CN118311262A - Biomarker for diagnosing beta-thalassemia and subtype and application thereof - Google Patents

Abstract

The invention relates to the technical field of biological medicines, in particular to a protein biomarker for diagnosing beta-thalassemia and application thereof. The present invention provides the use of a biomarker and/or a substance detecting said biomarker in the manufacture of a kit for diagnosing β -thalassemia and subtypes thereof; wherein the biomarkers are C1S, CLU, HBA, TF and IGHG4 proteins. The biomarkers can rapidly, accurately and clearly determine the occurrence and subtype of beta-thalassemia. Therefore, the auxiliary diagnosis kit for clinical application can be facilitated, and laboratory support is provided for screening, diagnosis and treatment of beta-thalassemia.

Description

Biomarker for diagnosing beta-thalassemia and subtype and application thereof

Technical Field

The invention relates to the technical field of biological medicines, in particular to a protein biomarker for diagnosing beta-thalassemia and subtype and application thereof.

Background

Beta-thalassemia is a hereditary blood disease caused by a defect in beta-globin chain synthesis caused by mutation of the beta-globin gene (HBB). It is estimated that about 1.5% of the population worldwide is the carrier of beta-thalassemia, with the most severely affected population being distributed in indian subcontinent, southeast asia and china. Ineffective erythropoiesis and hemolytic anemia are the most prominent disease features of beta-thalassemia, leading to multiple organ damage and developmental retardation in patients. The severity of this disease depends largely on genotype, and up to now more than 350 mutation types have been reported cumulatively. In addition, genetic polymorphisms in genes involved in regulation of erythropoiesis can regulate the clinical phenotype of β -thalassemia, further complicating clinical diagnosis of β -thalassemia.

At present, the first-line screening strategy of beta-thalassemia is to perform qualitative and quantitative analysis on different types of hemoglobin by combining red blood cell indexes. Because of the lack of sensitivity and specificity of this method, it is also desirable to exclude suspected diseases such as iron deficiency, structural hemoglobin variation, chronic anaemia, etc. if based solely on these assays. Another widely used screening method is a polymerase chain reaction (Polymerase Chain Reaction, PCR) based method, which allows the genotype of beta-thalassemia to be determined. However, only pre-specified mutations can be detected, resulting in insufficient diagnosis of rare mutations outside the detection range. Therefore, developing a high-efficiency screening method that complements existing methods would simplify the diagnostic process and reduce missed diagnosis.

Beta-thalassemia patients are generally classified into beta-thalassemia intermediate (beta-THALASSEMIA INTERMEDIA, TI) patients and heavy (beta-THALASSEMIA MAJOR, TM) patients. TM is characterized by severe anemia, transfusion dependent thalassemia, whereas TI patients vary in clinical severity from mild, moderate to severe anemia, requiring occasional or intermittent transfusion therapy. Differential diagnosis of TI and TM involves several non-objective parameters, including age and hemoglobin (Hb) content of the initial diagnosis of the disease, which may be inaccurate for patients in medium and low income areas due to delayed visits. Furthermore, in a particular region, new or rare mutations still need to be explored further to classify them as TI or TM, but the difficulty is that the boundaries between them are sometimes ambiguous. Significant differences in pathophysiological and clinical characteristics have been found for many years in different types of patients, including potential mechanisms of iron overload, development of complications, and therapeutic targets. For example, epidemiological data indicate that TI patients have a 4-fold greater incidence of thromboembolic events than TM patients; TM patients are prone to left ventricular insufficiency, while TI patients have left ventricular contractile function, which can lead to the development of pulmonary arterial hypertension, both of which can lead to heart failure. Therefore, timely and accurate diagnosis of TI and TM is critical for early intervention in the disease.

Extracellular vesicles (Extracellular vesicles, EVs) have particular advantages in disease diagnosis because they play a critical role in intercellular communication. Circulating microparticles from patients with β -thalassemia/Hb E can induce the expression of hemagglutination molecules, pro-inflammatory cytokines, and adhesion molecules in endothelial cells and promote cardiomyocyte proliferation, suggesting that composition in EVs is affected by β -thalassemia. Among the proteins of EVs, haptoglobin, hemagglutinin and cathepsin S are reported to be potential clinically relevant biomarkers of β -thalassemia hemolysis and inflammation levels; EVs-HSP70 is associated with ineffective erythropoiesis, hemolysis, and the severity of beta-thalassemia. Thus, proteins derived from plasma/serum EVs are worth further research to find biomarkers that can increase the diagnostic level of β -thalassemia.

Before experiments are carried out, the serum/plasma amount adopted by the method for EVs extraction is 150 mu L, and after EVs are enriched, the EVs protein can be finally obtained by about 150-200 mu g, namely, the EVs protein content of plasma is about 1-1.3 mu g/mu L, and the EVs protein content of plasma is about 50-70 mu g/mu L, so that the EVs protein obtained by the enrichment method only accounts for about 1/50-1/70 of the plasma protein, and accordingly, the EVs content in the plasma can be deduced to be very low.

Disclosure of Invention

To achieve the above object, the present invention provides a kit for detecting 5 protein markers of serum/plasma extracellular vesicles for diagnosis of β -thalassemia and the subtype thereof.

The kit for detecting 5 protein markers of serum/plasma extracellular vesicles for diagnosing beta-thalassemia and subtype thereof optionally comprises SVM beta-thalassemia consisting of 5 proteins and a subtype diagnosis model thereof, and can also comprise enzymes and reagents commonly used in proteomics, such as pancreatin, buffer solution and the like; can also contain standard substance and/or reference substance.

The 5 protein markers are C1S, CLU, TF, IGHG and HBA1.

The biological sample is extracellular vesicles of ex vivo plasma/serum.

Specifically, the present invention includes the following specific embodiments:

1. Use of a biomarker and/or a substance detecting said biomarker for the manufacture of a kit for diagnosing β -thalassemia and subtypes thereof;

Wherein the biomarkers are C1S, CLU, HBA, TF and IGHG4 proteins.

