Method for predicting pathotype and typing of myocardial cell variation and electronic device

Abstract

The invention provides a myocardial cell variation pathotype and typing prediction method and an electronic device. The prediction method comprises the following steps: acquiring a variant amino acid type of KCNQ1 protein of variant myocardial cells of a to-be-predicted sample; if the variant amino acid type is the variant amino acid type of the known pathogenicity type in the table 1, obtaining the pathogenicity type of myocardial cell variation; if the variant amino acid type does not belong to the variant amino acid type of the known pathogenic type in the table 1, acquiring the cFPD reading of the variant myocardial cell to be predicted; and inputting the cFPD reading into a prediction model of the pathogenicity type of myocardial cell variation, and outputting a prediction result of whether long QT pathogenicity exists or not, namely outputting the pathogenicity type of myocardial cell variation. The cFPD reading of the myocardial cells is detected through a quantifiable method, and the allele type of KCNQ1 is combined, so that the pathogenicity type of myocardial cell variation can be efficiently and accurately predicted.

Description

Technical Field

[0001] The present invention relates to the technical field of cytology, and in particular to a prediction method and an electronic device for the pathogenic type and typing of myocardial cell mutation. Background Art

[0002] Human pluripotent stem cells (hPSCs) have the potential to proliferate indefinitely in vitro and differentiate into cells of all germ layers, including the heart. The system for hPSCs to differentiate into cardiomyocytes in vitro has been very well developed, and can efficiently obtain spontaneously beating human pluripotent stem cell derived cardiomyocytes (hPSCs-CMs) in a short period of time (Publication No.: WO2014078414A1, CN106244526A). The process of hPSCs cardiomyocyte differentiation is consistent with the process of complete embryonic cardiomyocyte development in vivo. During the in vitro differentiation process, it sequentially expresses cardiomyocyte-specific structural proteins and ion channels, and the differentiated hPSCs-CMs have the complex phenotype and functional characteristics of cardiomyocytes in vivo, such as generating similar action potentials and showing spontaneous contractions. The application of hPSCs-CMs can overcome the barrier of obtaining primary cardiomyocytes and break the bottleneck of species differences. In addition, hPSCs-CMs are easy to obtain and can be prepared on a large scale and efficiently, making them an ideal tool for studying human heart disease.

[0003] With the development of next-generation sequencing technology, a large number of large-scale genetic screenings have been performed in healthy and diseased populations. However, the functional interpretation of genetic variants lags behind the identification of variants, resulting in the identification of a large number of variants of unknown significance (VUS) whose pathogenicity cannot be determined. In addition, due to the lack of functional assays, some benign and pathogenic variants are misclassified (PMID: 31752965, PMID: 29868193). This incomplete genetic framework reduces the value of large-scale genetic studies and limits the clinical application of genetic information. Therefore, how to provide a method to efficiently and accurately predict the pathogenicity caused by cardiomyocyte mutations is of great significance for actual clinical applications. Summary of the invention

[0004] The main purpose of the present invention is to provide a method and electronic device for predicting the pathogenic type and typing of cardiomyocyte mutation, so as to solve the problem in the prior art that it is difficult to effectively predict the pathogenic type of the pathogenic condition of cardiomyocyte mutation.

[0005] In order to achieve the above-mentioned purpose, according to the first aspect of the present invention, a method for predicting the pathogenic type of cardiomyocyte mutation is provided, and the prediction method comprises: obtaining the variant amino acid type of the KCNQ1 protein of the variant cardiomyocyte of the sample to be predicted; if the variant amino acid type is the variant amino acid type of the known pathogenic type in Table 1, the pathogenic type of the cardiomyocyte mutation is obtained; if the variant amino acid type does not belong to the variant amino acid type of the known pathogenic type in Table 1, the cFPD reading of the variant cardiomyocyte to be predicted is obtained; the cFPD reading is input into the prediction model of the pathogenic type of the cardiomyocyte mutation, and the prediction result of whether there is long QT pathogenicity is output, that is, the pathogenic type of the cardiomyocyte mutation is output.

[0006] Furthermore, the prediction model for the pathogenic type of cardiomyocyte variation is obtained by machine learning training using cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes of known pathogenicity; preferably, long QT-positive cardiomyocytes and long QT-negative cardiomyocytes are cardiomyocytes with KCNQ1 gene mutations.

