A profiling method for a respiratory microbiome and plasma microbial extracellular vesicles in intubated patients

Abstract

Extracellular vesicles (EVs) regulate various cellular and immunological functions in human diseases. There is growing interest in the clinical role of microbial EVs in pneumonia. However, research on the correlation between the lung microbiome, microbial EVs, and microbiomes from other body parts in pneumonia is lacking. The inventors investigated the co-occurrence of the lung microbiome and plasma-derived microbial EVs (mEVs) in 111 samples obtained from 60 mechanically ventilated patients (41 cases of pneumonia and 19 cases of non-pneumonia). Microbial correlations between the two samples were compared between pneumonia and non-pneumonia cases. The bacterial composition of plasma microbial EVs was distinct from the bacterial composition of the lung microbiome. There was a significantly higher correlation between the lung microbial community and plasma mEVs in non-pneumonia patients compared to pneumonia patients. In particular, the genera Acinetobacter and Lactobacillus showed high correlation coefficients in non-pneumonia patients. This indicates the beneficial effect of microbial EVs in modulating the host lung immune response through the delivery of EV components.

Description

Technology Field

[0001] The present invention relates to a method for profiling respiratory microorganisms and plasma microorganisms in intubated patients, and more specifically, to a method for predicting the likelihood of pneumonia by analyzing the bacterial composition between the lung microbiome of an intubated patient, microbial EVs, and the microbiome of other body parts. Background Technology

[0003] Extracellular vesicles (EVs) are small particles released from eukaryotic host cells or pathogens (1). Extracellular vesicles have emerged as a new mechanism for intercellular communication (2), including the delivery of bacteria-derived EVs that can regulate the host's innate immunity (3). Investigating microbiome EVs across various parts of the body can provide comprehensive insights into disease mechanisms and the influence of environmental factors.

[0004] Recently, increased migration of microbe-derived extracellular vesicles (mEVs) into the blood has been reported to be associated with the pathogenesis of type 2 diabetes mellitus (T2DM) (4). However, little is known about the association between plasma microbe-derived EVs and the respiratory microbiome, particularly in respiratory diseases such as pneumonia. Several studies have revealed that the gut microbiome plays an important role in the immune response of the lungs. Additionally, it has been observed that gut microbe-derived EVs enter the bloodstream and trigger various immune and metabolic responses (5-8). However, it is unclear whether the respiratory microbiome passes through the alveolar capillary barrier to enter the blood or whether plasma EVs affect the lung microbiome. Further research is needed to clarify the relationship between the respiratory microbiome and plasma mEVs. Therefore, in this invention, plasma mEVs and the respiratory microbiome of intubated patients were analyzed, and the microbial correlation between the two samples was explored. Identifying the associations and differences in genomic profiling between respiratory microbes and plasma mEVs can provide insights into the pathogenesis of pneumonia and the clinical role of plasma mEVs. Prior art literature