2. The use of item 1, wherein the β -thalassemia subtype includes intermediate and heavy duty.

3. The use according to item 1, wherein the substance for detecting a biomarker is a reagent for detecting the expression amounts of C1S, CLU, HBA1, TF and IGHG4 in a biological sample.

4. The use according to item 1, wherein the substance for detecting a biomarker comprises a substance for detecting the expression level of C1S, CLU, HBA, TF and IGHG4 in a biological sample by an enzyme-linked immunosorbent assay, immunofluorescence method, radioimmunoassay, co-immunoprecipitation method, immunoblotting method, high performance liquid chromatography, capillary gel electrophoresis, near infrared spectroscopy, mass spectrometry, immunochromatography, colloidal gold immunoassay, fluorescent immunochromatography, surface plasmon resonance, immuno-PCR or biotin-avidin technique.

5. The use according to item 3 or 4, wherein the substance for detecting the expression level of C1S, CLU, HBA, TF and IGHG4 in a biological sample comprises an antibody that binds to C1S, CLU, HBA1, TF and IGHG4 proteins, respectively; or the substances for detecting the expression level of the C1S, CLU, HBA, the TF and the IGHG4 in the biological sample can comprise synthetic peptide fragments corresponding to the C1S, CLU, HBA1, the TF and the IGHG4 proteins as standard substances for targeted mass spectrum detection.

6. The use according to item 3 or 4, wherein the biological sample is extracellular vesicles of ex vivo plasma/serum.

7. A composition comprising a substance for detecting a biomarker as described in item 3 or 4.

8. A kit, wherein the kit comprises the composition of item 7.

9. The use of the kit of item 8 for diagnosing β -thalassemia and subtypes thereof or for preparing a product for diagnosing β -thalassemia and subtypes thereof.

In a specific embodiment of the invention, the C1S protein is designated as complete C1S subcomponent, gene ID 716 (see https:// www.ncbi.nlm.nih.gov/Gene/: p09871, the content of C1S protein in plasma extracellular vesicles was higher than healthy controls, P <0.001, for both heavy and intermediate patients with β -thalassemia. The CLU protein, collectively known as clusterin (Gene ID:1191,Uniprot ID:P10909), was lower in plasma extracellular vesicles than in healthy controls, p <0.001, for both heavy and intermediate patients with beta-thalassemia. The TF protein is known collectively as serotransferrin (Gene ID:7018,Uniprot ID:P02787), and with respect to its content in plasma extracellular vesicles, patients with severe beta-thalassemia are lower than those with intermediate type and healthy controls, p <0.001. The IGHG4 protein, which is known collectively as immunoglobulin heavy constant gamma 4 (Gene ID:3503,Uniprot ID:P01861), was higher in the intermediate patients than in the heavy patients and healthy controls, with respect to its content in the plasma extracellular vesicles, p <0.001.HBA1 protein, collectively referred to as hemoglobin subunit alpha (Gene ID:3039,Uniprot ID:P69905), was found to be present in plasma extracellular vesicles in heavy patients below those of the intermediate type and healthy controls, with p <0.001.

The inventors modeled the expression levels of the 5 proteins in the plasma extracellular vesicles by a statistical method (e.g., a conventional statistical method such as a Support Vector Machine (SVM), random forest, etc.), and found that the combination of the expression levels of the 5 proteins can be used for diagnosis of β -thalassemia and subtypes thereof.

The establishment of the diagnosis models of the 5 protein markers, the SVM beta-thalassemia and the subtype thereof comprises the following steps:

1) Plasma/serum was collected from multiple patients, patients of intermediate type and healthy persons, partly for extracellular vesicle proteome studies and partly for plasma proteome studies;

2) Extracellular vesicle whole proteome differential expression profiling: screening out difference proteins of TM patient to healthy person, TI patient to healthy person and TM patient to TI patient from the whole protein group by adopting a t-test statistical test method and protein abundance difference multiple;

3) Extracellular vesicle-targeted quantitative proteome analysis: the monitoring list required in Parallel Reaction Monitoring (PRM) targeting mass spectrum detection consists of 2) differential proteins found by whole proteome differential expression profile analysis, and the targeted detected proteins are screened by adopting a t-test statistical test method to obtain the differential proteins subjected to targeting verification;

4) Plasma whole proteome differential expression profiling: screening out difference proteins of TM patient compared with healthy people, TI patient compared with healthy people and TM patient compared with TI patient from a plasma proteome by adopting a t-test statistical test method and the difference multiple of protein abundance, and performing comparative analysis on the difference proteins of the follow-up and extracellular vesicles subjected to targeted verification so as to prove that the superiority of extracellular vesicle proteins in diagnosis of the subtype belonged to the beta-thalassemia is higher than that of the plasma proteins;

5) Comparing with an index used for clinical screening of beta-thalassemia;

6) Comparison, plasma differential protein and plasma extracellular vesicle differential protein: on the one hand, determining the pathophysiological characteristics of the disease subtype by signal path enrichment analysis; on the other hand, the performance of the plasma differential protein and the plasma extracellular vesicle differential protein in distinguishing beta-thalassemia from healthy people and in distinguishing TM patients from TI patients are analyzed and compared by adopting a subject operating curve (receiver operating characteristic curve, ROC);

7) Screening out characteristic proteins with discrimination capability from extracellular vesicle difference proteins by adopting a Borata characteristic selection algorithm and a characteristic deletion method, namely C1S, CLU, HBA, TF and IGHG4 proteins;

8) Selecting proper kernel function and penalty factor by using SVM to build beta-thalassemia diagnosis model, and forming operable program by using packaging program;

The present invention aims to provide the 5 proteins as biomarkers for diagnosing β -thalassemia and the subtype thereof, and the above disclosed diagnostic model establishment method is only illustrative, and the skilled person can also detect the expression level of the 5 proteins in a larger sample size and model according to conventional statistical methods (e.g. SVM, random forest, kNN, etc.), and use the constructed model for diagnosing β -thalassemia and the subtype thereof.