[0007] Furthermore, the output standard of the prediction model is: when the cFPD reading is greater than or equal to 550ms, the output is that there is pathogenicity of long QT; when the cFPD reading is less than 550ms, the output is that there is no pathogenicity of long QT.

[0008] In order to achieve the above-mentioned purpose, according to the second aspect of the present invention, a method for constructing a prediction model for the pathogenic type of cardiomyocyte variation is provided, and the construction method comprises: obtaining cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes whose pathogenicity is known; performing machine learning training using the cFPD readings of long QT-positive and long QT-negative cardiomyocytes whose pathogenicity is known to obtain a prediction model for the pathogenic type of cardiomyocyte variation.

[0009] In order to achieve the above-mentioned purpose, according to the third aspect of the present invention, a method for predicting long QT typing caused by cardiomyocyte mutation is provided, and the prediction method comprises: predicting the pathogenic type of the mutated cardiomyocytes of the predicted sample by the above-mentioned prediction method; if the prediction result is the presence of long QT pathogenicity, analyzing the long QT typing of the mutated cardiomyocytes with different pathogenic mechanisms to obtain the prediction result of the long QT typing.

[0010] In order to achieve the above-mentioned purpose, according to the fourth aspect of the present invention, there is provided an electronic device for predicting the pathogenic type of myocardial cell mutation, the electronic device having a built-in prediction model for the pathogenic type of myocardial cell mutation, and the electronic device comprising: a gene information acquisition module, configured to obtain the variant amino acid type of the KCNQ1 protein of the variant myocardial cell of the sample to be predicted; a primary screening module, configured to obtain the pathogenic type of the myocardial cell mutation if the variant amino acid type is a variant amino acid type of a known pathogenic type in Table 1; if the variant amino acid type does not belong to the variant amino acid type of a known pathogenic type in Table 1, then obtain the cFPD reading of the variant myocardial cell to be predicted; a prediction module, configured to input the cFPD reading into the prediction model, and output a prediction result of whether there is pathogenicity of long QT, that is, output the pathogenic type of the myocardial cell mutation.

[0011] Furthermore, the prediction model is obtained by machine learning training using cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes with known pathogenicity; preferably, the long QT-positive cardiomyocytes and long QT-negative cardiomyocytes are cardiomyocytes with KCNQ1 gene mutations.

[0012] Furthermore, the output standard of the prediction model is: when the cFPD reading is greater than or equal to 550ms, the output is that there is pathogenicity of long QT; when the cFPD reading is less than 550ms, the output is that there is no pathogenicity of long QT.

[0013] In order to achieve the above-mentioned purpose, according to the fifth aspect of the present invention, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-mentioned method for predicting the pathogenic type of cardiomyocyte variation, the above-mentioned method for constructing a prediction model of the pathogenic type of cardiomyocyte variation, or the above-mentioned method for predicting the long QT typing caused by cardiomyocyte variation by executing the executable instructions.

[0014] In order to achieve the above-mentioned purpose, according to the sixth aspect of the present invention, a computer-readable storage medium is provided, and the computer-readable storage medium includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned method for predicting the pathogenic type of myocardial cell variation, the above-mentioned method for constructing a prediction model of the pathogenic type of myocardial cell variation, or the above-mentioned method for predicting the long QT typing caused by myocardial cell variation.

[0015] By applying the technical solution of the present invention, the cFPD readings of cardiomyocytes are detected by a quantifiable method. Combined with the allele type of KCNQ1, the pathogenic type of cardiomyocyte mutation can be predicted more efficiently and accurately, so as to further realize drug testing and screening for different pathogenicities, build an efficient and accurate in vitro drug evaluation system, and realize drug guidance for clinical precision treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0017] Figure 1 The peak diagram of DNA sequencing of genomic DNA in Example 1 of the present application after knocking out the KCNQ1 gene by CRISPR / CAS9 is shown;

[0018] Figure 2 The figure shows the Western Blot detection result of hPSCs with KCNQ1 gene knockout in Example 1 of the present application;

[0019] Figure 3 Microscopic observation images of wild-type stem cells and KCNQ1 knocked-out stem cell cardiomyocytes in Example 1 of the present application are shown;

[0020] Figure 4 The schematic diagram of the structure of the lentiviral vector in Example 1 of the present application is shown;

[0021] Figure 5 The microscopic observation diagram of the myocardial cells transfected with lentivirus in Example 1 of the present application is shown;

[0022] Figure 6 The figure shows the result of field potential detection of different amino acid mutation types of KCNQ1 by CardioExcyte Control 96 in Example 2 of the present application. DETAILED DESCRIPTION

[0023] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below in conjunction with the embodiments.

