Method for constructing phylogenetic tree for mining antibiotic resistance based on microbial mutation sites

Abstract

The invention discloses a method for constructing a phylogenetic tree for excavating antibiotic resistance based on microbial mutation sites, and relates to the technical field of microorganisms. The method comprises the following steps: step 1, database retrieval: determining a database according to a retrieval demand, analyzing a retrieval subject, extracting keywords, constructing a retrieval formula, and performing retrieval in the database to obtain drug resistance information and gene locus mutation data of microorganisms; 2, data collection and arrangement: screening and analyzing useful information from retrieval results, collecting research results about classification, action mechanisms, drug resistance conditions and the like of different antibiotics, creating a worksheet in Excel, and recording information such as microorganism names, strains, drug resistance conditions, drug resistance site mutation and the like; according to the application, a microorganism 23rRNA sequence is utilized to operate in a plurality of databases and software, and the method comprises the following steps: comparing homologous genes in an NCBI (National Center of Biotechnology Information) sub-website, constructing an evolutionary tree, retrieving drug resistance information in a plurality of professional websites, and counting and classifying antibiotics in Excel.

Description

Technical Field

[0001] The present invention relates to the field of microbial technology, and in particular to a method for constructing a phylogenetic tree based on microbial mutation sites to mine antibiotic resistance. Background Art

[0002] Mycoplasma pneumoniae is a common pathogen that causes respiratory tract infections and is an antibiotic. In recent years, the resistance of Mycoplasma pneumoniae to certain antimicrobial drugs has become increasingly serious. The most common resistance is to macrolide drugs. For example, azithromycin was once the first-line drug for the treatment of Mycoplasma pneumoniae infection. As time goes by, Mycoplasma pneumoniae strains resistant to macrolide drugs have appeared in some areas. The resistance mechanism mainly includes target site changes, that is, point mutations in the 23SrRNA gene, which leads to a decrease in the binding force between the drug and the ribosome. In addition, the overexpression of the active efflux pump system may also be involved in drug resistance. Secondly, there is also a potential risk of resistance to fluoroquinolones. Although fluoroquinolones are not the first choice for the treatment of Mycoplasma pneumoniae infection, they may be used in some cases. At present, resistance to fluoroquinolones is relatively rare, but with the increase in its use, there is also a potential risk of resistance.

[0003] Phylogenetic tree, also known as molecular evolutionary tree, is a tree-like branching graph that summarizes the relationship between various organisms. It is based on the sequence information of biological macromolecules (such as nucleic acids, proteins, etc.) and is constructed through a specific algorithm. In the evolutionary tree, each branch node represents a species or a sequence, and the length of the branch usually reflects the evolutionary distance between species or sequences. It displays the evolutionary relationship between organisms in a graphical way, and can clearly show the evolutionary distance between different species or sequences. At the same time, the tree also relies on a large amount of sequence data, which usually comes from the fields of genome sequencing, proteomics, etc., and has high scientificity and reliability. With the continuous generation of new sequence data and the continuous improvement of analysis methods, the phylogenetic tree can be continuously updated and improved to reflect the latest research results of biological evolution. When constructing the phylogenetic tree, BLAST sequence alignment is usually used, and the MEGA tree construction method is used to find the specific gene sequence of the mutation site between microbial genes, and then further retrieve the drug resistance antibiotic database and sequence judgment, and rank the drug resistance of different genes based on the evolutionary tree, which ultimately provides guidance for the precise use of clinical microorganisms.

[0004] In response to the above problems, the inventors proposed a method for constructing a phylogenetic tree based on microbial mutation sites to mine antibiotic resistance to solve the above problems. Summary of the invention

[0005] In order to solve the problem of microbial resistance to certain antimicrobial drugs, the present invention aims to provide a method for constructing a phylogenetic tree based on microbial mutation sites to mine antibiotic resistance.

[0006] In order to solve the above technical problems, the present invention adopts the following technical solution: a method for constructing a phylogenetic tree based on microbial mutation sites to mine antibiotic resistance, comprising the following steps:

[0007] Step 1: Database search: determine the database according to the search requirements, analyze the search topic, extract keywords, construct a search formula, and perform the search in the database to obtain the microbial resistance information and gene site mutation data;

[0008] Step 2: Data collection and organization: Screen and analyze useful information from the search results, collect research results on the classification, mechanism of action, and drug resistance of different antibiotics, and create a worksheet in Excel to record information such as microbial name, strain, drug resistance, and drug resistance site mutation;

[0009] Step 3: Classify antibiotics according to their chemical structure or mechanism of action, and record the names of antibiotics and their mechanisms of action in an Excel spreadsheet;

[0010] Step 4: Enter the drug-resistant genes and site mutations. Fill the known drug-resistant genes and site mutation information into the corresponding columns in the Excel table. If there are multiple drug-resistant genes or site mutations, use different colors to mark them.