The invention also provides a using method of the biomarker kit, which comprises the following steps:

1) Collecting a plasma sample to be tested;

2) Separating and enriching extracellular vesicles of plasma samples;

3) The detection of extracellular vesicle proteins can be performed by methods conventional in the art, such as mass spectrometry, high-throughput histology, or antibody detection. If mass spectrum detection is selected, extracellular vesicle protein is subjected to enzymolysis, peptide fragment concentration is measured, then 1 mug or peptide fragments with the same mass are taken for mass spectrum detection, and C1S, CLU, TF, IGHG and HBA 15 protein markers are used as targets for monitoring, so that mass spectrum signal intensity of 5 protein markers is calculated, and relative content or absolute content is obtained; or if other high-throughput histology analysis methods or antibody detection methods are selected, directly detecting the relative content or absolute content of 5 proteins;

4) And inputting the expression quantities of the 5 proteins into a constructed diagnosis model to obtain a diagnosis result of the sample to be tested.

The constructed diagnostic model may be an SVM model constructed according to the present invention, or a model constructed by a person skilled in the art according to a conventional statistical method (e.g., a random forest model, etc.), and in particular, in the case of detecting the protein content by other high-throughput histology methods other than mass spectrometry, etc., it is necessary for the person skilled in the art to reconstruct a machine learning model. Wherein for reconstructing the machine learning model, a plasma sample queue for diagnostic model construction is also collected in step 1), the model construction using plasma samples comprising: healthy human plasma samples, heavy beta-thalassemia patient plasma samples and intermediate beta-thalassemia patient plasma samples.

The invention relates to a protein screening and auxiliary diagnosis kit for serum/plasma extracellular vesicles, which is developed according to different proteins in serum/plasma extracellular vesicles of different subtype cases and healthy controls of beta-thalassemia.

In particular embodiments of the invention, asymmetric flow field flow techniques or other conventional techniques known in the art (e.g., ultracentrifugation, SEC, etc.) are used to isolate extracellular vesicles of the plasma/serum of a subject patient, and LC-MS/MS is used to analyze and identify proteins to determine the content of 5 protein biomarkers C1S, CLU, HBA1, TF, and IGHG4 in a sample to be tested.

The invention has the advantages that:

The invention provides protein biomarkers based on in vitro acquisition of proteins in plasma/serum extracellular vesicles from patients as diagnostic of beta-thalassemia and subtype detection diagnosis. The invention is based on the phenomenon that part of protein is differentially expressed in plasma/serum extracellular vesicles obtained from different patients with beta-thalassemia subtypes and normal human beings in vitro, so that the invention can be used as a marker for diagnosis and subtype detection of beta-thalassemia diseases, and the occurrence and subtype of beta-thalassemia can be rapidly, accurately and clearly determined by detecting the expression level of the protein in the plasma/serum extracellular vesicles obtained from the target patients in vitro through mass spectrometry. Therefore, the auxiliary diagnosis kit for clinical application can be facilitated, and laboratory support is provided for screening, diagnosis and treatment of beta-thalassemia.

Drawings

FIG. 1 is a schematic of a screening procedure for biomarkers of beta-thalassemia. The figure shows the screening procedure of the beta-thalassemia biomarker. EVs derived from human plasma are separated by an asymmetric flow field flow technique (AF 4), and TMT labeling quantitative proteomic analysis is carried out on the EVs separated from the combined plasma samples in a biomarker discovery stage to find out differential proteins; the differential protein was then validated using 60 plasma EVs samples and compared and analyzed by plasma proteomes paired with the cohort of this population, and finally biomarker combinations consisting of five EVs proteins were screened for use in diagnosis and subtype typing of β -thalassemia.

FIG. 2 is a representation of the effect of isolation and enrichment of extracellular vesicles in plasma. A. Analyzing the morphology of EVs by a transmission electron microscope; B. nanoparticle Tracking Analysis (NTA) (n=3) for isolation of plasma EVs by AF4 method c. EVs with a protein load of 10 μg and Western blot analysis (n=2) of whole plasma. The results showed that the EVs markers (CD 9, CD81, CD63, HSP90 and TSG 101) in the EVs fraction were significantly enriched compared to the whole plasma. Lipoprotein markers (APOA 1 and APOB) and plasma high-abundance proteins (albumin) are almost absent from the EVs fraction, indicating that the enriched EVs of the present invention are of high purity.

FIG. 3 is a graph of inter-subtype signal path characterization. A. Analysis of validated differential proteins in plasma extracellular vesicles, where there are 17 total TM vs. ctr validated differential proteins, 17 total TI vs. ctr validated differential proteins, and the wien plot shows the overlap between TM vs. ctr validated differential proteins and TI vs. ctr validated differential proteins. Gsva signaling pathway analysis of plasma extracellular vesicles whole proteomes. Gsea signaling pathway analysis of plasma whole proteomes. The proportion of differential immunoglobulin in plasma extracellular vesicles was 30.8% for d.tm vs.ti, and 7% in plasma.

FIG. 4 shows the results of the specificity and sensitivity test of the plasma EVs protein, the plasma protein and the screening markers commonly used in clinical practice in subtype diagnosis at present, wherein A is ROC curve showing the top 6 proteins with the highest AUC values for the patients suffering from beta-thalassemia and healthy controls in the plasma EVs, B is the top 6 proteins with the highest AUC values for the patients suffering from beta-thalassemia and intermediate type beta-thalassemia in the plasma EVs, C is the AUC value for the six proteins with the highest AUC ranks in the plasma for the patients suffering from heavy and intermediate type in the plasma for the six proteins with the highest AUC values for the patients suffering from heavy and intermediate type in the plasma, D is the AUC value for the clinical indicators commonly used in screening beta-thalassemia at present for the patients suffering from heavy and intermediate type in the patients for the distinction, and these indicators are clinically used only for the screening of thalassemia for the patients without distinction of subtype at present.