[0024] Glossary:

[0025] Human myocardial model: refers to any cardiomyocytes differentiated from human pluripotent stem cells with the potential for in vitro myocardial differentiation.

[0026] QT interval: includes ventricular depolarization and repolarization excitation time, represents the total duration of ventricular depolarization and repolarization process, and is the time from the starting point of QRS wave to the end point of T wave in electrocardiogram. QT change has important value in clinical electrocardiogram diagnosis, especially QT prolongation is of great significance for predicting malignant ventricular arrhythmia and sudden cardiac death.

[0027] KCNQ1: This gene encodes a voltage-gated potassium channel in the heart, mediating the slow delayed rectifier potassium channel current in phase III of myocardial slow repolarization, namely, IKs current. Gene mutations may cause downregulation of channel function, leading to prolonged QT interval and causing long QT syndrome (LQTS).

[0028] Long QT syndrome: Long QT syndrome (LQTS) is a cardiac electrophysiological abnormality characterized by a prolonged QT interval in the electrocardiogram, which may lead to serious arrhythmias such as Torsades de Pointes or even ventricular fibrillation, leading to sudden death. This syndrome is usually caused by mutations in ion channel genes, mainly involving ion channels in heart muscle cells, especially potassium ions (K + ), sodium ion (Na + ), calcium ions (Ca 2 + )aisle.

[0029] The clinical manifestations of long QT syndrome can include symptoms such as syncope, palpitations, heart palpitations, and angina pectoris, which usually occur after strenuous physical activity or emotional excitement. These symptoms are often caused by arrhythmias, especially when the QT interval is prolonged, the heart is prone to abnormal electrical activity, which affects the normal heart contraction and pumping function. Treatments for long QT syndrome include medication, pacemaker implantation, and other surgeries or treatments that may need to be considered in high-risk patients. Family history, clinical symptoms, and electrocardiogram examinations are key steps in diagnosing long QT syndrome. Timely detection and treatment can significantly reduce the patient's risk of serious arrhythmias and sudden death.

[0030] Globally, the prevalence of long QT syndrome is approximately between 1 in 2000 and 1 in 5000. Although its prevalence is low compared to some common cardiovascular diseases, the diagnosis and treatment of long QT syndrome in patients with abnormal electrocardiograms is crucial to avoid serious complications.

[0031] cFPD: standardized field potential duration, used to reflect the QT interval duration of the myocardial model.

[0032] As mentioned in the background technology, there are many mutations in the prior art that cannot clearly identify the pathogenic type of cardiomyocyte mutations. In order to be able to more accurately predict the pathogenic type of cardiomyocyte mutations, in a first typical embodiment of the present application, a method for predicting the pathogenic type of cardiomyocyte mutations is provided, and the prediction method includes: obtaining the variant amino acid type of the KCNQ1 protein of the variant cardiomyocyte of the sample to be predicted; if the variant amino acid type is a variant amino acid type of a known pathogenic type in Table 1, the pathogenic type of the cardiomyocyte mutation is obtained; if the variant amino acid type does not belong to the variant amino acid type of a known pathogenic type in Table 1, the cFPD reading of the variant cardiomyocyte to be predicted is obtained; the cFPD reading is input into the prediction model of the pathogenic type of the cardiomyocyte mutation, and the prediction result of whether there is long QT pathogenicity is output, that is, the pathogenic type of the cardiomyocyte mutation is output.

[0033] Table 1:

[0034]

[0035]

[0036]

[0037] SEQ ID NO: 1: (amino acid sequence of KCNQ1)

[0038] .

[0039] Using cFPD readings and KCNQ1 allele types to predict the pathogenic type of cardiomyocyte mutations of unknown pathogenic type can obtain relatively accurate results.

[0040] Using cardiomyocytes of known pathogenic types, the cFPD readings, the allele type of KCNQ1 and the pathogenic type are analyzed and judged to obtain a prediction model. In a preferred embodiment, the prediction model of the pathogenic type of cardiomyocyte variation is obtained by machine learning training using the cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes with known pathogenicity; preferably, long QT-positive cardiomyocytes and long QT-negative cardiomyocytes are cardiomyocytes with KCNQ1 gene mutations.

