Comparing sputum microbiota characteristics between severe and critically ill influenza patients
BackgroundCurrently, limited attention has been directed toward utilizing clinical cohorts as a starting point to elucidate alterations in the lower respiratory tract (LRT) microbiota following influenza A virus (IAV) infection.ObjectivesOur objective was to undertake a comparative analysis of the d...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-12-01
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| Series: | Frontiers in Cellular and Infection Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcimb.2023.1297946/full |
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| author | Zhixia Gu Zhixia Gu Zhixia Gu Zhixia Gu Yuanyuan Zhang Yuanyuan Zhang Yuanyuan Zhang Yuanyuan Zhang Xue Zhao Tingting Liu Tingting Liu Tingting Liu Tingting Liu Shugui Sheng Shugui Sheng Shugui Sheng Shugui Sheng Rui Song Rui Song Rui Song Ronghua Jin Ronghua Jin Ronghua Jin Ronghua Jin |
| author_facet | Zhixia Gu Zhixia Gu Zhixia Gu Zhixia Gu Yuanyuan Zhang Yuanyuan Zhang Yuanyuan Zhang Yuanyuan Zhang Xue Zhao Tingting Liu Tingting Liu Tingting Liu Tingting Liu Shugui Sheng Shugui Sheng Shugui Sheng Shugui Sheng Rui Song Rui Song Rui Song Ronghua Jin Ronghua Jin Ronghua Jin Ronghua Jin |
| author_sort | Zhixia Gu |
| collection | DOAJ |
| description | BackgroundCurrently, limited attention has been directed toward utilizing clinical cohorts as a starting point to elucidate alterations in the lower respiratory tract (LRT) microbiota following influenza A virus (IAV) infection.ObjectivesOur objective was to undertake a comparative analysis of the diversity and composition of sputum microbiota in individuals afflicted by severe and critically ill influenza patients.MethodsSputum specimens were procured from patients diagnosed with IAV infection for the purpose of profiling the microbiota using 16S-rDNA sequencing. To ascertain taxonomic differences between the severe and critically ill influenza cohorts, we leveraged Linear Discriminant Analysis Effect Size (LEfSe). Additionally, Spearman correlation analysis was employed to illuminate associations between sputum microbiota and influenza Ct values alongside laboratory indicators.ResultsOur study encompassed a total cohort of 64 patients, comprising 48 within the severe group and 16 within the critically ill group. Intriguingly, Bacteroidetes exhibited significant depletion in the critically ill cohort (p=0.031). The sputum microbiomes of the severe influenza group were hallmarked by an overrepresentation of Neisseria, Porphyromonas, Actinobacillus, Alloprevotella, TM7x, and Clostridia_UCG-014, yielding ROC-plot AUC values of 0.71, 0.68, 0.60, 0.70, 0.70, and 0.68, respectively. Notably, Alloprevotella exhibited an inverse correlation with influenza Ct values. Moreover, C-reactive protein (CRP) manifested a positive correlation with Haemophilus and Porphyromonas.ConclusionThe outcomes of this investigation lay the groundwork for future studies delving into the connection between the LRT microbiome and respiratory disorders. Further exploration is warranted to elucidate the intricate mechanisms underlying the interaction between IAV and Alloprevotella, particularly in disease progression. |
| first_indexed | 2024-03-08T22:29:53Z |
| format | Article |
| id | doaj.art-edf4829d8de84ce08d5ce2956ade7d58 |
| institution | Directory Open Access Journal |
| issn | 2235-2988 |
| language | English |
| last_indexed | 2024-03-08T22:29:53Z |
| publishDate | 2023-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cellular and Infection Microbiology |
| spelling | doaj.art-edf4829d8de84ce08d5ce2956ade7d582023-12-18T07:10:32ZengFrontiers Media S.A.Frontiers in Cellular and Infection Microbiology2235-29882023-12-011310.3389/fcimb.2023.12979461297946Comparing sputum microbiota characteristics between severe and critically ill influenza patientsZhixia Gu0Zhixia Gu1Zhixia Gu2Zhixia Gu3Yuanyuan Zhang4Yuanyuan Zhang5Yuanyuan Zhang6Yuanyuan Zhang7Xue Zhao8Tingting Liu9Tingting Liu10Tingting Liu11Tingting Liu12Shugui Sheng13Shugui Sheng14Shugui Sheng15Shugui Sheng16Rui Song17Rui Song18Rui Song19Ronghua Jin20Ronghua Jin21Ronghua Jin22Ronghua Jin23Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaBeijing Institute of Infectious Diseases, Beijing, ChinaNational Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, ChinaBeijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaBeijing Institute of Infectious Diseases, Beijing, ChinaNational Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, ChinaBeijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaBeijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaBeijing Institute of Infectious Diseases, Beijing, ChinaNational Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, ChinaBeijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaBeijing Institute of Infectious Diseases, Beijing, ChinaNational Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, ChinaBeijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaNational Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, ChinaBeijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaBeijing Institute of Infectious Diseases, Beijing, ChinaNational Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, ChinaNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, ChinaBackgroundCurrently, limited attention has been directed toward utilizing clinical cohorts as a starting point to elucidate alterations in the lower respiratory tract (LRT) microbiota following influenza A virus (IAV) infection.ObjectivesOur objective was to undertake a comparative analysis of the diversity and composition of sputum microbiota in individuals afflicted by severe and critically ill influenza patients.MethodsSputum specimens were procured from patients diagnosed with IAV infection for the purpose of profiling the microbiota using 16S-rDNA sequencing. To ascertain taxonomic differences between the severe and critically ill influenza cohorts, we leveraged Linear Discriminant Analysis Effect Size (LEfSe). Additionally, Spearman correlation analysis was employed to illuminate associations between sputum microbiota and influenza Ct values alongside laboratory indicators.ResultsOur study encompassed a total cohort of 64 patients, comprising 48 within the severe group and 16 within the critically ill group. Intriguingly, Bacteroidetes exhibited significant depletion in the critically ill cohort (p=0.031). The sputum microbiomes of the severe influenza group were hallmarked by an overrepresentation of Neisseria, Porphyromonas, Actinobacillus, Alloprevotella, TM7x, and Clostridia_UCG-014, yielding ROC-plot AUC values of 0.71, 0.68, 0.60, 0.70, 0.70, and 0.68, respectively. Notably, Alloprevotella exhibited an inverse correlation with influenza Ct values. Moreover, C-reactive protein (CRP) manifested a positive correlation with Haemophilus and Porphyromonas.ConclusionThe outcomes of this investigation lay the groundwork for future studies delving into the connection between the LRT microbiome and respiratory disorders. Further exploration is warranted to elucidate the intricate mechanisms underlying the interaction between IAV and Alloprevotella, particularly in disease progression.https://www.frontiersin.org/articles/10.3389/fcimb.2023.1297946/fullinfluenza virussevere and critically ill16s-rDNA sequencingbacteriacase control |
| spellingShingle | Zhixia Gu Zhixia Gu Zhixia Gu Zhixia Gu Yuanyuan Zhang Yuanyuan Zhang Yuanyuan Zhang Yuanyuan Zhang Xue Zhao Tingting Liu Tingting Liu Tingting Liu Tingting Liu Shugui Sheng Shugui Sheng Shugui Sheng Shugui Sheng Rui Song Rui Song Rui Song Ronghua Jin Ronghua Jin Ronghua Jin Ronghua Jin Comparing sputum microbiota characteristics between severe and critically ill influenza patients Frontiers in Cellular and Infection Microbiology influenza virus severe and critically ill 16s-rDNA sequencing bacteria case control |
| title | Comparing sputum microbiota characteristics between severe and critically ill influenza patients |
| title_full | Comparing sputum microbiota characteristics between severe and critically ill influenza patients |
| title_fullStr | Comparing sputum microbiota characteristics between severe and critically ill influenza patients |
| title_full_unstemmed | Comparing sputum microbiota characteristics between severe and critically ill influenza patients |
| title_short | Comparing sputum microbiota characteristics between severe and critically ill influenza patients |
| title_sort | comparing sputum microbiota characteristics between severe and critically ill influenza patients |
| topic | influenza virus severe and critically ill 16s-rDNA sequencing bacteria case control |
| url | https://www.frontiersin.org/articles/10.3389/fcimb.2023.1297946/full |
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