Method for analyzing antiviral and virus-promoting bacteria based on metatranscriptome sequencing and application

Abstract

The invention belongs to the technical field of biological detection, and discloses a method for analyzing antiviral and virus-promoting bacteria based on metatranscriptome sequencing and application. According to the method, on the basis of macro transcriptome sequencing, RPM values of virus and bacterial species are analyzed and calculated by utilizing different virus and bacterial marker genes. The RPM values of the viruses and the bacteria are utilized, and a correlation table showing the interaction relation between all the bacteria and the viruses is obtained through Spearman correlation analysis. According to the method, the problem that bacterial genomes are incomplete in metatranscriptome sequencing is solved, all viruses and bacteria in a single mosquito sample can be found, and the anti-virus and virus-promoting effects of the bacteria are detected through the rpoB gene of the bacteria and the load correlation of the virus RdRp. Moreover, the mosquito sample size involved in the method is large, the statistical reliability and ecological universality of research are enhanced, and the representativeness of research results is high.

Description

Technical Field

[0001] The present invention relates to the technical field of biological detection, and particularly relates to a method and application for analyzing antiviral and proviral bacteria based on metatranscriptome sequencing. Background Art

[0002] In traditional techniques, the search for antiviral and proviral bacteria mainly relies on laboratory screening and functional verification. These methods usually involve the isolation and purification of bacteria, the study of virus-host interactions, and virus infection experiments, etc. Generally, bacteria are isolated from natural environments (such as soil, water bodies, gut microbiota, etc.) or specific hosts (such as healthy or diseased animals), bacterial strains are cultured to obtain candidate strains capable of producing antiviral or proviral active substances, and in vitro experiments are used to screen bacteria or their metabolites that can inhibit or promote virus infection or replication, and finally in vitro or in vivo verification is carried out.

[0003] Currently, with the popularization and development of high-throughput sequencing technology, the research methods for discovering bacteria with antiviral or proviral effects have shifted from traditional experimental methods to modern methods that integrate high-throughput data analysis and functional verification. These new methods have higher sensitivity and resolution and can efficiently screen bacteria with specific functions in complex microbial communities. First, through 16S rRNA gene sequencing analysis, the microbial community structures of the healthy group and the virus-infected group are compared to screen bacteria that may be related to antiviral or proviral effects. The positive and negative correlation relationships between the abundance changes of specific bacteria and virus infection are searched for. Secondly, metagenomic sequencing can directly obtain the genomic information of all microorganisms in environmental samples, which is used to predict the functional potential of microorganisms and analyze the association between the functional genes of the bacterial community (such as genes related to the synthesis of antiviral active substances) and virus infection. Metatranscriptomics analyzes the gene expression profiles of the microbial community in the sample to reveal the dynamic mechanism of the bacteria-virus interaction.

[0004] However, metagenomic sequencing usually involves complex functional genes and pathways. The characteristics of metatranscriptomic sequencing result in that it is usually impossible to identify complete bacterial genomes, fewer bacterial species are found, and usually only disease-related viruses and related antiviral and proviral bacteria are concerned. Summary of the Invention

[0005] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method and application for analyzing antiviral and proviral bacteria based on metatranscriptome sequencing.

[0006] The present invention takes arboviruses (viruses that can replicate in the cells of hematophagous arthropods and vertebrates, can infect humans or other vertebrates, and are transmitted from infected hosts to susceptible hosts through vectors such as mosquitoes, ticks, sandflies or midges) as the starting point, and based on the metatranscriptome sequencing technology, provides a comprehensive and efficient method for exploring all antiviral and proviral bacteria in mosquitoes, which can more comprehensively explore the interaction relationship between all viruses and all bacteria in mosquitoes and screen out potential antiviral and proviral bacteria, covering pathogenic and non-pathogenic viruses, so as to reveal a more extensive microbial community dynamics.

[0007] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0008] In the first aspect, the present invention provides a method for analyzing antiviral and proviral bacteria based on metatranscriptome sequencing, comprising the following steps:

[0009] (1) Take mosquito samples from no less than 25 provinces and cities, extract nucleic acids, construct a transcriptome library and perform metatranscriptome sequencing;

[0010] (2) The data of the metatranscriptome sequencing is quality-controlled, and after assembly, the virus sequences are compared and annotated with the NR database; the virus sequences are translated through ORFfinder, the proteins are de-duplicated, and compared with the RdRp database to obtain RdRp-related proteins; the RdRp-related proteins and reference proteins are subjected to multiple sequence alignment, and after sequence trimming, an evolutionary tree is constructed; the annotated viruses corresponding to the virus species confirmed by the evolutionary tree are found, mapping quantification is performed, and the RPM value is calculated to obtain the identified virus species;