[0041] In order to further obtain more accurate prediction results, in a preferred embodiment, the output standard of the prediction model is: when the cFPD reading is greater than or equal to 550ms, the output is that there is pathogenicity of long QT; when the cFPD reading is less than 550ms, the output is that there is no pathogenicity of long QT.

[0042] In a second typical embodiment of the present application, a method for constructing a predictive model for the pathogenic type of cardiomyocyte variation is provided, the construction method comprising: obtaining cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes of known pathogenicity; performing machine learning training using the cFPD readings of long QT-positive and long QT-negative cardiomyocytes of known pathogenicity to obtain a predictive model for the pathogenic type of cardiomyocyte variation.

[0043] In a third typical embodiment of the present application, a method for predicting long QT typing caused by cardiomyocyte mutation is provided, and the prediction method comprises: predicting the pathogenic type of the mutated cardiomyocytes of the predicted sample by the above-mentioned prediction method; if the prediction result is the presence of long QT pathogenicity, analyzing the long QT typing of the mutated cardiomyocytes with different pathogenic mechanisms to obtain the prediction result of the long QT typing.

[0044] Since there are many different pathogenic mechanisms leading to long QT, i.e. long QT typing, if there is a long QT pathogenic mechanism related to abnormal response to catecholamine stimulation, beta-blockers can be considered in clinical medication. Further analysis of long QT typing can be performed based on the pathogenicity of long QT obtained by the above prediction method, and a suitable clinical medication plan can be obtained to achieve clinical application significance.

[0045] In a fourth typical embodiment of the present application, an electronic device for predicting the pathogenic type of cardiomyocyte mutation is provided, the electronic device has a built-in prediction model for the pathogenic type of cardiomyocyte mutation, and the electronic device includes: a gene information acquisition module, configured to obtain the variant amino acid type of the KCNQ1 protein of the variant cardiomyocyte of the sample to be predicted; a primary screening module, configured to obtain the pathogenic type of the cardiomyocyte mutation if the variant amino acid type is a variant amino acid type of a known pathogenic type in Table 1; if the variant amino acid type does not belong to the variant amino acid type of a known pathogenic type in Table 1, then obtain the cFPD reading of the variant cardiomyocyte to be predicted; a prediction module, configured to input the cFPD reading into the prediction model, and output a prediction result of whether there is pathogenicity of long QT, that is, output the pathogenic type of the cardiomyocyte mutation.

[0046] Using cardiomyocytes of known pathogenic types, the cFPD readings, the allele type of KCNQ1 and the pathogenic type are analyzed and judged to obtain a prediction model. In a preferred embodiment, the prediction model is obtained by machine learning training using the cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes with known pathogenicity; in a preferred embodiment, the long QT-positive cardiomyocytes and long QT-negative cardiomyocytes are cardiomyocytes with KCNQ1 gene mutations.

[0047] In a preferred embodiment, the output standard of the prediction model is: when the cFPD reading is greater than or equal to 550ms, the output is that the pathogenicity of long QT is present; when the cFPD reading is less than 550ms, the output is that the pathogenicity of long QT is not present.

[0048] In a fifth typical embodiment of the present application, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the above-mentioned method for predicting the pathogenic type of myocardial cell variation, the method for constructing a prediction model of the pathogenic type of myocardial cell variation, or the method for predicting the long QT typing caused by myocardial cell variation by executing the executable instructions.

[0049] In a sixth typical embodiment of the present application, a computer-readable storage medium is provided, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the above-mentioned method for predicting the pathogenic type of myocardial cell variation, the above-mentioned method for constructing a prediction model of the pathogenic type of myocardial cell variation, or the method for predicting the long QT typing caused by myocardial cell variation.

[0050] The present application is further described in detail below in conjunction with specific embodiments. These embodiments should not be construed as limiting the scope of protection claimed in the present application.

[0051] The H9 human embryonic stem cell line in the embodiments of the present application is derived from the Lanfeng Laboratory of the Fuwai Hospital of the Chinese Academy of Medical Sciences; 293T cells are derived from the Lanfeng Laboratory of the Fuwai Hospital of the Chinese Academy of Medical Sciences; lentivirus is derived from Yunzhou Biotechnology (Guangzhou) Co., Ltd.; human pluripotent stem cell culture medium is derived from Beijing Saibei Biotechnology Co., Ltd.; myocardial purification culture medium is derived from Beijing Saibei Biotechnology Co., Ltd.; and myocardial cell maintenance culture medium is derived from Beijing Saibei Biotechnology Co., Ltd.

