Pleural effusion microbiome differential diagnosis and prognosis evaluation method based on metagenome sequencing and application
By combining metagenomic sequencing and bioinformatics analysis with a three-level validation process and CNV analysis, the problem of information dispersion in pleural effusion sample analysis was solved, enabling efficient microbiome-based differential diagnosis and prognostic assessment, improving detection efficiency and accuracy, and expanding the clinical application boundaries of mNGS.
Patent Information
- Application Number
- CN202511866884.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-11
- Publication Date
- 2026-03-20
AI Technical Summary
Existing technologies for analyzing pleural effusion samples suffer from limitations such as single information dimension, fragmented analysis process, and limited application scenarios. They cannot systematically and effectively correlate the macroscopic characteristics of the microbiome with the type of pleural effusion, and they do not deeply explore the prognostic value of microbiome data.
A metagenomic next-generation sequencing (mNGS) combined with bioinformatics analysis, including a three-level validation process of Kraken2, Bowtie2, and BLAST, was used to construct a unified experimental workflow to obtain microbiome structural features and host genome information by sequencing and bioinformatics analysis of pleural effusion samples, combined with copy number variation (CNV) analysis.
It enables the simultaneous output of microbiome structural features and host genome copy number variation information from a single sequencing data, improving detection efficiency, reducing sample consumption and cost, providing technical support for the mutual complementarity of microbial ecological information and host genome stability, and assisting in differentiating pleural effusion types and assessing patient prognosis.
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Abstract
Description
Technical Field
[0001] This invention relates to the field of bioinformatics technology, specifically to a method and application for the identification, diagnosis, and prognostic assessment of pleural effusion microbiome based on metagenomic sequencing. Background Technology
[0002] In the fields of clinical testing and in vitro diagnostics, pleural effusion, as a complex body fluid sample, requires comprehensive bioinformatics analysis for revealing potential pathological conditions. Currently, analytical techniques for this type of sample suffer from significant limitations and fragmentation. Traditional microbial detection methods are not only time-consuming and low-throughput, but also fail to reveal the complete microbial community structure and diversity characteristics of the sample. Although metagenomic next-generation sequencing (mNGS) technology can unbiasedly acquire all microbial nucleic acid sequence information in a sample, enabling comprehensive analysis, existing technical solutions remain imperfect in practical applications for high-host-background samples like pleural effusion.
[0003] Specifically, existing analytical approaches often focus on detecting single pathogens or conducting microbial community studies in isolation. There is a lack of a technical solution that can systematically and effectively correlate the macroscopic characteristics of the microbiome with different types of pleural effusion, thus failing to provide objective evidence from a microbial ecological perspective for preliminary sample type identification. Furthermore, current technologies do not offer a systematic solution, necessitating independent experimental and data analysis processes to obtain information from different dimensions, increasing costs, time, and sample consumption.
[0004] However, current mNGS-based pleural effusion analysis is primarily limited to the traditional scope of pathogen identification. Existing technologies have not yet deeply explored the potential predictive value of microbiome data related to patient clinical outcomes, nor have they established effective analytical models, thus limiting the maximization of the data's value. Therefore, we propose a metagenomic sequencing-based method and application for the differential diagnosis and prognostic assessment of pleural effusion microbiome to alleviate or resolve the aforementioned problems.
[0005] The information disclosed above in this background section is only for enhancing the understanding of the background section of this invention, and therefore may include prior art that is not known to those skilled in the art. Summary of the Invention
[0006] To address the aforementioned technical problems, this invention provides a method and application for the differential diagnosis and prognostic assessment of pleural effusion microbiome based on metagenomic sequencing, thereby solving the problems of single information dimension, fragmented analysis process, and limited application scenarios in the prior art.
