Metagenome-based severe and death risk early warning model for acute severe pneumonia patients and construction method thereof

Abstract

The invention discloses a construction method of a severe case and death risk early warning model for acute severe pneumonia patients based on metagenome. The construction method comprises the following steps: (1) extracting sample DNA; (2) construction and sequencing of a DNA library; (3) preprocessing a sequencing result; (4) gene comparison; (5) annotating species; and (6) counting the species composition of each sample bacterial fond by using R software, converting the original reads number into the relative abundance value of the bacterial fond, and calculating the Shannon index of the species diversity of the sample bacterial species level by using R packet vecan. The sS I of the lower respiratory tract microbiome is identified by utilizing the metagenome, so that the 100-day death risk of the acute critical patient can be effectively identified, the clinical early-stage identification of people with high death risk is facilitated, the formulation of a treatment scheme is assisted, and the prognosis of the clinical patient is improved.

Description

Technical Field

[0001] The present invention relates to a method for constructing a death risk warning model for patients with clinical acute severe pneumonia, specifically a method for constructing a death risk warning model for patients with acute severe pneumonia based on metagenomics and its application, belonging to the technical field of biostatistics. Background Art

[0002] Lower respiratory tract infection (LRTI) is one of the main causes of morbidity and mortality globally, especially severely affecting children under 5 years old and the elderly over 60 years old. Acute severe pneumonia (asLRTI) is the most life-threatening form of lower respiratory tract infection, and the mortality rate of severe community-acquired pneumonia (sCAP) can be as high as 40%. The diagnostic criteria for sCAP are mainly based on the 2007 consensus guidelines of the Infectious Diseases Society of America (IDSA) / American Thoracic Society (ATS), including patients who require mechanical ventilation or vasopressor support for shock, or patients with three of the nine minor criteria and need to be admitted to the intensive care unit (ICU). Prognostic scoring tools, such as the Pneumonia Severity Index (PSI), calculate the pneumonia death risk index by using acute and chronic disease variables, but they are not a direct measure of pneumonia severity. The PSI score is highly dependent on the patient's chronic disease comorbidity status, with a complex scoring and low specificity.

[0003] The impact of the highly oxidative environment caused by severe lower respiratory tract infection on the lung microbiome is much higher than that on the microbial ecology of other parts, including the gut microbiome. Pulmonary inflammation and the lower respiratory tract microbiome (LRTM) form a two-way immunological ecological environment. LRTI leads to a decrease in the species diversity of LRTM, presenting a characteristic phenotype that is significantly differentiated from that of healthy LRTM, including a high bacterial load and a high relative abundance of specific dominant taxa. The LRTM characteristics of an individual also, in turn, affect the host's susceptibility to oxygen-induced lung injury. Due to the technical limitations of sampling lower respiratory tract samples and the high cost of sequencing, previous studies on LRTI and RTM mainly focused on areas where samples are relatively easy to collect, such as the oral cavity or nasopharynx, and the research on LRTM mainly focused on animal models. Although the anatomical distance between the sample collection area of tracheal aspirates and the alveoli is already very close, it still cannot fully reflect the microbial community characteristics of the lower respiratory tract.

[0004] Currently, the analysis of the microbiome in bronchoalveolar lavage fluid by metagenomic sequencing (mNGS) in clinical practice is mainly used to identify pathogens, and there are few studies on understanding the LRTM characteristics of asLRTI patients and their interaction with the host. In the past, microbiome studies mostly used sequencing technologies such as 16S, which could only identify to the genus level, and only metagenomics could identify to the species level. Summary of the Invention

[0005] The primary technical problem to be solved by the present invention is to provide an early warning model and its construction method for identifying the severe illness and death risks of patients with acute severe pneumonia that cannot be identified by PSI through the Shannon index of the species-level diversity of the lower respiratory tract microbiome by metagenomic analysis, as well as a data analysis method.

