Prediction method and electronic device for pathogenic type and classification of cardiomyocyte mutation

Abstract

The present invention provides a method and electronic device for predicting the pathogenic type and classification of cardiomyocyte mutations. 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 listed in Table 1, then obtaining the pathogenic type of the cardiomyocyte mutation; if the variant amino acid type does not belong to the variant amino acid type of a known pathogenic type listed in Table 1, then obtaining the cFPD reading of the cardiomyocyte of the variant to be predicted; inputting the cFPD reading into a prediction model for the pathogenic type of the cardiomyocyte mutation, and outputting a prediction result of whether long QT pathogenicity exists, i.e., outputting the pathogenic type of the cardiomyocyte mutation. By detecting the cFPD reading of the cardiomyocyte using a quantifiable method and combining it with the KCNQ1 allele type, the pathogenic type of the cardiomyocyte mutation can be predicted more efficiently and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of cytology, and in particular to a method and an electronic device for predicting the pathogenic type and typing of cardiomyocyte mutations. 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 in vitro differentiation system for hPSCs into cardiomyocytes is well established, enabling the efficient and short-term generation of spontaneously beating human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) (Publication Nos. WO2014078414A1, CN106244526A). The process of hPSC-derived cardiomyocyte differentiation mirrors the development of intact embryonic myocardium in vivo. During in vitro differentiation, hPSC-CMs sequentially express cardiomyocyte-specific structural proteins and ion channels. Furthermore, the resulting hPSC-CMs possess the complex phenotype and functional characteristics of in vivo cardiomyocytes, such as generating similar action potentials and exhibiting spontaneous contractions. The application of hPSCs-CMs can overcome the barrier of obtaining primary cardiomyocytes and break the bottleneck of species differences. 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 their identification, resulting in the identification of a large number of variants of unknown significance (VUS), whose pathogenicity cannot be determined. Furthermore, the lack of functional assays has led to the misclassification of some benign and pathogenic variants (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, developing a method to efficiently and accurately predict the pathogenicity caused by cardiomyocyte mutations is of great significance for practical 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 classification of myocardial cell mutations, so as to solve the problem in the prior art that it is difficult to effectively predict the pathogenic type of the pathogenic conditions of myocardial cell mutations.

[0005] To achieve the above-mentioned objectives, according to a first aspect of the present invention, a method for predicting the pathogenic type of cardiomyocyte mutation is provided, the prediction method comprising: obtaining the variant amino acid type of the KCNQ1 protein of the mutated 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, obtaining the pathogenic type of the cardiomyocyte mutation; if the variant amino acid type does not belong to the variant amino acid type of a known pathogenic type in Table 1, obtaining the cFPD reading of the mutated cardiomyocyte to be predicted; inputting the cFPD reading into a prediction model for the pathogenic type of the cardiomyocyte mutation, and outputting a prediction result of whether long QT pathogenicity exists, that is, outputting the pathogenic type of the cardiomyocyte mutation.

[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, the long QT-positive cardiomyocytes and long QT-negative cardiomyocytes are cardiomyocytes with KCNQ1 gene mutations.

[0007] Furthermore, the output criteria of the prediction model are: 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.

[0008] To achieve the above objectives, according to a second aspect of the present invention, a method for constructing a prediction 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 prediction model for the pathogenic type of cardiomyocyte variation.

[0009] To achieve the above-mentioned objectives, according to a third aspect of the present invention, a method for predicting long QT typing caused by cardiomyocyte mutation is provided, the prediction method comprising: predicting the pathogenic type of the mutated cardiomyocytes of the predicted sample using 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 long QT pathogenicity for different pathogenic mechanisms to obtain a 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, an electronic device for predicting the pathogenic type of cardiomyocyte mutation is provided, the electronic device having 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, 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 long QT pathogenicity, that is, output the pathogenic type of the cardiomyocyte 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 criteria of the prediction model are: 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.

[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 myocardial cell variation, the above-mentioned method for constructing a prediction model for the pathogenic type of myocardial cell variation, or the above-mentioned method for predicting the long QT type caused by myocardial cell 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, 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 for the pathogenic type of myocardial cell variation, or the above-mentioned method for predicting the long QT type caused by myocardial cell variation.

[0015] By applying the technical solution of the present invention, the cFPD readings of cardiomyocytes are detected through 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 pathogenicity, build an efficient and accurate in vitro drug evaluation system, and realize medication guidance for clinical precision treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

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

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

[0019] Figure 3 Shown are microscopic observation images of wild-type stem cells and KCNQ1 knockout stem cell cardiomyocytes in Example 1 of the present application;

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

[0021] Figure 5 shows a microscopic observation of lentivirus-transfected cardiomyocytes in Example 1 of the present application;

[0022] Figure 6 Graphs showing the results of field potential detection of different amino acid mutation types of KCNQ1 using CardioExcyte Control 96 in Example 2 of the present application are shown. 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 with reference to 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 on the electrocardiogram. QT changes are of great value in clinical electrocardiogram diagnosis, especially QT prolongation is of great significance in predicting malignant ventricular arrhythmias 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 the IKs current. Gene mutations may cause downregulation of channel function, leading to QT interval prolongation 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 (ECG), 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 cardiac 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 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 1 in 2,000 to 1 in 5,000. 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, in the prior art, there are many mutations in cardiomyocytes whose pathogenic types cannot be clearly identified. In order to be able to more accurately predict the pathogenic types 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 cardiomyocytes 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 cardiomyocytes to be predicted is obtained; the cFPD reading is input into a prediction model of the pathogenic type of the cardiomyocyte mutation, and a 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 variants of unknown pathogenic type can obtain relatively accurate results.

