In silico analysis of intestinal microbial instability and symptomatic markers in mice during the acute phase of
Bochen Hou1,2,3, Honglan Zhang1, Lina Zhou2
1Department of Neurology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, 400038, China.
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.
Insights
Severe burns disrupt gut microbiota diversity, increasing beneficial bacteria and altering metabolic pathways. Machine learning identified specific bacterial genera as potential biomarkers for burn injury assessment.
Area of Science:
- Microbiome research
- Burn injury pathophysiology
- Bioinformatics analysis
Background:
- Severe burns can destabilize gut flora, impacting patient recovery.
- Understanding gut microbiota in acute burn phases is crucial for disease assessment and identifying potential markers.
Purpose of the Study:
- To characterize gut microbiota changes in the acute phase of severe burn injury.
- To identify potential microbial biomarkers associated with burn phenotypes.
Main Methods:
- Established mouse models of deep III degree burns.
- Collected fecal samples for 16S rRNA sequencing at 4 and 6 hours post-burn.
- Utilized bioinformatic and machine learning analyses (alpha/beta diversity, Random Forest, XGBoost, SVM).
Main Results:
- Observed increased gut microbiota diversity post-burn, contrary to common belief.
- Significant shifts in bacterial phyla (decreased Firmicutes, increased Bacteroidetes) and genera were noted.
- Metabolic pathways like lipoic acid and seleno-compound metabolism were upregulated, while galactose and triglyceride metabolism decreased.
- Machine learning models accurately distinguished between burned and control groups and classified samples based on time post-burn.
Conclusions:
- Severe burns induce significant gut flora imbalance, increasing inflammatory microorganisms.
- Specific bacterial genera show potential as reliable biomarkers for severe burn phenotypes.
- Gut microbiota alterations affect multiple metabolic pathways crucial for recovery.
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