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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 沈立言 | zh_TW |
| dc.contributor.advisor | Lee Yan Sheen | en |
| dc.contributor.author | 蔡晞彤 | zh_TW |
| dc.contributor.author | Hailey Hei Tung Choi | en |
| dc.date.accessioned | 2023-08-15T17:21:25Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-15 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-04 | - |
| dc.identifier.citation | Abelaira, H. M., Réus, G. Z., & Quevedo, J. (2013). Animal models as tools to study the pathophysiology of depression. Brazilian Journal of Psychiatry, 35, S112-S120.
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C., Li, Q., Shao, W. H., Zhang, M. L., Cheng, K., Yang, D. Y., Fan, S. H., Chen, L., & Fang, L. (2012). Plasma metabonomics as a novel diagnostic approach for major depressive disorder. Journal of Proteome Research, 11(3), 1741-1748. Zhou, X., Liu, L., Zhang, Y., Pu, J., Yang, L., Zhou, C., Yuan, S., Zhang, H., & Xie, P. (2017). Metabolomics identifies perturbations in amino acid metabolism in the prefrontal cortex of the learned helplessness rat model of depression. Neuroscience, 343, 1-9. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/88683 | - |
| dc.description.abstract | 重度憂鬱症（MDD）是全世界最普遍的神經精神疾病之一。根據世界衛生組織（WHO）的數據，2020年全球有3.5億患者患有慢性壓力和憂鬱症。目前憂鬱症藥物副作用多且複雜，導致患者對抗憂鬱藥物的接受度低。因此，找尋更好的憂鬱症療法逐漸受到重視。生物體內氨基酸代謝物可進一步合成與憂鬱症相關的神經傳導物質，例如：甘胺酸（glycine）、天門冬胺酸（aspartic acid）、酪胺酸（tyrosine）、γ-氨基丁酸（GABA）等。因此，在憂鬱症的發展中起著至關重要的作用，近年來，液相層析質譜儀（LC-MS）的代謝體學是研究憂鬱症病理生理學的新興方法，因其可透過識別生物體內代謝紊亂下的新途徑和網絡，能有助選出預測、預防、與治療憂鬱症的氨基酸標記代謝物。許多中草藥能透過抑制炎症等方法改善生理系統失衡，從而表現出類抗憂鬱的功效；其副作用更比現有的憂鬱症藥物來得少，因而提高病人的服藥遵從性；因此，中草藥的互補醫學可能是改善憂鬱症的替代方法。天麻（Gastrodia elata Blume）是一種傳統中草藥，其活性物質天麻素（GAS）已被證明對大鼠的憂鬱症有益。然而，文獻中仍缺乏天麻水萃取物（WGE）和GAS對代謝物的作用。因此，本研究旨在利用代謝體學方法探討WGE和GAS對不可預期慢性輕度壓力（UCMS）誘導的憂鬱症大鼠模型血清氨基酸代謝體的變化，並解釋與抗憂鬱效用有關的潛在標記代謝物。在本研究中，WGE和GAS可以通過增加糖水偏好來緩解UCMS大鼠的類憂鬱行為。在血清氨基酸代謝體學分析中，以LC-MS分析，確定了39種氨基酸。稀疏偏最小二乘法判別分析（sPLS-DA）顯示各組之間的分離度有很大不同。多變量分析也揭示了組別之間具有差異的代謝物。此外，路徑分析與文獻回顧發現了給予WGE和GAS的憂鬱大鼠體內，有數條與憂鬱症相關而且被改變的潛在血清代謝物路徑，包括組氨酸代謝（histidine metabolism）；牛磺酸和次牛磺酸代謝（taurine and hypotaurine metabolism）；半胱氨酸和甲硫胺酸代謝（cysteine and methionine metabolism）；甘氨酸、絲氨酸和蘇氨酸代謝（glycine, serine, and threonine metabolism）；β-丙氨酸代謝（beta-alanine metabolism）；精氨酸的生物合成（arginine biosynthesis）；精氨酸與脯氨酸代謝（arginine and proline metabolism）。總括而言，本研究發現WGE與GAS於憂鬱症大鼠血清中21個胺基酸代謝物及7條潛在代謝路徑的變化。透過整理現有機轉，代謝體能有助於進一步理解 WGE 和 GAS 的抗憂鬱作用的潛在機轉，期望可用作未來開發保健食品的基礎。 | zh_TW |
| dc.description.abstract | Major depressive disorder (MDD) is one of the most prevalent neuropsychiatric disorders worldwide. According to the World Health Organization (WHO), there were 350 million patients suffering from chronic stress and depression globally in 2020. Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is a powerful tool to investigate the pathophysiology of depression by identifying the novel pathways and networks of metabolic disorders, amino acid metabolites play an essential role in the development of depression as they contribute to several neurotransmitters. Numerous medications have been developed to cure depression patients, including antidepressants. However, the side effects of the drug cause low acceptance from patients. Various food and herbs exhibit antidepressive-like activity; thus, using food and herbs as mind modulation is an alternative approach for depression amelioration. Gastrodia elata Blume (GE), a traditional Chinese medicine, and its active compound gastrodin has been proven beneficial for depression in rats. However, the role of water extract of GE (WGE) and gastrodin on the metabolites is still lacking in the literature. Therefore, this study aims to use the metabolomic method to discover the alterations in serum amino acid metabolome of unpredictable chronic mild stress (UCMS)-induced depression rat model by WGE and