[0011] (3) After quality control of the transcriptome library, ribosomal rRNA is removed, the host is removed and mixed and assembled to obtain a mixed contig sequence; the contig sequence is compared with the rpoB gene, translated, the proteins are de-duplicated, and compared with the rpoB gene again to obtain rpoB-related protein sequences; the rpoB-related protein sequences are subjected to HMM iterative search to obtain protein sequences with rpoB protein domains; the protein sequences with rpoB protein domains are compared and annotated with the NR database to screen out bacteria-related proteins; the bacteria-related proteins and reference proteins are subjected to multiple sequence alignment, and after sequence trimming, an evolutionary tree is constructed; the annotated bacteria corresponding to the bacteria species confirmed by the evolutionary tree are found, mapping quantification is performed, and the RPM value is calculated to obtain the identified bacteria species;

[0012] (4) The nucleic acid sequences of the virus species and the bacteria species are cleaned, mapped using nohostreads that remove the host, and the RPM values of the virus and bacteria species are calculated according to the norRNA reads data after removing ribosomes;

[0013] (5) Combine the RPM values of viruses and bacteria, screen for bacterial and viral species with a positive library of 20 or greater, and obtain a correlation table showing the interaction relationships between all bacteria and viruses through Spearman correlation analysis.

[0014] The method of the present invention detects the antiviral and proviral effects of bacteria through specific gene load correlations. Compared with existing methods, it can screen out all potential antiviral and proviral bacteria. It realizes the identification of all viral and bacterial species in a sample before the discovery of antiviral and proviral bacteria.

[0015] In a preferred embodiment of the method for analyzing antiviral and proviral bacteria based on metatranscriptome sequencing according to the present invention, in step (2), the raw data of the metatranscriptome sequencing is quality-controlled by the fastp software to obtain clean reads, and the ribosomal rRNA is removed using the bowtie2 software to obtain norRNA reads data; the host-related sequences are removed using the bowtie2 software to obtain nohost reads; the clean reads of a single transcriptome library are assembled using the megahit software to obtain contig sequences, and then the diamond software is used to annotate the spliced contig sequences through the NR database, and the relevant sequences annotated as viruses are screened out;

[0016] The virus-related sequences are translated through ORFfinder, and protein sequences with a fragment size of at least 200 bp are screened out and compared with the RdRp database to obtain RdRp-related proteins;

[0017] The obtained RdRp-related proteins are de-duplicated using the cd-hit software based on a threshold of 0.9 and subjected to multiple sequence alignment together with the reference proteins of NCBI. The aligned sequences are trimmed using the TrimAL software and used for phylogenetic tree construction; the contig sequences corresponding to the virus species confirmed by the phylogenetic tree are found, the sequences are cleaned, and further mapped and quantified using the nohost reads data through the bowtie2 software, and the RPM value is calculated based on the norRNA reads data after removing ribosomes.

[0018] As a further preferred embodiment of the method for analyzing antiviral and proviral bacteria based on metatranscriptome sequencing according to the present invention, when screening for virus-related sequences, a preliminary screening is performed according to the keyword "virus", and the rows containing the keyword "Ortervirales" are deleted to exclude retroviruses.

[0019] As a preferred embodiment of the method for analyzing antiviral and proviral bacteria based on metatranscriptome sequencing according to the present invention, in step (3), the nohost reads of the transcriptome library are assembled by the megahit software according to mosquito genera to obtain a mixed contig sequence; the diamond software is used to align the mixed contig sequence with the bacterial rpoB reference gene of GTDB, and the contig sequence aligned with the reference rpoB gene is extracted;

[0020] The bacterial rpoB-related sequence is translated by ORFfinder, and protein sequences with a fragment size of at least 200 bp are screened, and the cd-hit software is used to deduplicate them based on a threshold of 0.98; the deduplicated proteins are aligned with the bacterial rpoB reference gene again, and the related protein sequences aligned with the reference rpoB gene are extracted;

[0021] The obtained rpoB gene-related proteins are searched twice by iteration using HMM and the rpoB gene hmm profile of GTDB to find protein sequences with rpoB protein domains;

[0022] The diamond software is used to align and annotate the protein sequences with rpoB protein domains obtained with the NR database, and bacterial-related proteins are screened out;

[0023] The bacterial-related proteins and the rpoB reference proteins of GTDB are used together for multiple sequence alignment by the MAFFT software, and the aligned sequences are trimmed by the TrimAL software and used for phylogenetic tree construction;

[0024] For the finally confirmed bacterial species after merging, the nohost reads data is mapped and quantified by the bowtie2 software, and the RPM value is calculated according to the norRNA reads data after removing ribosomes.

