samples), R2X represents the explainability of X variables, and Q2 represents the predictability of the model.

[0099] 6.3 Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA)

[0100] Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) was performed on the model using SIMCA software (V14, Umetrics AB, Umea, Sweden) to maximize the differences between the predictive components within the model. OPLS-DA was first performed on the data using LOG transformation + UV formatting, and then modeling was performed on the first and second principal components. The quality of the model was verified using 7-fold cross-validation, and R2Y (representing the explainability of Y variables) and Q2 (representing the predictability of the model) obtained after cross-validation were used to evaluate the effectiveness of the model. After that, the effectiveness of the model was further verified by randomly changing the order of the classification variable y multiple times (n = 200) and obtaining different random Q2 values.

[0101] 6.4 Screening and identification of differential metabolites

[0102] Irrelevant orthogonal signals were filtered out through OPLS-DA analysis, so that the differential metabolites obtained were more reliable. In this project, the Variable Importance in the Projection (VIP) value (threshold > 1) of the first principal component of the OPLS-DA model was used in combination with the p value (threshold 0.05) of the student's t-test (t-test) to find differentially expressed metabolites.

[0103] 7. Results

[0104] 7.1 Metabolite identification and ion detection

[0105] Serological ELISA detection and pathological tissue results also confirmed that all experimental mouse groups were successfully infected with spirochetes ( Figure 1 、 Figure 2). In addition, we found some mice with lameness or dragging of the hind limbs 15 days after infection. Each organ was represented by 80 samples, and after pre-processing of the raw data, such as filtering, missing value imputation and standardization, the remaining peak values were as follows: heart 529; liver 471 cases; spleen 487 cases; lung 581 cases; kidney 489 cases; brain, 510; joint, 336. The PCA total score plot of the seven organs ( Figure 3 ), PCA scores of each organ at different time points, PCA results showed the distribution of the original data. OPLS-DA plot and permutation test of each time point of the seven organs, DPLS-DA showed a higher level of component separation and better understanding of the variables responsible for classification. The parameter results showed that the model was stable, with good fitting and prediction.

[0106] 7.2 Screening of differential metabolites

[0107] A large number of differential metabolites were obtained from the seven organs by three screening conditions: FC, P value and VIP (heart: 44 metabolites, liver: 43 metabolites, spleen: 48 metabolites, lung: 20 metabolites, kidney: 45 metabolites, brain: 31 metabolites, joint: 29 metabolites), see Figure 4 .

[0108] Figures 5 to 11The up- and down-regulation of the differential metabolites in each organ is shown (red in the figure indicates up-regulation, blue indicates down-regulation), and the top three most significantly different metabolites are labeled. Applicable to heart of 7 DPI (up-regulation: 22), 15 DPI (up-regulation: 13, down-regulation: 6), 21 DPI (up-regulation: 1, down-regulation: 2), 30 DPI (up-regulation: 9, down-regulation: 5); liver of 7 DPI (up-regulation: 7), 15 DPI (up: 18, down-regulation: 5), 21 DPI (up-regulation: 15, down-regulation: 2), 30 DPI (up-regulation; 14, down-regulation: 3); spleen 7 DPI (up-regulation: 8, down-regulation: 2), 15 DPI (up-regulation: 5, down-regulation: 5), 21 DPI (up-regulation: 17; down-regulation: 4), 30 DPI (up-regulation: 5, down: 25); lung 7 DPI (up-regulation: 1, down-regulation: 13), 15 DPI (down-regulation: 4), 21 DPI (up-regulation: 6, down-regulation: 1), 30 DPI (up-regulation: 1, down-regulation: 11); kidney 7 DPI (up-regulation: 13, down-regulation: 8), 15 DPI (up-regulation: 7, down: 10), 21 DPI (up-regulation: 6; down-regulation: 7), 30 DPI (up-regulation: 6, down-regulation: 10); brain at 7 DPI (up-regulation: 5, down-regulation: 6), 15 DPI (down-regulation: 6, down-regulation: 9), 21 DPI (up: 10, down-regulation: 1), 30 DPI (up-regulation: 5, down-regulation: 4); joint in 7 DPI (down-regulation: 4), 15 DPI (up-regulation: 4, down-regulation: 18), 21 DPI (up-regulation: 6), 30 DPI (up-regulation: 1, down-regulation: 1) joint.

[0109] The common differential metabolites of the 7 organs at the 4 time points were also analyzed (Table 2), Figures 12 to 18 Among these common differential metabolites,

[0110] 11 in the heart (ornithine 1, hypoxanthine 1, methyl phosphate, beta-mannosyl glyceride 2, 2-hydroxypyridine, linoleic acid, analyte 307, cytidine monophosphate 1, 3-hydroxybutyric acid, unknown, glucose 1),

[0111] 13 in the liver (creatine, 2'-deoxycytidine 5'-triphosphate dehydrogenase, guanine 1, histidine 2, phenylalanine 1, L-isothreonine 1, uracil, nicotinamide stearate, melibiose 1, sorbitol, terephthalic acid, 1-monopalmitin),

[0112] 15 in the spleen (D-trehalose 2, pantothenic acid, N-acetyl-L-aspartate 1, pyruvic acid, ethanolamine, lauric acid, citric acid, 5'-methylthioadenosine 1, oxoproline, unknown, bifendate b epoxide 2, uric acid, hypoxanthine 1, tyrosine 1, threonic acid),

[0113] Lung 8 (hydroxylamine, glucose-6-phosphate 1, glucose 1, lactic acid, palmitelaidic acid, linoleic acid, fructose-6-phosphate, 1,5-anhydroglucitol),

[0114] Kidney 15 (mannose 1, putrescine 2, pantothenic acid, L-cysteine, benzylalcohol b epoxide 2, lignan, adenosine, palmitelaidic acid, 2-hydroxybutyric acid, xylitol, myristic acid, beta-glycerophosphate, oxoproline, oleic acid, D-(glycerol 1-phosphate)),

[0115] Brain 12 (creatine, 2,3-dihydroxypyridine, lysine, analyte 975, ribose-5-phosphate 2, linoleic acid methyl ester, monostearate, palmitic acid, galactose 1, hydroxylamine, hypoxanthine 1, galactose 2),

[0116] Joint 3 (beta-mannosyl glycerol 2, myo-inositol, maltose).

