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Future Microbiology Volume 14, 2019 - Issue 5
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Research Article

Septic Patients in the Intensive Care Unit Present Different Nasal Microbiotas

Xi-lan Tan1 Department of Environmental Health,School of Public Health,Southern Medical University,Guangzhou,PR China;2 Division of Infection Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
,
Hai-yue Liu3 State Key Laboratory of Organ Failure Research,Division of Laboratory Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
,
Jun Long3 State Key Laboratory of Organ Failure Research,Division of Laboratory Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
,
Zhaofang Jiang3 State Key Laboratory of Organ Failure Research,Division of Laboratory Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
,
Yuemei Luo1 Department of Environmental Health,School of Public Health,Southern Medical University,Guangzhou,PR China;3 State Key Laboratory of Organ Failure Research,Division of Laboratory Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
,
Xin Zhao3 State Key Laboratory of Organ Failure Research,Division of Laboratory Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
,
Shumin Cai4 Department of Intensive Care Medicine,Nanfang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
,
Xiaozhu Zhong2 Division of Infection Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
,
Zhongran Cen5 Division of Intensive Care Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
,
Jin Su6 Chronic Airways Diseases Laboratory,Department of Respiratory & Critical Care Medicine,Nanfang Hospital,Southern Medical University,Guangzhou,PR ChinaView further author information
&
Hongwei Zhou1 Department of Environmental Health,School of Public Health,Southern Medical University,Guangzhou,PR China;3 State Key Laboratory of Organ Failure Research,Division of Laboratory Medicine,Zhujiang Hospital,Southern Medical University,Guangzhou,PR ChinaCorrespondence[email protected]
View further author information
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Pages 383-395 | Received 15 Dec 2018, Accepted 08 Feb 2019, Published online: 26 Feb 2019

Figures & data

Table 1. Baseline characteristics of the patients with sepsis.

Figure 1. Comparison of nasal microbiota between septic patients and healthy subjects.

(A) α diversity between the septic patients and healthy subjects. (B) Stacked bar chart of bacteria at the genus level between the septic patients and healthy controls.(C)Principal coordinate analysis based on unweighted UniFrac distances. The red dots represent patients with sepsis, and the blue dots represent the controls. (D) significantly different taxa between the healthy participants and septic patients were determined using the linear discriminant analysis effect size (LEfSe). The data show increasing levels of Gammaproteobacteria, Pseudomonadales, Proteobacteria, Pseudomonas, Moraxellaceae, Acinetobacter, Enterobacteriaceae and Klebsiella in the patients. (E) Machine-learning classification based on nasal microbiota using random forest algorithms.

LDA: Linear discriminant analysis.

Figure 1. Comparison of nasal microbiota between septic patients and healthy subjects. (A) α diversity between the septic patients and healthy subjects. (B) Stacked bar chart of bacteria at the genus level between the septic patients and healthy controls.(C)Principal coordinate analysis based on unweighted UniFrac distances. The red dots represent patients with sepsis, and the blue dots represent the controls. (D) significantly different taxa between the healthy participants and septic patients were determined using the linear discriminant analysis effect size (LEfSe). The data show increasing levels of Gammaproteobacteria, Pseudomonadales, Proteobacteria, Pseudomonas, Moraxellaceae, Acinetobacter, Enterobacteriaceae and Klebsiella in the patients. (E) Machine-learning classification based on nasal microbiota using random forest algorithms.LDA: Linear discriminant analysis.
Figure 1. Comparison of nasal microbiota between septic patients and healthy subjects. (A) α diversity between the septic patients and healthy subjects. (B) Stacked bar chart of bacteria at the genus level between the septic patients and healthy controls.(C)Principal coordinate analysis based on unweighted UniFrac distances. The red dots represent patients with sepsis, and the blue dots represent the controls. (D) significantly different taxa between the healthy participants and septic patients were determined using the linear discriminant analysis effect size (LEfSe). The data show increasing levels of Gammaproteobacteria, Pseudomonadales, Proteobacteria, Pseudomonas, Moraxellaceae, Acinetobacter, Enterobacteriaceae and Klebsiella in the patients. (E) Machine-learning classification based on nasal microbiota using random forest algorithms.LDA: Linear discriminant analysis.
Figure 2. Differences in the results of the principal coordinate analysis of nasal microbiota between healthy subjects and patients with sepsis.

(A) Principal coordinate analysis (PCoA) of septic patients and healthy subjects based on unweighted UniFrac distances. (B) PCoA of healthy subjects based on unweighted UniFrac distances. (C) PCoA of patients with sepsis based on unweighted UniFrac distances. The orange circles represent the samples (nasal microbiota) with the Acinetobacter type, the purple circles represent the samples with the Corynebacterium type and the green circles represent the samples with the Staphylococcus type.

Figure 2. Differences in the results of the principal coordinate analysis of nasal microbiota between healthy subjects and patients with sepsis. (A) Principal coordinate analysis (PCoA) of septic patients and healthy subjects based on unweighted UniFrac distances. (B) PCoA of healthy subjects based on unweighted UniFrac distances. (C) PCoA of patients with sepsis based on unweighted UniFrac distances. The orange circles represent the samples (nasal microbiota) with the Acinetobacter type, the purple circles represent the samples with the Corynebacterium type and the green circles represent the samples with the Staphylococcus type.
Figure 3. Relationship between the nasal bacterial type in the patients with sepsis and their length of stay in the intensive care unit.

(A) Comparison of the α diversity among the three nasal bacterial types in patients with sepsis. (B) Comparison of the length of intensive care unit stay among the three nasal bacterial types in patients with sepsis. (C) Comparison of the stacked bar charts of the genera in the three nasal bacterial types in patients with sepsis.

ICU: Intensive care unit; PD: Phylogenetic diversity.

Figure 3. Relationship between the nasal bacterial type in the patients with sepsis and their length of stay in the intensive care unit. (A) Comparison of the α diversity among the three nasal bacterial types in patients with sepsis. (B) Comparison of the length of intensive care unit stay among the three nasal bacterial types in patients with sepsis. (C) Comparison of the stacked bar charts of the genera in the three nasal bacterial types in patients with sepsis.ICU: Intensive care unit; PD: Phylogenetic diversity.
Figure 4. Relationship between the nasal bacterial type of patients with sepsis and the number of antibiotic use days in the intensive care unit.
Figure 4. Relationship between the nasal bacterial type of patients with sepsis and the number of antibiotic use days in the intensive care unit.
Figure 5. Mortality rates of the three nasal bacterial types based on different lengths of intensive care unit stay.

(A) Mortality of the Corynebacterium type based on different lengths of intensive care unit (ICU) stay. (B) Mortality of the Staphylococcus type based on different lengths of ICU stay. (C) Mortality of the Acinetobacter type based on different lengths of ICU stay.

Figure 5. Mortality rates of the three nasal bacterial types based on different lengths of intensive care unit stay. (A) Mortality of the Corynebacterium type based on different lengths of intensive care unit (ICU) stay. (B) Mortality of the Staphylococcus type based on different lengths of ICU stay. (C) Mortality of the Acinetobacter type based on different lengths of ICU stay.

Table 2. The nasal bacterial types and its association with length of stay and mortality.

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