[0007] To achieve the above objectives, the present invention provides a method for analyzing the microbiome of pleural effusion samples, comprising the following steps:
[0008] Next-generation metagenomic sequencing was performed on isolated pleural effusion samples to obtain sequencing reads;
[0009] Bioinformatics analysis was performed on the sequencing reads, and based on the validation results, the microbial composition information of the pleural effusion sample was determined; the bioinformatics analysis included:
[0010] (A) The sequencing reads were compared with the human reference genome hg19 to separate reads of non-human origin;
[0011] (B) Use the Kraken2 tool to classify the non-human readings against the NCBI reference sequence database;
[0012] (C) The classified sequences were compared and verified with the microbial RefSeq database using the Bowtie2 tool;
[0013] (D) Candidate reads with inconsistent results between Kraken2 and Bowtie2 were validated using BLAST alignment against a nucleotide database.
[0014] Preferably, the metagenomic next-generation sequencing includes:
[0015] Genomic DNA was extracted from the sample using a nucleic acid extraction kit;
[0016] Sequencing libraries were constructed on an NGS automated library preparation system using a total DNA library preparation kit.
[0017] The library was sequenced using the Illumina NextSeq500 platform and a 75-cycle high-output sequencing kit.
[0018] Preferably, in step (B), a microbial species can be confirmed as effectively detected only if all of the following conditions are met:
[0019] Sequencing data underwent quality control, with library concentration > 10 pM, Q20 > 85%, and Q30 > 80%.
[0020] The species was not detected in the negative control of the same sequencing run, or the ratio of the species per million reads in the sample to the species per million reads in the negative control was ≥ 5.
[0021] Preferably, the bioinformatics analysis further includes a diversity analysis step, which includes:
[0022] Calculate the Alpha diversity index of the sample, the index being selected from at least one of the Shannon index, Inverse Simpson index, Chao1 index, and ACE index; and / or perform principal coordinate analysis based on Bray-Curtis dissimilarity to assess Beta diversity.
[0023] Preferably, the method further includes a copy number variation analysis step based on the sequencing reads:
[0024] Readings aligned to the human reference genome hg19 were used for CNV analysis;
[0025] Among them, the samples used for CNV analysis must meet the following quality thresholds: (1) at least 1 million human host sequences; (2) GC content ratio less than 0.44.
[0026] A pleural effusion microbiome sequencing kit for use in the above methods, the kit comprising: a nucleic acid extraction kit, a total DNA library preparation kit, and a 75-cycle high-output sequencing kit for the Illumina NextSeq500 platform.
[0027] The application of the microbial composition data obtained by the above method in the preparation of an article used to help distinguish the type of pleural effusion sample of unknown category, the application including: comparing the microbial composition data with the reference microbial composition of pleural effusion of known type;
[0028] The known types include transudative pleural effusion, parapneumonic pleural effusion, tuberculous pleural effusion, and malignant pleural effusion.
[0029] The comparison includes comparing alpha diversity. If the microbial alpha diversity of the unidentified sample is higher than that of a reference microbial composition representing exudative pleural effusion, then the product provides information to help distinguish the unidentified sample as transudative pleural effusion.
[0030] The application of the microbial composition data obtained by the above method in the preparation of an article used to assist in assessing the prognostic status of samples derived from patients with malignant pleural effusion, the application including: detecting the relative abundance of the genera Cutibacterium and Pseudomonas in the microbial composition data.
[0031] Compared with the prior art, the beneficial effects of the present invention are:
[0032] This invention establishes a unified mNGS experimental workflow and a parallel bioinformatics analysis path, enabling the simultaneous output of two different dimensions of technical information from a single sequencing data stream: microbiome structural characteristics and host genome copy number variation. This overcomes the drawback of existing technologies where microbial and genomic analyses need to be performed separately. It not only significantly improves detection efficiency and reduces sample consumption and detection costs, but also allows microbial ecological information and host genome stability information to corroborate and complement each other, providing technical support for a comprehensive evaluation of sample characteristics.
[0033] This invention, through systematic analysis, reveals statistically significant differences in microbial alpha diversity between transudative and exudative pleural effusions, and also demonstrates a clear distinction in community structure characterized by beta diversity. Utilizing microbiome ecological characteristics to aid in differentiating between TrPE and EPE provides a novel technical approach.