FIG. 5 shows differential diagnosis of β -thalassemia subtypes and healthy controls based on plasma/serum extracellular vesicle proteins provided by an embodiment of the present invention. A. The PRM-verified extracellular vesicle-differentiated proteins were subjected to Boruta feature selection, with top 6 proteins being TF, C1S, IGHG4, CLU, HBB and HBA, respectively. B. By SVM method to trim the number of protein features, we tested the importance of each feature with an average score (scored by F1-micro) in a 5-fold cross-validation. When only the HBB protein was deleted from the model, i.e., only the 5 proteins in the table, the score was highest (score: 0.912), i.e., the SVM machine learning model constructed from these five proteins was best for the three groups of classifications. C. 15 samples were tested using the SVM beta-thalassemia and the subtype diagnostic model, with each 15 samples achieving the correct classification (C) and AUC values of 1 (D). E based on the results of principal component analysis of all the validated differential proteins and 5 protein markers in extracellular vesicles, it can be seen from the figure that three groups can be clearly divided based on 5 protein markers.

Fig. 6 is a graph showing the change in abundance of 5 protein markers in plasma EVs and plasma, respectively, and it can be seen that the degree of difference in these 5 proteins between the three groups is significantly higher in plasma EVs than in plasma. (p <0.05, p <0.01, p < 0.001).

Detailed Description

The patients and healthy participants to which the present invention relates are recruited by a first affiliated hospital of the university of Guangxi medical science (Guangxi Zhuang June autonomous region of China). All participants provided written informed consent, and for patients under 18 years old, had his parents or legal guardians consented. Two weeks after receiving the last transfusion, the patient provided their blood sample and completed a questionnaire including the first transfusion, the treatment and other medical conditions. The study recruited 286 β -thalassemia patients and 51 healthy controls, and patients who included both plasma proteomics and plasma extracellular vesicle proteomics received blood transfusion and iron chelation treatment, and none received splenectomy. Whole blood was collected into EDTA evacuated blood vessels and centrifuged at 3000×g for 10 minutes. Plasma collected from the supernatant was stored at-80 ℃ until use. This study was designed and carried out according to the declaration of helsinki. The first affiliated hospital ethics committee of the university of western medicine approved the study.

The above samples are used by Hua big gene clinical laboratory (Shenzhen in China) to complete the diagnosis of mutation type of patients by using Gap-PCR and SNP detection, wherein the intermediate type beta-thalassemia patient is beta ⁺β⁺/β⁺β⁰, and the heavy type beta-thalassemia patient is beta ⁰β⁰. The gene mutations are categorized as follows: the β ⁺ mutation includes -28A>G(HBB:c.-78A>G),-29A>G(HBB:c.-79A>G),codon 26G>A(HbE,HBB:c.79G>A),IVS-II-654C654>T(HBB:c.316-197C>T) and IVS-II-5G > C (HBB: c.315+5G > C). The β ⁰ mutation includes codons 41/42-TTCT(HBB:c.126_129delCTTT),codon 17A>T(HBB:c.52A>T),codons 71/72+A(HBB:c.216_217insA),IVS-I-1G>T(HBB:c.92+1G>T),codon 43G>T(HBB:c.130G>T),IVS-I-130G>C(HBB:c.93-1G>C),codon37 G>A(HBB:c.114G>A),codons 27/28+C(HBB:c.84_85insC), and codon 30A > G (HBB: c.91A > G).

Clinical parameters include HbF, SF, hbA ₂, HGB, MCV and MCH as measured by conventional techniques. Comprises a CELL-DYN full-automatic hematology analyzer (Abbott Diagnostics) for measuring MCV and MCH and HGB; hbF and HbA ₂ were measured by high performance liquid chromatography (VARIANT II, bio-Rad); serum Ferritin (SF) was measured using electrochemiluminescence immunoassay (Cobas e601, roche).

The invention is based on the differential expression profile of the whole proteome of the plasma extracellular vesicles and the differential expression profile of the whole proteome of the plasma, compares the advantages and disadvantages of two types of clinical sample proteins as diagnostic biomarkers through ROC curve analysis, proposes the advantages of the plasma extracellular vesicles in the diagnosis of the subtype of the beta-thalassemia, and further adopts a Borata characteristic selection and characteristic deletion method to select 5 plasma extracellular vesicle protein marker combinations (figure 1), wherein the screening process comprises the following aspects:

1. Plasma from 20 heavy patients, 20 intermediate patients and 20 healthy persons were selected for subsequent extracellular vesicle proteomics and plasma proteomics studies;

2. Plasma extracellular vesicle proteomics studies fall into two phases, the first phase being a biomarker discovery phase, with the aim of determining differential proteins from the extracellular vesicle whole proteome for subsequent validation; the second stage is biomarker verification, screening and diagnosis model construction stage, and aims to monitor the differential protein determined in the first stage, and protein biomarker combinations with beta-thalassemia and subtype diagnosis performance are screened out through a ROC curve analysis method, a Borata feature selection method and a feature deletion method. The method comprises the following specific steps:

1) Plasma extracellular vesicle proteomics study first phase: biomarker discovery phase.

To reduce the effect of individual differences on the biomarker discovery phase, the same group of patients were mixed with each 7 human plasma to one plasma sample, and three groups of TM patients, TI patients and Ctrl were mixed with two plasma samples each, giving a total of 6 mixed samples. The 6 pooled plasma samples were taken at 150 μl each, dead cells and platelets were removed by ultrafiltration at 2500×g for 10min, the centrifuged supernatant was transferred to a new EP tube, and an equal volume of PBS solution filtered through a 0.22 μm filter was added to dilute the plasma.

Extracellular vesicles in 6 mixed samples diluted with PBS were separated and enriched using an asymmetric flow field flow in combination with an ultraviolet absorption detector and a multi-angle excitation light scattering detector (AF 4-UV/MALS). The conditions for AF4-UV/MALS isolation and enrichment of plasma extracellular vesicles were as follows: a mylar shim with a thickness of 350 μm, a regenerated cellulose membrane with a retention of 10kDa, a flow rate into the detector of 1.0mL/min, a focusing flow rate of 1.5mL/min, a focusing time of 8min (focusing 1min before sample injection, focusing 5min after sample injection), a constant cross flow rate of 3mL/min for 5min, a linear gradient cross flow rate of 15min decreasing from 3mL/min to 1mL/min, a 20min decreasing from 1mL/min to 0.1mL/min, a 5min decreasing from 0.1mL/min to 0, and a sample injection volume of 300. Mu.L (PBS and plasma 1:1 (v/v) mixed dilution).