[0006] Maas SLN et al., Trends Cell Biol. 2017;27(3):172-88.Fujita Y et al., Am J Respir Cell Mol Biol. 2018;58(5):560-5. Schorey JS et al., EMBO Rep. 2015;16(1):24-43.Nah G et al., Sci Rep. 2019;9(1):20136.Shukla SD et al., Clin Transl Immunology. 2017;6(3):e133.Budden KF et al., Nat Rev Microbiol. 2017;15(1):55-63.Romano M. Front Microbiol. 2017;8:721. Choi Y et al., Sci Rep. 2015;5:15878.Kim MR et al., Allergy. 2012;67(10):1271-81. Choi Y et al., Allergy Asthma Immunol Res. 2017;9(2):101-6. Zhou Y et al., Nature. 2012;486(7402):207-14. Vishnivetskaya T, et al. Genome Biol. 2013;14(1):R1. Matthay MA et al., Annu Rev Pathol. 2011;6:147-63. Muraca M et al., Discov Med. 2015;19(106):343-8. Daou Y et al., Front Med (Lausanne). 2022;9:985141. Behrens F et al., Int J Mol Sci. 2021;22(8). Codemo M et al., mBio. 2018;9(2). Tiku V et al., Sci Rep. 2021;11(1):618. Athman JJ et al., J Immunol. 2017;198(5):2028-37. Vidakovics ML et al., PLoS Pathog. 2010;6(1):e1000724. Asensio CJ et al., Vaccine.2011;29(8):1649-56. Olaya-Abril A et al., J Proteomics. 2014;106:46-60. Lee WH et al., Exp Mol Med. 2015;47(9):e183. Huang W et al., Front Microbiol. 2019;10:1379.Pulido MR et al., Vaccine. 2020;38(4):719-24.Huang W et al., PLoS One. 2014;9(6):e100727.McConnell MJ et al., Vaccine. 2011;29(34):5705-10. Cai W et al., J Immunol Res. 2019;2019:2835256.Dominguez Rubio AP et al., Front Microbiol. 2022;13:864720.Al-Nedawi K et al., FASEB J. 2015;29(2):684-95.Li M et al., BMC Microbiol. 2017;17(1):66.Seo MK et al., J Dairy Sci. 2018;101(10):8662-71. The problem to be solved

[0007] The present invention aims to provide a method for predicting the likelihood of pneumonia by analyzing the bacterial composition between the lung microbiome of an intubated patient and microbial EVs in other tissues, such as plasma. means of solving the problem

[0009] Intercellular communication mediated by bacterial EVs is associated with the pathogenesis of lung disease (2, 5). In previous studies by the inventors, we investigated the long-distance migration of EVs derived from gut microbes and their effects on intestinal permeability (4, 8, 9). Emerging new evidence suggests the existence of an intestinal-lung axis, in which EVs produced by gut commensal bacteria contribute to mucosal tolerance and can protect the human body from lung disease (10).

[0010] However, research on the distribution of the lung microbiome across different body parts and the effects of EVs derived from other organs on lung inflammation is lacking. The inventors found that lung and plasma microbial communities have distinct compositions and form separate clusters, which is consistent with previous studies demonstrating weak correlations in EV composition across different body parts (11, 12). Nah et al. demonstrated that patients with type 2 diabetes mellitus (T2DM) have impaired intestinal permeability and exhibit higher correlations between stool, serum, and urine samples compared to healthy individuals (4). Based on increased lung permeability in acute lung injuries such as acute respiratory distress syndrome (ARDS) (13), we hypothesized that correlations between body parts would be lower in healthy subjects compared to patients with pneumonia. However, contrary to our expectations, the correlation between lung microbial communities and plasma mEVs was higher in individuals without pneumonia than in patients with pneumonia.

[0011] We found that the migration of EVs derived from protective bacteria in the absence of pneumonia is more likely to increase than the migration of EVs derived from respiratory pathogens into plasma due to increased alveolar-capillary permeability in pneumonia (14).

[0012] Macia et al. reported that under homeostatic conditions, EVs of commensal microorganisms inhibit pathogenic colonization and regulate immune responses to resistance (15). Recent studies have shown that EVs of Gram-negative and Gram-positive bacteria can have both harmful and beneficial effects on pneumonia (16).

[0013] Outer membrane vesicles (OMVs) interact with respiratory epithelium and trigger inflammatory responses in the innate and adaptive immune systems during pneumonia (17-20). However, bacterial-derived OMVs such as B. pertussis, S. pneumoniae, K. pneumoniae, and A. baumannii have also been considered potential vaccine candidates based on their ability to induce potent antibody responses and beneficial biochemical properties (21-24).

[0014] Several studies have demonstrated that natural OMV derived from A. baumannii provides protection against clinical isolates, including pan-resistant strains, and reduces mortality (24-27). Cai et al. demonstrated that OMV derived from A. baumannii can induce dendritic cell activation, thereby promoting Th2 activation and humoral immune responses (28). Similarly, in our study, increased intercellular communication of Acinetobacter EVs in non-pneumonia patients can be interpreted in terms of the immunogenicity and protective potential of EVs.

