A single-stranded DNA screening method for regulating gene mRNA expression
A computational and experimental approach using Python and database analysis identifies single-stranded DNAs that regulate gene mRNA expression, addressing the inefficiencies of existing methods by predicting and verifying transcription factor binding and mRNA expression changes.
Patent Information
- Application Number
- CN202111437868.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2021-11-29
- Publication Date
- 2024-12-20
- Estimated Expiration
- 2041-11-29
AI Technical Summary
Existing methods lack efficient and systematic approaches for screening single-stranded DNA sequences that regulate gene mRNA expression, particularly through computational analysis and experimental verification.
A method involving Python programming to analyze DNA sequences for complementary and reverse complementary repeat pairs, predict transcription factor binding sites using the JASPAR database, and verify mRNA expression changes in transfected cells to identify single-stranded DNAs that up- or down-regulate gene expression.
This method significantly enhances the efficiency and accuracy of screening single-stranded DNAs that regulate gene mRNA expression by leveraging computational tools and experimental validation, enabling the identification of sequences with regulatory functions.
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Figure CN114203258B_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a single-stranded DNA screening method for regulating gene mRNA expression, belonging to the technical field of genetics. Background Art
[0002] In the genome of eukaryotes, there are many repetitive sequences, which play a variety of roles in the expression of cellular genes, including palindromic repeat sequences, inverted repeat sequences, mirror repeat sequences, complementary repeat sequences, etc.
[0003] There are many cis-acting elements upstream of eukaryotic cell genes, such as enhancers, silencers, promoters, etc., which regulate the expression of downstream genes. When eukaryotic cells transcribe genes, the chromosome structure relaxes, the DNA double helix unwinds and binds to transcription factors. In this process, various types of repetitive sequences upstream of the gene may form various spatial structures of the DNA chain through complementary pairing, thereby affecting the binding of DNA and transcription factors, thereby regulating gene expression. Nucleic acid aptamers are nucleic acid sequences that can bind to specific protein domains by forming a certain stable spatial structure, thereby playing a certain biological function. By screening specific types of repetitive sequences in the genome, it is possible to find small segments of nucleic acid sequences that have good binding properties with transcription factors, thereby screening sequences that have a regulatory effect on gene expression.
[0004] There are studies in existing reports showing that single-stranded DNA can affect the expression of certain genes. But it is not the repeated single-stranded DNA sequence. At present, there are few studies on the function of repeated single-stranded DNA in cells, and there are even fewer methods that use computer program languages for calculation screening and verification in experiments. The present invention is a technology with original significance. Summary of the invention
[0005] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art and provide a single-stranded DNA screening method for regulating gene mRNA expression.
[0006] To achieve the above object, the present invention adopts the following technical solutions:
[0007] A single-stranded DNA screening method for regulating gene mRNA expression, the specific steps are as follows:
[0008] (1) First, analyze the upstream sequence of the gene using Python language to obtain all complementary repeat sequences and reverse complementary repeat sequence pairs, and derive the sequence length and sequence starting position to obtain the candidate sequence;
[0009] (2) Using the JASPAR database to predict transcription factor binding sites for candidate sequences, the transcription factors with scores greater than 9 were screened out, and the corresponding sequences were numbered to obtain pre-screened sequences;
[0010] (3) The pre-screened sequence is then transfected into cells, an internal reference is set, and changes in the mRNA expression of the corresponding gene are examined. Sequences with statistically significant changes are screened out, which are single-stranded DNA that regulates the expression of gene mRNA.
[0011] Preferably, the single-stranded DNA comprises a single-stranded DNA sequence that up-regulates or down-regulates gene mRNA expression.
[0012] Preferably, the TRRUST database can be used to analyze the transcription factors corresponding to the obtained single-stranded DNA sequences to determine their targets and effects for further research.
[0013] Preferably, the specific method of step (1) is as follows: read in the sequence information and the length range of the repeated sequence, loop for each length n, establish a set data structure, and for each sequence at position [i, i+n-1] (i represents the relative position of the base, and n represents the number of bases increasing in increments), if its reverse repeat / complementary repeat sequence is in the set, store it and the sequence in the set as a repeated sequence pair. After each judgment is completed, add the sequence [i, i+n-1] to the set, and judge the next [i+1, i+n] until i≤5001-n. Finally, output the sequence, length, position and other information of all retrieved repeated sequence pairs as the program running result.
[0014] Preferably, in step (3), the transfected cells are K562 cells.
[0015] Preferably, in step (3), the standard of statistical difference is t<0.05.
[0016] Beneficial effects of the present invention:
[0017] The present invention uses a program to analyze complex DNA sequence information, greatly improving efficiency and time. At the same time, the program is used to compare and analyze relevant information in the database to predict the probability of binding between the sequence and the transcription factor, thereby screening the repetitive sequence information of important transcription factors that regulate gene expression. The predicted single-stranded DNA sequence is artificially synthesized and verified by cytological experiments. Finally, the single-stranded DNA that regulates gene mRNA expression is obtained.
[0018] The key innovation of the present invention is that for the first time, single-stranded DNA is screened by means of standardized processes such as sequence information retrieval, program design, transcription factor binding prediction, and experimental verification. The screened single-stranded DNA has the ability to regulate the expression level of mRNA of a specific screened gene in cells, and a specific single-stranded DNA may have the function of up-regulating or down-regulating the gene.