And (3) carrying out characterization on the extracellular vesicles obtained by separation and enrichment by adopting a transmission electron microscope, nanoparticle Tracking Analysis (NTA) and immunoblotting analysis (western blot). The transmission electron microscope result shows the form of the extracellular vesicles (figure 2A), the nanoparticle tracking analysis shows that the sizes of the extracellular vesicles are distributed between 50nm and 250nm (figure 2B), and the immunoblotting analysis shows that extracellular vesicles markers, plasma high-abundance proteins and lipoprotein markers are enriched, so that the extracellular vesicles obtained by the enrichment method have high purity (figure 2C).

Carrying out enzymolysis on the extracellular vesicle protein obtained by enrichment, wherein the enzymolysis process comprises the following steps: a. concentrating the enriched extracellular vesicle fraction with a 3kDa ultrafiltration tube; b. after protein concentration was determined by BCA method, 100 μg of protein solution was taken, urea solids were added to a final concentration of 8M, and 10 μl of 50mM NH ₄HCO₃ was added to make the total volume approximately 150 μl; c. adding 1M Dithiothreitol (DTT) to make the final concentration of the dithiothreitol 20mM, and reducing the dithiothreitol in a water bath at 37 ℃ for 1h; d. cooling to room temperature, adding 1M iodoacetamide (IAM, iodoacetamide) to make its final concentration 40mM, and placing in dark, and reacting at room temperature for 45min to block sulfhydryl end of protein; e. the enzyme is as follows: protein = 1:50 mass ratio 2. Mu.g of intracellular protease (Lys-C) was added and incubated for 3h at 37 ℃; f. diluting the urea concentration of the sample to below 2M with 25mM NH4HCO 3; g. the enzyme is as follows: protein = 1:50 mass ratio, adding 2 mug trypsin (trypsin), and carrying out water bath enzymolysis at 37 ℃ for 16h; h. the cleavage reaction was stopped by adding 0.1% FA (pH < 3.5) to a final concentration.

After the enzymolysis is finished, a TMT-6plex kit (purchased from the Simer femto) is adopted to mark peptide fragments obtained by the enzymolysis, in order to improve the protein identification number of the whole proteome, the marked peptide fragments are subjected to pre-separation by adopting a high-pH reversed-phase liquid chromatography, and finally, the whole proteome detection is carried out by adopting a liquid chromatography-mass spectrometry technology (LC-MS/MS) to obtain mass spectrum data.

The LC-MS/MS instrument was a Q Exactive mass spectrometer (Thermo FISHER SCIENTIFIC) equipped with an EASY-nLC 1000HPLC system, the analytical liquid phase, mass spectrometry conditions were as follows, the column was packed with dr. Maisch GmbH ReproSil-Pur C18 AQ packing, germany: specification 75 μm id×20cm,3.0 μm; the mobile phase is: a: water containing 0.1% formic acid, B: acetonitrile containing 0.1% formic acid; the flow rate is 300nL/min; gradient elution: 0min:96% A+4% B,5min:90% A+10% B,55min:78% A+22% B,70min:68% A+32% B,71min:10% A+90% B;78min:10% A+90% B. Ion source: an electrospray ion source; scanning positive ions; electrospray voltage: 2.1KV; tandem mass spectrum collision energy 32%; dynamic exclusion time 50s; separation mass window 2.0Da; the mass range of the primary mass spectrum is 300-1600m/z; a primary mass spectrometry resolution of 70,000; secondary mass spectrometry resolution 17,500.

The mass spectrum data is retrieved to obtain whole proteome quantitative information. This is done using Protein Discoverer (version 2.2, zemoer) software, where the search parameters are set as follows: the database selects a human library downloaded from Uniprot on 2-5 days 2018, the whole enzyme digestion of the trypsins is selected, 2 missed digestion sites are allowed at most, the amino acid length of the enzyme digestion peptide fragment is set to be 6-144, the cysteine Carbamidomethylation modification, the TMT modification of the N end of the peptide fragment and the lysine are set to be fixed modification, the methionine oxidation is set to be variable modification, the mass errors of parent ions and fragment sub-ions are respectively set to be 10ppm and 0.02Da, and the false positive rate FDR of the protein and the peptide fragment is set to be 1%.

Differential proteins were determined from whole proteome data. The p-values for the two sets of comparisons were calculated using the t-test statistical test method. The screening conditions of the differential protein are as follows: fold change >1.2 and p <0.05 is an up-regulating protein, fold change <0.8 and p <0.05 is a down-regulating protein.

According to the above data retrieval method, a total of 375 plasma extracellular vesicle proteins were identified in this example. A total of 57 differential proteins were found in comparison to the intermediate patients and healthy controls; a total of 61 differential proteins were found in the heavy patients and healthy controls; the comparative heavy and intermediate patients had a total of 10 differential proteins. Wherein the C1S, CLU and TF proteins are differential proteins in both the intermediate patient control and the heavy patient control, HBA1 is a differential protein specific to the heavy patient control, IGHG4 is a differential protein specific to the intermediate patient control

2) Plasma extracellular vesicle proteomics study second phase: and (3) verifying and screening biomarkers.

The 60 plasma samples were individually subjected to extracellular vesicle extraction, and extracellular vesicle enrichment method and proteolytic process were as above. And carrying out Parallel Reaction Monitoring (PRM) targeted quantitative analysis on the peptide fragment sample obtained by enzymolysis. The LC-MS/MS instrument used at this stage was Orbitrap Eclipse Tribrid mass spectrometer (Thermo FISHER SCIENTIFIC) equipped with an EASY-nLC 1200HPLC system.

Before performing the official test, the PRM test method needs to be established. The peptide fragment was quantified using the peptide fragment quantification kit (Thermo Scientific ^TMPierce^TM Quantitative Colorimetric PEPTIDE ASSAY) from the company sameiaway. 1 mug of each sample is respectively taken for equal amount mixing after being quantified by a peptide fragment kit, and 10X iRT standard peptide fragments (Biognosys) are added for data-dependent mass spectrometry so as to construct a spectrum chart library, and a parent ion monitoring list which can be used for subsequent targeted quantitative analysis is obtained.