[0006] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the early warning model for the severe illness and death risks of patients with acute severe pneumonia based on metagenomics.

[0007] The third technical problem to be solved by the present invention is to provide a data analysis system for the early warning model of the severe illness and death risks of patients with acute severe pneumonia based on metagenomics.

[0008] To achieve the above technical objectives, the present invention adopts the following technical solutions:

[0009] A construction method for an early warning model of the severe illness and death risks of patients with acute severe pneumonia based on metagenomics, comprising the following steps:

[0010] (1) Extract sample DNA;

[0011] (2) Construction and sequencing of the DNA library;

[0012] (3) Pretreatment of the sequencing results;

[0013] (4) Gene alignment;

[0014] (5) Species annotation;

[0015] (6) Use R software to statistically analyze the species composition of bacteria in each sample, convert the original read counts into relative abundance values of bacteria, and use the R package vegan to calculate the Shannon index of the species diversity of bacteria at the species level in the samples.

[0016] Preferably, in the step (2), the specific steps for the construction and sequencing of the DNA library are as follows: Use a DNA library construction kit to construct the library, randomly fragment the DNA with an ultrasonic disruptor, complete the entire library preparation through end repair, A-tailing, adding sequencing adapters, purification, and PCR amplification; after the library construction is completed, first perform preliminary quantification, detect the inserted fragments of the library after dilution, and after the inserted fragments meet the expectations, accurately quantify the effective concentration of the library. The effective concentration of the library > 3 nM to ensure the library quality; after passing the library inspection, mix different libraries according to the requirements of the effective concentration and the target output data volume and then perform sequencing.

[0017] Preferably, in the step (3), the preprocessing of the sequencing results is as follows: preprocess the raw data obtained by the sequencing platform to obtain the effective data for subsequent analysis.

[0018] Preferably, the preprocessing includes the following sub-steps:

[0019] a) Remove reads containing low-quality bases;

[0020] b) Remove reads with N bases;

[0021] c) Remove overlapping reads with Adapter;

[0022] d) Align with the host database and filter out reads from the host.

[0023] Preferably, in the step (4), the specific steps of gene alignment are as follows: Align the Clean Data of each sample to the initial gene catalog, calculate the number of reads mapped to the genes in each sample, and filter out genes with the number of reads ≤ 2 in each sample to obtain the final gene catalog for subsequent analysis.

[0024] Preferably, in the step (5), the specific steps of species annotation are as follows:

[0025] a) Align Unigenes with the sequences of bacteria, fungi, archaea, and viruses extracted from the NR database of NCBI;

[0026] b) For the alignment results of each sequence, select the results with evalue ≤ the minimum evalue * 1. Since each sequence may have multiple alignment results, the LCA algorithm is used to determine the species annotation of the sequence;

[0027] c) Starting from the LCA annotation results and the gene abundance table, obtain the abundance information and gene number table of each sample at each taxonomic level, i.e., kingdom, phylum, class, order, family, genus, and species. For the abundance of a certain species in a certain sample, it is equal to the sum of the gene abundances annotated as that species; for the number of genes of a certain species in a certain sample, it is equal to the number of genes with non-zero abundance among the genes annotated as that species.

[0028] Preferably, the construction method further includes step (7) result analysis: Analyze the Shannon index obtained in step (6), and perform result analysis with the Shannon index > 2.931 as the standard. The 100-day death ratio of patients with acute severe pneumonia with a Shannon index > 2.931 is significantly lower than that of the sSI < 2.931 group.

[0029] A warning model for the risk of severe illness and death in patients with acute severe pneumonia based on metagenomics, which is constructed by the above construction method.

[0030] A computer-readable storage medium storing a computer program, which when executed by a processor implements the above method for constructing a warning model for the risk of severe illness and death in patients with acute severe pneumonia based on metagenomics.