[0025] As a further preferred embodiment of the method for analyzing antiviral and proviral bacteria based on metatranscriptome sequencing according to the present invention, the bacterial protein sequences confirmed by the phylogenetic tree are merged at the "species" level; the merging criteria include:

[0026] i. Its position on the phylogenetic tree shows that it is the same species of bacteria;

[0027] ii. Use diamond makedb to construct a database with the protein sequences in "i", and perform a blastp alignment of the protein sequences in "i" with this database; find their corresponding cds sequences, use makeblastdb to construct a nucleic acid database and perform a blastn alignment of the cds with this database. If the identity threshold of blastp or blastn meets 98% and the hitlength meets 100bp, it is considered to be the same species.

[0028] iii. After cleaning the cds of the bacterial species merged in steps i and ii, use the bowtie2 software to perform mapping of nohost reads on it, and calculate the RPM value based on the norRNA reads data after removing ribosomes. Perform Spearman correlation analysis based on the RPM value to calculate whether there is a correlation between different bacterial species, and further confirm the position of the bacterial species with a correlation coefficient greater than or equal to 0.8 and a P value less than 0.05 on the phylogenetic tree, so as to determine whether its high correlation is due to belonging to the same species or a real interaction relationship. The cds that meet the above conditions are merged into the same bacterial species.

[0029] As a preferred embodiment of the method for analyzing antiviral and proviral bacteria based on metatranscriptome sequencing according to the present invention, in step (5), in the table, the from and to columns respectively represent different bacterial or viral species; cor is the size of the correlation coefficient; the p value is used to measure the statistical significance of the result. When the p value < 0.05, the result is considered to have statistical significance; the P value threshold is less than 0.05. When the correlation coefficient between the bacterium and the virus is less than 0, the bacterium is a potential antiviral bacterium, and the smaller the cor correlation coefficient, the greater the correlation; when the correlation coefficient between the bacterium and the virus is greater than 0, the bacterium is a potential proviral bacterium, and the greater the cor correlation coefficient, the greater the correlation.

[0030] Second, the present invention provides a method for analyzing antiviral and proviral bacteria, including the following steps:

[0031] Obtain the metatranscriptome sequencing data of the sample to be tested; use the above method for analyzing antiviral and proviral bacteria based on metatranscriptome sequencing to obtain the RPM value data of the virus and bacteria in the sample to be tested; merge the RPM values of the virus and bacteria, screen the bacterial and viral species with a positive library greater than or equal to 20, and through Spearman correlation analysis, obtain a correlation table showing the interaction relationships existing between all bacteria and viruses, so as to obtain the types of antiviral and proviral bacteria.

[0032] In a third aspect, the present invention provides a virus-promoting bacterium, including Cutibacterium, Flavobacterium, Methylobacterium, Comamonas, Pseudomonas, Wolbachia, Escherichia.

[0033] In a fourth aspect, the present invention provides an antiviral bacterium, including unclassified CAIZLB01, unclassified Patescibacteria, Cutibacterium, unclassified JAAZZY01, unclassified UBA10799, Pseudomonas, Wolbachia, Escherichia.

[0034] In a fifth aspect, the present invention relates to the application of the method for analyzing antiviral and virus-promoting bacteria based on metatranscriptomic sequencing, the method for analyzing antiviral and virus-promoting bacteria, the virus-promoting bacterium, and the antiviral bacterium in any of the following fields, including:

[0035] i. Analyzing antiviral and virus-promoting bacteria;

[0036] ii. Detecting or developing antiviral probiotics;

[0037] iii. Preparing a therapeutic drug or adjuvant therapeutic drug for viral diseases;

[0038] iv. Preparing an antibiotic for virus-promoting bacteria;

[0039] V. Microbiome regulation of virus-promoting bacteria.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] The present invention first identifies viral species through quality control - assembly - NR database alignment and annotation - viral sequence screening - translation - protein deduplication - RdRp database alignment - multiple sequence alignment - sequence trimming - phylogenetic tree construction, and identifies bacterial species through quality control - ribosomal rRNA removal - host removal - hybrid assembly - rpoB gene alignment - translation - protein deduplication - rpoB gene alignment - HMM iterative search - NR database alignment and annotation - bacterial sequence screening - multiple sequence alignment - sequence trimming - phylogenetic tree construction. Then, it cleans the nucleic acid sequences of viruses and bacteria, uses the nohost reads after host removal for mapping, and calculates the RPM values of viral and bacterial species based on the norRNA reads data after ribosome removal. Using the RPM values of viruses and bacteria, a correlation table is obtained through Spearman correlation analysis, which shows the interaction relationships existing between all bacteria and viruses. Based on metatranscriptomic data, the present invention uses different viral and bacterial marker genes to make up for the problem of incomplete bacterial genomes in metatranscriptomic sequencing, can discover all viruses and bacteria in a single mosquito sample, and detects the antiviral and proviral effects of bacteria through the load correlation between the rpoB gene of bacteria and RdRp of viruses. Moreover, the mosquito samples involved in the present invention are large in quantity, coming from nearly 30 provinces, municipalities and autonomous regions across the country, enhancing the statistical reliability and ecological universality of the research, and the representativeness of the research results is strong. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic diagram of the technical route of the present invention;