[0034] This invention employs a three-level validation process using Kraken2, Bowtie2, and BLAST, combined with background noise reduction quality control standards, to improve the specificity and reliability of microbial identification results. Furthermore, by setting clear pre-analysis quality control thresholds for CNV analysis and utilizing a fusion LASSO algorithm to optimize the copy number ratio, a highly accurate CNV analysis workflow is constructed.
[0035] This invention verifies a statistical association between the relative abundance of specific microbial genera in malignant pleural effusion samples and patient clinical outcomes, providing a solid technical basis for using microbial composition data as a novel, non-invasive prognostic assessment tool, and expanding the clinical application boundaries and technical value of mNGS data.
[0036] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description
[0037] Figure 1 This is a schematic diagram of the microbial community composition of different types of pleural effusion samples in this invention. It mainly shows the typical microbial community characteristics of transudative pleural effusion (TrPE), exudative pleural effusion (EPE), and their subcategories, parapneumonic effusion (PaE), tuberculous effusion (TPE), and malignant effusion (MPE). The size and number of the model graphs correspond to the relative abundance of key microbial taxa, illustrating that pleural effusions of different etiologies have unique microbiome structures.
[0038] Figure 2This is a flowchart illustrating the workflow of the combined analysis of pleural effusion microbiome and copy number variation based on metagenomic next-generation sequencing in this invention. Detailed Implementation
[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. It should be noted that the drawings are schematic and not illustrated to scale. For clarity and convenience, the relative sizes and proportions of the parts shown in the drawings have been exaggerated or reduced in size. Any size is only illustrative and not limiting.
[0040] Example 1: Collection and Clinical Grouping of Pleural Effusion Samples
[0041] With the approval of the ethics committee and informed consent obtained from the patients, a total of 49 patients with radiologically confirmed pleural effusion (PE) by computed tomography (CT) and / or ultrasound were recruited. After rigorous screening, 44 participants were ultimately included in the analysis. After collecting sufficient fluid and sending it for clinically indicated tests, the remaining fluid was aseptically collected into test tubes containing DNA stabilizing solution and immediately stored in an ultra-low temperature freezer at -80°C for later use.
[0042] Based on clinical diagnostic criteria such as the Light criteria, patients were divided into four groups: transudative pleural effusion (TrPE, n=7); parapneumonia pleural effusion (PaE, n=12); tuberculous pleural effusion (TPE, n=11); and malignant pleural effusion (MPE, n=14). Among them, PaE, TPE, and MPE were classified as exudative pleural effusion (EPE, n=37).
[0043] Example 2: Genomic DNA extraction, sequencing library construction, and metagenomic sequencing
[0044] Metagenomic next-generation sequencing (mNGS) was performed on the isolated pleural effusion samples obtained in Example 1. The specific steps are as follows:
[0045] Genomic DNA extraction: Total genomic DNA was extracted from a 200 μL pleural effusion sample using a nucleic acid extraction kit (Cat. MD013, MatriDx Biotech Corp., Hangzhou, China).
[0046] Sequencing library preparation: Sequencing libraries were constructed using the Total DNA Library Preparation Kit (Cat. MD001T, MatriDx Biotech Corp.) on the NGS Automated Library Preparation System (Cat. MD005, MatriDx Biotech Corp.).
[0047] Sequencing: The prepared library was sequenced on the Illumina NextSeq500 sequencing platform (Illumina, San Diego, CA, USA) using a 75-cycle high-output sequencing kit.
[0048] Strictly monitor sequencing quality to ensure that each sample produces 10-20 million raw reads. In particular, samples used for subsequent copy number variation (CNV) analysis must meet the following two quality thresholds, otherwise they should be excluded from CNV analysis: (1) containing at least 1 million host sequences aligned to the human genome; (2) the GC content ratio of the sequencing data is less than 0.44.
[0049] Example 3: Bioinformatics Analysis Workflow, the overall workflow is attached. Figure 2 As shown.
[0050] Bioinformatics analysis was performed on the sequencing data generated in Example 2. This process can handle pathogen detection, microbiome analysis, and tumor CNV analysis in parallel.
