Application of LncRNA in detection of hypoxia tolerance

Abstract

The invention relates to application of LncRNA in detection of hypoxia tolerance. A group of LncRNA markers, including one or more of Lnc NEAT1 and Lnc MALAT1, are obtained by screening a whole transcriptome ceRNA by using TED deletion cells, and can be applied to construction of a prediction model of hypoxia tolerance, and a new tool and a new idea are provided for research of a hypoxia tolerance molecular mechanism.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine, and in particular relates to the application of LncRNA in detecting hypoxia tolerance. Background Art

[0002] People who have lived at lower altitudes for a long time may suffer from headaches, dizziness, fatigue, poor sleep, acute cerebral edema, pulmonary edema and other life-threatening diseases or injuries due to the reduced oxygen content when they enter a low-oxygen environment such as plateaus, high altitudes, deep seas, etc., whereas people who have lived at high altitudes for a long time can usually tolerate hypoxia and will not suffer from hypoxia-related diseases or injuries.

[0003] Hypoxia tolerance refers to the ability of a person to withstand a decrease in oxygen concentration under hypoxic conditions. People who exercise regularly usually have a higher tolerance to hypoxia, while people who are usually weak or do not like to exercise or are quiet have a relatively poorer tolerance to hypoxia. The higher the tolerance to hypoxia, the less impact a special environment has on a person. Studying the molecular mechanism of hypoxia tolerance is extremely important for the selection, prevention and treatment of hypoxic injury reactions in special occupational groups.

[0004] Therefore, the present invention provides a group of LncRNAs that can be used to detect the body's tolerance to hypoxia, which is beneficial for selecting people in special occupations, or can be used to perform a simple test of the body's tolerance to hypoxia before going to a special environment, which helps to prepare in advance and avoid altitude sickness or other diseases or injuries caused by hypoxia. Summary of the invention

[0005] Based on the above purpose, the present invention proposes the following technical solution: In a first aspect, the present invention provides a group of LncRNA markers, wherein the LncRNA markers include one or more of Lnc NEAT1 and Lnc MALAT1.

[0006] Long non-coding RNA (LncRNA) is a non-coding RNA with a length greater than 200 nucleotides. Studies have shown that LncRNA plays an important role in many life activities such as dosage compensation effect, epigenetic regulation, cell cycle regulation and cell differentiation regulation, and has become a hot topic in genetic research.

[0007] The second aspect of the present invention provides the use of a reagent for detecting the expression level of a LncRNA marker in the preparation of a product for detecting hypoxia tolerance, wherein the LncRNA marker is the LncRNA marker described in the first aspect of the present invention.

[0008] Furthermore, the detection is performed on the sample to be tested.

[0009] In the present invention, the sample to be tested for the detection of the LncRNA marker refers to a composition obtained from or derived from a subject (e.g., an individual of interest), comprising cell entities and / or other molecular entities characterized and / or identified based on physical, biochemical, chemical and / or physiological characteristics. The sample to be tested can be from the subject's blood and other fluid samples and tissue samples of biological origin, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample can be solid tissue, such as from fresh, frozen and / or preserved organ or tissue samples, biopsy tissues or aspirates; blood or any blood component; body fluids; cells from any time of individual pregnancy or development; or plasma. The term sample to be tested includes biological samples that have been processed in any way after they are obtained, such as reagent treatment, stabilization, or enrichment for certain components (such as proteins or polynucleotides), or embedded in a semi-solid or solid matrix for sectioning purposes. The cell is an animal cell, such as a cell from a human, gorilla, monkey, horse, cow, sheep, pig, donkey, camel, dog, rabbit, cat, rat, mouse, fish, bird or insect as is well known in the art. The subject includes a mammal, preferably a primate, and particularly preferably a human.