[0052] Example 1 Testing Standard Training

[0053] 1. Construction of KCNQ1 knockout H9 human embryonic stem cell line

[0054] CRISPR / CAS9 genome editing technology was used to knock out the target gene KCNQ1.

[0055] 1.1 Design 2 or more sgRNAs targeting the KCNQ1 gene, the coding sequence of sgRNA is shown in SEQ ID NO: 2, ATGCTACACGTCGACCGCCAGGG (SEQ ID NO: 2), and construct CRISPR / CAS9 plasmid;

[0056] 1.2 Transfect 293T cells with CRISPR / CAS9 plasmid, extract genomic DNA of surviving cells after Puromycin screening for PCR, and observe the peak strength of DNA sequencing results (such as Figure 1 As shown) and TA clone sequencing ratio to detect sgRNA activity;

[0057] 1.3 hPSCs were cultured in a human pluripotent stem cell culture medium at 37°C in an incubator containing 5% carbon dioxide. After the cells were fully grown, human pluripotent stem cell digestion solution was used for digestion and passage.

[0058] 1.4 After the confluence of hPSCs reaches 70-80%, the highly active CRISPR / CAS9 plasmid is transfected into hPSCs by electroporation;

[0059] 1.5 Use Puromycin to screen out KCNQ1 gene knockout hPSCs and verify KCNQ1 gene knockout by Western Blot (such as Figure 2 shown).

[0060] 2. Directed differentiation of cardiomyocytes

[0061] 2.1 Using a known cardiomyocyte differentiation protocol (Patent Publication No. CN106244526A), combined with a Wnt pathway promoter (small molecule drug CHIR99021) and inhibitor (small molecule drug IWP2) for cardiomyocyte-directed differentiation;

[0062] 2.2 Use a sugar-free myocardial purification medium containing lactic acid to purify myocardial cells and obtain myocardial cells with a purity of more than 95%;

[0063] 2.3 Use cardiomyocyte maintenance medium to culture cardiomyocytes at 37°C in an incubator containing 5% carbon dioxide. Replace the medium every 2 to 3 days to obtain successfully differentiated cardiomyocytes (such as Figure 3 As shown, WT-CMs are normal stem cell-differentiated cardiomyocytes, and KO-CMs are KCNQ1-knockout stem cell-differentiated cardiomyocytes).

[0064] 3. Construction of human myocardial models with different KCNQ1 mutations

[0065] Different KCNQ1 mutant genes were overexpressed in the KCNQ1 knockout myocardial model by lentiviral transfection to obtain a large number of human myocardial cell models with different KCNQ1 gene mutations.

[0066] 3.1 A lentiviral vector overexpressing different KCNQ1 mutant genes was designed and constructed using a known method and packaged. The vector contained a 5'LTR and a 3'LTR of a ψ sequence, a target gene sequence (KCNQ1 gene) between the 5'LTR and the 3'LTR, and a promoter sequence and a translation initiation sequence operably linked to the target gene sequence (the lentiviral vector structure is as shown in FIG. Figure 4 shown);

[0067] 3.2 Virus titer test, according to 1×10 5 The target gene knockout cardiomyocytes were inoculated in each well of a 24-well plate, and the virus was added on the next day. The myocardial maintenance medium was replaced after 12 to 20 hours. The fluorescence was recorded by photographing 72 hours after infection, and the cells were collected to extract genomic DNA and protein. The optimal infection titer was determined by quantitative PCR and Western Blot experiments. The optimal infection titer refers to the viral titer that can make the expression level of mutant genes and mutant proteins close to that of wild-type cardiomyocytes.

[0068] 3.3 Using the optimal infection titer (MOI = 20) to overexpress different KCNQ1 mutant genes in KCNQ1 knockout cardiomyocytes (transfection status as shown in Figure 5 As shown), human cardiomyocytes expressing different KCNQ1 mutant genes were constructed.

[0069] Among them, the mutation sites of different KCNQ1 mutant genes are shown in Table 2.

