scholarly journals Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Jiahui Wang ◽  
Yelei Han ◽  
Jing Feng
Keyword(s):  
Next Generation Sequencing ◽  
Human Cytomegalovirus ◽  
Predictive Value ◽  
Pulmonary Infection ◽  
Next Generation ◽  
Promising Technique ◽  
Infection Diagnosis ◽  
Conventional Tests ◽  
Conventional Test ◽  
Generation Sequencing

Abstract Background Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. Methods From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared. Results The sensitivity of mNGS in mixed pulmonary infection diagnosis was much higher than that of conventional test (97.2% vs 13.9%; P < 0.01), but the specificity was the opposite (63.2% vs 94.7%; P = 0.07). The positive predictive value of mNGS was 83.3% (95% CI, 68.0–92.5%), and the negative predictive value was 92.3% (95% CI, 62.1–99.6%). A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1 (1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1 (1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected by mNGS was Human cytomegalovirus in our study, which was identified in 19 (34.5%) cases of mixed pulmonary infection. Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7 (12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection. Conclusions mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity. Trial registration (retrospectively registered): ChiCTR1900023727. Registrated 9 JUNE 2019.

scholarly journals Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis

2019 ◽  
Author(s):  
Jiahui Wang ◽  
Yelei Han ◽  
Yang Zhou ◽  
Jing Feng
Keyword(s):  
Next Generation Sequencing ◽  
Human Cytomegalovirus ◽  
Predictive Value ◽  
Pulmonary Infection ◽  
Next Generation ◽  
Promising Technique ◽  
Infection Diagnosis ◽  
Conventional Tests ◽  
Conventional Test ◽  
Generation Sequencing

Abstract Background. Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. Methods. From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared. Results.The sensitivity of mNGS in mixed pulmonary infection diagnose was much higher than that of conventional test (97.2％ vs 13.9％;P<0.01) , but the specificity was the opposite (63.2％ vs 94.7％;P=0.07) . The positive predictive value of mNGS was 83.3% (95% CI: 68.0-92.5%) , and the negative predictive value was 92.3% (95% CI: 62.1-99.6%) . A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1(1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1(1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected was Human cytomegalovirus in our study, which was identified in 19(34.5%) cases of mixed pulmonary infection. Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7(12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection. Conclusions. mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity.

scholarly journals Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis

2019 ◽  
Author(s):  
Jiahui Wang ◽  
Yelei Han ◽  
Yang Zhou ◽  
Jing Feng
Keyword(s):  
Next Generation Sequencing ◽  
Predictive Value ◽  
Pulmonary Infection ◽  
Fungal Infections ◽  
Next Generation ◽  
Promising Technique ◽  
Infection Diagnosis ◽  
Conventional Tests ◽  
Conventional Test ◽  
Generation Sequencing

Abstract Background. Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce. Methods. From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared. Results. The sensitivity of mNGS in mixed pulmonary infection diagnose was much higher than that of conventional test (97.2％ vs 13.9％; P< 0.01) , but the specificity was the opposite (63.2％ vs 94.7％; P= 0.07) . The positive predictive value of mNGS was 83.3% (95% CI: 68.0-92.5%) , and the negative predictive value was 92.3% (95% CI: 62.1-99.6%) . A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1(1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1(1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected was H uman cytomegalovirus in our study, which was identified in 19(34.5%) cases of mixed pulmonary infection . Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7(12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection. Conclusions. mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity.

scholarly journals Metagenomic next-generation sequencing for mixed pulmonary infection diagnosis

2019 ◽  
Author(s):  
Jiahui Wang ◽  
Yelei Han ◽  
Jing Feng
Keyword(s):  
Next Generation Sequencing ◽  
Predictive Value ◽  
Pulmonary Infection ◽  
Fungal Infections ◽  
Next Generation ◽  
Promising Technique ◽  
Infection Diagnosis ◽  
Conventional Tests ◽  
Conventional Test ◽  
Generation Sequencing

Abstract Background: Metagenomic next-generation sequencing (mNGS) is emerging as a promising technique for pathogens detection. However, reports on the application of mNGS in mixed pulmonary infection remain scarce.Methods: From July 2018 to March 2019, 55 cases were enrolled in this retrospective analysis. Cases were classified into mixed pulmonary infection (36 [65.5%]) and non-mixed pulmonary infection (19 [34.5%]) according to primary diagnoses. The performances of mNGS and conventional test on mixed pulmonary infection diagnosis and pathogen identification were compared.Results: The sensitivity of mNGS in mixed pulmonary infection diagnose was much higher than that of conventional test (97.2％ vs 13.9％; P< 0.01) , but the specificity was the opposite (63.2％ vs 94.7％; P= 0.07) . The positive predictive value of mNGS was 83.3% (95% CI: 68.0-92.5%) , and the negative predictive value was 92.3% (95% CI: 62.1-99.6%) . A total of 5 (9.1%) cases were identified as mixed pulmonary infection by both conventional tests and mNGS, however, the pathogens identification results were consistent between these two methods in only 1(1.8%) case. In summary, the pathogens detected by mNGS in 3 (5.5%) cases were consistent with those by conventional test, and only 1(1.8%) case was mixed pulmonary infection. According to our data, mNGS had a broader spectrum for pathogen detection than conventional tests. In particular, application of mNGS improved the diagnosis of pulmonary fungal infections. Within the 55 cases, mNGS detected and identified fungi in 31 (56.4%) cases, of which only 10 (18.2%) cases were positive for the same fungi by conventional test. The most common pathogen detected was H uman cytomegalovirus in our study, which was identified in 19(34.5%) cases of mixed pulmonary infection . Human cytomegalovirus and Pneumocystis jirovecii, which were detected in 7(12.7%) cases, were the most common co-pathogens in the group of mixed pulmonary infection.Conclusions: mNGS is a promising technique to detect co-pathogens in mixed pulmonary infection, with potential benefits in speed and sensitivity.

