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Guo J, Zhao J, Han P, Wu Y, Zheng K, Huang C, Wang Y, Chen C, Guo Q. Finding the best predictive model for hypertensive depression in older adults based on machine learning and metabolomics research. Front Psychiatry 2024; 15:1370602. [PMID: 38993388 PMCID: PMC11236531 DOI: 10.3389/fpsyt.2024.1370602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 06/10/2024] [Indexed: 07/13/2024] Open
Abstract
Objective Depression is a common comorbidity in hypertensive older adults, yet depression is more difficult to diagnose correctly. Our goal is to find predictive models of depression in hypertensive patients using a combination of various machine learning (ML) methods and metabolomics. Methods Methods We recruited 379 elderly people aged ≥65 years from the Chinese community. Plasma samples were collected and assayed by gas chromatography/liquid chromatography-mass spectrometry (GC/LC-MS). Orthogonal partial least squares discriminant analysis (OPLS-DA), volcano diagrams and thermograms were used to distinguish metabolites. The attribute discriminators CfsSubsetEval combined with search method BestFirst in WEKA software was used to find the best predicted metabolite combinations, and then 24 classification methods with 10-fold cross-validation were used for prediction. Results 34 individuals were considered hypertensive combined with depression according to our criteria, and 34 subjects with hypertension only were matched according to age and sex. 19 metabolites by GC-MS and 65 metabolites by LC-MS contributed significantly to the differentiation between the depressed and non-depressed cohorts, with a VIP value of more than 1 and a P value of less than 0.05. There were multiple metabolic pathway alterations. The metabolite combinations screened with WEKA for optimal diagnostic value included 12 metabolites. The machine learning methods with AUC values greater than 0.9 were bayesNet and random forests, and their other evaluation measures are also better. Conclusion Altered metabolites and metabolic pathways are present in older adults with hypertension combined with depression. Methods using metabolomics and machine learning performed quite well in predicting depression in hypertensive older adults, contributing to further clinical research.
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Affiliation(s)
- Jiangling Guo
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- Graduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jingwang Zhao
- Shanghai YangZhi Rehabilitation Hospital (Shanghai Sunshine Rehabilitation Center), School of Medicine, Tongji University, Shanghai, China
| | - Peipei Han
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Yahui Wu
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Kai Zheng
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Chuanjun Huang
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yue Wang
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Cheng Chen
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health, Fujian Medical University, Fuzhou, Fujian, China
| | - Qi Guo
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
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Han P, Chen X, Liang Z, Liu Y, Yu X, Song P, Zhao Y, Zhang H, Zhu S, Shi X, Guo Q. Metabolic signatures and risk of sarcopenia in suburb-dwelling older individuals by LC-MS-based untargeted metabonomics. Front Endocrinol (Lausanne) 2024; 15:1308841. [PMID: 38962681 PMCID: PMC11220188 DOI: 10.3389/fendo.2024.1308841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 06/04/2024] [Indexed: 07/05/2024] Open
Abstract
Background Untargeted metabonomics has provided new insight into the pathogenesis of sarcopenia. In this study, we explored plasma metabolic signatures linked to a heightened risk of sarcopenia in a cohort study by LC-MS-based untargeted metabonomics. Methods In this nested case-control study from the Adult Physical Fitness and Health Cohort Study (APFHCS), we collected blood plasma samples from 30 new-onset sarcopenia subjects (mean age 73.2 ± 5.6 years) and 30 healthy controls (mean age 74.2 ± 4.6 years) matched by age, sex, BMI, lifestyle, and comorbidities. An untargeted metabolomics methodology was employed to discern the metabolomic profile alterations present in individuals exhibiting newly diagnosed sarcopenia. Results In comparing individuals with new-onset sarcopenia to normal controls, a comprehensive analysis using liquid chromatography-mass spectrometry (LC-MS) identified a total of 62 metabolites, predominantly comprising lipids, lipid-like molecules, organic acids, and derivatives. Receiver operating characteristic (ROC) curve analysis indicated that the three metabolites hypoxanthine (AUC=0.819, 95% CI=0.711-0.927), L-2-amino-3-oxobutanoic acid (AUC=0.733, 95% CI=0.598-0.868) and PC(14:0/20:2(11Z,14Z)) (AUC= 0.717, 95% CI=0.587-0.846) had the highest areas under the curve. Then, these significant metabolites were observed to be notably enriched in four distinct metabolic pathways, namely, "purine metabolism"; "parathyroid hormone synthesis, secretion and action"; "choline metabolism in cancer"; and "tuberculosis". Conclusion The current investigation elucidates the metabolic perturbations observed in individuals diagnosed with sarcopenia. The identified metabolites hold promise as potential biomarkers, offering avenues for exploring the underlying pathological mechanisms associated with sarcopenia.