[0070] Table 2:

[0071] serial number Mutation site serial number Mutation site 1 R25P 15 G325R 2 P63S 16 A341V 3 Y111C 17 R366Q 4 L114P 18 S373P 5 V129G 19 S546L 6 G189R 20 K557E 7 R190Q 21 T587M 8 D202H 22 G589D 9 I204F 23 R594Q 10 V207M 24 P448R 11 R243C 25 P408A 12 G269S 26 V521L 13 T309I 27 G516S 14 T322A

[0072] 4. Phenotypic identification of human cardiomyocytes expressing different KCNQ1 mutant genes

[0073] 4.1 Wild-type cardiomyocytes and human cardiomyocytes with known pathogenic KCNQ1 mutations obtained in step 3 (the mutation types are shown in Table 2) were cultured in myocardial maintenance medium at 37°C in an incubator containing 5% carbon dioxide for 25 days. Wild-type cardiomyocytes and human cardiomyocytes with different KCNQ1 mutations were digested and inoculated at 50,000 cells / well on a 96-well electrode plate coated with matrix gel in advance. Continue to culture for 3 to 5 days, and subsequent detection can be performed after the cardiomyocytes are stably and spontaneously beating on the electrode plate;

[0074] 4.2 Use the CardioExcyte 96 system to test the electrophysiological activity of cardiomyocytes. Use the Recording mode of CardioExcyte Control96 to start the test, and perform a 20-second test every 48 hours, for a total of 3 times;

[0075] 4.3 Export the test results using the Replay mode of CardioExcyte Control 96. Each online analysis (OA) data point is based on a 20-second scan, including multiple parameters such as Field Potential Duration (FPD), Mean Beat, and Area under Curve;

[0076] 4.4 The obtained cFPD readings and reported pathogenicity were used for machine learning training to obtain the judgment criteria: when the cFPD reading is greater than or equal to 550ms, long QT pathogenicity exists; when the cFPD reading is less than 550ms, long QT pathogenicity does not exist.

[0077] Among them, the cFPD readings of human cardiomyocytes expressing different KCNQ1 mutant genes are shown in Table 3.

[0078] Table 3:

[0079]

[0080] 5. Based on the cFPD readings of the cardiomyocytes of the sample to be tested, the disease type of the cardiomyocytes of the sample to be tested is obtained, and targeted large-scale drug testing and screening are achieved to provide guidance for clinical precision drug use.

[0081] Example 2 Testing Standard Verification

[0082] The method of step 4 above was used to perform cFPD detection on pathogenic cardiomyocytes with known gene mutations, and the cFPD readings obtained were used to determine the pathogenicity, which was compared with the actual situation. The specific data are shown in Table 4. It was found that the accuracy of pathogenicity detection using the cFPD readings of the present application was high, and it has certain application significance in the actual pathogenicity judgment of cardiomyocytes.

[0083] Among them, samples with unknown pathogenicity were tested for field potential using CardioExcyte Control 96, such as Figure 6 As shown, R25P and V129G, which were judged as long QT, showed arrhythmia phenotype, while P63S, which was judged as benign, had normal electrophysiology, which proved the accuracy of the judgment criteria of the present application.

[0084] Table 4:

[0085]

[0086]

[0087] Example 3 Determination of long QT typing

[0088] In order to further analyze the pathogenic mechanism of long QT pathogenicity obtained by the above detection, i.e., long QT typing, 27 myocardial models with long QT pathogenicity in Table 5 below were treated with isoprotereno (ISO treatment) to determine the pathogenic mechanism of long QT pathogenicity. By observing the cFPD change rate before and after ISO treatment, as shown in Table 5, it can be seen that the pathogenic mechanism of myocardial cell variant samples (G189R, R190Q, V207M, G269S, A341V and G589D) with no obvious cFPD change before and after ISO treatment (less than 5%) may be related to abnormal response to catecholamine stimulation, and beta-blockers can be considered for clinical medication to achieve drug testing and screening for different pathogenic typing, build an efficient and accurate in vitro drug evaluation system, and achieve medication guidance for clinical precision treatment.

[0089] Table 5:

[0090]

[0091]

[0092] From the above description, it can be seen that the above embodiments of the present invention achieve the following technical effects: by detecting the cFPD readings of cardiomyocytes by a quantifiable method, combined with the allele type of KCNQ1, the pathogenic type of cardiomyocyte mutation can be predicted more efficiently and accurately, so as to further realize drug testing and screening for different pathogenicities, build an efficient and accurate in vitro drug evaluation system, and realize drug guidance for clinical precision treatment.