gastrodin (GAS), and to interpret potential marker metabolites related to metabolic pathways involved in antidepressant-like effects of them. In this study, WGE and GAS could alleviate the depressive-like behavior of UCMS rats by increasing the sucrose preference. In serum amino acid metabolomic analysis, 39 amino acids were identified based on LC-MS analysis. The sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) showed substantially different separations among the groups. Multivariate analysis also reveals the differentially expressed metabolites. Moreover, the pathway analysis uncovered several changes in serum metabolites linked to twenty-one candidate metabolite markers and seven potential pathways in the depressed rats with WGE and GAS treatments, including histidine metabolism; taurine and hypotaurine metabolism; cysteine and methionine metabolism; glycine, serine, and threonine metabolism; beta-alanine metabolism; arginine biosynthesis; arginine and proline metabolism. In summary, this study provided a comprehensive understanding of metabolic alterations in the serum, helping to understand the potential mechanisms underlying the antidepressant-like effects of WGE and GAS. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-15T17:21:25Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-15T17:21:25Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Table of Contents
Acknowledgements I Abstract II 中文摘要 IV Table of Contents VI List of Table XI List of Figure XII Abbreviation XVII 1. INTRODUCTION 1 2. LITERATURE REVIEW 3 2.1 Depression 3 2.1.1 Epidemiology and global prevalence of depression 3 2.1.2 Classification of depression 4 2.1.3 Diagnosis of depression 4 2.1.4 Treatments of depression 5 2.1.5 Pathophysiological mechanism of depression 12 2.1.5.1 Psychological 12 2.1.5.2 Pathophysiological 13 2.1.5.2.1 Neuroendocrine system (hypothalamic-pituitary-adrenal, HPA axis) 13 2.1.5.2.2 Neural plasticity and neurogenesis 14 2.1.5.2.3 Neurotrophic and brain-derived neurotrophic factor (BDNF) hypothesis 15 2.1.5.2.4 Neurotransmitters 16 2.1.5.2.4.1 Monoamine hypothesis 16 2.1.5.2.4.2 Glutamate hypothesis 17 2.1.5.2.5 Neuroinflammation 18 2.1.5.3 Genetics 20 2.1.6 Animal model of depression 20 2.1.6.1 Learned helplessness model (LH) 21 2.1.6.2 Unpredictable chronic mild stress (UCMS) 21 2.1.6.3 Chronic social defeat stress (CSDS) 22 2.1.6.4 Chronic restraint stress model (CRS) 23 2.2 Metabolomics and depression 24 2.2.1 Metabolomics 24 2.2.2 Analyses of metabolomics 25 2.2.3 Applications of metabolomics 28 2.2.4 Metabolomics and depression 30 2.2.5 Amino acid metabolism involved in depression 30 2.3 Gastrodia elata Blume 33 2.3.1 Introduction of Gastrodia elata Blume 33 2.3.2 Potential active ingredient of Gastrodia elata Blume 34 2.3.3 Physiological functions of Gastrodia elata Blume 37 2.3.3.1 Neurotransmitters regulations 37 2.3.3.2 Anti-oxidation 37 2.3.3.3 Anti-inflammation 38 2.3.3.4 Neurotrophic effects 38 2.3.3.5 Neuroplasticity 39 2.3.4 Application of metabolomics on Traditional Chinese Medicines (TCMs) used in MDD treatment 40 3. HYPOTHESES 41 4. EXPERIMENTAL DESIGN 42 5. MATERIALS AND METHOD 44 5.1 Experimental materials 44 5.1.1 Water extract of Gastrodia elata Blume 44 5.1.2 Chemicals and reagents 44 5.1.2.1 Animal experiment-related chemicals 44 5.1.2.2 Serum metabolomics analysis 44 5.1.3 Experimental apparatus 45 5.1.3.1 Unpredictable chronic mild stress (UCMS) 45 5.1.3.2 Animal sacrifice and sample collection 45 5.1.3.3 Serum metabolomics analysis 45 5.2 Experimental method 46 5.2.1 WGE and GAS dosage 46 5.2.2 Animal model 46 5.2.2.1 Animal feeding and experimental groups 46 5.2.2.2 Unpredictable chronic mild stress (UCMS) procedure 47 5.2.2.3 Sucrose preference test (SPT) 47 5.2.2.4 Sacrifice and sample collection 48 5.2.3 Serum metabolomics analysis 49 