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Affiliation(s)
- Peipei Han
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Jiangwan Hospital of Shanghai Hongkou District, Shanghai University of Medicine and Health Science Affiliated First Rehabilitation Hospital, Shanghai, China
| | - Xiaoyu Chen
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Zhenwen Liang
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Yuewen Liu
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xing Yu
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Peiyu Song
- Jiangwan Hospital of Shanghai Hongkou District, Shanghai University of Medicine and Health Science Affiliated First Rehabilitation Hospital, Shanghai, China
| | - Yinjiao Zhao
- Jiangwan Hospital of Shanghai Hongkou District, Shanghai University of Medicine and Health Science Affiliated First Rehabilitation Hospital, Shanghai, China
| | - Hui Zhang
- Jiangwan Hospital of Shanghai Hongkou District, Shanghai University of Medicine and Health Science Affiliated First Rehabilitation Hospital, Shanghai, China
| | - Shuyan Zhu
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Xinyi Shi
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
| | - Qi Guo
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai, China
- Jiangwan Hospital of Shanghai Hongkou District, Shanghai University of Medicine and Health Science Affiliated First Rehabilitation Hospital, Shanghai, China
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Guo J, Han P, Zheng Y, Wu Y, Zheng K, Huang C, Wang Y, Chen C, Qi Y, Chen X, Tao Q, Zhai J, Guo Q. Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS. Sci Rep 2024; 14:10303. [PMID: 38705886 PMCID: PMC11070417 DOI: 10.1038/s41598-024-60836-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 04/28/2024] [Indexed: 05/07/2024] Open
Abstract
Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.
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Affiliation(s)
- Jiangling Guo
- Graduate School, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Peipei Han
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China
| | | | - Yahui Wu
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Kai Zheng
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Chuanjun Huang
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yue Wang
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Cheng Chen
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China
- School of Health, Fujian Medical University, Fuzhou, Fujian, China
| | - Yiqiong Qi
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
| | - Xiaoyu Chen
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China
| | - Qiongying Tao
- Jiading Subdistrict Community Health Center, Shanghai, China
| | - Jiayi Zhai
- Jiading Subdistrict Community Health Center, Shanghai, China
| | - Qi Guo
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, 279 Zhouzhu Highway, Pudong New Area, Shanghai, 201318, China.
- Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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Han P, Yuan C, Chen X, Hu Y, Hu X, Xu Z, Guo Q. Metabolic signatures and potential biomarkers of sarcopenia in suburb-dwelling older Chinese: based on untargeted GC-MS and LC-MS. Skelet Muscle 2024; 14:4. [PMID: 38454497 PMCID: PMC10921582 DOI: 10.1186/s13395-024-00337-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 02/07/2024] [Indexed: 03/09/2024] Open
Abstract
BACKGROUND Untargeted metabolomics can be used to expand our understanding of the pathogenesis of sarcopenia. However, the metabolic signatures of sarcopenia patients have not been thoroughly investigated. Herein, we explored metabolites associated with sarcopenia by untargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) and identified possible diagnostic markers. METHODS Forty-eight elderly subjects with sarcopenia were age and sex matched with 48 elderly subjects without sarcopenia. We first used untargeted GC/LC-MS to analyze the plasma of these participants and then combined it with a large number of multivariate statistical analyses to analyze the data. Finally, based on a multidimensional analysis of the metabolites, the most critical metabolites were considered to be biomarkers of sarcopenia. RESULTS According to variable importance in the project (VIP > 1) and the p-value of t-test (p < 0.05), a total of 55 metabolites by GC-MS and 85 metabolites by LC-MS were identified between sarcopenia subjects and normal controls, and these were mostly lipids and lipid-like molecules. Among the top 20 metabolites, seven phosphatidylcholines, seven lysophosphatidylcholines (LysoPCs), phosphatidylinositol, sphingomyelin, palmitamide, L-2-amino-3-oxobutanoic acid, and palmitic acid were downregulated in the sarcopenia group; only ethylamine was upregulated. Among that, three metabolites of LysoPC(17:0), L-2-amino-3-oxobutanoic acid, and palmitic acid showed very good prediction capacity with AUCs of 0.887 (95% CI = 0.817-0.957), 0.836 (95% CI = 0.751-0.921), and 0.805 (95% CI = 0.717-0.893), respectively. CONCLUSIONS These findings show that metabonomic analysis has great potential to be applied to sarcopenia. The identified metabolites could be potential biomarkers and could be used to study sarcopenia pathomechanisms.