Mass spectral data used for spectral library construction was analyzed using SpectroDive v 10.4.4 (Biognosys) software. Parent ions corresponding to the differential proteins identified in the whole proteome were subjected to the following parametric screening to create a PRM detection list: parent ion mass to charge ratio range 350-1500; parent ion charge number, 2-3; a missed cut site, 0; peptide length 8-25; peptide fragment mass-to-charge ratio range, 300-1800; maximum sub-ion charge, 2; ion type, b, y ions; selecting top 6 fragment ions; after the retention time correction with iRT, the retention time window was set to ±5min.

And after the parent ion detection list is determined, formally acquiring PRM data of the sample. Mass spectral parameters for PRM experiments were set as follows: tandem mass spectrum collision energy 30%; the resolution of the first-level spectrogram is 60,000, the maximum ion implantation time is 20ms, and the AGC is 4e5; the resolution of the secondary mass spectrum is 30,000, and the separation mass window is 1.0Da; maximum ion implantation time 80ms, agc is 2e5. The PRM original file obtained was processed using SpectroDive v 10.4. And selecting parent ions with q-value less than 0.01 in software, excluding sub-ion signal peaks with obvious interference signals around peak tips, and quantifying more than three ion pairs for each parent ion.

In two sets of comparisons of PRM targeted quantitation, the p-value was calculated using the t-test statistical test method. proteins with p <0.05 were considered to have significant differences.

3. Plasma proteome differential expression profiling

After removing 14 high abundance proteins from 10. Mu.L of plasma by Top14 immunoaffinity column (Sieimer's femto), the buffer was replaced with 8M urea/50 mM NH ₄HCO₃ by 3kDa concentration tube and the total volume was about 150. Mu.L, and then the protein was subjected to enzymatic hydrolysis, as above. After obtaining the peptide fragment, the peptide fragment is marked by adopting TMT 10-plex reagent, and after the marked peptide fragment is pre-analyzed by high pH reversed phase chromatography, the whole proteome detection is carried out. The LC-MS/MS parameters and data retrieval methods employed here are the same as the first stage biomarker discovery stage of the plasma extracellular vesicle proteomics study. Calculating p value of the identified protein by adopting a t-test statistical test method, wherein the screening conditions of the differential protein are as follows: fold change >1.2 and p <0.05 is an up-regulating protein, fold change <0.8 and p <0.05 is a down-regulating protein.

4. Signal path analysis of plasma extracellular vesicle whole proteome data and plasma whole proteome data to mine subtype characteristics between subgroups

First, the difference proteins in extracellular vesicles, which were confirmed by TM vs. ctr, were 17 in total, and the difference proteins in TI vs. ctr were also 17 in total, and fig. 3A shows the overlap between TM vs. ctr and TI vs. ctr difference proteins, and the present invention found that the proteins present specifically by TI vs. ctr were mostly immunoglobulins, while the proteins present specifically by TM vs. ctr were mainly hemoglobin and lipoproteins. Thus, it is speculated that TM patients may have more severe lipid metabolism abnormalities, while TI immune abnormalities are more severe.

To confirm the above, the plasma extracellular vesicle whole proteome and plasma whole proteome data were analyzed using signal pathway enrichment, specifically, the genome variation analysis (GSVA) and the genome enrichment analysis (GSEA) were performed on the plasma extracellular vesicle whole proteome data and the plasma whole proteome data, respectively. Analysis of plasma extracellular vesicle whole proteome GSVA showed that compared to TM patients, the cellular oxidative detoxification and immune system up-regulation, and lipid metabolism down-regulation in TI patients (fig. 3B). Furthermore, the GSEA analysis results for plasma whole-proteome data were up-regulated in both TI patient immune response and antioxidant activity related gene sets, and down-regulated in lipid metabolism compared to TM patients (fig. 3C). Thus, the results of plasma and plasma extracellular vesicles whole proteomic signaling consistently indicate that TI patients have a more pronounced degree of immune dysfunction than TM patients.

Furthermore, the differential immunoglobulin ratio of TM to TI in plasma extracellular vesicles was significantly higher than plasma (30.8% vs.7%, fig. 3D), confirming that the deregulation level of immunoglobulins is more pronounced in extracellular vesicles and therefore plasma extracellular vesicle proteins are more advantageous in subtype discrimination.

In conclusion, the immune dysfunction in TI patients is more pronounced than in TM patients and this difference is more pronounced in extracellular vesicles, thus the immune related differential proteins in extracellular vesicles are more advantageous in subfraction type diagnosis than in plasma.

5. The performance of the common indicators for clinical screening of plasma extracellular vesicle-differentiated proteins, plasma-differentiated proteins, and beta-thalassemia in subtype diagnosis was analyzed by comparison of subject operating curves (receiver operating characteristic curve, ROC).

The subject operating Curve (receiver operating characteristic Curve, ROC) analysis can be used to compare the identification capacity of different differential proteins as diagnostic markers for diseases, and when comparing multiple diagnostic methods of the same disease, the advantage and the disadvantage of each diagnostic method can be intuitively distinguished according to the Area Under the ROC Curve (AUC) value of each diagnostic method, and the diagnostic method represented by the larger AUC value (maximum 1) has better performance. Thus, the protein with the best performance for identifying the beta-thalassemia disease or subtype is selected from the differential proteins by ROC curve analysis.

In identifying β -thalassemia patients and healthy controls, the proteins with the highest AUC values were C1S protein and CLU protein, which separated 40 patients and 20 healthy persons by AUC = 100%, respectively (fig. 4A); in identifying severe and intermediate patients with β -thalassemia, proteins with AUC values greater than 85% were TF protein, HBA1 protein, IGHG4 protein, HBB protein, KLKB1 protein in order, which differentiated 20 severe and 20 intermediate patients by classification capacity with AUC of 96.8%, 90.2%, 89.5%, 88.8% and 88.5%, respectively (fig. 4B). At the same time, we also comparatively analyzed the AUC values corresponding to these several proteins in plasma, which are all smaller than the subgroup capacity in plasma, and we also analyzed the first 6 proteins with the highest AUC values in the subgroup discrimination of plasma proteins, only one protein with higher AUC value (fig. 4C), so that the extracellular vesicle protein of plasma is more advantageous in the discrimination of the subtypes.