[0043] Figure 2 is a phylogenetic tree constructed by PhyML;

[0044] Figure 3 is a phylogenetic tree constructed by the iqtree software;

[0045] Figure 4 is the visualization result of the correlation between bacteria and viruses; Spearman correlation analysis is performed based on the RPM values, cor is the magnitude of the correlation coefficient; the p - value is used to measure the statistical significance of the result, and when the p - value < 0.05, the result is considered to have statistical significance; the P - value threshold is less than 0.05. When the correlation coefficient between bacteria and viruses is less than 0, the bacterium is a potential antiviral bacterium, and the smaller the cor correlation coefficient, the greater the correlation; when the correlation coefficient between bacteria and viruses is greater than 0, the bacterium is a potential proviral bacterium, and the greater the cor correlation coefficient, the greater the correlation; different colors in the figure represent the magnitude of the correlation coefficient, red is the positive correlation, i.e., the correlation coefficient between the proviral bacterium and the virus, blue is the negative correlation, i.e., the correlation coefficient between the antiviral bacterium and the virus, and different shapes represent different mosquito species. DETAILED DESCRIPTION OF THE INVENTION

[0046] To better illustrate the purpose, technical solution and advantages of the present invention, the present invention will be further described below in conjunction with specific embodiments. Those skilled in the art should understand that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] Unless otherwise specified, the test methods used in the examples are all conventional methods; the materials, reagents, etc. used, unless otherwise specified, can all be obtained from commercial channels.

[0048] Example:

[0049] 1. Original data

[0050] Mosquito samples were collected in 24 provinces, autonomous regions and municipalities directly under the Central Government, such as Anhui, Heilongjiang, Beijing, Yunnan, Guangdong, Gansu, Guangxi, Guizhou, Henan, Hebei, Hainan, Inner Mongolia, Hunan, Jiangxi, Tibet, Zhejiang, Ningxia, Shandong, Shaanxi, Sichuan, Shanghai, Shanxi, Hubei, Xinjiang, etc., and nucleic acids were extracted respectively. The transcriptome library was constructed and the metatranscriptome was sequenced by BGI Tech Solutions Co., Ltd. in Shenzhen.

[0051] 2. Virus discovery

[0052] (1) The original data of metatranscriptome sequencing was quality-controlled by the fastp software to obtain clean reads. The ribosomal rRNA was removed using the bowtie2 software to obtain norRNA reads data. The host-related sequences were removed using the bowtie2 software to obtain nohost reads.

[0053] (2) The clean reads of a single transcriptome library were assembled using the megahit software to obtain contig sequences. Subsequently, the assembled contig sequences were annotated through the NR database using the diamond software, and the sequences annotated as viruses were screened out. Specifically: preliminary screening was carried out according to the keyword "virus", and the lines containing the keyword "Ortervirales" were deleted to exclude retroviruses.

[0054] (3) The virus-related sequences were translated through ORFfinder, and the protein sequences with a fragment size of at least 200 bp were screened out and compared with the RdRp database (diamond software) to obtain RdRp-related proteins.

[0055] (4) The obtained RdRp-related proteins were de-duplicated using the cd-hit software based on a threshold of 0.9 and were subjected to multiple sequence alignment (MAFFT software) together with the reference proteins downloaded from NCBI. The aligned sequences were trimmed using the TrimAL software and were used for phylogenetic tree construction. The phylogenetic tree constructed by PhyML is shown in Figure 2For the virus species confirmed by the phylogenetic tree, find their corresponding contig sequences, clean the sequences, and further perform mapping quantification on the nohost reads data using the bowtie2 software. Calculate the RPM value based on the norRNA reads data after removing ribosomes.

[0056] 3. Bacteria discovery

[0057] (1) The nohost reads of the constructed transcriptome library were assembled by the megahit software according to the genus Culex to obtain the mixed contig sequences.

[0058] (2) Subsequently, use the diamond software to align the mixed contig sequences with the bacterial rpoB reference gene downloaded from GTDB, and extract the contig sequences that are aligned with the reference rpoB gene.