[0040] Using cardiomyocytes of known pathogenic types, cFPD readings, KCNQ1 allele types, and pathogenic types are analyzed and determined to obtain a prediction model. In a preferred embodiment, 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.

[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 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.

[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, the prediction method comprising: predicting the pathogenic type of the mutated cardiomyocytes of the predicted sample using 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 long QT pathogenicity with different pathogenic mechanisms to obtain a prediction result of the long QT typing.

[0044] Because there are multiple different pathogenic mechanisms, or long QT typing, for long QT pathogenesis related to an abnormal response to catecholamine stimulation, beta-blockers may be considered in clinical practice. Based on the pathogenicity of long QT obtained through the above-mentioned prediction methods, further analysis of long QT typing can be performed to develop appropriate clinical medication plans, thereby realizing clinical application significance.

[0045] In a fourth typical embodiment of the present application, an electronic device for predicting the pathogenic type of myocardial cell mutation is provided, the electronic device having a built-in prediction model for the pathogenic type of myocardial cell 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 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, 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 long QT pathogenicity, that is, output the pathogenic type of the myocardial cell mutation.

[0046] Using cardiomyocytes of known pathogenicity, cFPD readings, KCNQ1 allele types, and pathogenicity are analyzed and determined to obtain a prediction model. In a preferred embodiment, the prediction model is obtained by machine learning training using cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes of 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 criteria of the prediction model are: 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 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 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 for the pathogenic type of myocardial cell variation, or the method for predicting the long QT type caused by myocardial cell variation.

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

[0051] The H9 human embryonic stem cell line in the embodiments of this application is derived from the Lan Feng Laboratory of Fuwai Hospital, Chinese Academy of Medical Sciences; 293T cells are derived from the Lan Feng Laboratory of Fuwai Hospital, 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 two or more sgRNAs targeting the KCNQ1 gene, with the coding sequence of sgRNA as shown in SEQ ID NO: 2, ATGCTACACGTCGACCGCCAGGG (SEQ ID NO: 2), and construct CRISPR / CAS9 plasmids;

[0056] 1.2 Transfect 293T cells with CRISPR / CAS9 plasmid, extract genomic DNA of surviving cells after Puromycin screening and perform PCR. 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 using human pluripotent stem cell culture medium at 37°C in an incubator containing 5% carbon dioxide. After the cells were confluent, they were digested and passaged using human pluripotent stem cell digestion buffer.

[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 KCNQ1 gene knockout hPSCs and verify KCNQ1 gene knockout by Western Blot (e.g. Figure 2 shown).

[0060] 2. Directed differentiation of cardiomyocytes

[0061] 2.1 Using a known cardiomyocyte differentiation protocol (Patent Publication No. CN106244526A), a Wnt pathway promoter (small molecule drug CHIR99021) and inhibitor (small molecule drug IWP2) were used 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] By overexpressing different KCNQ1 mutant genes in the KCNQ1 knockout myocardial model through lentiviral transfection, a large number of human myocardial cell models with different KCNQ1 gene mutations were obtained.

[0066] 3.1 Using known methods, a lentiviral vector overexpressing different KCNQ1 mutant genes was designed and constructed and packaged. The vector contained a 5'LTR and a 3'LTR of the 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 follows: Figure 4 shown);

[0067] 3.2 Virus titer test, according to 1×10 5 Target gene knockout cardiomyocytes were inoculated into each well of a 24-well plate. Virus was added on the next day, and the myocardial maintenance medium was replaced 12 to 20 hours later. Fluorescence was recorded and photographed 72 hours after infection. The cells were then harvested 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 levels of mutant genes and mutant proteins close to those of wild-type cardiomyocytes.

[0068] 3.3 Using the optimal infection titer (MOI = 20), different KCNQ1 mutant genes were overexpressed in KCNQ1 knockout cardiomyocytes (transfection status as shown in Figure 2). Figure 5 As shown in Figure 2 ), 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 characterization of human cardiomyocytes expressing different KCNQ1 mutations

[0073] 4.1 Wild-type cardiomyocytes and human cardiomyocytes with known pathogenic KCNQ1 mutations (mutation types are listed in Table 2) obtained in Step 3 were cultured in cardiomyocyte maintenance medium at 37°C in an incubator containing 5% carbon dioxide for 25 days. After digestion, wild-type cardiomyocytes and human cardiomyocytes with different KCNQ1 mutations were plated onto pre-coated Matrigel-coated 96-well electrode plates at a density of 50,000 cells / well. Culture was continued for 3-5 days. Subsequent testing was performed after the cardiomyocytes were stably beating spontaneously on the electrode plates.