5.2.3.1 Preparation of standard curves and quality control (QC) 49 5.2.3.2 Preparation of serum samples 49 5.2.3.3 Sample analysis – Metabolites identification 50 5.2.3.4 Data export and statistical analysis 51 5.2.3.5 Multivariate statistical analysis 51 5.2.3.6 Biomarkers identification and pathway analysis 52 6. RESULTS 53 6.1 Body weight, food intake, and water intake 53 6.2 Sucrose preference test (SPT) 54 6.3 Serum metabolomics analysis 55 6.3.1 Comparison between the CON group and UCMS group 55 6.3.2 Comparison between the UCMS group and WGE or GAS treatment group 56 6.3.3 Comparison between UCMS group and FLUO treatment group 57 6.3.4 Multivariate statistical analysis of metabolomics data 58 6.3.4.1 Principal Component Analysis (PCA) 58 6.3.4.2 Partial Least Squares-Discriminant Analysis (PLS-DA) 58 6.3.4.3 Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) 59 6.3.4.4 Heatmap 59 6.3.5 Pathways analysis of serum metabolomics 59 6.3.5.1 Metabolic pathways altered by UCMS in rats 60 6.3.5.2 Metabolic pathways altered by WGE and GAS in UCMS rats 60 6.3.5.3 Metabolic pathways altered by FLUO in UCMS rats 61 6.3.6 Amino acid pathways and network altered by UCMS, WGE, and GAS 61 7. DISCUSSION 62 7.1 The effects of the UCMS animal model and the intervention of WGE towards body weight, food, and water intakes 62 7.2 The effects of the UCMS animal model and the intervention of WGE towards week 5 sucrose preference 63 7.3 Multivariate statistical analysis of metabolomics data 64 7.3.1 Principal Component Analysis (PCA) 64 7.3.2 Partial Least Squares-Discriminant Analysis (PLS-DA) 65 7.3.3 Sparse Partial Least Squares-Discriminant Analysis (sPLS-DA) 65 7.3.4 Heatmap 66 7.4 Biochemical interpretation of amino acid biomarkers or the metabolic pathways in UCMS animal model and the intervention of WGE and GAS 67 7.4.1 Histidine metabolism 67 7.4.2 Taurine and hypotaurine metabolism 69 7.4.3 Cysteine and methionine metabolism 70 7.4.4 Glycine, serine, and threonine metabolism 72 7.4.5 Beta-alanine metabolism 74 7.4.6 Arginine biosynthesis 75 7.4.7 Arginine and proline metabolism 77 7.4.8 D-glutamine and D-glutamate metabolism 78 7.4.9 Alanine, aspartate, and glutamate metabolism 80 7.4.10 Phenylalanine, tyrosine, and tryptophan biosynthesis 81 7.4.11 Other possible amino acid metabolites or metabolic pathways altered by WGE and GAS in UCMS rats 82 8. CONCLUSION 85 9. REFERENCES 124 10. APPENDICES 142 | - |
| dc.language.iso | en | - |
| dc.subject | 不可預期慢性輕度壓力（UCMS） | zh_TW |
| dc.subject | 氨基酸 | zh_TW |
| dc.subject | 代謝體學 | zh_TW |
| dc.subject | 天麻（Gastrodia elata Blume） | zh_TW |
| dc.subject | 重度憂鬱症（MDD） | zh_TW |
| dc.subject | 神經傳遞物質 | zh_TW |
| dc.subject | major depressive disorder (MDD) | en |
| dc.subject | neurotransmitters | en |
| dc.subject | amino acids | en |
| dc.subject | metabolomics | en |
| dc.subject | Gastrodia elata Blume | en |
| dc.subject | unpredictable chronic mild stress (UCMS) | en |
| dc.title | 以代謝體學探討天麻與天麻素於大鼠血清胺基酸代謝物的抗憂鬱機轉 | zh_TW |
| dc.title | Serum amino acids metabolomic investigation of Gastrodia elata Blume and gastrodin on rats with depressive-like behaviors | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 鄭美玲;郭錦樺 | zh_TW |
| dc.contributor.oralexamcommittee | Mei Ling Cheng;Ching Hua Kuo | en |
| dc.subject.keyword | 天麻（Gastrodia elata Blume）,重度憂鬱症（MDD）,代謝體學,神經傳遞物質,氨基酸,不可預期慢性輕度壓力（UCMS）, | zh_TW |
| dc.subject.keyword | Gastrodia elata Blume,major depressive disorder (MDD),metabolomics,neurotransmitters,amino acids,unpredictable chronic mild stress (UCMS), | en |
| dc.relation.page | 203 | - |
| dc.identifier.doi | 10.6342/NTU202300958 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-08 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 食品科技研究所 | - |
| 顯示於系所單位： | 食品科技研究所 | |
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