[0059] (3) Translate the bacterial rpoB-related sequences through ORFfinder, screen the protein sequences with a fragment size of at least 200bp, and use the cd-hit software to deduplicate them based on a threshold of 0.98. The deduplicated proteins are aligned again with the bacterial rpoB reference gene (diamond software), and the protein sequences that are aligned with the reference rpoB gene are extracted.

[0060] (4) The obtained rpoB-related proteins are searched for protein sequences with the rpoB protein domain through two iterative searches using HMM and the rpoB gene hmm profile downloaded from GTDB.

[0061] (5) Subsequently, use the diamond software to align and annotate the protein sequences with the rpoB protein domain obtained with the NR database, and screen out the bacteria-related proteins, specifically: screen according to the keyword "Bacteria".

[0062] (6) Align the bacteria-related proteins with the rpoB reference proteins downloaded from GTDB using the MAFFT software for multiple sequence alignment. The aligned sequences are trimmed using the TrimAL software and used for phylogenetic tree construction. The phylogenetic tree constructed by the iqtree software is shown in Figure 3 .

[0063] (7) The bacterial protein sequences confirmed by the phylogenetic tree (the sequences involved in the phylogenetic tree, the tree structure is correct) are merged at the "species" level according to three criteria:

[0064] i. Their positions in the phylogenetic tree indicate that they are the same species of bacteria;

[0065] ii. Use diamond makedb to construct a database with the protein sequences in "i", and perform a blastp alignment of the protein sequences in "i" with this database; find their corresponding cds sequences, use makeblastdb to construct a nucleic acid database and perform a blastn alignment of the cds with this database. If the identity threshold of blastp or blastn meets 98% and the hitlength meets 100bp, they are considered to be of the same species.

[0066] iii. After cleaning the cds of the bacterial species merged in the previous two steps, use the bowtie2 software to perform mapping of nohost reads on it, and calculate the RPM value based on the norRNA reads data after removing ribosomes. Perform a Spearman correlation analysis based on the RPM value to calculate whether there is a correlation between different bacterial species, and further confirm the position of the bacterial species with a correlation coefficient greater than or equal to 0.8 and a P value less than 0.05 on the phylogenetic tree, so as to judge whether its high correlation is due to belonging to the same species or a real interaction relationship. The cds that meet the above conditions are merged into the same bacterial species.

[0067] (8) For the finally confirmed bacterial species after merging, use the nohost reads data to perform mapping quantification through the bowtie2 software, and calculate the RPM value based on the norRNA reads data after removing ribosomes.

[0068] 4. Anti / virus promoting analysis

[0069] Merge the virus and bacteria quantification results (i.e., the tables of virus RPM values and bacteria RPM values), screen the bacterial and virus species with a positive library greater than or equal to 20, and obtain a correlation table through Spearman correlation analysis.

[0070] Table 1 Correlation between bacteria and mosquito-related viromes (the sum of the rpm of all mosquito-related viruses, reflecting the integrity)

[0071] mosquito species Bacteria Virome cor p Culex_tritaeniorhynchus unclassified CAIZLB01 mosquito-related virome -0.58017 0.002267 Culex_tritaeniorhynchus unclassified Patescibacteria mosquito-related virome -0.46697 0.029778 Culex_tritaeniorhynchus Cutibacterium mosquito-related virome 0.251267 0.019845 Culex_pipiens Flavobacterium mosquito-related virome 0.427826 0.038162 Armigeres_subalbatus Cutibacterium mosquito-related virome 0.473684 0.042171 Culex_tritaeniorhynchus Methylobacterium mosquito-related virome 0.538462 0.049989 Culex_pipiens Comamonas mosquito-related virome 0.581739 0.003392 Armigeres_subalbatus Pseudomonas mosquito-related virome 0.637363 0.022289