[0011] Step 5: Construct a phylogenetic tree. Take the specific genes of microorganisms as the research object, use the BLAST tool to align homologous genes, and use MEGA software to construct a phylogenetic tree to analyze the relationship between microorganisms and the evolutionary trend of drug resistance.

[0012] Preferably, the databases described in step 1 include CARD, Yaoyuan.com, National Bacterial Resistance Monitoring Network, DRESIS, MegaRes, EBI and NCBI,

[0013] Preferably, the steps of aligning homologous genes and constructing a phylogenetic tree according to step 5 include:

[0014] S1: Obtain the target gene sequence, and search and download the complete gene sequence in databases such as NCBI;

[0015] S2: Use BLAST tool to compare the downloaded target gene sequences and screen out homologous gene sequences;

[0016] S3: Import the screened homologous gene sequences into MEGA software for sequence comparison analysis;

[0017] S4: Select an appropriate construction algorithm and set relevant parameters in the MEGA software to construct a phylogenetic tree.

[0018] Preferably, the method further comprises the following steps:

[0019] Compare the affinity of evolutionary trees, observe the topological structure of evolutionary trees, including the length of branches and the position of nodes, and analyze the affinity between microorganisms and the evolutionary trend of drug resistance;

[0020] Drug resistance analysis, combining the drug resistance information and phylogenetic tree in Excel tables, conducts drug resistance analysis on different types of microorganisms to explore the commonalities and differences in drug resistance mechanisms, as well as possible response strategies.

[0021] Preferably, the method is used to study the drug resistance of microorganisms such as Mycoplasma pneumoniae, and to reveal their drug resistance evolutionary trends and homology relationships by constructing a phylogenetic tree.

[0022] Preferably, the drug resistance analysis also includes counting the use of antibiotics by various mycoplasmas and entering them into a table to more systematically study the current status and development trend of mycoplasma resistance.

[0023] A method for constructing a phylogenetic tree based on microbial mutation sites to mine antibiotic resistance. The phylogenetic tree is used to reveal the relationship between microorganisms and the evolutionary trend of resistance, and provides strong support for the rational use of antibiotics, population antibiotic resistance screening, resistance research and medical research.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. This application uses microbial 23rRNA sequences to operate in multiple databases and software, including comparing homologous genes and constructing evolutionary trees on NCBI sub-sites, retrieving drug resistance information on multiple professional websites, and statistically classifying antibiotics in Excel;

[0026] 2. This application constructs an evolutionary tree using Mycoplasma pneumoniae as an example, and also retrieves the resistance of homologous fragments and counts the frequency of Mycoplasma pneumoniae antibiotics, providing strong evidence for exploring the mechanism of Mycoplasma pneumoniae resistance to antibiotics and related research, and providing guidance for clinical population typing. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 Schematic diagram of the process of searching for antibiotics for the present invention.

[0029] Figure 2 It is a schematic diagram of the phylogenetic tree structure of homologous sequences of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Examples, such as Figure 1-2 As shown, the present invention provides a method for constructing a phylogenetic tree based on microbial mutation sites to mine antibiotic resistance, comprising the following steps:

[0032] Step 1: Database search: determine the database according to the search requirements, analyze the search topic, extract keywords, construct a search formula, and perform the search in the database to obtain the microbial resistance information and gene site mutation data;

[0033] Step 2: Data collection and organization: Screen and analyze useful information from the search results, collect research results on the classification, mechanism of action, and drug resistance of different antibiotics, and create a worksheet in Excel to record information such as microbial name, strain, drug resistance, and drug resistance site mutation;

[0034] Step 3: Classify antibiotics according to their chemical structure or mechanism of action, and record the names of antibiotics and their mechanisms of action in an Excel spreadsheet;

[0035] Step 4: Enter the drug-resistant genes and site mutations. Fill the known drug-resistant genes and site mutation information into the corresponding columns in the Excel table. If there are multiple drug-resistant genes or site mutations, use different colors to mark them.

