PDF(Pubmed)
Abstract：
BACKGROUND: Severe burns may alter the stability of the intestinal flora and affect the patient\'s recovery process. Understanding the characteristics of the gut microbiota in the acute phase of burns and their association with phenotype can help to accurately assess the progression of the disease and identify potential microbiota markers.
METHODS: We established mouse models of partial thickness deep III degree burns and collected faecal samples for 16 S rRNA amplification and high throughput sequencing at two time points in the acute phase for independent bioinformatic analysis.
RESULTS: We analysed the sequencing results using alpha diversity, beta diversity and machine learning methods. At both time points, 4 and 6 h after burning, the Firmicutes phylum content decreased and the content of the Bacteroidetes phylum content increased, showing a significant decrease in the Firmicutes/Bacteroidetes ratio compared to the control group. Nine bacterial genera changed significantly during the acute phase and occupied the top six positions in the Random Forest significance ranking. Clustering results also clearly showed that there was a clear boundary between the communities of burned and control mice. Functional analyses showed that during the acute phase of burn, gut bacteria increased lipoic acid metabolism, seleno-compound metabolism, TCA cycling, and carbon fixation, while decreasing galactose metabolism and triglyceride metabolism. Based on the abundance characteristics of the six significantly different bacterial genera, both the XGboost and Random Forest models were able to discriminate between the burn and control groups with 100% accuracy, while both the Random Forest and Support Vector Machine models were able to classify samples from the 4-hour and 6-hour burn groups with 86.7% accuracy.
CONCLUSIONS: Our study shows an increase in gut microbiota diversity in the acute phase of deep burn injury, rather than a decrease as is commonly believed. Severe burns result in a severe imbalance of the gut flora, with a decrease in probiotics and an increase in microorganisms that trigger inflammation and cognitive deficits, and multiple pathways of metabolism and substance synthesis are affected. Simple machine learning model testing suggests several bacterial genera as potential biomarkers of severe burn phenotypes.
METHODS: We established mouse models of partial thickness deep III degree burns and collected faecal samples for 16 S rRNA amplification and high throughput sequencing at two time points in the acute phase for independent bioinformatic analysis.
RESULTS: We analysed the sequencing results using alpha diversity, beta diversity and machine learning methods. At both time points, 4 and 6 h after burning, the Firmicutes phylum content decreased and the content of the Bacteroidetes phylum content increased, showing a significant decrease in the Firmicutes/Bacteroidetes ratio compared to the control group. Nine bacterial genera changed significantly during the acute phase and occupied the top six positions in the Random Forest significance ranking. Clustering results also clearly showed that there was a clear boundary between the communities of burned and control mice. Functional analyses showed that during the acute phase of burn, gut bacteria increased lipoic acid metabolism, seleno-compound metabolism, TCA cycling, and carbon fixation, while decreasing galactose metabolism and triglyceride metabolism. Based on the abundance characteristics of the six significantly different bacterial genera, both the XGboost and Random Forest models were able to discriminate between the burn and control groups with 100% accuracy, while both the Random Forest and Support Vector Machine models were able to classify samples from the 4-hour and 6-hour burn groups with 86.7% accuracy.
CONCLUSIONS: Our study shows an increase in gut microbiota diversity in the acute phase of deep burn injury, rather than a decrease as is commonly believed. Severe burns result in a severe imbalance of the gut flora, with a decrease in probiotics and an increase in microorganisms that trigger inflammation and cognitive deficits, and multiple pathways of metabolism and substance synthesis are affected. Simple machine learning model testing suggests several bacterial genera as potential biomarkers of severe burn phenotypes.
摘要：
背景：严重烧伤可能会改变肠道菌群的稳定性并影响患者的康复过程。了解烧伤急性期肠道微生物群的特征及其与表型的关联有助于准确评估疾病的进展并确定潜在的微生物群标志物。
方法：我们建立了部分厚度深III度烧伤小鼠模型，并在急性期的两个时间点收集粪便样本进行16SrRNA扩增和高通量测序，以进行独立的生物信息学分析。
结果：我们使用α多样性分析了测序结果，β多样性和机器学习方法。在这两个时间点上,燃烧后4和6小时，Firmicutes门含量下降，拟杆菌门含量增加，与对照组相比，Firmicutes/拟杆菌比率显着降低。9个细菌属在急性期发生了显着变化，并在随机森林显著性排名中排名前六名。聚类结果还清楚地表明，烧伤小鼠和对照小鼠的群落之间存在明确的边界。功能分析显示在烧伤的急性期,肠道细菌增加硫辛酸代谢，硒化合物代谢，TCA循环,和碳固定，同时降低半乳糖代谢和甘油三酯代谢。根据六种明显不同的细菌属的丰度特征，XGboost和随机森林模型都能够以100%的准确度区分烧伤组和对照组，而随机森林和支持向量机模型均能够以86.7%的准确率对4小时和6小时烧伤组的样本进行分类。
结论：我们的研究表明，深度烧伤急性期肠道菌群多样性增加，而不是通常认为的减少。严重烧伤导致肠道菌群严重失衡，随着益生菌的减少和引发炎症和认知缺陷的微生物的增加，代谢和物质合成的多个途径受到影响。简单的机器学习模型测试表明几种细菌属作为严重烧伤表型的潜在生物标志物。
方法：我们建立了部分厚度深III度烧伤小鼠模型，并在急性期的两个时间点收集粪便样本进行16SrRNA扩增和高通量测序，以进行独立的生物信息学分析。
结果：我们使用α多样性分析了测序结果，β多样性和机器学习方法。在这两个时间点上,燃烧后4和6小时，Firmicutes门含量下降，拟杆菌门含量增加，与对照组相比，Firmicutes/拟杆菌比率显着降低。9个细菌属在急性期发生了显着变化，并在随机森林显著性排名中排名前六名。聚类结果还清楚地表明，烧伤小鼠和对照小鼠的群落之间存在明确的边界。功能分析显示在烧伤的急性期,肠道细菌增加硫辛酸代谢，硒化合物代谢，TCA循环,和碳固定，同时降低半乳糖代谢和甘油三酯代谢。根据六种明显不同的细菌属的丰度特征，XGboost和随机森林模型都能够以100%的准确度区分烧伤组和对照组，而随机森林和支持向量机模型均能够以86.7%的准确率对4小时和6小时烧伤组的样本进行分类。
结论：我们的研究表明，深度烧伤急性期肠道菌群多样性增加，而不是通常认为的减少。严重烧伤导致肠道菌群严重失衡，随着益生菌的减少和引发炎症和认知缺陷的微生物的增加，代谢和物质合成的多个途径受到影响。简单的机器学习模型测试表明几种细菌属作为严重烧伤表型的潜在生物标志物。