[0072] Table 2 Correlation between bacteria and mosquito-related viruses

[0073] mosquito species Bacteria Virus cor p Anopheles_sinensis Cutibacterium Mosquito.orthomyxo.like.virus.YN3 -0.88811 9.17E-05 Culex_pipiens unclassified JAAZZY01 Mosquito.hepe.like.virus.GD2 -0.7972 0.003161 Armigeres_subalbatus unclassified UBA10799 Mosquito.narna.like.virus.SC1 -0.75758 0.015921 Culex_tritaeniorhynchus unclassified JAAZZY01 Mosquito.luteo.like.virus.YN2 -0.74545 0.011866 Culex_tritaeniorhynchus Cutibacterium Mosquito.mono.like.virus.SHX3 -0.67133 0.020442 Culex_pipiens unclassified JAAZZY01 Mosquito.narna.like.virus.ZJ1 -0.50762 0.017099 Culex_pipiens Pseudomonas Mosquito.orthomyxo.like.virus.SHX2 -0.5049 0.04079 Culex_pipiens Wolbachia Mosquito.picorna.like.virus.YN4 -0.40441 0.020298 Culex_tritaeniorhynchus Escherichia Mosquito.mono.like.virus.SHX3 -0.39271 0.015306 Culex_tritaeniorhynchus Escherichia Mosquito.flavi.like.virus.ZJ2 -0.27159 0.033055 Armigeres_subalbatus Escherichia Mosquito.narna.like.virus.SC1 -0.22372 0.031309 Aedes_albopictus Wolbachia Mosquito.bunya.like.virus.HEN1 0.190152 0.010442 Armigeres_subalbatus Wolbachia Mosquito.toti.like.virus.SHX3 0.362021 0.020569 Culex_pipiens Wolbachia Mosquito.mono.like.virus.XZ3 0.371378 0.001944 Culex_pipiens Wolbachia Mosquito.hepe.like.virus.ZJ2 0.459439 0.000229 Culex_pipiens Escherichia Mosquito.narna.like.virus.SC1 0.718182 0.016799

[0074] All the interaction relationships between bacteria and viruses are shown in this table. The "from" and "to" columns represent different bacterial or viral species respectively. "cor" represents the magnitude of the correlation coefficient, and the p-value is used to measure the statistical significance of the results. When the p-value < 0.05, the results are generally considered to have statistical significance. The p-value threshold is less than 0.05. When the correlation coefficient between a bacterium and a virus is less than 0, the bacterium is a potential antiviral bacterium; when the correlation coefficient between a bacterium and a virus is greater than 0, the bacterium is a potential pro-viral bacterium.

[0075] In summary, in the present invention, virus species are first identified by quality control - assembly - NR database alignment and annotation - virus sequence screening - translation - protein deduplication - RdRp database alignment - multiple sequence alignment - sequence trimming - phylogenetic tree construction, and bacterial species are identified by quality control - removal of ribosomal rRNA - removal of host - mixed assembly - rpoB gene alignment - translation - protein deduplication - rpoB gene alignment - HMM iterative search - NR database alignment and annotation - bacterial sequence screening - multiple sequence alignment - sequence trimming - phylogenetic tree construction. The nucleic acid sequences of viruses and bacteria are cleaned, and the nohost reads after removing the host are used for mapping. The RPM values of virus and bacterial species are calculated based on the norRNA reads data after removing ribosomes. Using the RPM values of viruses and bacteria, through Spearman correlation analysis, a correlation table is obtained. All the interaction relationships between bacteria and viruses are shown in this table. When the p-value < 0.05, the results are considered to have statistical significance. The p-value threshold is less than 0.05. When the correlation coefficient between a bacterium and a virus is less than 0, the bacterium is a potential antiviral bacterium, and the smaller the cor correlation coefficient, the greater the correlation; when the correlation coefficient between a bacterium and a virus is greater than 0, the bacterium is a potential pro-viral bacterium, and the greater the cor correlation coefficient, the greater the correlation.

[0076] Using this method, it is possible to explore the interaction relationships between all viruses and bacteria in mosquitoes and screen out all potential antiviral and proviral bacteria, including disease-related viruses and other viruses, as well as both known virus-bacteria species and unknown virus-bacteria species. For example, Wolbachia, Escherichia, and Pseudomonas have antiviral effects on some viruses, while Wolbachia, Escherichia, and Stenotrophomonas have proviral effects on some other viruses. This method not only greatly reduces the waste of human, material, and financial resources of researchers but also can greatly expand the database of antiviral and proviral bacteria. In addition, the discovery of antiviral and proviral bacteria is of great significance in studying the virus infection mechanism and developing new treatment strategies, providing a new perspective for understanding the occurrence and development of infectious diseases. The practical application prospects are as follows: developing antiviral probiotics as an adjuvant treatment method for viral diseases; designing antibiotics or microbiome regulation technologies against proviral bacteria to reduce the severity of virus infections. In the present invention, through the above method, 11 bacteria with antiviral or proviral effects were discovered in the libraries of more than 4,000 individual mosquitoes of five mosquito species, including: Cutibacterium, Flavobacterium, Methylobacterium, Comamonas, Pseudomonas, Wolbachia, Escherichia, unclassified CAIZLB01, unclassified Patescibacteria, unclassified JAAZZY01, unclassified UBA10799; among which 5 are positively correlated with the mosquito-related virome, including: Cutibacterium, Flavobacterium, Methylobacterium, Comamonas, Pseudomonas; 2 are negatively correlated with the mosquito-related virome, including: unclassified CAIZLB01, unclassified Patescibacteria; 2 bacteria have proviral effects, including: Wolbachia, Escherichia, and 6 bacteria have antiviral effects, including: Cutibacterium, Escherichia, Pseudomonas, Wolbachia, unclassified JAAZZY01, unclassified UBA10799.