[0031] A data analysis system based on the above warning model, comprising the following modules:

[0032] A receiving module: used to receive the retrieval requirements of the user;

[0033] A matching module: used to match the retrieval requirements of the user with the constructed metagenomic database to obtain the retrieval matching results;

[0034] An output module: used to output the matching results.

[0035] Compared with the prior art, the present invention has the following technical effects:

[0036] (1) The analysis of the lower respiratory tract microbiome by the metagenomics provided by the present invention can identify to the species level, is more accurate than the prior art, has a large amount of data, and has no bias in identifying pathogens.

[0037] (2) The present invention solves the technical problem that at present, metagenomics in clinical applications is mainly used for the identification of pathogens to assist in formulating treatment strategies clinically, and cannot accurately judge the death risk of patients with severe pneumonia. The present invention measures the species diversity-Shannon index (sSI) at the species level in BALF by metagenomic sequencing analysis method, correlates with the 100-day clinical outcome of patients with acute severe pneumonia, constructs an ROC curve, and finds that the area under the curve is 0.6843, the diagnostic sensitivity is 72.2%, the specificity is 68.2%, the sSI critical value is taken as 2.931, and the 100-day death ratio of patients with acute severe pneumonia with sSI>2.931 is significantly lower than that of the group with sSI<2.931, indicating that the present invention can identify the death risk that PSI cannot identify, better assist in clinically predicting the 100-day death risk of patients with acute severe pneumonia, helps in clinically early identifying high-risk death populations, assisting in formulating treatment plans, and improving the prognosis of clinical patients. Description of the Drawings

[0038] Figure 1 It is a specific Shannon index diagram in the embodiment of the present invention.

[0039] Figure 2 It is a diagram for analyzing high-risk and low-risk deaths using the Shannon index in the present invention;

[0040] Figure 3In the prior art, there is no significant difference in the commonly used PSI score in clinical practice between the two groups of high and low death risks;

[0041] Figure 4A It is an analysis diagram of all species of metagenome calculated using the Shannon index;

[0042] Figure 4B It is an analysis diagram of bacterial genera of metagenome calculated using the Shannon index;

[0043] Figure 4C It is an analysis diagram of bacterial species of metagenome calculated using the Shannon index. Specific Embodiments

[0044] The technical content of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0045] In the specific embodiments of the present invention, the meanings of all technical terms and scientific terms are the same as those commonly understood by those skilled in the art. To better explain the present invention, some key terms are defined below. The terms "comprising", "including", "having", "containing" or "involving" are inclusive or open-ended and do not exclude other unenumerated elements or method steps. The term "consisting of" is considered a preferred embodiment of "comprising". If a group is defined as including at least a certain number of embodiments, this should also be understood as disclosing a group preferably consisting only of these embodiments.

[0046] The indefinite or definite article used when referring to a singular noun, such as "a" or "an", "the", includes the plural form of the noun. The term "about" represents an accuracy range that those skilled in the art can understand and still ensure the technical effect of the feature being discussed, usually representing ±10% deviation from the indicated value, preferably ±5%.

[0047] In addition, the terms first, second, third, (a), (b), (c), etc. in the description and claims are used to distinguish similar elements and are not necessarily descriptive of an order or time sequence. It should be understood that the terms so applied can be interchanged in appropriate circumstances, and the embodiments described in the present invention can be implemented in an order different from that described or illustrated in the present invention.

[0048] Example 1: Analysis method for warning data of severe illness and death risk of patients with acute severe pneumonia based on metagenomics

[0049] S1. After admission, 2 ml of bronchoalveolar lavage fluid (BALF) is obtained from patients with acute severe pneumonia through a bronchoscope and collected in a sterile container;

[0050] S2. Place 2 ml of BALF in an EP tube, centrifuge it at 3000 revolutions for 5 minutes in a centrifuge, and collect 1 ml of the supernatant in an EP tube;