[0051] 3.1 Copy Number Variation (CNV) Analysis Process:
[0052] Sequencing reads were compared with the human reference genome (hg19), and the uniquely matched reads were selected for CNV analysis.
[0053] The reference genome is divided into fixed-length contiguous windows. The read depth of each window is calculated and normalized. The normalized read depth of each window is divided by the average depth of the reference dataset to obtain the copy number ratio for that window. The log2-transformed copy number ratio is smoothed using the fused LASSO method. Adjacent windows with similar smoothed copy number ratios are merged into fragments, and their chromosomal positions and average ratios are recorded. Based on the average copy number of the fragments and the normal copy number of the corresponding chromosome, the absolute copy number of each fragment is calculated and compared with a preset threshold to ultimately determine CNVs.
[0054] 3.2 Pathogen Detection and Microbiome Analysis Workflow:
[0055] The unaligned reads remaining after human genome alignment, i.e., reads from non-human sources, will be used for subsequent microbial analysis. Species identification and validation will be conducted.
[0056] Primary classification (rapid screening): Using Kraken2 software, non-human readings are quickly compared and classified against the NCBI reference sequence database.
[0057] Secondary verification (rigorous alignment): Using Bowtie2 software, the sequences classified by Kraken2 were rigorously aligned and verified with the microbial RefSeq database.
[0058] Level 3 arbitration (inconsistency ruling): For candidate readings where Kraken2 and Bowtie2 analysis results are inconsistent, a final ruling is made by comparing the nucleotide database using BLAST (version 2.9.0+).
[0059] Microorganisms identified through the above process are reported as true positives only if they meet all of the following conditions:
[0060] (1) Sequencing data passed quality control, i.e., library concentration > 10 pM, Q20 > 85%, and Q30 > 80%.
[0061] (2) Effectively distinguish background contamination, i.e., the species is not present in the negative control of the same batch of sequencing, or the ratio of the species' RPM value in the sample to the RPM value of the negative control (RPM). 样本 / RPM 阴性对照 ≥ 5.
[0062] Based on the final confirmed species list, a microbial composition profile was generated. Alpha diversity indices, including Shannon, Inverse Simpson, Chao1, and ACE indices, were calculated using R software, and Beta diversity was assessed using principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarity. The significance of differences between groups was tested using permuted multivariate analysis of variance (PERMANOVA).
[0063] Example 4: Microbiome Analysis Results and Application Examples
[0064] Analysis of 44 samples using the above methods revealed that the microbial alpha diversity of transudative pleural effusion (TrPE) was significantly higher than that of the combined group of all exudative pleural effusions (EPE). Principal coordinate analysis showed significant differences in the microbial community structure between TrPE and EPE, and the microbial composition of the four groups (TrPE, PaE, TPE, and MPE) also exhibited distinct characteristics, as shown in the attached figure. Figure 1 As shown.
[0065] For a pleural effusion sample of unknown type, if its microbial alpha diversity is significantly higher than the known EPE reference range after testing by the method of this invention, and its microbial community structure is closer to the TrPE reference cluster in the PCoA diagram, then the result can provide important objective evidence for clinicians to help determine whether it is a transudative effusion.
[0066] Microbiome and CNV characteristics for the identification and prognostic assessment of malignant pleural effusion:
[0067] In a diagnostic application, CNV analysis showed that significant chromosomal copy number abnormalities were detected in MPE samples, but not in non-MPE samples. In a study of 35 patients, this method demonstrated a sensitivity of 80%, a specificity of 92.6%, and an AUC of 0.882 in identifying MPE.
[0068] Prognostic applications and survival analysis of MPE patients revealed that their microbial composition was associated with patient prognosis. Specifically, the relative abundance of the genus *Cutibacterium* was positively correlated with longer overall survival, while the relative abundance of the genus *Pseudomonas* was negatively correlated with poorer prognosis.
[0069] If a characteristic CNV pattern and a high relative abundance of the genus Cutibacterium are detected simultaneously in an MPE sample using the method of this invention, the result can provide valuable reference information for clinicians to assist in assessing the patient's prognosis, which may be relatively good.
