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Identification and Quantification of Deranged Metabolites in Critically Ill Patients Using NMR-Based Metabolomics
Published on: November 29, 2024
Transcriptome and Experimental Verification Identified Candidate Biomarkers Related to Mitochondrial Metabolism in
Ran Zhou1, Hao Fang2,3, Herui Li1
1Respiratory Department, The First People's Hospital of Yunnan Province (The Affiliated Hospital of Kunming University of Science and Technology), Kunming, China.
Background:
Mitochondrial metabolism (MM) abnormalities have been implicated in multiple diseases, but their contribution to sepsis-associated encephalopathy (SAE) remains insufficiently understood. The purpose of this research was to explore the candidate biomarkers associated with MM in SAE and the underlying mechanisms.
Methods:
The relevant transcriptome data were acquired from the public databases. MM-related genes were scoured from published articles. Candidate biomarker identification integrated differential expression profiling, protein-protein interaction network construction, machine learning algorithms, receiver operating characteristic curve evaluation, and expression quantification. Immune infiltration, multilayer perceptron network, Gene Set Enrichment Analysis, molecular regulatory network, drug prediction, and molecular docking were applied to probe the mechanisms of candidate biomarkers in SAE. The expression levels of candidate biomarkers were further validated in the brain tissues of the lipopolysaccharides -induced SAE mouse model.
Results:
Four MM-RGs-INSIG1, SREBF1, CIDEC, and PNPLA3-were identified as candidate biomarkers, and their expression patterns in the rodent model were consistent with bioinformatics predictions. Within the GSE135838 dataset, the multilayer perceptron model demonstrated good performance in distinguishing SAE from control samples. Differential immune cells included resting mast cells, naive B cells, and activated natural killer cells. Gene Set Enrichment Analysis indicated significant enrichment in the allograft rejection pathway. The regulatory network involved transcription factors (e.g., THRB), microRNAs (miRNAs) (e.g., hsa-mir-29c-3p), and long noncoding RNAs (e.g., KCNQ1OT1). Furthermore, experimental validation confirmed that these candidate biomarkers were significantly downregulated in SAE samples.
Conclusion:
This study identified and experimentally validated four MM-related candidate biomarkers in SAE, revealing their potential roles in immune dysregulation. These candidate biomarkers require further validation in independent cohorts and in a more diverse range of animal models before clinical translation.
