Integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patients
ABSTRACT This study investigated the diagnostic potential of mNGS for detecting MTB in pulmonary tuberculosis patients. We analyzed pulmonary microbiome data to assess its impact on mNGS diagnostic accuracy and explored the association between microbiome profiles and clinical diagnosis. Bronchoalveo...
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2025-08-01
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| Series: | Microbiology Spectrum |
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| Online Access: | https://journals.asm.org/doi/10.1128/spectrum.01563-24 |
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| author | Hongli Sun Qiuyue Chen Dong Zhang Long Hu Song Li Minya Lu Yao Wang Huiting Su Yi Gao Jiayu Guo Ying Zhao Juan Du Cun Liu Han Xia Yingchun Xu Xiaojun Ge Qiwen Yang |
| author_facet | Hongli Sun Qiuyue Chen Dong Zhang Long Hu Song Li Minya Lu Yao Wang Huiting Su Yi Gao Jiayu Guo Ying Zhao Juan Du Cun Liu Han Xia Yingchun Xu Xiaojun Ge Qiwen Yang |
| author_sort | Hongli Sun |
| collection | DOAJ |
| description | ABSTRACT This study investigated the diagnostic potential of mNGS for detecting MTB in pulmonary tuberculosis patients. We analyzed pulmonary microbiome data to assess its impact on mNGS diagnostic accuracy and explored the association between microbiome profiles and clinical diagnosis. Bronchoalveolar lavage fluid samples were collected from 236 patients with pulmonary infections, and the diagnostic performance of mNGS was compared with traditional methods in detecting MTB. Furthermore, the incidence of false negatives and false positives, as well as the characteristics of the lung microbiota in TB patients, was analyzed to improve the diagnostic precision of mNGS. We observed that among all detection methods, mNGS showed the highest sensitivity (73.33%), followed by X-pert (60.00%), culture (53.33%), RT-PCR (53.33%), and sputum smear (23.33%). Notably, mNGS produced 3 false positive results in 236 samples, yielding a specificity of 98.54%. Analysis of the pulmonary microbiome revealed significant differences in both α-diversity and β-diversity between patients with TB and uninfected controls (P<0.05). Shannon index and Chao1 index were identified as significant predictors associated with MTB infection. ROC curve analysis demonstrated an AUC of 0.765, indicating good discriminatory performance. This study suggested that integrating wet-laboratory techniques with bioinformatics analysis can further enhance the diagnostic accuracy of mNGS for TB. Furthermore, microbiome analysis holds significant potential for the diagnosis of MTB infection.IMPORTANCEThis study focuses on the application of next-generation sequencing (NGS) technology in detecting Mycobacterium tuberculosis in bronchoalveolar lavage fluid and explores the impact of M. tuberculosis infection on the pulmonary microbiome. By optimizing the methods and conducting microbial analyses, the accuracy of metagenomic NGS for detecting M. tuberculosis has been improved. |
| format | Article |
| id | doaj-art-d8b7a6a7d8804b20922526a3dd587fdb |
| institution | Kabale University |
| issn | 2165-0497 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | Microbiology Spectrum |
| spelling | doaj-art-d8b7a6a7d8804b20922526a3dd587fdb2025-08-20T03:39:28ZengAmerican Society for MicrobiologyMicrobiology Spectrum2165-04972025-08-0113810.1128/spectrum.01563-24Integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patientsHongli Sun0Qiuyue Chen1Dong Zhang2Long Hu3Song Li4Minya Lu5Yao Wang6Huiting Su7Yi Gao8Jiayu Guo9Ying Zhao10Juan Du11Cun Liu12Han Xia13Yingchun Xu14Xiaojun Ge15Qiwen Yang16Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory, The second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Scientific Affairs, Hugobiotech Co., Ltd., Beijing, ChinaDepartment of Scientific Affairs, Hugobiotech Co., Ltd., Beijing, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory, The Affiliated Qingdao Third People’s Hospital of Qingdao University, Qingdao, Shandong, ChinaDepartment of Scientific Affairs, Hugobiotech Co., Ltd., Beijing, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaDepartment of Clinical Laboratory, The second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, ChinaDepartment of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaABSTRACT This study investigated the diagnostic potential of mNGS for detecting MTB in pulmonary tuberculosis patients. We analyzed pulmonary microbiome data to assess its impact on mNGS diagnostic accuracy and explored the association between microbiome profiles and clinical diagnosis. Bronchoalveolar lavage fluid samples were collected from 236 patients with pulmonary infections, and the diagnostic performance of mNGS was compared with traditional methods in detecting MTB. Furthermore, the incidence of false negatives and false positives, as well as the characteristics of the lung microbiota in TB patients, was analyzed to improve the diagnostic precision of mNGS. We observed that among all detection methods, mNGS showed the highest sensitivity (73.33%), followed by X-pert (60.00%), culture (53.33%), RT-PCR (53.33%), and sputum smear (23.33%). Notably, mNGS produced 3 false positive results in 236 samples, yielding a specificity of 98.54%. Analysis of the pulmonary microbiome revealed significant differences in both α-diversity and β-diversity between patients with TB and uninfected controls (P<0.05). Shannon index and Chao1 index were identified as significant predictors associated with MTB infection. ROC curve analysis demonstrated an AUC of 0.765, indicating good discriminatory performance. This study suggested that integrating wet-laboratory techniques with bioinformatics analysis can further enhance the diagnostic accuracy of mNGS for TB. Furthermore, microbiome analysis holds significant potential for the diagnosis of MTB infection.IMPORTANCEThis study focuses on the application of next-generation sequencing (NGS) technology in detecting Mycobacterium tuberculosis in bronchoalveolar lavage fluid and explores the impact of M. tuberculosis infection on the pulmonary microbiome. By optimizing the methods and conducting microbial analyses, the accuracy of metagenomic NGS for detecting M. tuberculosis has been improved.https://journals.asm.org/doi/10.1128/spectrum.01563-24Mycobacterium tuberculosismetagenomic next-generation sequencingpulmonary microbiomebronchoalveolar lavage fluid |
| spellingShingle | Hongli Sun Qiuyue Chen Dong Zhang Long Hu Song Li Minya Lu Yao Wang Huiting Su Yi Gao Jiayu Guo Ying Zhao Juan Du Cun Liu Han Xia Yingchun Xu Xiaojun Ge Qiwen Yang Integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patients Microbiology Spectrum Mycobacterium tuberculosis metagenomic next-generation sequencing pulmonary microbiome bronchoalveolar lavage fluid |
| title | Integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patients |
| title_full | Integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patients |
| title_fullStr | Integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patients |
| title_full_unstemmed | Integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patients |
| title_short | Integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patients |
| title_sort | integrative study of pulmonary microbiome and clinical diagnosis in pulmonary tuberculosis patients |
| topic | Mycobacterium tuberculosis metagenomic next-generation sequencing pulmonary microbiome bronchoalveolar lavage fluid |
| url | https://journals.asm.org/doi/10.1128/spectrum.01563-24 |
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