[0074] 4.2 Perform electrophysiological activity testing of cardiomyocytes using the CardioExcyte 96 system. Start the test using the Recording mode of the CardioExcyte Control 96, performing a 20-second test every 48 hours for a total of three times.

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

[0076] 4.4 Using the obtained cFPD readings and reported pathogenicity, machine learning training was performed to obtain the judgment criteria: when the cFPD reading is greater than or equal to 550ms, long QT pathogenicity is present; when the cFPD reading is less than 550ms, long QT pathogenicity is not present.

[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 determined, and targeted large-scale drug testing and screening are achieved, providing 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. The cFPD readings obtained were used to determine pathogenicity and 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 determination of cardiomyocytes.

[0083] Among them, samples with unknown pathogenicity mutation types were tested by CardioExcyte Control 96 for field potential detection, such as Figure 6 As shown, R25P and V129G, which were judged to be long QT, showed arrhythmia phenotype, while P63S, which was judged to be 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 Type

[0088] To further analyze the pathogenic mechanisms of long QT pathogenicity (LQT genotypes) identified in the aforementioned tests, 27 myocardial models with LQT pathogenicity (Table 5) were treated with isoproterenol (ISO) to determine the underlying mechanisms. By observing the change in cFPD before and after ISO treatment, as shown in Table 5, it was determined that the pathogenic mechanisms of the cardiomyocyte variants (G189R, R190Q, V207M, G269S, A341V, and G589D) with no significant (less than 5%) change in cFPD before and after ISO treatment may be related to abnormal responses to catecholamine stimulation. Therefore, the use of beta-blockers in clinical medications may be considered to enable drug testing and screening for different LQT genotypes, establish an efficient and accurate in vitro drug evaluation system, and provide guidance for precise clinical treatment.

[0089] Table 5:

[0090]

[0091]

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

[0093] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection 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 cardiomyocytes of the variant 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 the known pathogenic type in Table 1, obtaining the cFPD reading of the cardiomyocyte of the variant to be predicted; Inputting the cFPD reading into a prediction model of the pathogenic type of cardiomyocyte variation, and outputting a prediction result of whether there is pathogenicity of long QT, that is, outputting the pathogenic type of the cardiomyocyte variation; Table 1: ， 。 2. The prediction method according to claim 1, characterized in that The prediction model for the pathogenic type of the cardiomyocyte variation is obtained by performing machine learning training using the cFPD readings of long QT-positive cardiomyocytes and long QT-negative cardiomyocytes with known pathogenicity.

3. The prediction method according to claim 2, characterized in that The long QT-positive cardiomyocytes and the long QT-negative cardiomyocytes are cardiomyocytes with KCNQ1 gene mutations.

4. 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 long QT pathogenicity is output; When the cFPD reading is less than 550 ms, the absence of long QT pathogenicity is output.

5. A method for constructing a prediction model for the pathogenic type of cardiomyocyte mutation, characterized in that: The construction method comprises: Obtain cFPD readings for 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.

6. 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 according to any one of claims 1 to 4; If the prediction result is that there is long QT pathogenicity, the long QT typing analysis with different pathogenic mechanisms is performed on the cardiomyocytes with the long QT pathogenic mutation to obtain the prediction result of the long QT typing.

7. 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 cardiomyocytes of the variant sample to be predicted; The primary screening module is 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, obtain the cFPD reading of the cardiomyocyte mutation 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 variation; Table 1: ， ， 。 8. The electronic device according to claim 7, wherein: 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.

9. The electronic device according to claim 8, wherein: The long QT-positive cardiomyocytes and the long QT-negative cardiomyocytes are cardiomyocytes with KCNQ1 gene mutations.

10. The electronic device according to claim 7, wherein: The output criteria of the prediction model are: When the cFPD reading is greater than or equal to 550 ms, the presence of long QT pathogenicity is output; When the cFPD reading is less than 550 ms, the absence of long QT pathogenicity is output.

11. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; Wherein, the processor is configured to execute the method for predicting the pathogenic type of myocardial cell variation described in any one of claims 1 to 4, the method for constructing a prediction model for the pathogenic type of myocardial cell variation described in claim 5, or the method for predicting the long QT typing caused by myocardial cell variation described in claim 6 by executing the executable instructions.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored computer program, wherein, when the computer program is run, the device where the computer-readable storage medium is located is controlled to execute the method for predicting the pathogenic type of myocardial cell variation according to any one of claims 1 to 4, the method for constructing a prediction model for the pathogenic type of myocardial cell variation according to claim 5, or the method for predicting the long QT typing caused by myocardial cell variation according to claim 6.

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