[0015] Our data also revealed that Lactobacillus species with a correlation coefficient between lung and plasma mEVs > 0.5 are associated with non-pneumonia status. The beneficial effects of probiotics against infectious diseases have been reported to be mediated by probiotic-derived EVs that inhibit pathogens, strengthen barrier function, and modulate immunity (29). Consistent with our findings, previous studies have shown that EVs derived from other strains of Lactobacillus possess anti-inflammatory properties beneficial to the host (30-32).

[0016] Through the present invention, we have made it possible to gain a comprehensive understanding of the microbiome and communication via EVs by directly comparing the lung microbiome and plasma microbial EVs using human samples.

[0017] In conclusion, we observed a significantly higher correlation between the lung microbiome and plasma EVs in non-pneumonia patients than in pneumonia patients. This indicates the potential of EVs as a tool for host-bacterial communication.

[0019] The present invention

[0020] (a) A step of collecting plasma samples and endotracheal aspirate samples separated from a patient on mechanical ventilation between 1 and 7 days after intubation;

[0021] (b) A step of separating the supernatant containing extracellular vesicles from the collected plasma sample by centrifugation;

[0022] (c) A step of removing foreign substances from the plasma supernatant containing extracellular vesicles;

[0023] (d) A step of extracting DNA from plasma extracellular vesicles;

[0024] (e) Step of extracting DNA from an endotracheal aspirate sample;

[0025] (f) A step of amplifying the plasma extracellular vesicle DNA extracted in step (d) and the tracheal aspirate DNA extracted in step (e) using primers, respectively;

[0026] (d) A step of analyzing the amplified plasma extracellular vesicle DNA sequence and the tracheal aspirate DNA sequence;

[0027] (a) a step of performing alpha diversity analysis and beta diversity analysis from the readings obtained from the sequence analysis results; and

[0028] (i) A step of predicting that the likelihood of pneumonia is low if there are microorganisms with a correlation coefficient of > 0.5 based on alpha diversity analysis and beta diversity analysis results; the present invention relates to a method for profiling extracellular vesicle microorganisms, comprising: (j) a step of predicting that the likelihood of pneumonia is low if there are microorganisms with a correlation coefficient of > 0.5 based on alpha diversity analysis and beta diversity analysis results.

[0029] In addition, the present invention relates to a method for profiling extracellular vesicle microorganisms, wherein the sample of step (a) is obtained on the 5th to 7th day after intubation.

[0030] In addition, the present invention relates to a method for profiling extracellular vesicle microorganisms, which analyzes the alpha diversity of step (a) using the Chao1 index.

[0031] In addition, the present invention relates to a method for profiling extracellular vesicle microorganisms, which evaluates the beta diversity of step (a) using the Bray-Curtis disparity index.

[0032] In addition, the above step (a) of the present invention relates to a method for profiling extracellular vesicle microorganisms, which evaluates the correlation between intratracheal aspirate microorganisms and plasma extracellular vesicles using Spearman sequence correlation coefficients.

[0033] In addition, the present invention

[0034] (a) A step of collecting plasma samples and endotracheal aspirate samples separated from a patient on mechanical ventilation between 1 and 7 days after intubation;

[0035] (b) A step of separating the supernatant containing extracellular vesicles from the collected plasma sample by centrifugation;

[0036] (c) A step of removing foreign substances from the plasma supernatant containing extracellular vesicles;

[0037] (d) A step of extracting DNA from plasma extracellular vesicles;

[0038] (e) Step of extracting DNA from an endotracheal aspirate sample;

[0039] (f) A step of amplifying the plasma extracellular vesicle DNA extracted in step (d) and the tracheal aspirate DNA extracted in step (e) using primers, respectively;

[0040] (d) A step of analyzing the amplified plasma extracellular vesicle DNA sequence and the tracheal aspirate DNA sequence;