[0117] In addition to their substance type and distribution in the heat map ( Figure 7 ), in terms of the number of analyte categories, carbohydrates and carbohydrate conjugates, carboxylic acids and derivatives, and fatty acyls were the major groups. In descending order, these were imidazopyrimidines, amines, purine nucleosides, hydroxy acids and derivatives thereof, keto acids and derivatives thereof, organophosphates and derivatives thereof, phenylpropionic acids, sugar acids and derivatives thereof, and other such categories.

[0118] 7.3 Finding potential biomarkers

[0119] Receiver operating characteristic (ROC) curves were used with an online platform to identify potential biomarkers. The area under the ROC curve (AUC) represents the size of the area under the ROC curve (0.5-1). The value of AUC is the size of the area under the ROC curve. In general, the larger the AUC, the better the performance (AUC > 0.8), details as shown in Figures 19 to 25 .

[0120] Heart-specific biomarkers were hypoxanthine 1, ornithine 1, glucose 1, unknown (RI: 17.99340, count: 39, mass: 319).

[0121] Liver biomarkers were L-isothreonine 1, phenylalanine 1,2'-deoxycytidine 5'-triphosphate dehydrogenase, histidine 2, guanine 1.

[0122] Spleen biomarkers included 5'-methylthioadenosine 1, benzylalcohol b epoxide 2, lauric acid, oxoproline, and uric acid.

[0123] Lung biomarkers were lactic acid, 1,5-anhydroglucitol.

[0124] The biomarker for the kidney is a lignan, L-cysteine, biphenyl-4-yl-ethanol, b-epoxide 2, adenosine.

[0125] The potential biomarkers for the brain are hydroxylamine, 2,3-dihydroxy-pyridine, galactose 1, galactose 2, monostearate.

[0126] The biomarker for the joint is pantothenic acid, palmitoleic acid.

[0127] The specific information of these potential biomarkers in each organ is shown in Figure 8 .

[0128] The above merely preferred embodiments of the present application, can not be used to limit the scope of the present application. For the changes and improvements of the present application, should be included within the scope of the protection of the present application.

Claims

1. A method for screening a diagnostic marker for Lyme disease, characterized by, The screening method comprises the following steps: (1) Establishing an infection model and collecting samples; An experimental group and a control group are set up, and the experimental animals in the experimental group are infected with Borrelia garinii; Samples are collected from multiple organs at different time points after infection; The samples include heart, liver, spleen, lung, kidney, brain and joint; (2) Non-targeted metabolomics analysis; Metabolites in the samples are detected by gas chromatography-time of flight mass spectrometry; Differential metabolites are obtained by analyzing and screening the metabolites; (3) Screening to obtain diagnostic markers for Lyme disease; The differential metabolites obtained in step (2) are analyzed by receiver operating characteristic curve, and metabolites with an area under the curve >0.8 are selected as diagnostic markers.

2. A diagnostic kit for Lyme disease, characterized by comprising the antibody of claim 1. The Lyme disease diagnostic kit comprises reagents for detecting the diagnostic markers of claim 1.

3. The kit of claim 2, wherein The reagents comprise detection probes or antibodies for at least one of the following metabolites: hypoxanthine, ornithine, glucose, L-allothreonine, phenylalanine, histidine, guanine, 5'-methylthioadenosine, lauric acid, oxoproline, lactic acid, 1,5-anhydroglucitol, lignoceric acid, L-cysteine, hydroxylamine, 2,3-dihydroxypyridine, galactose, monostearic acid, beta-mannosyl glyceric acid, and maltose.

4. Use of the Lyme disease diagnostic markers of claim 1 in the preparation of an early diagnostic for Lyme disease.

5. Use according to claim 4, characterized in that, When the organ for early diagnosis is the heart, the Lyme disease diagnostic markers comprise hypoxanthine, ornithine, and glucose; When the organ for early diagnosis is the liver, the Lyme disease diagnostic markers comprise L-allothreonine, phenylalanine, histidine, and guanine; When the organ for early diagnosis is the spleen, the Lyme disease diagnostic markers comprise 5'-methylthioadenosine, lauric acid, and oxoproline; When the organ for early diagnosis is the lung, the Lyme disease diagnostic markers comprise lactic acid and 1,5-anhydroglucitol; When the organ for early diagnosis is the kidney, the Lyme disease diagnostic markers comprise lignoceric acid and L-cysteine; When the organ for early diagnosis is the brain, the Lyme disease diagnostic markers comprise hydroxylamine, 2,3-dihydroxypyridine, and galactose; When the organ for early diagnosis is the joint, the Lyme disease diagnostic markers comprise beta-mannosyl glyceric acid and maltose.

6. Use of the Lyme disease diagnostic markers of claim 1 in the preparation of a drug target for Lyme disease.

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