[0036] Step 5: Construct a phylogenetic tree. Take the specific genes of microorganisms as the research object, use the BLAST tool to align homologous genes, and use MEGA software to construct a phylogenetic tree to analyze the relationship between microorganisms and the evolutionary trend of drug resistance.

[0037] The databases in step one include CARD, Drug Source Network, National Bacterial Resistance Monitoring Network, DRESIS, MegaRes, EBI and NCBI.

[0038] The steps of aligning homologous genes and constructing a phylogenetic tree according to step 5 include:

[0039] S1: Obtain the target gene sequence, and search and download the complete gene sequence in databases such as NCBI;

[0040] S2: Use BLAST tool to compare the downloaded target gene sequences and screen out homologous gene sequences;

[0041] S3: Import the screened homologous gene sequences into MEGA software for sequence comparison analysis;

[0042] S4: Select an appropriate construction algorithm and set relevant parameters in the MEGA software to construct a phylogenetic tree.

[0043] The following steps are also included:

[0044] Compare the affinity of evolutionary trees, observe the topological structure of evolutionary trees, including the length of branches and the position of nodes, and analyze the affinity between microorganisms and the evolutionary trend of drug resistance;

[0045] Drug resistance analysis, combining the drug resistance information and phylogenetic tree in Excel tables, conducts drug resistance analysis on different types of microorganisms to explore the commonalities and differences in drug resistance mechanisms, as well as possible response strategies.

[0046] The method was used to study the drug resistance of microorganisms such as Mycoplasma pneumoniae, and to reveal their resistance evolutionary trends and homology relationships by constructing a phylogenetic tree.

[0047] Resistance analysis also includes statistics on the use of antibiotics by various mycoplasmas and entering them into a table to more systematically study the current status and development trends of mycoplasma resistance.

[0048] A method for constructing a phylogenetic tree based on microbial mutation sites to mine antibiotic resistance, characterized in that the phylogenetic tree is used to reveal the relationship between microorganisms and the evolutionary trend of resistance, and to provide strong support for the rational use of antibiotics, screening of antibiotic resistance in the population, resistance research and medical research.

[0049] When searching a database, you should first determine the database based on your search needs, then analyze the search topic, extract keywords, and use logical operators to build a search formula. The database usually provides a variety of search fields and search condition settings, such as time range, document type, journal range, etc. After entering the search formula and setting the conditions, perform the search, then filter and analyze the search results, and save useful results for subsequent viewing and use.

[0050] The databases, literature websites and software used in the patent of this invention are: CARD, Yaoyuan.com, National Bacterial Resistance Monitoring Network, DRESIS, MegaRes, EBI, NCBI, CARD (The Comprehensive Antibiotic Resistance Database): a comprehensive antibiotic resistance database, which contains rich information on antibiotic resistance genes, their encoded products, antibiotic resistance mechanisms and other related data. The database classifies and organizes various antibiotic resistance genes and their variants through strict literature screening and sequence analysis tools. The National Bacterial Resistance Monitoring Network (China Antimicrobial Resistance Surveillance System, CARSS): is an important bacterial resistance monitoring system in my country, Yaoyuan.com: is a medical portal website engaged in Internet drug information services, which can provide various types of antibiotic drug information, medication knowledge, the latest antibiotic research progress and other content. It is a platform for understanding comprehensive antibiotic information, DRESIS: the introduction of a comprehensive database describing resistance information is to systematically provide all There are existing types of molecular mechanisms of drug resistance, which widely covers the most extensive diseases of all existing databases, as well as drugs with the largest number of clinically / experimentally verified resistance data. MegaRes: focuses on the collection and organization of antibiotic resistance genes and resistance-related information. It has made detailed classifications and annotations of the resistance genes of various microorganisms, which is convenient for users to quickly retrieve and understand the resistance characteristics of specific microorganisms. EBI (European Bioinformatics Institute): The database of the European Bioinformatics Institute also contains rich microbial information and related research data. In terms of antibiotic resistance, EBI provides some sequence information, functional annotations and related research reports on microbial resistance genes. Users can use this database to understand the resistance of microorganisms in Europe and the latest progress in international antibiotic resistance research. NCBI: It contains a large amount of microbial genome information and related research literature. By searching on NCBI, you can obtain a lot of research data on the sensitivity and resistance of microorganisms to antibiotics, including experimental results, gene sequences, etc.