[0041] (a) A step of performing alpha diversity analysis and beta diversity analysis at the genus level from the readings obtained from the sequence analysis results; and

[0042] (j) A step of predicting that the likelihood of pneumonia is low when, as a result of performing the alpha diversity analysis and beta diversity analysis steps above, one or more of the genus Acinetobacter and the genus Lactobacillus have a correlation coefficient > 0.5; the present invention relates to a method for profiling extracellular vesicle microorganisms, comprising: (j) a step of predicting that the likelihood of pneumonia is low when, as a result of performing the alpha diversity analysis and beta diversity analysis steps above, the correlation coefficient of one or more of the genus Acinetobacter and the genus Lactobacillus is > 0.5.

[0043] In addition, the present invention

[0044] (a) A step of collecting plasma samples and endotracheal aspirate samples separated from a patient on mechanical ventilation between 1 and 7 days after intubation;

[0045] (b) A step of separating the supernatant containing extracellular vesicles from the collected plasma sample by centrifugation;

[0046] (c) A step of removing foreign substances from the plasma supernatant containing extracellular vesicles;

[0047] (d) A step of extracting DNA from plasma extracellular vesicles;

[0048] (e) Step of extracting DNA from an endotracheal aspirate sample;

[0049] (f) A step of amplifying the plasma extracellular vesicle DNA extracted in step (d) and the tracheal aspirate DNA extracted in step (e) using primers, respectively;

[0050] (d) A step of analyzing the amplified plasma extracellular vesicle DNA sequence and the tracheal aspirate DNA sequence;

[0051] (a) A step of performing alpha diversity analysis and beta diversity analysis at the genus level from the readings obtained from the sequence analysis results; and

[0052] (j) A step of predicting that there is a high probability of pneumonia if, as a result of performing the alpha diversity analysis and beta diversity analysis steps above, one or more species of the genus Corynebacterium and the genus Stenotrophomonas are dominant; the present invention relates to a method for profiling microorganisms in tracheal aspirates, comprising: (j) a step of predicting that there is a high probability of pneumonia if, as a result of performing the alpha diversity analysis and beta diversity analysis steps above, one or more species of the genus Corynebacterium and the genus Stenotrophomonas are dominant.

[0053] In addition, the present invention

[0054] (a) A step of collecting plasma samples and endotracheal aspirate samples separated from a patient on mechanical ventilation between 1 and 7 days after intubation;

[0055] (b) A step of separating the supernatant containing extracellular vesicles from the collected plasma sample by centrifugation;

[0056] (c) A step of removing foreign substances from the plasma supernatant containing extracellular vesicles;

[0057] (d) A step of extracting DNA from plasma extracellular vesicles;

[0058] (e) Step of extracting DNA from an endotracheal aspirate sample;

[0059] (f) A step of amplifying the plasma extracellular vesicle DNA extracted in step (d) and the tracheal aspirate DNA extracted in step (e) using primers, respectively;

[0060] (d) A step of analyzing the amplified plasma extracellular vesicle DNA sequence and the tracheal aspirate DNA sequence;

[0061] (a) A step of performing alpha diversity analysis and beta diversity analysis at the genus level from the readings obtained from the sequence analysis results; and

[0062] (j) A step of predicting that the likelihood of pneumonia is low if, as a result of performing the alpha diversity analysis and beta diversity analysis steps above, one or more of the genera Prevotella and Alloprevotella are dominant; the present invention relates to a method for profiling microorganisms in tracheal aspirates, comprising: (j) a step of predicting that the likelihood of pneumonia is low if, as a result of performing the alpha diversity analysis and beta diversity analysis steps above, one or more of the genera Prevotella and Alloprevotella are dominant.

