Identification of Critical Signature in Post‐Traumatic Stress Disorder Using Bioinformatics Analysis and in Vitro Analyses
ABSTRACT Background Post‐traumatic stress disorder (PTSD) is a complex psychiatric condition that emerges following exposure to trauma and significantly affects daily functioning. Current research is focused on identifying effective treatments for PTSD. Advances in bioinformatics provide opportuniti...
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| Format: | Article |
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Wiley
2025-01-01
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| Series: | Brain and Behavior |
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| Online Access: | https://doi.org/10.1002/brb3.70243 |
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| author | Lifen Liu Yang Liu Rui Li Yue Teng Shuyang Zhao Jinhong Chen Changjiang Li Xinyu Hu Lin Sun |
| author_facet | Lifen Liu Yang Liu Rui Li Yue Teng Shuyang Zhao Jinhong Chen Changjiang Li Xinyu Hu Lin Sun |
| author_sort | Lifen Liu |
| collection | DOAJ |
| description | ABSTRACT Background Post‐traumatic stress disorder (PTSD) is a complex psychiatric condition that emerges following exposure to trauma and significantly affects daily functioning. Current research is focused on identifying effective treatments for PTSD. Advances in bioinformatics provide opportunities to elucidate the underlying mechanisms of PTSD. Methods RNA sequencing (RNA‐seq) datasets were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using GEO2R. Weighted gene co‐expression network analysis (WGCNA) was employed to examine gene correlation patterns. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for functional annotation and enrichment analysis, respectively. The MCODE plugin in Cytoscape software was utilized to analyze the protein–protein interaction (PPI) network. Anxiety and depression in a mice stress model were assessed using the open‐field test (OFT), elevated plus maze test (EPMT), and forced swimming test (FST). Real‐time quantitative PCR (qRT‐PCR) was conducted to validate key genes in stress‐exposed models. Results A total of 157 common upregulated DEGs and 53 common downregulated DEGs were identified in the amygdala (AMY) and the hippocampus (HIP). Notably enriched pathways included neuroactive ligand‐receptor interaction, mechanistic target of rapamycin (mTOR) signaling pathway, nicotine addiction, and dopaminergic synapse. The PPI network identified four hub genes, with key pathways associated with nicotine addiction and dopaminergic synapse. qRT‐PCR validation confirmed that the expression trends of these four genes were consistent with microarray data. Behavioral tests (OFT, EMPT, and FST) revealed significant changes. Conclusion This study utilized bioinformatics and in vitro experiments to identify genes and pathways potentially crucial for PTSD development. Key genes were validated in a mouse model, providing insights into potential target genes for PTSD treatment. |
| format | Article |
| id | doaj-art-4565992b7c444762a7795f6bce46fe5e |
| institution | Kabale University |
| issn | 2162-3279 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Brain and Behavior |
| spelling | doaj-art-4565992b7c444762a7795f6bce46fe5e2025-01-29T13:36:39ZengWileyBrain and Behavior2162-32792025-01-01151n/an/a10.1002/brb3.70243Identification of Critical Signature in Post‐Traumatic Stress Disorder Using Bioinformatics Analysis and in Vitro AnalysesLifen Liu0Yang Liu1Rui Li2Yue Teng3Shuyang Zhao4Jinhong Chen5Changjiang Li6Xinyu Hu7Lin Sun8School of PsychologyShandong Second Medical UniversityWeifangShandongPeople's Republic of ChinaDepartment of Psychological Counseling CenterWeifang Mental Health Center, Weian Road, Weifang, Shandong ChinaSchool of PsychologyShandong Second Medical UniversityWeifangShandongPeople's Republic of ChinaFaculty of PsychologySouthwest University, Chongqing ChinaDepartment of Bioscience and TechnologyShandong Second Medical UniversityWeifangShandongPeople's Republic of ChinaCollege of Extended EducationShandong Second Medical UniversityWeifangShandongPeople's Republic of ChinaSchool of PsychologyShandong Second Medical UniversityWeifangShandongPeople's Republic of ChinaCAS Key Laboratory of Mental Health, Institute of PsychologyChinese Academy of SciencesBeijingPeople's Republic of ChinaSchool of PsychologyShandong Second Medical UniversityWeifangShandongPeople's Republic of ChinaABSTRACT Background Post‐traumatic stress disorder (PTSD) is a complex psychiatric condition that emerges following exposure to trauma and significantly affects daily functioning. Current research is focused on identifying effective treatments for PTSD. Advances in bioinformatics provide opportunities to elucidate the underlying mechanisms of PTSD. Methods RNA sequencing (RNA‐seq) datasets were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified using GEO2R. Weighted gene co‐expression network analysis (WGCNA) was employed to examine gene correlation patterns. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for functional annotation and enrichment analysis, respectively. The MCODE plugin in Cytoscape software was utilized to analyze the protein–protein interaction (PPI) network. Anxiety and depression in a mice stress model were assessed using the open‐field test (OFT), elevated plus maze test (EPMT), and forced swimming test (FST). Real‐time quantitative PCR (qRT‐PCR) was conducted to validate key genes in stress‐exposed models. Results A total of 157 common upregulated DEGs and 53 common downregulated DEGs were identified in the amygdala (AMY) and the hippocampus (HIP). Notably enriched pathways included neuroactive ligand‐receptor interaction, mechanistic target of rapamycin (mTOR) signaling pathway, nicotine addiction, and dopaminergic synapse. The PPI network identified four hub genes, with key pathways associated with nicotine addiction and dopaminergic synapse. qRT‐PCR validation confirmed that the expression trends of these four genes were consistent with microarray data. Behavioral tests (OFT, EMPT, and FST) revealed significant changes. Conclusion This study utilized bioinformatics and in vitro experiments to identify genes and pathways potentially crucial for PTSD development. Key genes were validated in a mouse model, providing insights into potential target genes for PTSD treatment.https://doi.org/10.1002/brb3.70243bioinformaticsDEGsmicroarrayPTSD |
| spellingShingle | Lifen Liu Yang Liu Rui Li Yue Teng Shuyang Zhao Jinhong Chen Changjiang Li Xinyu Hu Lin Sun Identification of Critical Signature in Post‐Traumatic Stress Disorder Using Bioinformatics Analysis and in Vitro Analyses Brain and Behavior bioinformatics DEGs microarray PTSD |
| title | Identification of Critical Signature in Post‐Traumatic Stress Disorder Using Bioinformatics Analysis and in Vitro Analyses |
| title_full | Identification of Critical Signature in Post‐Traumatic Stress Disorder Using Bioinformatics Analysis and in Vitro Analyses |
| title_fullStr | Identification of Critical Signature in Post‐Traumatic Stress Disorder Using Bioinformatics Analysis and in Vitro Analyses |
| title_full_unstemmed | Identification of Critical Signature in Post‐Traumatic Stress Disorder Using Bioinformatics Analysis and in Vitro Analyses |
| title_short | Identification of Critical Signature in Post‐Traumatic Stress Disorder Using Bioinformatics Analysis and in Vitro Analyses |
| title_sort | identification of critical signature in post traumatic stress disorder using bioinformatics analysis and in vitro analyses |
| topic | bioinformatics DEGs microarray PTSD |
| url | https://doi.org/10.1002/brb3.70243 |
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