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Affiliation(s)
- Peipei Han
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- College of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China
- Jiangwan Hospital of Shanghai Hongkou District, Shanghai University of Medicine and Health Science Affiliated First Rehabilitation Hospital, Shanghai, China
| | - Chunhua Yuan
- Comprehensive Surgical Rehabilitation Ward, Shanghai Health Rehabilitation Hospital, Shanghai, China
| | - Xiaoyu Chen
- College of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China
| | - Yuanqing Hu
- College of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China
| | - Xiaodan Hu
- College of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China
| | - Zhangtao Xu
- College of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China
| | - Qi Guo
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China.
- College of Rehabilitation Sciences, Pudong New Area, Shanghai University of Medicine and Health Sciences, 279 Zhouzhu Highway, Shanghai, 201318, China.
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Wu Y, Yuan C, Han P, Guo J, Wang Y, Chen C, Huang C, Zheng K, Qi Y, Li J, Xue Z, Lu F, Liang D, Gao J, Li X, Guo Q. Discovery of potential biomarkers for osteoporosis using LC/GC-MS metabolomic methods. Front Endocrinol (Lausanne) 2024; 14:1332216. [PMID: 38298188 PMCID: PMC10828954 DOI: 10.3389/fendo.2023.1332216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 12/27/2023] [Indexed: 02/02/2024] Open
Abstract
Purpose For early diagnosis of osteoporosis (OP), plasma metabolomics of OP was studied by untargeted LC/GC-MS in a Chinese elderly population to find possible diagnostic biomarkers. Methods A total of 379 Chinese community-dwelling older adults aged ≥65 years were recruited for this study. The BMD of the calcaneus was measured using quantitative ultrasound (QUS), and a T value ≤-2.5 was defined as OP. Twenty-nine men and 47 women with OP were screened, and 29 men and 36 women were matched according to age and BMI as normal controls using propensity matching. Plasma from these participants was first analyzed by untargeted LC/GC-MS, followed by FC and P values to screen for differential metabolites and heatmaps and box plots to differentiate metabolites between groups. Finally, metabolic pathway enrichment analysis of differential metabolites was performed based on KEGG, and pathways with P ≤ 0.05 were selected as enrichment pathways. Results We screened metabolites with FC>1.2 or FC<1/1.2 and P<0.05 and found 33 differential metabolites in elderly men and 30 differential metabolites in elderly women that could be potential biomarkers for OP. 2-Aminomuconic acid semialdehyde (AUC=0.72, 95% CI 0.582-0.857, P=0.004) is highly likely to be a biomarker for screening OP in older men. Tetradecanedioic acid (AUC=0.70, 95% CI 0.575-0.818, P=0.004) is highly likely to be a biomarker for screening OP in older women. Conclusion These findings can be applied to clinical work through further validation studies. This study also shows that metabolomic analysis has great potential for application in the early diagnosis and recurrence monitoring of OP in elderly individuals.