[0077] Based on metatranscriptomic data, the present invention first identifies all virus and bacteria species in the data according to their respective marker genes, and cleans the corresponding nucleic acid sequences. Based on the correlation of marker gene loads, the present invention calculates the loads of each virus and bacteria species in each library respectively, and calculates the correlation coefficient using Spearman correlation analysis. The present invention uses different virus and bacteria marker genes to make up for the problem of incomplete bacterial genomes in metatranscriptomic sequencing, can discover all viruses and bacteria in a single mosquito sample, and detects the antiviral and proviral effects of bacteria through the load correlation between the rpoB gene of bacteria and the RdRp of viruses. Moreover, the mosquito samples involved in the present invention are large in quantity, coming from nearly 30 provinces, municipalities and autonomous regions across the country, enhancing the statistical reliability and ecological universality of the research, and the representativeness of the research results is strong.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the protection scope of the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the essence and scope of the technical solutions of the present invention.

Claims

1. A method for analyzing antiviral and proviral bacteria based on macrotranscriptome sequencing, characterized in that: The following steps are involved: (1) Collect mosquito samples from no less than 25 provinces and cities, extract nucleic acids, construct transcriptome libraries and perform metatranscriptome sequencing; (2) The data of the macrotranscriptome sequencing were quality controlled, assembled, and compared with the NR database to annotate the viral sequence; the viral sequence was translated and protein duplicates were removed by ORFfinder, and RdRp-related proteins were obtained by comparison with the RdRp database; multiple sequences of the RdRp-related proteins were aligned with reference proteins, and an evolutionary tree was constructed after sequence pruning; Find the corresponding annotated virus for the virus species confirmed by the evolutionary tree, perform quantitative mapping, calculate the RPM value, and identify the virus species; (3) After quality control of the transcriptome library, the ribosomal rRNA is removed, the host is removed and mixed and assembled to obtain a mixed contig sequence; the contig sequence is aligned with the rpoB gene, translated and protein duplicated, and aligned with the rpoB gene again to obtain rpoB-related protein sequences; the rpoB-related protein sequence is subjected to HMM iterative search to obtain a protein sequence with an rpoB protein domain; the protein sequence with the rpoB protein domain is aligned and annotated with the NR database to screen bacterial-related proteins; the bacterial-related proteins are multiple-sequence aligned with the reference proteins, and an evolutionary tree is constructed after sequence pruning; Find the corresponding annotated bacteria for the bacterial species confirmed by the evolutionary tree, perform quantitative mapping, calculate the RPM value, and identify the bacterial species; (4) cleaning the nucleic acid sequences of the virus species and the bacterial species, mapping them using nohost reads after removing the host, and calculating the RPM values ​​of the virus and bacterial species based on the norRNA reads data after removing the ribosomes; (5) The RPM values ​​of viruses and bacteria were combined to screen the bacterial and viral species with a positive library of 20 or more. Through Spearman correlation analysis, a correlation table showing the interaction relationship between all bacteria and viruses was obtained.

2. The method for analyzing antiviral and proviral bacteria based on macrotranscriptome sequencing according to claim 1, characterized in that: In step (2), the raw data of the macrotranscriptome sequencing is quality controlled by fastp software to obtain cleanreads, and the ribosomal rRNA is removed by bowtie2 software to obtain norRNA reads data; the host-related sequences are removed by bowtie2 software to obtain nohost reads; the clean reads of the single transcriptome library are assembled by megahit software to obtain contig sequences, and then the spliced ​​contig sequences are annotated by diamond software through the NR database, and the related sequences annotated as viruses are screened out; The virus-related sequence is translated by ORFfinder, protein sequences with a fragment size of at least 200 bp are screened, and the sequences are compared with the RdRp database to obtain RdRp-related proteins; The obtained RdRp-related proteins were deduplicated using cd-hit software based on a threshold of 0.9, and multiple sequence alignment was performed together with the reference proteins of NCBI. The aligned sequences were trimmed using TrimAL software for phylogenetic tree construction; the corresponding contig sequences of the virus species confirmed by the phylogenetic tree were found, the sequences were cleaned, and further mapped and quantified using nohost reads data using bowtie2 software, and the RPM values ​​were calculated based on the norRNAreads data after removing the ribosomes.

3. The method for analyzing antiviral and proviral bacteria based on macrotranscriptome sequencing according to claim 2, characterized in that: When the screening annotations were virus-related sequences, a preliminary screening was performed based on the keyword "virus", and the lines containing the keyword "Ortervirales" were deleted to exclude retroviruses.