[0051] S3. Extract the genomic DNA of the sample using the CTAB extraction method, and use 1% agarose gel electrophoresis (AGE) to detect and analyze the purity and integrity of the DNA. Use dsDNA Assay Kit in 2.0 Flurometer (Life Technologies, CA, USA) to quantify the DNA. Take an appropriate amount of the sample in a centrifuge tube and dilute the sample with sterile water until the OD value is between 1.8 and 2.0;

[0052] S4. Library construction and sequencing on the machine: Take 1 μg of genomic DNA from the sample and use Ultra DNA Library Prep Kit for Illumina (NEB, USA) to construct the library. Randomly fragment it into fragments with a length of about 350 bp using a Covaris ultrasonic disruptor, and complete the entire library preparation through steps such as end repair, adding A tails, adding sequencing adapters, purification, and PCR amplification;

[0053] After the library construction is completed, first use Qubit 2.0 for preliminary quantification, dilute the library to 2 ng / μL, and then use Agilent 2100 to detect the insert size of the library. After the insert size meets the expectations, use the Q-PCR method to accurately quantify the effective concentration of the library (library effective concentration > 3 nM) to ensure the library quality; after passing the library inspection, pool different libraries according to the requirements of the effective concentration and the target output data volume and then perform Illumina PE150 sequencing;

[0054] S5. Pretreatment of sequencing results: Use Readfq (V8, https: / / github.com / cjfields / readfq) to preprocess the raw data (Raw Data) obtained from the Illumina HiSeq sequencing platform to obtain the effective data (Clean Data) for subsequent analysis. The specific method is:

[0055] a) Remove reads containing low-quality bases (default quality threshold < 38) that exceed a certain proportion (default length value is 40 bp);

[0056] b) Remove reads with a certain proportion of N bases (default length value is 10 bp);

[0057] c) Remove reads with an overlap with the Adapter exceeding a certain threshold (default length value is 15 bp).

[0058] d) Compare with the host database and filter out reads that may originate from the host. By default, use the Bowtie2 software (version 2.2.4, http: / / bowtie-bio.sourceforge.net / bowtie2 / index.shtml), with parameter settings: -end-to-end, --sensitive, -1200, -X400.

[0059] S6. Gene alignment:

[0060] Use Bowtie2 (Bowtie2.2.4) to align the Clean Data of each sample to the initial gene catalogue, and calculate the number of reads that the genes are aligned to in each sample. Parameter settings: --end-to-end, --sensitive, -I200, -X 400; Filter out genes with the number of reads ≤ 2 in each sample to obtain the final gene catalogue (Unigenes) for subsequent analysis;

[0061] S7. Species annotation:

[0062] a) Use the DIAMOND software (v0.9.9.110, https: / / github.com / bbuchfink / diamond / ) to align Unigenes with the sequences of bacteria, fungi, archaea, and viruses extracted from the NCBI NR database (Version 2018-01-02, https: / / www.ncbi.nlm.nih.gov / ). Parameter settings: blastp, -ele-5;

[0063] b) For the alignment results of each sequence, select the result with evalue ≤ the minimum evalue * 1. Since each sequence may have multiple alignment results, adopt the LCA algorithm (applied to the taxonomic classification of the MEGAN software, (https: / / en.wikipedia.org / wiki / Lowest_common_ancestor)) to determine the species annotation of the sequence;

[0064] c) Starting from the LCA annotation results and the gene abundance table, obtain the abundance information and gene number table of each sample at each taxonomic level (kingdom, phylum, class, order, family, genus, species). For the abundance of a certain species in a certain sample, it is equal to the sum of the gene abundances annotated as that species; for the number of genes of a certain species in a certain sample, it is equal to the number of genes with non-zero abundance among the genes annotated as that species;

[0065] S8. Use R software to count the species composition of bacteria in each sample, convert the original read counts into relative abundance values of bacteria, and use the R package vegan to calculate the Shannon value of species diversity at the species level of the samples.