[0019] The present invention is universal in the process of screening and searching for single-stranded DNA regulating mRNA expression of many other genes, and is applicable to the screening and verification of single-stranded DNA regulating gene expression of multiple genes. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] Figure 1 It is a schematic diagram of HBB sequence information;
[0021] Figure 2 It is a python algorithm flowchart;
[0022] Figure 3 The expression of HBB gene mRNA. DETAILED DESCRIPTION
[0023] The present invention is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that the following description is only for explaining the present invention and does not limit its contents.
[0024] 1. HBB upstream sequence query
[0025] The human HBB gene was found in the NCBI database at 5227071-5225464 on human chromosome 11. The upstream 5730bp sequence was obtained, which was 5232800-5227071 on human chromosome 11. ( Figure 1 )
[0026] 2. Program design and analysis results
[0027] The program was written in Python to analyze the upstream 5730bp sequence. The above sequence information was analyzed to obtain all complementary repeat sequences and reverse complementary repeat sequence pairs in the 5730bp sequence, and their lengths and sequence start positions (calculated based on the distance from the start of the HBB gene) were derived.
[0028] Specific idea: read in the sequence information and the length range of the repeat sequence, loop for each length n, establish a set data structure, and for each sequence at position [i,i+n-1] (i represents the relative position of the base, and n represents the number of bases that increase in increments), if its reverse repeat / complementary repeat sequence is in the set, store it and the sequence in the set as a repeat sequence pair. After each judgment is completed, add the sequence [i,i+n-1] to the set, and judge the next [i+1,i+n] until i≤5001-n. Finally, output the sequence, length, position and other information of all retrieved repeat sequence pairs as the result of the program operation. ( Figure 2 )
[0029] The program analysis results are shown in Tables 1 and 2.
[0030] Table 1. Analysis of complementary repeat sequences in the 5730 bp upstream of the HBB gene
[0031]
[0032]
[0033] Table 2. Analysis of reverse complementary repeat sequences in the 5730 bp upstream of the HBB gene
[0034]
[0035] 3. Screening of potential transcription binding site sequences
[0036] The JASPAR database was used to predict the transcription factor binding sites of the candidate sequences, and the transcription factors with scores greater than 9 were screened out. The corresponding sequences were numbered and 12 sequences were obtained. The results are shown in Table 3.
[0037] Table 3. Transcription factor binding prediction
[0038]
[0039]
[0040] 4. RT-qPCR results
[0041] The changes in HBB gene mRNA expression after the 12 sequences were transfected into K562 cells (ATCC, USA) were plotted. Figure 3(A to L are the mRNA expression of HBB gene of sequences 1 to 12, respectively). GAPDH gene (Shanghai Biosynthetics) was used as the internal reference, and the statistical difference standard was set to t<0.05. After different sequences were introduced into cells, the expression of HBB was compared with that of the blank control group NC. A t-test was performed between the repeated times, and it was found that sequences 1, 2, 3, 4, 5, 6, 11, and 12 did not show statistical differences. Sequences 7, 8, 9, and 10 showed significant differences, and the expression levels were lower than those of the blank control group. Among them, sequences 7 and 8 were significantly lower after introduction, suggesting that these sequences may have a greater impact on the transcription of the HBB gene.
[0042] 5. Sequence-corresponding transcription factors and mechanism prediction
[0043] The transcription factors corresponding to sequences 7, 8, 9, and 10 were analyzed using the TRRUST database to identify targets, effects, and references in previous literature for further study (Table 4).
[0044] Table 4. Sequence downstream effect prediction analysis
[0045]
[0046]
[0047] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Based on the technical solution of the present invention, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present invention.
Claims
1. A method for screening single-stranded DNA for regulating gene mRNA expression, characterized in that: The specific steps are as follows: (1) First, analyze the upstream sequence of the gene using Python language to obtain all complementary repeat sequences and reverse complementary repeat sequence pairs, and derive the sequence length and sequence starting position to obtain the candidate sequence; (2) Use the JASPAR database to predict transcription factor binding sites for candidate sequences, screen out transcription factors with scores greater than 9, number the corresponding sequences, and obtain pre-screening sequences; (3) Then, the pre-screened sequence is transfected into cells, an internal reference is set, and the changes in the mRNA expression of the corresponding gene are examined. The sequences with statistically significant changes are screened out, which are single-stranded DNAs that regulate the expression of gene mRNA; The specific method of step (1) is as follows: read in the sequence information and the length range of the repeated sequence, loop for each length n, establish a set data structure, for each sequence at position [i, i+n-1], if its inverted repeat / complementary repeat sequence is in the set, store it and the sequence in the set as a repeated sequence pair, each time a judgment is completed, add the sequence [i, i+n-1] to the set, and judge the next [i+1, i+n] until i≤5001-n, and finally output the sequence, length, and position information of all retrieved repeated sequence pairs as the program running result.
2. The screening method according to claim 1, characterized in that The single-stranded DNA includes a single-stranded DNA sequence that up-regulates or down-regulates gene mRNA expression.
3. The screening method according to claim 1, characterized in that The TRRUST database can also be used to analyze the transcription factors corresponding to the obtained single-stranded DNA sequences to determine their targets and effects for further research.
4. The screening method according to claim 1, characterized in that In step (3), the transfected cells are K562 cells.
5. The screening method according to claim 1, characterized in that In step (3), the standard for statistical difference was t < 0.05.