[0051] Classify antibiotics, collect and organize data: obtain homologous gene data from BLAST comparison of NCBI's sub-website, and input resistance information and gene site mutation data obtained by querying various antibiotics and bacterial resistance websites, conduct database surveys, collect existing research results on the classification, mechanism of action, and resistance of different antibiotics, create a worksheet in Excel, and set the following column titles: microbial name, strain, resistance status, and resistance site mutation.

[0052] Classification of antibiotics: According to the chemical structure or mechanism of action of antibiotics, the collected antibiotics are roughly divided into several categories, such as β-lactams, macrolides, aminoglycosides, tetracyclines, etc. Then, the names of antibiotics involved in the data from different sources are filled in the corresponding columns in the Excel table one by one, and the main mode of action of the antibiotic is briefly described in the "Mechanism of Action" column.

[0053] Drug-resistance gene and site mutation entry: For each antibiotic, fill in the known drug-resistance gene and site mutation information into the corresponding columns based on the query results and literature research. If there are multiple drug-resistance genes or site mutations for one antibiotic, they will be marked with different colors.

[0054] Analysis and organization of antibiotics: Use the screening function of Excel to screen out specific categories of antibiotics, antibiotics with specific levels of resistance, or antibiotics with specific resistance genes, etc., and combine literature research and classification results to summarize and analyze the resistance of different categories of antibiotics, explore the commonalities and differences in resistance mechanisms, and possible response strategies.

[0055] First, obtain the target gene, open the NCBI website (https: / / www.ncbi.nlm.nih.gov / ), enter "Mycoplasma 23SrRNA" in the search bar, select the nucleic acid database for search, find the complete Mycoplasma 23SrRNA sequence from the search results, ensure its integrity and reliability, click the download button, save the sequence to a computer folder, open the nucleic acid BLAST tool, upload the downloaded Mycoplasma 23SrRNA sequence, select the standard database, run BLAST alignment, wait for the alignment results to return, carefully screen out 17 homologous sequence genes from the alignment results, and save these 17 homologous sequence genes into a separate file for subsequent use.

[0056] Next, align the sequences, open the MEGA software, select the "Open" option in the "File" menu, import the original mycoplasma 23SrRNA sequence and the 17 previously saved homologous sequences into the MEGA software, find "Alignment" in the menu bar, and click the "MegaBlast" function in the drop-down menu. The MEGA software will align and analyze the 18 gene sequences. Wait for the alignment to be completed and view the statistical information of the alignment results, including the sequence similarity percentage, number of matches, number of mismatches, number of gaps, etc. These statistical information can preliminarily reflect the degree of similarity between the sequences. A high similarity percentage usually indicates that the sequences are closely related, while a low similarity may mean that the relationship is distant. If the similarity of two sequences reaches more than 90%, it means that they may be close in evolution. Then, construct the phylogenetic tree. In the MEGA software, make sure that the 18 gene sequences that have completed sequence alignment are selected, select the "Phylogeny" option in the menu bar, and select "Construct Phylogenetic Tree" in the drop-down menu. In the pop-up dialog box, select an appropriate construction algorithm, such as Neighbor-Joining or Maximum Parsimony, etc., set relevant parameters, such as bootstrap value (usually set to 1000 repetitions to evaluate the reliability of the tree), evolutionary model (select an appropriate model according to the sequence characteristics, such as GTR, JC, etc.), etc., click the "OK" button to start building the phylogenetic tree, wait for the construction to be completed, and view the generated evolutionary tree graph (such as Figure 2 ) and related data.

[0057] Finally, the affinity of the evolutionary tree was compared and the topological structure of the evolutionary tree was observed, including the length of the branches and the position of the nodes. The branch length indicates the evolutionary distance. Shorter branches mean fewer evolutionary changes between the two species and a closer relationship. The position of the node reflects the evolutionary relationship of the species. Species near the common ancestor node usually have a closer relationship. For example, if two mycoplasmas share a closer node on the evolutionary tree and the branch length is shorter, then they are likely to have a higher affinity. Mycoplasmas with high homology will be in a closer position on the evolutionary tree and have a higher branch support. Their homology can be judged by observing the distribution of mycoplasmas in the evolutionary tree. If multiple mycoplasmas form a tight cluster on the evolutionary tree, it means that they have a higher homology. On the contrary, if the mycoplasmas are scattered in different positions of the evolutionary tree and the branch support is low, then their homology may be low.

