ABSTRACT
Objective:To screen important genes and characterize their functions and signaling pathways by weighted gene co-expression network analysis (WGCNA) on the gene expression profile of brain tissue after traumatic brain injury (TBI) so as to provide a reference for the mechanism research and treatment of TBI.Methods:The rat TBI gene expression profile GSE2871 was downloaded from the Gene Expression Omnibus (GEO). The expression profile of 8 799 genes of all 47 rat brain tissue samples was analyzed by WGCNA. After calculating and selecting the β-weighted soft threshold, undirected weighted gene network was constructed to identify gene sets with a high degree of correlation. Sample information was obtained from the database to calculate the correlation between each trait of the samples and modules. Gene ontology (GO) analysis and KEGG pathway analysis were performed for the genes in modules related to injury severity and sampling side in order to unvail the biological processes and pathways involved. The gene-module correlation and gene-trait correlation in these key modules were calculated and hub genes were selected.Results:All the rat brain tissue samples and genes in GSE2871 were included in the WGCNA analysis. A total of 22 modules were obtained, which were marked as modules A to V. Modules E, G, T and U were significantly associated with the sampling side. Modules E and G were significantly related to injury severity . GO analysis and KEGG pathway analysis indicated that the genes in modules E and G with significant relation to injury severity and sampling side were mainly implicated in leukocyte migration, cell chemotaxis, various immune response regulation, etc. The involved pathways included antigen processing and presentation pathways, cell factor-cytokine receptor interaction, interleukin-17 signaling pathway, etc. While modules T and U with significant relation to the sampling side were mainly implicated in hypoxia response, cell metabolism, cell membrane ion channel regulation, signal transduction, etc. The pathways involved were neurodegenerative disease signaling pathways, ribosomes, autophagy, neuroactive ligand-receptor interactions, etc. Among the key modules significantly relating to traits, Tuba1b/1c, Ifitm3, Cebpd, Nfkbia, Serinc3, Pmpcb and Cyp4a8 were selected as hub genes of the above key modules.Conclusion:The genes significantly relating to rat TBI are mainly involved in pathophysiological links such as immune activation, inflammatory response, abnormal energy metabolism, calcium channel disorders, abnormal autophagy and cell apoptosis.
ABSTRACT
The vegetative state is a complex condition with unclear mechanisms and limited diagnostic, prognostic, and therapeutic methods. In this study, we aimed to explore the proteomic profile of tears from patients in a traumatic vegetative state and identify potential diagnostic markers using tears-a body fluid that can be collected non-invasively. Using iTRAQ quantitative proteomic technology, in the discovery phase, tear samples collected from 16 patients in a traumatic vegetative state and 16 normal individuals were analyzed. Among 1080 identified tear proteins, 57 were upregulated and 15 were downregulated in the patients compared to the controls. Bioinformatics analysis revealed that the differentially-expressed proteins were mainly involved in the wound response and immune response signaling pathways. Furthermore, we verified the levels of 7 differentially-expressed proteins in tears from 50 traumatic vegetative state patients and 50 normal controls (including the samples used in the discovery phase) using ELISA. The results showed that this 7-protein panel had a high discrimination ability for traumatic vegetative state (area under the curve = 0.999). In summary, the altered tear proteomic profile identified in this study provides a basis for potential tear protein markers for diagnosis and prognosis of the traumatic vegetative state and also provides novel insights into the mechanisms of traumatic vegetative state.
Subject(s)
Adult , Aged , Aged, 80 and over , Female , Humans , Male , Middle Aged , Young Adult , Biomarkers , Metabolism , Chromatography, Liquid , Enzyme-Linked Immunosorbent Assay , Eye Proteins , Metabolism , Mass Spectrometry , Persistent Vegetative State , Metabolism , Proteome , Proteomics , ROC Curve , Tears , MetabolismABSTRACT
Prognostication of coma patients after brain injury is important, yet challenging. In this study, we evaluated the predictive value of amplitude-integrated electroencephalography (aEEG) for neurological outcomes in coma patients. From January 2013 to January 2016, 128 coma patients after acute brain injury were prospectively enrolled and monitored with aEEG. The 6-month neurological outcome was evaluated using the Cerebral Performance Category Scale. aEEG monitoring commenced at a median of 7.5 days after coma onset. Continuous normal voltage predicted a good 6-month neurological outcome with a sensitivity of 93.6% and specificity of 85.2%. In contrast, continuous extremely low voltage, burst-suppression, or a flat tracing was correlated with poor 6-month neurological outcome with a sensitivity of 76.5% and specificity of 100%. In conclusion, aEEG is a promising predictor of 6-month neurological outcome for coma patients after acute brain injury.