[0010] Furthermore, the reagents for detecting the level of LncRNA markers include reagents used in any of the following methods: RNA extraction, RNA sequence analysis, detection methods based on PCR principles, Southern hybridization methods, Northern hybridization methods, dot hybridization methods, fluorescence in situ hybridization methods, DNA microarray methods, ASO methods, and high-throughput sequencing platform methods.

[0011] Furthermore, the reagents may also include LncRNA expression auxiliary detection reagents, which include but are not limited to: reaction reagents that visualize the amplicon corresponding to the primer, such as reagents that visualize the amplicon by agarose gel electrophoresis, enzyme-linked gel electrophoresis, chemiluminescence, in situ hybridization, fluorescence detection, etc.; RNA extraction reagents; reverse transcription reagents; cDNA amplification reagents; standard substances used to prepare standard curves; and positive controls.

[0012] Furthermore, the product also includes reagents for processing the sample to be tested.

[0013] Furthermore, the product is selected from the group consisting of probe sets, primer sets, kits, chips, test strips, high-throughput sequencing, systems, equipment, and devices.

[0014] The third aspect of the present invention provides the use of LncRNA markers in the study of the molecular mechanism of hypoxia tolerance, wherein the LncRNA markers are the LncRNA markers described in the first aspect of the present invention.

[0015] The fourth aspect of the present invention provides the use of the LncRNA marker described in the first aspect of the present invention in constructing a prediction model for hypoxia tolerance.

[0016] The fifth aspect of the present invention provides a method for constructing a hypoxia tolerance prediction model, the method comprising the steps of: obtaining LncRNA marker expression level data of the sample to be tested, and constructing a hypoxia tolerance prediction model based on the LncRNA marker expression level data.

[0017] The LncRNA marker is the LncRNA marker described in the first aspect of the present invention.

[0018] Furthermore, if the expression level of the LncRNA marker is higher than the reference value, the sample to be tested has a better ability to tolerate hypoxia; if the expression level of the LncRNA marker is lower than the reference value, the sample to be tested has a poor ability to tolerate hypoxia.

[0019] The sixth aspect of the present invention provides a prediction system for hypoxia tolerance, the system comprising a judgment component, which is a computing device for judging hypoxia tolerance according to the expression level of a LncRNA marker, wherein the LncRNA marker is the LncRNA marker described in the first aspect of the present invention.

[0020] Optionally, the judging component may make a judgment through a computing model constructed by the method described in the fifth aspect of the present invention.

[0021] Optionally, the judgment component can make a judgment by comparing the expression level of the LncRNA marker described in the first aspect of the present invention with a reference value.

[0022] Furthermore, if the expression level of the LncRNA marker is higher than the reference value, the sample to be tested has a better ability to tolerate hypoxia; if the expression level of the LncRNA marker is lower than the reference value, the sample to be tested has a poor ability to tolerate hypoxia.

[0023] Furthermore, the prediction system may also include one or more of a detection device, an input device, an output device, and a communication device.

[0024] Furthermore, the detection device is used to detect the expression level of LncRNA markers in the sample to be tested.

[0025] Furthermore, the input device is used to input the detection result of the LncRNA marker expression level, and the LncRNA marker expression level can be detected by the detection device.

[0026] Furthermore, the output device is used to output the judgment result of the judgment component.

[0027] A seventh aspect of the present invention provides a computer device for predicting hypoxia tolerance, the device comprising a memory and a processor.

[0028] The memory is used to store program instructions.

[0029] The processor is used to execute program instructions. When the program instructions are executed, it is used to perform the following operations: obtain the LncRNA marker expression level data of the sample to be tested, input the LncRNA marker expression level data into the prediction model constructed based on the method described in the fifth aspect of the present invention, and obtain the prediction result of the sample to be tested.

[0030] The eighth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the following method when executed by a processor: obtaining LncRNA marker expression level data of a sample to be tested, inputting the LncRNA marker expression level data into a prediction model constructed based on the method described in the fifth aspect of the present invention, and obtaining a prediction result of the sample to be tested.

