Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis
Abstract At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This study enrolled 136 patients from 2020 to 2021 and collected bronchoalveolar lavage fluid (BALF) specimens. We used metatranscriptome to analy...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-09-01
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| Series: | npj Biofilms and Microbiomes |
| Online Access: | https://doi.org/10.1038/s41522-024-00548-y |
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| _version_ | 1850029897882271744 |
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| author | Hongbin Chen Tianqi Qi Siyu Guo Xiaoyang Zhang Minghua Zhan Si Liu Yuyao Yin Yifan Guo Yawei Zhang Chunjiang Zhao Xiaojuan Wang Hui Wang |
| author_facet | Hongbin Chen Tianqi Qi Siyu Guo Xiaoyang Zhang Minghua Zhan Si Liu Yuyao Yin Yifan Guo Yawei Zhang Chunjiang Zhao Xiaojuan Wang Hui Wang |
| author_sort | Hongbin Chen |
| collection | DOAJ |
| description | Abstract At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This study enrolled 136 patients from 2020 to 2021 and collected bronchoalveolar lavage fluid (BALF) specimens. We used metatranscriptome to analyze the lower respiratory tract microbiome (LRTM) and host immune response. The diversity of the LRTM in LRTIs significantly decreased, manifested by a decrease in the abundance of normal microbiota and an increase in the abundance of opportunistic pathogens. The upregulated differentially expressed genes (DEGs) in the LRTIs group were mainly enriched in infection immune response-related pathways. Klebsiella pneumoniae had the most significant increase in abundance in LRTIs, which was strongly correlated with host infection or inflammation genes TNFRSF1B, CSF3R, and IL6R. We combined LRTM and host transcriptome data to construct a machine-learning model with 12 screened features to discriminate LRTIs and non-LRTIs. The results showed that the model trained by Random Forest in the validate set had the best performance (ROC AUC: 0.937, 95% CI: 0.832–1). The independent external dataset showed an accuracy of 76.5% for this model. This study suggests that the model integrating LRTM and host transcriptome data can be an effective tool for LRTIs diagnosis. |
| format | Article |
| id | doaj-art-e95d0edbbf5746f89aba253fcb8c7d7a |
| institution | DOAJ |
| issn | 2055-5008 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Biofilms and Microbiomes |
| spelling | doaj-art-e95d0edbbf5746f89aba253fcb8c7d7a2025-08-20T02:59:23ZengNature Portfolionpj Biofilms and Microbiomes2055-50082024-09-0110111010.1038/s41522-024-00548-yIntegrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosisHongbin Chen0Tianqi Qi1Siyu Guo2Xiaoyang Zhang3Minghua Zhan4Si Liu5Yuyao Yin6Yifan Guo7Yawei Zhang8Chunjiang Zhao9Xiaojuan Wang10Hui Wang11Department of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Aerospace Center HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalDepartment of Clinical Laboratory, Peking University People’s HospitalAbstract At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This study enrolled 136 patients from 2020 to 2021 and collected bronchoalveolar lavage fluid (BALF) specimens. We used metatranscriptome to analyze the lower respiratory tract microbiome (LRTM) and host immune response. The diversity of the LRTM in LRTIs significantly decreased, manifested by a decrease in the abundance of normal microbiota and an increase in the abundance of opportunistic pathogens. The upregulated differentially expressed genes (DEGs) in the LRTIs group were mainly enriched in infection immune response-related pathways. Klebsiella pneumoniae had the most significant increase in abundance in LRTIs, which was strongly correlated with host infection or inflammation genes TNFRSF1B, CSF3R, and IL6R. We combined LRTM and host transcriptome data to construct a machine-learning model with 12 screened features to discriminate LRTIs and non-LRTIs. The results showed that the model trained by Random Forest in the validate set had the best performance (ROC AUC: 0.937, 95% CI: 0.832–1). The independent external dataset showed an accuracy of 76.5% for this model. This study suggests that the model integrating LRTM and host transcriptome data can be an effective tool for LRTIs diagnosis.https://doi.org/10.1038/s41522-024-00548-y |
| spellingShingle | Hongbin Chen Tianqi Qi Siyu Guo Xiaoyang Zhang Minghua Zhan Si Liu Yuyao Yin Yifan Guo Yawei Zhang Chunjiang Zhao Xiaojuan Wang Hui Wang Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis npj Biofilms and Microbiomes |
| title | Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis |
| title_full | Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis |
| title_fullStr | Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis |
| title_full_unstemmed | Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis |
| title_short | Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis |
| title_sort | integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis |
| url | https://doi.org/10.1038/s41522-024-00548-y |
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