[0066] After obtaining the metagenomic data, the inventors once calculated the alpha diversity of all species (including bacteria, fungi, and archaea) in the metagenome using the classical Shannon index calculation method. The results showed that there was no difference in species diversity between the groups with poor prognosis and good prognosis of severe pneumonia, as Figure 4A shown. Therefore, using the metagenomic data and calculating the Shannon index for all species in the metagenome cannot accurately assess the risk of death from severe pneumonia.

[0067] To address the above problems, the inventors started with the bacteria, which account for the largest proportion in the lower respiratory tract microbial community, and calculated the Shannon index of the bacteria in the samples. The research found that when species were identified to the genus level of bacteria, there was still no significant difference in species diversity between the groups with poor prognosis and good prognosis of severe pneumonia, as Figure 4B shown. Therefore, using the metagenomic data and calculating the Shannon index for bacteria in the metagenome cannot accurately assess the risk of death from severe pneumonia.

[0068] The inventors further studied and improved the Shannon index calculation method and found that when species were identified to the species level of bacteria, the Shannon index showed a significant difference, as Figure 4C shown. The following is a further specific description and data.

[0069] The analysis of the lower respiratory tract microbiome by metagenomics can identify species at the species level, which is more accurate than the prior art, and has a large amount of data and unbiased pathogen identification. Currently, metagenomics is mainly applied clinically for pathogen identification to assist in formulating treatment strategies. In the present invention, the species diversity - Shannon index (sSI) at the species level in BALF was measured by metagenomic sequencing analysis method, correlated with the 100 - day clinical outcome of patients with acute severe pneumonia, and an ROC curve was constructed. It was found that the area under the curve was 0.6843, the diagnostic sensitivity was 72.2%, the specificity was 68.2%, and the sSI critical value was taken as 2.931, as Figure 1 shown.

[0070] Table 1 Diagnostic performance of the test set

[0071]

[0072] The present invention also found that the 100 - day death proportion of patients with acute severe pneumonia with sSI > 2.931 was significantly lower than that of the group with sSI < 2.931, as Figure 2As shown. Moreover, the commonly used PSI score in clinical practice showed no significant difference between the two groups and could not accurately identify the death risks of these two groups. For example, Figure 3 As shown. The sSI of the lower respiratory tract microbiome identified by metagenomics in the present invention can effectively identify the 100-day death risk of acute severe patients, which helps to clinically identify high-risk death populations at an early stage, assist in formulating treatment plans, and improve the prognosis of clinical patients.

[0073] Example 2: Verification of the analysis method for warning severe illness and death risk of patients with acute severe pneumonia based on metagenomics Experiment

[0074] To verify the performance of the sSI value in predicting adverse outcomes in the 100-day outcomes of acute severe pneumonia patients in the training set cohort, the present invention recruited 28 acute severe pneumonia patients in another medical center, collected the 100-day clinical outcomes, and used the critical value of sSI 2.931 of the present invention for prediction. The specific method was the same as that in Example 1.

[0075] Experimental results and analysis: The diagnostic sensitivity of the present invention using the sSI 2.931 critical value was 92.9%, the specificity was 50%, the positive predictive value was 65%, and the negative predictive value was 87.5%. The verification experiment results proved that the method and index of the present invention had high sensitivity in clinical practice and could be used as a predictive index for predicting adverse outcomes in the 100-day outcomes of acute severe pneumonia patients, as shown in Table 2 and Table 3.

[0076] Table 2 Basic characteristics of the samples included in the training set and the validation set

[0077] Training set Validation set Number 40 28 Male (%) 33(82.5) 22(78.6) Age 72(63,79) 76(54,81)

[0078] Table 3 Diagnostic performance of the validation set

[0079]

Claims

1. A method for constructing a metagenome-based early warning model for the risk of severe illness and death in patients with acute severe pneumonia, characterized in that The steps include: (1) Extracting DNA from samples; (2) Construction and sequencing of DNA libraries; (3) Preprocessing of sequencing results; (4) Gene comparison; (5) Species annotation; (6) R software was used to count the species composition of the bacterial class of each sample, the original read counts were converted into the relative abundance value of the bacterial class, and the R package vegan was used to calculate the Shannon index of species diversity at the bacterial species level of the sample.