[0063] In addition, the present invention

[0064] (a) A step of collecting plasma samples and endotracheal aspirate samples separated from a patient on mechanical ventilation between 1 and 7 days after intubation;

[0065] (b) A step of separating the supernatant containing extracellular vesicles from the collected plasma sample by centrifugation;

[0066] (c) A step of removing foreign substances from the plasma supernatant containing extracellular vesicles;

[0067] (d) A step of extracting DNA from plasma extracellular vesicles;

[0068] (e) Step of extracting DNA from an endotracheal aspirate sample;

[0069] (f) A step of amplifying the plasma extracellular vesicle DNA extracted in step (d) and the tracheal aspirate DNA extracted in step (e) using primers, respectively;

[0070] (d) A step of analyzing the amplified plasma extracellular vesicle DNA sequence and the tracheal aspirate DNA sequence;

[0071] (a) a step of metagenomics analysis of plasma extracellular vesicles; and

[0072] (j) A step of predicting a low probability of pneumonia when an increase in the genus Cutibacterium is observed as a result of the metagenomics analysis above; the present invention relates to a method for profiling extracellular vesicle microorganisms, comprising: (j) a step of predicting a low probability of pneumonia when an increase in the genus Cutibacterium is observed as a result of the metagenomics analysis above. Effects of the invention

[0074] The method of the present invention enables determining whether the likelihood of pneumonia is high or low through the microbial correlation between microorganisms in aspirated trachea and extracellular vesicles in plasma. Brief explanation of the drawing

[0076] Figure 1 shows the results of alpha and beta diversity analysis. (a) The observed OTU and Chao1 alpha diversity of the subject was analyzed for comparison between endotracheal aspirate (hereinafter referred to as "ETA" interchangeably in this specification and drawings) and plasma samples. (b) General phyla and genera of ETA and plasma are shown. Overlapping circles in the Venn diagram represent the number of microorganisms common to both sites. (c) PCoA analysis results visualized in the Bray-Curtis disequivalence index for beta diversity of body parts. Figures 2a and 2b show the microbial composition of the phyla and genera of intubated patients. The numbers represent the relative proportions of the microbiome of ETA and plasma mEV. Figure 3 shows beta diversity between body parts and microbial composition in pneumonia and non-pneumonia patients. (a) Results of PCoA analysis using the Bray-Curtis dissimilarity index between the ETA microbiome and plasma mEVs in pneumonia and non-pneumonia groups. (b) Microbial composition of ETA and plasma mEVs in pneumonia and non-pneumonia groups. Figure 4 compares the correlation coefficients between ETA and plasma mEV according to pneumonia status. The correlation coefficient was higher in the non-pneumonia group than in the pneumonia group. Specific details for implementing the invention

[0077] The composition of the present invention is described in more detail below with reference to specific embodiments. However, it is obvious to those skilled in the art that the scope of the present invention is not limited only to the description of the embodiments.

[0079] Example 1: Experimental Subject

[0080] This prospective study was conducted in the Intensive Care Unit (ICU) of Chuncheon Sacred Heart Hospital. A total of 111 samples were obtained from 60 subjects who received mechanical ventilation upon admission to the ICU between July 2017 and August 2018. Patients under 18 years of age, those who started mechanical ventilation 48 hours after admission to the ICU or received mechanical ventilation for less than 7 days, those with neuromuscular diseases such as amyotrophic lateral sclerosis (4), and those unable to provide informed consent were excluded. Endotracheal aspirate (ETA) and plasma samples were collected after fasting for at least 8 hours. 41 subjects were diagnosed with pneumonia and treated according to international guidelines. Demographic characteristics, acute respiratory distress syndrome; The presence of ARDS, disease severity (Acute Physiological and Chronic Health Assessment [APACHE] II and Sequential Organ Failure Assessment [SOFA] scores), and clinical outcomes (all-cause mortality within 28 days, in-hospital death, duration of mechanical ventilation) were collected. This study was approved by the Institutional Review Board of Chuncheon Sacred Heart Hospital (Approval No.: 2017-47). Written informed consent was obtained from the participants, and the study was conducted in accordance with approved guidelines.