[0058] Retrieve the homologous fragments of Mycoplasma pneumoniae resistance and the occurrence of Mycoplasma pneumoniae antibiotics in the database. Specifically, by investigating the database, enter the information on the use of antibiotics by various types of mycoplasmas mentioned in different databases into a table. Combined with the previous work such as database retrieval, data collection and organization, antibiotic classification, entry of resistance genes and site mutations, resistance level assessment, and literature source annotation, we can more systematically study the current status and development trend of mycoplasma resistance.

[0059]

[0060] Mycoplasma resistance statistics

[0061] This implementation case, through searching on multiple professional websites, building evolutionary trees by comparing homologous genes, analyzing microbial resistance, classifying and organizing antibiotic information using Excel, and counting the frequency of Mycoplasma pneumoniae antibiotics in literature databases, provides strong support for the rational use of antibiotics, population antibiotic resistance screening, resistance research, and medical research.

[0062] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for constructing a phylogenetic tree based on microbial mutation sites to mine antibiotic resistance, characterized in that: The steps include: Step 1: Database search: determine the database according to the search requirements, analyze the search topic, extract keywords, construct a search formula, and perform the search in the database to obtain the microbial resistance information and gene site mutation data; Step 2: Data collection and organization: Screen and analyze useful information from the search results, collect research results on the classification, mechanism of action, and drug resistance of different antibiotics, and create a worksheet in Excel to record information such as microbial name, strain, drug resistance, and drug resistance site mutation; Step 3: Classify antibiotics according to their chemical structure or mechanism of action, and record the names of antibiotics and their mechanisms of action in an Excel spreadsheet; Step 4: Enter the drug-resistant genes and site mutations. Fill the known drug-resistant genes and site mutation information into the corresponding columns in the Excel table. If there are multiple drug-resistant genes or site mutations, use different colors to mark them. Step 5: Construct a phylogenetic tree. Take the specific genes of microorganisms as the research object, use the BLAST tool to align homologous genes, and use MEGA software to construct a phylogenetic tree to analyze the relationship between microorganisms and the evolutionary trend of drug resistance.

2. A method for constructing a phylogenetic tree based on microbial mutation sites to mine antibiotic resistance as claimed in claim 1, characterized in that: The databases described in step 1 include CARD, Drug Source Network, National Bacterial Resistance Monitoring Network, DRESIS, MegaRes, EBI and NCBI.

3. A method for constructing a phylogenetic tree based on microbial mutation site mining for antibiotic resistance according to claim 1, characterized in that: The steps of aligning homologous genes and constructing a phylogenetic tree according to step five include: S1: Obtain the target gene sequence, and search and download the complete gene sequence in databases such as NCBI; S2: Use BLAST tool to compare the downloaded target gene sequences and screen out homologous gene sequences; S3: Import the screened homologous gene sequences into MEGA software for sequence comparison analysis; S4: Select an appropriate construction algorithm and set relevant parameters in the MEGA software to construct a phylogenetic tree.

4. A method for constructing a phylogenetic tree based on microbial mutation site mining for antibiotic resistance according to claim 1, characterized in that: The following steps are also included: Compare the affinity of evolutionary trees, observe the topological structure of evolutionary trees, including the length of branches and the position of nodes, and analyze the affinity between microorganisms and the evolutionary trend of drug resistance; Drug resistance analysis, combining the drug resistance information and phylogenetic tree in Excel tables, conducts drug resistance analysis on different types of microorganisms to explore the commonalities and differences in drug resistance mechanisms, as well as possible response strategies.

5. A method for constructing a phylogenetic tree based on microbial mutation site mining for antibiotic resistance according to claim 1, characterized in that: The method is used to study the drug resistance of microorganisms such as Mycoplasma pneumoniae, and to reveal their drug resistance evolutionary trends and homology relationships by constructing a phylogenetic tree.

6. A method for constructing a phylogenetic tree based on microbial mutation site mining for antibiotic resistance according to claim 4, characterized in that: The drug resistance analysis also includes statistically analyzing the use of antibiotics by various mycoplasmas and entering the data into a table to more systematically study the current status and development trend of mycoplasma resistance.

7. A method for constructing a phylogenetic tree based on microbial mutation site mining for antibiotic resistance according to any one of claims 1 to 6, characterized in that: This phylogenetic tree is used to reveal the relationship between microorganisms and the evolutionary trend of drug resistance, and to provide strong support for the rational use of antibiotics, screening of antibiotic resistance in the population, drug resistance research and medical research.

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