1
|
Zhou GQ, Wang X, Gao P, Qin TZ, Guo L, Zhang ZW, Huang ZF, Lin JJ, Jing YT, Wang HN, Wang CP, Ding GR. Intestinal microbiota via NLRP3 inflammasome dependent neuronal pyroptosis mediates anxiety-like behaviour in mice exposed to 3.5 GHz radiofrequency radiation. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 927:172391. [PMID: 38608899 DOI: 10.1016/j.scitotenv.2024.172391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/08/2024] [Accepted: 04/08/2024] [Indexed: 04/14/2024]
Abstract
The rapid development of 5G communication technology has increased public concern about the potential adverse effects on human health. Till now, the impacts of radiofrequency radiation (RFR) from 5G communication on the central nervous system and gut-brain axis are still unclear. Therefore, we investigated the effects of 3.5 GHz (a frequency commonly used in 5G communication) RFR on neurobehavior, gut microbiota, and gut-brain axis metabolites in mice. The results showed that exposure to 3.5 GHz RFR at 50 W/m2 for 1 h over 35 d induced anxiety-like behaviour in mice, accompanied by NLRP3-dependent neuronal pyroptosis in CA3 region of the dorsal hippocampus. In addition, the microbial composition was widely divergent between the sham and RFR groups. 3.5 GHz RFR also caused changes in metabolites of feces, serum, and brain. The differential metabolites were mainly enriched in glycerophospholipid metabolism, tryptophan metabolism, and arginine biosynthesis. Further correlation analysis showed that gut microbiota dysbiosis was associated with differential metabolites. Based on the above results, we speculate that dysfunctional intestinal flora and metabolites may be involved in RFR-induced anxiety-like behaviour in mice through neuronal pyroptosis in the brain. The findings provide novel insights into the mechanism of 5G RFR-induced neurotoxicity.
Collapse
Affiliation(s)
- Gui-Qiang Zhou
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; School of Public Health, Shandong Second Medical University, Weifang, China
| | - Xing Wang
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, China
| | - Peng Gao
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, China
| | - Tong-Zhou Qin
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, China
| | - Ling Guo
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, China
| | - Zhao-Wen Zhang
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, China
| | - Zhi-Fei Huang
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; School of Public Health, Shandong Second Medical University, Weifang, China
| | - Jia-Jin Lin
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, China
| | - Yun-Tao Jing
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, China
| | - Hao-Nan Wang
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, China
| | - Chun-Ping Wang
- School of Public Health, Shandong Second Medical University, Weifang, China.
| | - Gui-Rong Ding
- Department of Radiation Protection Medicine, Faculty of Preventive Medicine, Fourth Military Medical University, Xi'an, China; Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, Xi'an, China.
| |
Collapse
|
2
|
Su CH, Ko LW, Jung TP, Onton J, Tzou SC, Juang JC, Hsu CY. Extracting Stress-Related EEG Patterns From Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency. IEEE Trans Neural Syst Rehabil Eng 2024; 32:1817-1827. [PMID: 38683718 DOI: 10.1109/tnsre.2024.3394471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution.
Collapse
|
3
|
Cheng L, Wu H, Cai X, Zhang Y, Yu S, Hou Y, Yin Z, Yan Q, Wang Q, Sun T, Wang G, Yuan Y, Zhang X, Hao H, Zheng X. A Gpr35-tuned gut microbe-brain metabolic axis regulates depressive-like behavior. Cell Host Microbe 2024; 32:227-243.e6. [PMID: 38198925 DOI: 10.1016/j.chom.2023.12.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/29/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
Gene-environment interactions shape behavior and susceptibility to depression. However, little is known about the signaling pathways integrating genetic and environmental inputs to impact neurobehavioral outcomes. We report that gut G-protein-coupled receptor, Gpr35, engages a microbe-to-brain metabolic pathway to modulate neuronal plasticity and depressive behavior in mice. Psychological stress decreases intestinal epithelial Gpr35, genetic deletion of which induces depressive-like behavior in a microbiome-dependent manner. Gpr35-/- mice and individuals with depression have increased Parabacteroides distasonis, and its colonization to wild-type mice induces depression. Gpr35-/- and Parabacteroides distasonis-colonized mice show reduced indole-3-carboxaldehyde (IAld) and increased indole-3-lactate (ILA), which are produced from opposing branches along the bacterial catabolic pathway of tryptophan. IAld and ILA counteractively modulate neuroplasticity in the nucleus accumbens, a brain region linked to depression. IAld supplementation produces anti-depressant effects in mice with stress or gut epithelial Gpr35 deficiency. Together, these findings elucidate a gut microbe-brain signaling mechanism that underlies susceptibility to depression.
Collapse
Affiliation(s)
- Lingsha Cheng
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Haoqian Wu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Xiaoying Cai
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Youying Zhang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Siqi Yu
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Yuanlong Hou
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Zhe Yin
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Qingyuan Yan
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China
| | - Qiong Wang
- Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China
| | - Taipeng Sun
- Department of Psychosomatics and Psychiatry, Southeast University Affiliated Zhongda Hospital, Nanjing 210009, China
| | - Guangji Wang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, Southeast University Affiliated Zhongda Hospital, Nanjing 210009, China.
| | - Xueli Zhang
- Department of Pharmacy, Southeast University Affiliated Zhongda Hospital, Nanjing 210009, China.
| | - Haiping Hao
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
| | - Xiao Zheng
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China; Laboratory of Metabolic Regulation and Drug Target Discovery, School of Pharmacy, China Pharmaceutical University, Nanjing 210009, China.
| |
Collapse
|