4. The method for analyzing antiviral and proviral bacteria based on macrotranscriptome sequencing according to claim 1, characterized in that: In step (3), the nohost reads of the transcriptome library are mixed and assembled according to the mosquito genus using megahit software to obtain a mixed contig sequence; the mixed contig sequence is compared with the bacterial rpoB reference gene of GTDB using diamond software, and the contig sequence of the reference rpoB gene is extracted; The bacterial rpoB-related sequences were translated using ORFfinder, and protein sequences with a fragment size of at least 200 bp were screened. The duplicates were removed using cd-hit software based on a threshold of 0.

98. The deduplicated proteins were again compared with the bacterial rpoB reference gene, and the protein sequences related to the reference rpoB gene were extracted. The obtained rpoB gene-related proteins were searched twice iteratively using HMM and GTDB's rpoB gene hmm profile to find protein sequences with rpoB protein domains; Diamond software was used to compare and annotate the obtained protein sequences with the rpoB protein domain with the NR database, and bacterial-related proteins were screened out; The bacterial-related proteins were aligned with the rpoB reference protein of GTDB using MAFFT software, and the aligned sequences were trimmed using TrimAL software for phylogenetic tree construction; The final bacterial species confirmed by merging were mapped and quantified using the nohost reads data using bowtie2 software, and the RPM value was calculated based on the norRNA reads data after removing the ribosomes.

5. The method for analyzing antiviral and proviral bacteria based on macrotranscriptome sequencing according to claim 4, characterized in that: The bacterial protein sequences confirmed by the evolutionary tree are merged at the "species" level; the merging criteria include: i. The position on the evolutionary tree indicates that they are the same bacterium; ii. Use diamond makedb to construct a database of the protein sequence in "i", and perform blastp comparison between the protein sequence in "i" and the database; find out the corresponding cds sequence, use makeblastdb to construct a nucleic acid database, and perform blastn comparison between the cds and the database. If the identity threshold of blastp or blastn meets 98% and the hitlength meets 100bp, it is considered to be the same species; iii. After cleaning the cds of the bacterial species merged in steps i and ii, use bowtie2 software to map the nohost reads, and calculate the RPM value based on the norRNA reads data after removing the ribosomes. Perform Spearman correlation analysis based on the RPM value to calculate whether there is a correlation between different species of bacteria, and further confirm the position of bacterial species with a correlation coefficient greater than or equal to 0.8 and a P value less than 0.05 on the evolutionary tree, so as to determine whether the high correlation is due to belonging to the same species or a real interaction relationship. The cds that meet the above conditions are merged into the same bacterial species.

6. The method for analyzing antiviral and proviral bacteria based on macrotranscriptome sequencing according to claim 1, characterized in that: In step (5), in the table, the from and to columns represent different bacterial or viral species respectively; cor is the size of the correlation coefficient; the p value is used to measure the statistical significance of the result, and the result is considered to be statistically significant when the p value is <0.05; the P value threshold is less than 0.

05. When the correlation coefficient between bacteria and viruses is less than 0, the bacteria is a potential antiviral bacterium, and the smaller the cor correlation coefficient, the greater the correlation; when the correlation coefficient between bacteria and viruses is greater than 0, the bacteria is a potential proviral bacterium, and the larger the cor correlation coefficient, the greater the correlation.

7. A method for analyzing antiviral and proviral bacteria, characterized in that The following steps are involved: Obtain the macrotranscriptome sequencing data of the sample to be tested; obtain the RPM value data of the virus and bacteria of the sample to be tested by using the method described in any one of claims 1-6; combine the RPM values ​​of the virus and bacteria, screen the bacteria and virus species with a positive library greater than or equal to 20, and perform Spearman correlation analysis to obtain a correlation table showing the interaction relationship between all bacteria and viruses, and obtain antiviral and proviral bacterial species.

8. A virus-promoting bacterium, characterized in that Including Cutibacterium, Flavobacterium, Methylobacterium, Comamonas, Pseudomonas, Wolbachia, Escherichia.

9. An antiviral bacterium, characterized in that Including unclassified CAIZLB01, unclassifiedPatescibacteria, Cutibacterium, unclassified JAAZZY01, unclassified UBA10799, Pseudomonas, Wolbachia, Escherichia.

10. Use of the method according to any one of claims 1 to 6, the method according to claim 7, the virus-promoting bacteria according to claim 8, and the antiviral bacteria according to claim 9 in any of the following fields, characterized in that: include: i. Analysis of antiviral and proviral bacteria; ii. Detect or develop antiviral probiotics; iii. Preparation of drugs for the treatment of viral diseases or auxiliary treatment drugs; iv. Preparation of virus-promoting bacterial antibiotics; V. Microbiome regulation by virus-promoting bacteria.

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