2. The method for constructing a metagenome-based early warning model for severe illness and death risk in patients with acute severe pneumonia as claimed in claim 1, characterized in that In the step (2), the specific steps of constructing and sequencing the DNA library are as follows: constructing the library using a DNA library construction kit, randomly breaking the DNA into fragments using an ultrasonic disruptor, and completing the entire library preparation through end repair, adding A tails, adding sequencing adapters, purification, and PCR amplification; after the library construction is completed, a preliminary quantification is first performed, and the inserted fragments of the library are detected after diluting the library. After the inserted fragments meet the expectations, the effective concentration of the library is accurately quantified, and the effective concentration of the library is greater than 3nM to ensure the quality of the library; after the library inspection is qualified, different libraries are mixed according to the effective concentration and the target data volume requirements and then sequenced.

3. The method for constructing a metagenome-based early warning model for severe illness and death risk in patients with acute severe pneumonia as claimed in claim 1, characterized in that In the step (3), the sequencing result preprocessing is: preprocessing the raw data obtained by the sequencing platform to obtain valid data for subsequent analysis.

4. The method for constructing a metagenome-based early warning model for severe illness and death risk in patients with acute severe pneumonia as claimed in claim 3, characterized in that The pre-processing comprises the following sub-steps: a) Remove reads containing low-quality bases; b) Remove the reads with N bases; c) Remove the reads that overlap with the Adapter; d) Compare with the host database and filter out reads originating from the host.

5. The method for constructing a metagenome-based early warning model for severe illness and death risk in patients with acute severe pneumonia as claimed in claim 1, characterized in that In step (4), the specific steps of gene alignment are: aligning the Clean Data of each sample to the initial gene catalog, calculating the number of reads of the gene aligned in each sample, filtering out genes with a read number ≤ 2 in each sample, and obtaining the final gene catalog for subsequent analysis.

6. The method for constructing a metagenome-based early warning model for severe illness and death risk in patients with acute severe pneumonia as claimed in claim 1, characterized in that In step (5), the specific steps of species annotation are: a) Compare Unigenes with bacterial, fungal, archaeal and viral sequences extracted from NCBI's NR database; b) For each sequence alignment result, select the result with evalue ≤ minimum evalue*1 and use the LCA algorithm to determine the species annotation of the sequence; c) Based on the LCA annotation results and gene abundance table, the abundance information and gene number table of each sample at each classification level, i.e., kingdom, phylum, class, order, family, genus and species, were obtained. The abundance of a species in a sample was equal to the sum of the abundances of genes annotated as the species; the number of genes of a species in a sample was equal to the number of genes with non-zero abundance among the genes annotated as the species.

7. The method for constructing a metagenome-based early warning model for severe illness and death risk in patients with acute severe pneumonia as claimed in claim 1, characterized in that The method also includes step (7) of analyzing the calculation results: analyzing the Shannon index obtained in step (6), and using the Shannon index > 2.931 as the standard for result analysis. The 100-day mortality rate of patients with acute severe pneumonia whose Shannon index > 2.931 was significantly lower than that of the group with sSI < 2.

931.

8. A metagenomics-based early warning model for the risk of severe illness and death in patients with acute severe pneumonia, characterized by The method is constructed by any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored therein, characterized in that When the computer program is executed by the processor, the metagenomic-based early warning model for severe illness and death risk in patients with acute severe pneumonia as described in claim 8 is implemented.

10. An analysis system based on the early warning model of claim 8, characterized in that Includes the following modules: Receiving module: used to receive the user's search requirements; Matching module: used to match the user's search requirements with the constructed metagene database to obtain search matching results; Output module: used to output matching results.

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