[0070] Example 5: Reagent Kit and System
[0071] Based on the above method, a kit for pleural effusion microbiome and CNV sequencing was assembled. This kit includes, but is not limited to: a nucleic acid extraction kit (Cat. MD013, MatriDx Biotech Corp.), a total DNA library preparation kit (Cat. MD001T, MatriDx Biotech Corp.), and a 75-cycle high-output sequencing kit for the Illumina NextSeq500 platform.
[0072] Meanwhile, the present invention includes a computer-readable storage medium storing a computer program, which, when executed by a processor, enables the implementation of the bioinformatics analysis steps described in Embodiment 3.
[0073] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Furthermore, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0074] Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A method for analyzing the microbiome of pleural effusion samples, characterized in that, Includes the following steps: Next-generation metagenomic sequencing was performed on isolated pleural effusion samples to obtain sequencing reads; Bioinformatics analysis was performed on the sequencing reads, and the microbial composition information of the pleural effusion sample was determined based on the validation results; The bioinformatics analysis includes: (A) The sequencing reads were compared with the human reference genome hg19 to separate reads of non-human origin; (B) Use the Kraken2 tool to classify the non-human readings against the NCBI reference sequence database; (C) The classified sequences were compared and verified with the microbial RefSeq database using the Bowtie2 tool; (D) Candidate reads with inconsistent results between Kraken2 and Bowtie2 were validated using BLAST alignment against a nucleotide database.
2. The method according to claim 1, characterized in that, The metagenomic next-generation sequencing includes: Genomic DNA was extracted from the sample using a nucleic acid extraction kit; Sequencing libraries were constructed on an NGS automated library preparation system using a total DNA library preparation kit. The library was sequenced using the Illumina NextSeq500 platform and a 75-cycle high-output sequencing kit.
3. The method according to claim 1, characterized in that, In step (B), a microbial species can only be confirmed as effectively detected if all of the following conditions are met: Sequencing data underwent quality control, with library concentration > 10 pM, Q20 > 85%, and Q30 > 80%. The species was not detected in the negative control of the same sequencing run, or the ratio of the species per million reads in the sample to the species per million reads in the negative control was ≥ 5.
4. The method according to claim 1, characterized in that, The bioinformatics analysis also includes a diversity analysis step, which includes: Calculate the Alpha diversity index of the sample, the index being selected from at least one of the Shannon index, Inverse Simpson index, Chao1 index, and ACE index; and / or perform principal coordinate analysis based on Bray-Curtis dissimilarity to assess Beta diversity.
5. The method according to claim 1, characterized in that, The method further includes a copy number variation analysis step based on the sequencing reads: Readings aligned to the human reference genome hg19 were used for CNV analysis; Among them, the samples used for CNV analysis must meet the following quality thresholds: (1) at least 1 million human host sequences; (2) GC content ratio less than 0.
44.
6. A pleural effusion microbiome sequencing kit for implementing the method of claim 2, characterized in that, The kit includes: a nucleic acid extraction kit, a total DNA library preparation kit, and a 75-cycle high-output sequencing kit for the Illumina NextSeq500 platform.
7. The application of microbial composition data obtained by the method of any one of claims 1-5 in the preparation of an article, said article being used to assist in distinguishing the type of pleural effusion samples of unknown classification, characterized in that, The applications include: The microbial composition data were compared with the reference microbial composition of known types of pleural effusion; The known types include transudative pleural effusion, parapneumonic pleural effusion, tuberculous pleural effusion, and malignant pleural effusion.
8. The application according to claim 7, characterized in that, The comparison includes a comparison of alpha diversity. When the microbial alpha diversity of the unidentified sample is higher than that of a reference microbial composition representing exudative pleural effusion, the product provides information to help distinguish the unidentified sample as transudative pleural effusion.
9. The use of microbial composition data obtained by the method of any one of claims 1-5 in the preparation of an article for assisting in the assessment of the prognostic status of samples derived from patients with malignant pleural effusion, characterized in that, The application includes detecting the relative abundance of the genera *Cutibacterium* and *Pseudomonas* in the microbial composition data.