[0031] Advantages and beneficial effects of the present invention: The present invention uses TED-deficient cells to screen the whole transcriptome ceRNA to obtain a group of LncRNA markers, including one or more of Lnc NEAT1 and Lnc MALAT1. It can be used to construct a prediction model for hypoxia tolerance, providing new tools and new ideas for studying the molecular mechanism of hypoxia tolerance. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is the sequence alignment of the Lnc NEAT1 whole transcriptome sequencing.

[0033] Figure 2 qPCR detection of 293T-WT cells and 293T-TED - / - Results of Lnc NEAT1 expression in cells.

[0034] Figure 3 This is the sequence alignment of the Lnc MALAT1 whole transcriptome sequencing.

[0035] Figure 4 qPCR detection of 293T-WT cells and 293T-TED - / - Results of Lnc MALAT1 expression in cells.

[0036] Figure 5 Schematic diagram of the prediction system structure for hypoxia tolerance.

[0037] Figure 6 Schematic diagram of the computer device structure for predicting hypoxia tolerance. DETAILED DESCRIPTION

[0038] The present invention is further described below in conjunction with specific embodiments, but the protection scope of the present invention is not limited thereto, and any technician familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, shall be equivalently replaced or changed, and all shall be covered within the protection scope of the present invention. The materials, reagents, etc. used in the following examples, unless otherwise specified, can be obtained from commercial channels.

[0039] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0040] Example 1. Verify the sequence results of whole transcriptome sequencing 1. 293T-WT cells and 293T-TED - / - The cells were subjected to whole transcriptome sequencing (RNA-seq) and ceRNA analysis. According to the whole transcriptome ceRNA screening results, LncRNA NEAT1 was downregulated after 2 hours of hypoxia and upregulated at 24 hours; LncRNA NEAT2 (MALAT1) was upregulated under normoxia.

[0041] 2. Align the sequences according to the whole transcriptome sequencing results, select the sequence regions with obvious differences (or high abundance) to design qPCR primers, and detect the expression levels at 0, 2h, 24h and 48h.

[0042] Lnc NEAT1 (Chromosome 11, NC_000011.10 (65422798..65445540)) (YJ peak) alignment results are as follows Figure 1 As shown in Figure 1, the primers used for qPCR were NEAT1-qPCR-F1: 5'-GTGGCTGTTGGAGTCGGTAT-3' (SEQ ID NO: 1), NEAT1-qPCR-R1: 5'-ACCACGGTCCATGAAGCATT-3' (SEQ ID NO: 2). The qPCR results are shown in Figure 1. Figure 2 As shown, Lnc NEAT1 is expressed in 293T-TED - / - The expression of TNF-α in 293T cells was higher than that in 293T-WT cells, and increased with the increase of hypoxia time in the early stage of hypoxia.

[0043] Lnc MALAT1 (hg38, Chromosome 11, NC_000011.10 (65497738..65506516))(hg19, 65, 265, 209-65, 273, 987, YJ peak chr11: 65268346-65270060) alignment results are as follows Figure 3 As shown in Figure 2, the primers used for qPCR were NEAT2-qPCR-F: 5'-TGTTGGCACGAACACCTTCA-3' (SEQ ID NO: 3), NEAT2-qPCR-R: 5'-AAAGCTGCACTGTGCTGTACT-3' (SEQ ID NO: 4). The qPCR results are shown in Figure 2. Figure 4 As shown, after hypoxia treatment, Lnc MALAT1 was expressed in 293T-TED - / - The expression of 293T cells was higher than that of 293T-WT cells.

[0044] Figure 5 A schematic diagram showing the structure of the hypoxia tolerance prediction system provided by the present invention.

[0045] The system is programmed or otherwise configured to include a data acquisition unit 101 , a data classification unit 102 , and an output unit 103 .

[0046] The data acquisition unit 101 is used to acquire the LncRNA marker expression data in the sample, wherein the metabolite marker is one or more of Lnc NEAT1 and Lnc MALAT1.