Finally, several indicators, including fetal hemoglobin (Hb F), hemoglobin a ₂(hemoglobin A₂,Hb A₂), serum ferritin (serum ferritin, SF), mean red blood cell volume (Mean Corpuscular Volume, MCV), mean red blood cell hemoglobin content (Mean corpuscular hemoglobin, MCH), which are clinically used for the evaluation of β -thalassemia disease, were analyzed for AUC of less than 85% when distinguishing between heavy and intermediate patients (fig. 4D). The above procedure narrows the screening range of potential biomarkers.

6. And screening out the characteristic protein with the discrimination capability by using a Borata characteristic selection algorithm and a characteristic deletion method.

The Boruta algorithm is a method of comparing the importance of different proteins in distinguishing between multiple classes of patients. Thus, we further used the Boruta algorithm to evaluate the importance of all proteins to determine the importance of the proteins in differentiating three classes of populations simultaneously, including severe beta thalassemia patients, intermediate patients and healthy controls. TF, C1S, IGHG4, CLU, HBB, HBA1 were the first six proteins of greatest importance, ordered by importance (fig. 5A).

In the feature deletion method, a support vector machine (support vector machine, SVM) classifier in machine learning is adopted for carrying out class II classification, and a support vector machine is used for carrying out OnevsRestclassifier to process three classification problems. First, the GRIDSEARCHCV function in the python Scikit-learn package is used to find the optimal values for the SVM model hyper-parameters (kernel, C and gamma). GRIDSEARCHCV all combinations of predefined parameter values are used and model performance of each combination is evaluated using 5-fold cross-validation. Finally, a kernel parameter c=1 and a linear kernel support vector machine are selected as classification models, and the cross validation accuracy is optimal to be 0.98. Each protein was tested for importance to six protein combinations using a 5-fold cross-validation method. The importance of these six proteins to the diagnostic model was confirmed by deleting one protein at a time and evaluated using the micro-F1 score (F1 score is a weighted average of accuracy and recall). Finally, when HBB protein was deleted, the model diagnostic effect was optimal, micro-F1 scored 0.93, so we combined 5 proteins of C1S, CLU, HBA1, TF and IGHG4 as biomarkers (fig. 5B).

7. Construction of SVM diagnostic model from 5 protein marker combinations

Five protein markers are used, 45 cases of modeling and 15 cases of samples are used for verification, kernel of the hyper-parameters of the SVM model is a linear kernel support vector machine, the kernel parameters C=1, and OnevsRestclassifier are used for processing three classification problems. And judging the verification samples of the unknown labels by using a classification hyperplane decision function to obtain the predicted labels of the samples to be classified in three groups, namely classifying the samples into the group when the predicted value is greater than 0, and judging the samples to be classified into other two types of labels when the predicted value is less than 0.

8. Evaluating diagnostic performance of SVM diagnostic models

The diagnosis test is carried out on 15 samples by utilizing the SVM beta-thalassemia and the subtype diagnosis model, the diagnosis result of the SVM diagnosis model is compared with the diagnosis result of the genotype diagnosis, the result is shown in a confusion matrix of FIG. 5C, namely, the group prediction of the 15 samples by utilizing the SVM model is 100% correct, so that the sensitivity, the specificity, the positive prediction value and the negative prediction value of the SVM model are 100%. Meanwhile, the ROC curve (figure 5D) of the model is obtained, and as can be seen from figure 5D, the model judges that the AUC values of the ROC curves of the healthy control group, the intermediate beta-thalassemia patient group and the heavy beta-thalassemia patient group are all 1, so that the model has high classification accuracy.

The following describes the above kit and specific steps thereof by way of specific examples.

Experimental method

1) The 15 plasma samples to be tested are respectively taken to obtain 150 mu L of plasma, 2500 Xg of the plasma is adopted to carry out super-separation for 10min to remove dead cells and platelets, the supernatant after centrifugation is transferred to a new EP tube, and PBS solution filtered by a 0.22 mu m filter membrane is added into the supernatant in an equal volume to dilute the plasma.

2) The extracellular vesicles in the plasma sample are separated and enriched by using an asymmetric flow field flow in combination with an ultraviolet absorption detector and a multi-angle excitation light scattering detector (AF 4-UV/MALS). The conditions were as follows: a mylar shim with a thickness of 350 μm, a regenerated cellulose membrane with a retention of 10kDa, a flow rate into the detector of 1.0mL/min, a focusing flow rate of 1.5mL/min, a focusing time of 8min (focusing 1min before sample injection, focusing 5min after sample injection), a constant cross flow rate of 3mL/min for 5min, a linear gradient cross flow rate of 15min decreasing from 3mL/min to 1mL/min, a 20min decreasing from 1mL/min to 0.1mL/min, a 5min decreasing from 0.1mL/min to 0, and a sample injection volume of 300. Mu.L (PBS and plasma 1:1 (v/v) mixed dilution).

3) The enriched extracellular vesicle fraction was concentrated in a 3kDa ultrafiltration tube, and after measuring the protein concentration by the BCA method, 100. Mu.g of the protein solution was taken, urea solid was added to a final concentration of 8M, and 10. Mu.L of 50mM NH ₄HCO₃ was added to make the total volume about 150. Mu.L.

4) 1M Dithiothreitol (DTT) was added to a final concentration of 20mM and reduced in a water bath at 37℃for 1 hour.

5) Cooled to room temperature, 1M iodoacetamide (IAM, iodoacetamide) was added to a final concentration of 40mM, and left to react in the dark at room temperature for 45min, and the thiol terminus of the protein was blocked.

6) The enzyme is as follows: protein = 1: 50. Mu.g of intracellular protease (Lys-C) was added and incubated at 37℃for 3h.

7) The urea concentration of the above samples was diluted to below 2M with 25mm nh4hco 3.