[0093] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting the pathogenic type of cardiomyocyte mutation, characterized in that: The prediction method comprises: Obtaining the variant amino acid type of the KCNQ1 protein of the variant cardiomyocytes of the sample to be predicted; If the variant amino acid type is a variant amino acid type of a known pathogenic type in Table 1, the pathogenic type of the cardiomyocyte mutation is obtained; If the variant amino acid type does not belong to the variant amino acid type of known pathogenic type in Table 1, obtaining the cFPD reading of the myocardial cell with the variant to be predicted; The cFPD reading is input into a prediction model of the pathogenic type of cardiomyocyte variation, and a prediction result of whether there is pathogenicity of long QT is output, that is, the pathogenic type of the cardiomyocyte variation is output.

2. The prediction method according to claim 1, characterized in that: The prediction model of the pathogenic type of the cardiomyocyte variation is obtained by machine learning training using the cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes with known pathogenicity; Preferably, the long QT-positive cardiomyocytes and the long QT-negative cardiomyocytes are cardiomyocytes with KCNQ1 gene mutations.

3. The prediction method according to claim 1, characterized in that: The output criteria of the prediction model are: When the cFPD reading is greater than or equal to 550 ms, the presence of pathogenicity of long QT is output; When the cFPD reading is less than 550 ms, the absence of pathogenicity of long QT is outputted.

4. A method for constructing a prediction model for the pathogenic type of cardiomyocyte mutation, characterized in that: The construction method comprises: Obtain cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes of known pathogenicity; The cFPD readings of long QT-positive and long QT-negative cardiomyocytes with known pathogenicity are used for machine learning training to obtain a prediction model for the pathogenic type of the cardiomyocyte variation.

5. A method for predicting long QT typing caused by cardiomyocyte mutation, characterized in that: The prediction method comprises: Predicting the pathogenic type of the mutated cardiomyocytes of the sample to be predicted by the prediction method described in any one of claims 1 to 3; If the prediction result is the presence of long QT pathogenicity, the long QT typing with different pathogenic mechanisms is analyzed for the myocardial cells with the long QT pathogenicity mutation to obtain the prediction result of the long QT typing.

6. An electronic device for predicting the pathogenic type of cardiomyocyte mutation, characterized in that: The electronic device has a built-in prediction model of the pathogenic type of cardiomyocyte mutation, and the electronic device includes: A gene information acquisition module is configured to acquire the variant amino acid type of the KCNQ1 protein of the variant cardiomyocytes of the sample to be predicted; The primary screening module is configured to obtain the pathogenic type of the myocardial cell mutation if the variant amino acid type is a variant amino acid type of a known pathogenic type in Table 1; if the variant amino acid type does not belong to the variant amino acid type of a known pathogenic type in Table 1, obtain the cFPD reading of the myocardial cell mutation to be predicted; The prediction module is configured to input the cFPD reading into the prediction model and output a prediction result of whether there is pathogenicity of long QT, that is, output the pathogenic type of the cardiomyocyte variation.

7. The electronic device according to claim 6, characterized in that: The prediction model is obtained by machine learning training using the cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes with known pathogenicity; Preferably, the long QT-positive cardiomyocytes and the long QT-negative cardiomyocytes are cardiomyocytes with KCNQ1 gene mutations.

8. The electronic device according to claim 6, characterized in that: The output criteria of the prediction model are: When the cFPD reading is greater than or equal to 550 ms, the presence of pathogenicity of long QT is output; When the cFPD reading is less than 550 ms, the absence of pathogenicity of long QT is outputted.

9. An electronic device, characterized in that: include: processor; as well as A memory, configured to store executable instructions of the processor; Wherein, the processor is configured to execute the method for predicting the pathogenic type of cardiomyocyte variation described in any one of claims 1 to 3, the method for constructing a prediction model for the pathogenic type of cardiomyocyte variation described in claim 4, or the method for predicting the long QT typing caused by cardiomyocyte variation described in claim 5 by executing the executable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for predicting the pathogenic type of cardiomyocyte variation according to any one of claims 1 to 3, the method for constructing a prediction model for the pathogenic type of cardiomyocyte variation according to claim 4, or the method for predicting the long QT typing caused by cardiomyocyte variation according to claim 5.

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