[0047] The data classification unit 102 is used to perform classification prediction based on the prediction model to obtain a classification result of whether the sample to be tested has a better or worse ability to tolerate hypoxia. If the expression level of the LncRNA marker is higher than the reference value, a classification result of a better ability to tolerate hypoxia is obtained; if the expression level of the LncRNA marker is lower than the reference value, a classification result of a poor ability to tolerate hypoxia is obtained.

[0048] In some embodiments, the method of constructing the prediction model is known to those skilled in the art, and the steps of associating the LncRNA expression level with a certain possibility or risk can be implemented and realized in different ways. Preferably, the metabolite marker expression level is mathematically associated with the fundamental question of whether it is resistant to hypoxia. The determination of the LncRNA marker can be combined with other clinical features by any suitable prior art mathematical method, and the prediction model can be constructed by a machine learning algorithm.

[0049] In some embodiments, the method for constructing the prediction model includes: obtaining the expression level of the LncRNA marker of the training set sample and the clinical characteristics corresponding to the sample to be tested, the clinical characteristics include hypoxia-tolerant population and hypoxia-intolerant population; constructing a model based on the expression level of the LncRNA marker and the clinical characteristics, using machine learning to obtain the constructed prediction model, and verifying the effectiveness of the constructed prediction model. The machine learning includes algorithm models developed using various development tools; the development tools include TensorFlow, Scikit Learn, PyTorch, OpenNN, RapidMiner, Azure Machine Learning, Apache Mahout, Shogun, KNIME, Vertex AI, H2Oai, Anaconda, Keras, Tableau, Fast.ai, Catalyst, Amazon ML, MLJAR, Spell, and one or more. The algorithm model includes one or more of convolutional neural network, autoencoder, deep belief network, linear regression, logistic regression, Lasso regression, Ridge regression, linear discriminant analysis, K nearest neighbor algorithm, decision tree, perceptron, support vector machine, ensemble learning, correlation analysis, naive Bayes, AdaBoost, GBDT, XGBoost, LightGBM, CatBoost or random forest.

[0050] In the present invention, the term "reference value" refers to a value that is statistically related to a specific result when compared to an analysis result. In some embodiments, the reference value is determined based on the statistical conclusions of the expression levels of LncRNAs in wild-type cells and TED-deficient cells in hypoxia or low oxygen environments. In some embodiments, the reference value is determined based on the statistical conclusions of the differential LncRNA expression levels in hypoxia-tolerant and hypoxia-intolerant populations. Some such studies are shown in the Examples section of this article, but studies from the literature and the experience of users of the methods described herein can also be used to generate or adjust reference values.

[0051] The output unit 103 is used to output the classification result.

[0052] The system may be the user's electronic device or a computer system located remotely from the electronic device.

[0053] Figure 6 A schematic diagram showing the structure of a computer device for predicting hypoxia tolerance provided by the present invention.

[0054] The computer device 200 includes a processor 201 and a memory 202 coupled to the processor 201. The memory 202 stores program instructions. When the program instructions are executed by the processor 201, the processor 201 executes the program of the above-mentioned hypoxia tolerance prediction system. The computer device 200 may be a mobile electronic device.

[0055] The processor 201 may also be referred to as a central processing unit (CPU). The processor 201 may be an integrated circuit chip having signal processing capabilities. The processor 201 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0056] The storage medium of the embodiment of the present invention stores program instructions that can implement the above-mentioned method for predicting hypoxia tolerance, wherein the program instructions can be stored in the above-mentioned storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or a computer device such as a computer, a server, a mobile phone, or a tablet.