[0082] Example 2: EV Separation and DNA Extraction

[0083] ETA samples and plasma samples were collected within 7 days of the initiation of mechanical ventilation. Plasma samples were obtained using BD Vacutainers (BD, Franklin Lakes, NJ, USA) and centrifuged at 2000 g for 15 minutes at 4°C. To separate EVs from the plasma samples, differential centrifugation was performed at 10,000 g for 10 minutes at 4°C using a microcentrifuge (Labogene 1730R; BMS, Seoul, Korea) to separate the pellet and supernatant. Subsequently, the supernatant was filtered through a 0.22 µm filter Bacteria and foreign substances were removed by filtration. Then, the EV was boiled at 100°C for 40 minutes and centrifuged at 13,000 rpm at 4°C for 30 minutes. EV DNA extraction was performed using the DNeasy PowerSoil Kit (Qiagen, Hilden, Germany), and quantification was performed using the QIAxpert system (Qiagen). For ETA samples, total DNA extraction was performed using a commercially available microbial DNA isolation kit (Qiagen, Hilden, Germany).

[0085] Example 3: Paired-end reads sequencing and data processing

[0086] The extracted DNA was amplified using primers designed to target the V3–V4 regions of the prokaryotic 16S rRNA gene. The primers were as follows: 16S_V3_F (5'-TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG-3'), 16S_V4_R (5'-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC-3'). The amplicon library was quantified and sequenced using the MiSeq platform (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. Paired-end reads were merged using FLASH software, and reads shorter than 350 bp or longer than 550 bp were excluded from further analysis. Chimeric sequences and singletons were removed using the VSEARCH and SILVA Gold databases. Sequencing leads with a similarity of 97% or higher were clustered into operational classifications using the SILVA128 database. Samples that failed PCR or had a low number of reads (<150) were excluded from quality control.

[0088] Example 4: Statistical Analysis

[0089] A total of 120 samples were collected from 60 subjects (17 females and 43 males). Nine samples were excluded due to an insufficient number of reads, resulting in a total of 111 samples being used. Biodiversity and community similarity analyses were performed using R software (version 4.0.4). Alpha and beta diversity were calculated using QIIME. Beta diversity was assessed using the Bray-Curtis disequivalence index, and alpha diversity was measured using the Chao1 index. Taxa with a relative abundance > 0.5% were considered dominant. The correlation between lung microbes and plasma mEVs was evaluated using Spearman correlations. Permutation-based p-values ​​were calculated from 100,000 permutation replicates because they are robust against nonnormality and small sample sizes. Genera with a correlation coefficient greater than 0.5 and a false discovery rate less than 0.05 were considered statistically significant.

[0091] result

[0092] There were 41 cases of pneumonia and 19 cases of non-pneumonia. The baseline characteristics of the subjects are presented in Table 1. There were no significant differences among clinical outcomes, including age, sex, Charlson Comorbidity Index, disease severity (SOFA and APACHE II scores), mortality, and duration of mechanical ventilation. As expected, the non-pneumonia group showed a higher PaO2 / FiO2 ratio, while the pneumonia group showed high C-reactive protein levels. Neurosurgical conditions and cardiopulmonary arrest were identified more frequently in the non-pneumonia group as causes of intubation.

[0094] Result 1: Microbial diversity between respiratory microbial communities and plasma-derived EVs

[0095] Alpha diversity was much higher in ETA samples than in plasma mEV (Fig. 1). Beta diversity analysis using Principal Coordinate Analysis (PCoA) revealed significant differences between the plasma mEV and ETA microbial communities at both the phylum and genus levels. At the phylum level, 22 strains were found to be common between the ETA microbiome and plasma mEV. Proteobacteria, Firmicutes, Actinobacteria, Bacteroidetes, and Fusobacteria were the dominant taxa, accounting for >0.5% of relative abundance.