scholarly journals Application of Metagenomic Next-Generation Sequencing in the Diagnosis of Pulmonary Infectious Pathogens From Bronchoalveolar Lavage Samples

2021 ◽  
Vol 11 ◽  
Author(s):  
Yuqian Chen ◽  
Wei Feng ◽  
Kai Ye ◽  
Li Guo ◽  
Han Xia ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Bronchoalveolar Lavage ◽  
Predictive Value ◽  
Pulmonary Infection ◽  
Diversity Index ◽  
Microbial Culture ◽  
Pulmonary Infections ◽  
Next Generation ◽  
Underlying Diseases ◽  
Generation Sequencing

BackgroundMetagenomic next-generation sequencing (mNGS) is a powerful method for pathogen detection. In this study, we assessed the value of mNGS for bronchoalveolar lavage (BAL) samples in the diagnosis of pulmonary infections.MethodsFrom February 2018 to April 2019, BAL samples were collected from 235 patients with suspected pulmonary infections. mNGS and microbial culture were performed to evaluate the effectiveness of mNGS in pulmonary infection diagnosis.ResultsWe employed mNGS to evaluate the alpha diversity, results suggesting that patients with confirmed pathogens had a lower microbial diversity index compared to that of patients with uncertain pathogens. For the patients admitted to the respiratory intensive care unit (RICU) or on a ventilator, they experienced a lower diversity index than that of the patients in the general ward or not on a ventilator. In addition, mNGS of BAL had a diagnostic sensitivity of 88.89% and a specificity of 14.86% in pulmonary infection, with 21.16% positive predictive value (PPV) and 83.87% negative predictive value (NPV). When rare pathogens were excluded, the sensitivity of mNGS decreased to 73.33%, and the specificity increased to 41.71%. For patients in the simple pulmonary infection group and the immunocompromised group, the main infection types were bacterial infection (58.33%) and mixed-infection (43.18%). Furthermore, mNGS had an advantage over culture in describing polymicrobial ecosystem, demonstrating the microbial distribution and the dominant strains of the respiratory tract in patients with different underlying diseases.ConclusionsThe study indicated that mNGS of BAL samples could provide more accurate diagnostic information in pulmonary infections and demonstrate the changes of respiratory microbiome in different underlying diseases. This method might play an important role in the clinical use of antimicrobial agents in the future.

Clinical Utility of In-house Metagenomic Next-generation Sequencing for the Diagnosis of Lower Respiratory Tract Infections and Analysis of the Host Immune Response

2020 ◽  
Vol 71 (Supplement_4) ◽  
pp. S416-S426
Author(s):  
Hongbin Chen ◽  
Yuyao Yin ◽  
Hua Gao ◽  
Yifan Guo ◽  
Zhao Dong ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Respiratory Tract ◽  
Predictive Value ◽  
Lower Respiratory Tract ◽  
Respiratory Tract Infections ◽  
Next Generation ◽  
Lower Respiratory Tract Infections ◽  
Dna And Rna ◽  
Tract Infections ◽  
Generation Sequencing

Abstract Background Only few pathogens that cause lower respiratory tract infections (LRTIs) can be identified due to limitations of traditional microbiological methods and the complexity of the oropharyngeal normal flora. Metagenomic next-generation sequencing (mNGS) has the potential to solve this problem. Methods This prospective observational study sequentially enrolled 93 patients with LRTI and 69 patients without LRTI who visited Peking University People’s Hospital in 2019. Pathogens in bronchoalveolar lavage fluid (BALF) specimens were detected using mNGS (DNA and RNA) and traditional microbiological assays. Human transcriptomes were compared between LRTI and non-LRTI, bacterial and viral LRTI, and tuberculosis and nontuberculosis groups. Results Among 93 patients with LRTI, 20%, 35%, and 65% of cases were detected as definite or probable pathogens by culture, all microbiological tests, and mNGS, respectively. Our in-house BALF mNGS platform had an approximately 2-working-day turnaround time and detected more viruses and fungi than the other methods. Taking the composite reference standard as a gold standard, it had a sensitivity of 66.7%, specificity of 75.4%, positive-predictive value of 78.5%, and negative-predictive value of 62.7%. LRTI-, viral LRTI–, and tuberculosis-related differentially expressed genes were respectively related to immunity responses to infection, viral transcription and response to interferon-γ pathways, and perforin 1 and T-cell receptor B variable 9. Conclusions Metagenomic DNA and RNA-seq can identify a wide range of LRTI pathogens, with improved sensitivity for viruses and fungi. Our in-host platform is likely feasible in the clinic. Host transcriptome data are expected to be useful for the diagnosis of LRTIs.

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