[0057] It should be understood that the system, device and method described in the present invention can be implemented in other ways. For example, the device embodiment described above is only schematic. For example, the division of the unit is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms. The unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0058] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0059] The present invention has been described in detail above. For those skilled in the art, without departing from the purpose and scope of the present invention, and without the need to carry out unnecessary experimental conditions, the present invention can be implemented in a wide range under equivalent parameters, concentrations and conditions. Although the present invention provides embodiments, it should be understood that the present invention can be further improved. In a word, according to the principles of the present invention, the application is intended to include any changes, uses or improvements to the present invention, including departure from the disclosed scope in the application, and changes made with conventional techniques known in the art.

Claims

1. A group of LncRNA markers, characterized in that: The LncRNA markers include one or more of Lnc NEAT1 and Lnc MALAT1.

2. The use of a reagent for detecting the expression level of a LncRNA marker in the preparation of a product for detecting hypoxia tolerance, characterized in that: The LncRNA marker is the LncRNA marker according to claim 1.

3. The use according to claim 2, characterized in that: The test is performed on the sample to be tested; Preferably, the reagents for detecting the level of LncRNA markers include reagents used in any of the following methods: RNA extraction, RNA sequence analysis, detection methods based on PCR principles, Southern hybridization methods, Northern hybridization methods, dot hybridization methods, fluorescence in situ hybridization methods, DNA microarray methods, ASO methods, and high-throughput sequencing platform methods.

4. The use according to claim 2, characterized in that: The product also includes reagents for processing samples to be tested; Preferably, the product is selected from the group consisting of a probe set, a primer set, a kit, a chip, a test paper, a high-throughput sequencing, a system, a device, and an apparatus.

5. Application of LncRNA markers in the study of the molecular mechanism of hypoxia tolerance, characterized in that: The LncRNA marker is the LncRNA marker according to claim 1.

6. Use of the LncRNA marker according to claim 1 in constructing a prediction model for hypoxia tolerance.

7. A method for constructing a prediction model for hypoxia tolerance, characterized in that: The method comprises the following steps: obtaining LncRNA marker expression level data of the sample to be tested, and constructing a hypoxia tolerance prediction model based on the LncRNA marker expression level data; The LncRNA marker is the LncRNA marker according to claim 1; Preferably, if the expression level of the LncRNA marker is higher than the reference value, the sample to be tested has a better ability to tolerate hypoxia; if the expression level of the LncRNA marker is lower than the reference value, the sample to be tested has a poor ability to tolerate hypoxia.

8. A prediction system for hypoxia tolerance, characterized in that: The system comprises a judgment component, which is a computing device for judging the hypoxia tolerance ability according to the expression level of the LncRNA marker, wherein the LncRNA marker is the LncRNA marker according to claim 1; Optionally, the judging component may make a judgment through a computing model constructed by the method according to claim 7; Optionally, the judgment component can make a judgment by comparing the expression level of the LncRNA marker according to claim 1 with a reference value; Preferably, if the expression level of the LncRNA marker is higher than the reference value, the sample to be tested has a better ability to tolerate hypoxia; if the expression level of the LncRNA marker is lower than the reference value, the sample to be tested has a poor ability to tolerate hypoxia; Preferably, the prediction system may also include one or more of a detection device, an input device, an output device, and a communication device; Preferably, the detection device is used to detect the expression level of LncRNA markers in the sample to be tested; Preferably, the input device is used to input the detection result of the LncRNA marker expression level, and the LncRNA marker expression level can be detected by the detection device; Preferably, the output device is used to output the judgment result of the judgment component.

9. A computer device for predicting hypoxia tolerance, characterized in that: The device comprises a memory and a processor; The memory is used to store program instructions; The processor is used to execute program instructions. When the program instructions are executed, it is used to perform the following operations: obtain the LncRNA marker expression level data of the sample to be tested, input the LncRNA marker expression level data into the prediction model constructed based on the method described in claim 7, and obtain the prediction result of the sample to be tested.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the following method when executed by a processor: obtaining LncRNA marker expression level data of a sample to be tested, inputting the LncRNA marker expression level data into a prediction model constructed based on the method of claim 7, and obtaining a prediction result of the sample to be tested.

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