8) The enzyme is as follows: protein = 1:50, 2. Mu.g trypsin (trypsin) was added thereto, and the mixture was subjected to enzymolysis overnight at 37℃in a water bath for 16 hours.

9) The cleavage reaction was terminated by adding 0.1% FA (pH < 3.5) to a final concentration to obtain plasma EVs peptide fragment samples.

10 Parallel Reaction Monitoring (PRM) targeted quantitative mass spectrometry of the plasma EVs peptide fragment samples obtained above. The specific operation is as follows:

After 1. Mu.g of each sample was mixed in equal amounts, 10X iRT standard peptide (Biognosys) was added and the PRM-MS/MS analyzer was a Orbitrap Eclipse Tribrid mass spectrometer (Thermo FISHER SCIENTIFIC) equipped with EASY-nLC 1200HPLC system, and mass spectrometry data acquisition was performed using DDA mode for the construction of a spectral library. 5 protein biomarkers are screened from the spectrogram library to form a detection list. Spectral library data analysis was done using SpectroDive v 10.4.4 (Biognosys) software. The following parameter screens were performed on 5 protein biomarkers to build a PRM detection list: parent ion mass to charge ratio range 350-1500; parent ion charge number, 2-3; a missed cut site, 0; peptide length 8-25; peptide fragment mass-to-charge ratio range, 300-1800; maximum sub-ion charge, 2; ion type, b, y ions; selecting top 6 fragment ions; after the retention time correction with iRT, the retention time window was set to ±5min.

After the detection list is constructed, PRM data acquisition is carried out, and targeted quantitative mass spectrum data are obtained. Mass spectral parameters for PRM experiments were set as follows: tandem mass spectrum collision energy 30%; the resolution of the first-level spectrogram is 60,000, the maximum ion implantation time is 20ms, and the AGC is 4e5; the resolution of the secondary mass spectrum is 30,000, and the separation mass window is 1.0Da; maximum ion implantation time 80ms, agc 2e5;

The PRM original file obtained was processed using SpectroDive v 10.4. Selecting a peptide segment with q-value less than 0.01, excluding sub-ion signal peaks with obvious interference signals around peak tips, and quantifying more than three ion pairs for each parent ion. Table 1 shows the relative expression levels of 5 protein markers detected in 15 samples to be tested using the PRM technique.

TABLE 1

Second, result diagnosis

And (3) introducing mass spectrum signals or relative expression amounts of 5 protein markers of the extracellular vesicles of the sample to be detected into an SVM model established by us to obtain a predicted value (table 2), wherein each sample to be detected can respectively obtain one predicted value in three groups of a healthy control group, an intermediate beta-thalassemia patient group and a heavy beta-thalassemia patient group, if the predicted value is greater than 0, the sample to be detected is judged to belong to the group, and if the predicted value is less than 0, the sample to be detected is judged to belong to the other two groups.

TABLE 2

Comparing the diagnosis result of the SVM diagnosis model with the diagnosis result of the genotype subtype in the prior art, the result is shown in a confusion matrix of FIG. 5C, namely, the group prediction of the 15 samples by using the SVM model is 100% correct, so that the sensitivity, the specificity, the positive prediction value and the negative prediction value of the SVM model are 100%. Meanwhile, the ROC curve (figure 5D) of the model is obtained, and as can be seen from figure 5D, the model judges that the AUC values of the ROC curves of the healthy control group, the intermediate beta-thalassemia patient group and the heavy beta-thalassemia patient group are all 1, so that the model has high classification accuracy.

Taken together, the present invention uses only the SVM model as an example, showing that the 5 proteins of C1S, CLU, HBA, TF and IGHG4 can be used as biomarker combinations for diagnosing beta-thalassemia and its subtypes. The person skilled in the art can also use any conventional protein detection method to detect the expression level of the 5 proteins in the extracellular vesicles in the biological sample which is diagnosed, and perform machine learning modeling according to a conventional statistical method, and use the constructed machine learning model to determine the beta-thalassemia and the subtype of the sample to be detected.

The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the invention thereto, but to limit the invention thereto, and any modifications, equivalents, improvements and equivalents thereof may be made without departing from the spirit and principles of the invention.

Claims (9)

1. Use of a biomarker and/or a substance detecting said biomarker for the manufacture of a kit for diagnosing β -thalassemia and subtypes thereof;

Wherein the biomarkers are C1S, CLU, HBA, TF and IGHG4 proteins.

2. The use of claim 1, wherein the β -thalassemia subtype includes intermediate and heavy duty.

3. The use according to claim 1, wherein the substance for detecting a biomarker is a reagent for detecting the expression level of C1S, CLU, HBA1, TF and IGHG4 in a biological sample.

4. The use according to claim 1, wherein the substance for detecting a biomarker comprises a substance for detecting the expression level of C1S, CLU, HBA, TF and IGHG4 in a biological sample by enzyme-linked immunosorbent assay, immunofluorescence method, radioimmunoassay, co-immunoprecipitation method, immunoblotting method, high performance liquid chromatography, capillary gel electrophoresis, near infrared spectroscopy, mass spectrometry, immunochromatography, colloidal gold immunoassay, fluorescent immunochromatography, surface plasmon resonance technique, immuno-PCR technique or biotin-avidin technique.

5. The use according to claim 3 or 4, wherein the substance for detecting the expression level of C1S, CLU, HBA, TF and IGHG4 in a biological sample comprises antibodies that bind to C1S, CLU, HBA1, TF and IGHG4 proteins, respectively; or the substances for detecting the expression level of the C1S, CLU, HBA, the TF and the IGHG4 in the biological sample can comprise synthetic peptide fragments corresponding to the C1S, CLU, HBA1, the TF and the IGHG4 proteins as standard substances for targeted mass spectrum detection.

6. The use according to claim 3 or 4, wherein the biological sample is extracellular vesicles of ex vivo plasma/serum.

7. A composition comprising a substance for detecting a biomarker as claimed in claim 3 or 4.

8. A kit, wherein the kit comprises the composition of claim 7.

9. Use of the kit of claim 8 for diagnosing β -thalassemia and its subtypes or for the preparation of a product for diagnosing β -thalassemia and its subtypes.