[0096] At the genus level, a total of 898 genera and 351 genera were identified in the ETA microbial community and plasma mEVs, respectively. Of these, 279 genera were found at both sampling locations. The microbial taxonomic profiles showed significant differences between the ETA microbial community and plasma mEVs (Fig. 2). The four most abundant phyla in both the ETA microbial community and plasma mEVs were Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. However, Streptococcus and Enterobacter were much more abundant in the ETA microbial community than in plasma mEVs, while Staphylococcus, Escherichia-Shigella, and Klebsiella were much more abundant in plasma mEVs. No genus showed a significantly high correlation between the two regions.

[0098] Result 2: 16S rRNA Microbiome

[0099] In both pneumonia and non-pneumonia cases, the composition of plasma mEVs was distinct from the composition of the ETA microbiome, which is consistent with the results of all 111 samples. Beta-diversity analysis using PCoA revealed significant differences between plasma mEVs and the ETA microbiome in both pneumonia and non-pneumonia patients (Fig. 3, P < 0.001). Different taxa in the lung microbiome and plasma mEVs were identified between pneumonia and non-pneumonia patients. In ETA microbiome analysis, Corynebacterium and Stenotrophomonas were dominant in the pneumonia group, while Prevotella and Alloprevotella were dominant in the non-pneumonia group. Metagenomic analysis of plasma mEVs showed a significant increase in Cutibacterium in the non-pneumonia group compared to the pneumonia group.

[0100] Interestingly, the correlation between plasma mEV and the ETA microbiome differed significantly between pneumonia and non-pneumonia patients (Fig. 4). Non-pneumonia patients showed a significantly higher correlation between plasma mEV and the ETA microbiome compared to pneumonia patients. Genus-level analysis of the correlation between the ETA microbiome and plasma mEV revealed that Acinetobacter and Lactobacillus had correlation coefficients > 0.5 across all sampling sites and were associated with non-pneumonia patients. However, no genera were associated with pneumonia (Table 2).

[0101]

[0102]

Claims

Claim 1 (a) collecting plasma samples and endotracheal aspirate samples isolated from a patient on mechanical ventilation between 1 and 7 days after intubation; (b) separating the supernatant containing extracellular vesicles from the collected plasma samples by centrifugation; (c) removing foreign substances from the plasma supernatant containing extracellular vesicles; (d) extracting DNA from the plasma extracellular vesicles; (e) extracting DNA from the endotracheal aspirate samples; (f) amplifying the plasma extracellular vesicle DNA extracted in step (d) and the endotracheal aspirate DNA extracted in step (e), respectively, using primers; (g) analyzing the amplified plasma extracellular vesicle DNA sequences and endotracheal aspirate DNA sequences; (h) performing alpha diversity analysis and beta diversity analysis from the readings obtained from the sequence analysis results; and (i) based on the results of the alpha diversity analysis and beta diversity analysis, Acinetobacter among the microbial composition of the plasma extracellular vesicles and endotracheal aspirates A method for profiling plasma extracellular vesicle microbes and tracheal aspirate microbes, comprising the step of predicting a low probability of pneumonia when the correlation coefficient of one or more of the genera Acinetobacter and Lactobacillus is > 0.

5. Claim 2 A method for profiling plasma extracellular vesicle microorganisms and tracheal aspirate microorganisms according to claim 1, wherein the sample of step (a) is obtained on the 5th to 7th day after intubation. Claim 3 A method for profiling plasma extracellular vesicle microbes and tracheal aspirate microbes, wherein the alpha diversity of step (a) is analyzed using the Chao1 index. Claim 4 A method for profiling plasma extracellular vesicle microbes and tracheal aspirate microbes, wherein the beta diversity of step (a) is evaluated using the Bray-Curtis disequivalence index. Claim 5 A method for profiling plasma extracellular vesicle microbes and tracheal aspirate microbes, wherein step (i) evaluates the microbial correlation between tracheal aspirate microbes and plasma extracellular vesicle microbes using Spearman sequence correlation coefficients. Claim 6 delete Claim 7 delete Claim 8 delete Claim 9 delete

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