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Affiliation(s)
- Yahui Wu
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Chunhua Yuan
- Comprehensive surgical rehabilitation ward, Shanghai Health Rehabilitation Hospital, Shanghai, China
| | - Peipei Han
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Jiangling Guo
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- Graduate School of Shanghai University of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yue Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Cheng Chen
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- School of Health, Fujian Medical University, Fujian, China
| | - Chuanjun Huang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Kai Zheng
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Yiqiong Qi
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
- Department of Sport Rehabilitation, Shanghai University of Sport, Shanghai, China
| | - Jiajin Li
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Zhengjie Xue
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Fanchen Lu
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
| | - Dongyu Liang
- Clinical Research Center, Jiading District Central Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Jing Gao
- General Practice Clinic, Pujiang Community Health Service Center in Minhang District, Shanghai, China
| | - Xingyan Li
- Shanghai Hongkou District Jiangwan Hospital Affiliated Shanghai University of Medicine & Health Sciences, Shanghai, China
| | - Qi Guo
- Department of Rehabilitation Medicine, Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, Shanghai, China
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Rangel SC, da Silva MD, da Silva AL, dos Santos JDMB, Neves LM, Pedrosa A, Rodrigues FM, Trettel CDS, Furtado GE, de Barros MP, Bachi ALL, Romano CM, Nali LHDS. Human endogenous retroviruses and the inflammatory response: A vicious circle associated with health and illness. Front Immunol 2022; 13:1057791. [PMID: 36518758 PMCID: PMC9744114 DOI: 10.3389/fimmu.2022.1057791] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 10/31/2022] [Indexed: 11/24/2022] Open
Abstract
Human Endogenous Retroviruses (HERVs) are derived from ancient exogenous retroviral infections that have infected our ancestors' germline cells, underwent endogenization process, and were passed throughout the generations by retrotransposition and hereditary transmission. HERVs comprise 8% of the human genome and are critical for several physiological activities. Yet, HERVs reactivation is involved in pathological process as cancer and autoimmune diseases. In this review, we summarize the multiple aspects of HERVs' role within the human genome, as well as virological and molecular aspects, and their fusogenic property. We also discuss possibilities of how the HERVs are possibly transactivated and participate in modulating the inflammatory response in health conditions. An update on their role in several autoimmune, inflammatory, and aging-related diseases is also presented.
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Affiliation(s)
- Sara Coelho Rangel
- UNISA Research Center, Universidade Santo Amaro, Post-Graduation in Health Sciences, São Paulo, Brazil
| | | | - Amanda Lopes da Silva
- Laboratório de Virologia, Instituto de Medicina Tropical de São Paulo, Universidade de São Paulo, São Paulo, Brazil
| | | | - Lucas Melo Neves
- UNISA Research Center, Universidade Santo Amaro, Post-Graduation in Health Sciences, São Paulo, Brazil
| | - Ana Pedrosa
- CNC-Center for Neuroscience and Cell Biology, CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, (3004-504), Coimbra, Portugal
| | | | - Caio dos Santos Trettel
- Interdisciplinary Program in Health Sciences, Institute of Physical Activity Sciences and Sports (ICAFE), Cruzeiro do Sul University, São Paulo, Brazil
| | - Guilherme Eustáquio Furtado
- Polytechnic Institute of Coimbra, Applied Research Institute, Rua da Misericórdia, Lagar dos Cortiços – S. Martinho do Bispo, Coimbra, Portugal
| | - Marcelo Paes de Barros
- Interdisciplinary Program in Health Sciences, Institute of Physical Activity Sciences and Sports (ICAFE), Cruzeiro do Sul University, São Paulo, Brazil
| | - André Luis Lacerda Bachi
- UNISA Research Center, Universidade Santo Amaro, Post-Graduation in Health Sciences, São Paulo, Brazil
| | - Camila Malta Romano
- Laboratório de Virologia, Instituto de Medicina Tropical de São Paulo, Universidade de São Paulo, São Paulo, Brazil,Hospital das Clínicas HCFMUSP (LIM52), Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
| | - Luiz Henrique Da Silva Nali
- UNISA Research Center, Universidade Santo Amaro, Post-Graduation in Health Sciences, São Paulo, Brazil,*Correspondence: Luiz Henrique Da Silva Nali, ;
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