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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 郭柏秀(Po-Hsiu Kuo) | |
dc.contributor.author | Yu-Chu Ella Chung | en |
dc.contributor.author | 鍾宇筑 | zh_TW |
dc.date.accessioned | 2021-06-16T17:14:11Z | - |
dc.date.available | 2025-09-02 | |
dc.date.copyright | 2020-09-02 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-04-10 | |
dc.identifier.citation | 7 References
1 Aagaard, K., Ma, J., Antony, K.M., Ganu, R., Petrosino, J., Versalovic, J., 2014. The placenta harbors a unique microbiome. Sci Transl Med 6(237), 237ra265-237ra265. 2 Alcock, J., Maley, C.C., Aktipis, C., 2014. Is eating behavior manipulated by the gastrointestinal microbiota? Evolutionary pressures and potential mechanisms. Bioessays 36(10), 940-949. 3 American Psychiatric Association, 2013. Diagnostic and statistical manual of mental disorders (DSM-5®). American Psychiatric Pub. 4 Amir, A., McDonald, D., Navas-Molina, J.A., Kopylova, E., Morton, J.T., Zech Xu, Z., et al., 2017. Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns. mSystems 2(2). 5 Anderson, M.J., Walsh, D.C., 2013. PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol Monogr 83(4), 557-574. 6 Baer, L., Blais, M.A., 2010. Handbook of clinical rating scales and assessment in psychiatry and mental health. Springer. 7 Bailey, M.T., Coe, C.L., 1999. Maternal separation disrupts the integrity of the intestinal microflora in infant rhesus monkeys. Dev Psychobiol 35(2), 146-155. 8 Bailey, M.T., Dowd, S.E., Galley, J.D., Hufnagle, A.R., Allen, R.G., Lyte, M., 2011. Exposure to a social stressor alters the structure of the intestinal microbiota: implications for stressor-induced immunomodulation. Brain Behav Immun 25(3), 397-407. 9 Ballenger, J.C., 2000. Anxiety and Depression: Optimizing Treatments. Prim Care Companion J Clin Psychiatry 2, 71-79. 10 Balvociute, M., Huson, D.H., 2017. SILVA, RDP, Greengenes, NCBI and OTT - how do these taxonomies compare? BMC Genomics 18(Suppl 2), 114. 11 Beck, A.T., Steer, R.A., Carbin, M.G., 1988. Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clin Psychol Rev 8(1), 77-100. 12 Benedetti, F., Serretti, A., Colombo, C., Barbini, B., Lorenzi, C., Campori, E., et al., 2003. Influence of CLOCK gene polymorphism on circadian mood fluctuation and illness recurrence in bipolar depression. Am J Med Genet B 123(1), 23-26. 13 Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet, C.C., Al-Ghalith, G.A., et al., 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37(8), 852-857. 14 Bora, E., Harrison, B.J., Davey, C.G., Yucel, M., Pantelis, C., 2012. Meta-analysis of volumetric abnormalities in cortico-striatal-pallidal-thalamic circuits in major depressive disorder. Psychol Med 42(4), 671-681. 15 Borcard, D., Legendre, P., 2002. All-scale spatial analysis of ecological data by means of principal coordinates of neighbour matrices. Ecol Model 153(1-2), 51-68. 16 Bravo, J.A., Forsythe, P., Chew, M.V., Escaravage, E., Savignac, H.M., Dinan, T.G., et al., 2011. Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. Proc Natl Acad Sci U S A 108(38), 16050-16055. 17 Burke, H.M., Davis, M.C., Otte, C., Mohr, D.C., 2005. Depression and cortisol responses to psychological stress: a meta-analysis. Psychoneuroendocrino 30(9), 846-856. 18 Cai, N., Bigdeli, T.B., Kretzschmar, W., Li, Y., Liang, J., Song, L., et al., 2015. Sparse whole-genome sequencing identifies two loci for major depressive disorder. Nature 523, 588-591. 19 Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13(7), 581-583. 20 Cani, P.D., Neyrinck, A.M., Fava, F., Knauf, C., Burcelin, R.G., Tuohy, K.M., et al., 2007. Selective increases of bifidobacteria in gut microflora improve high-fat-diet-induced diabetes in mice through a mechanism associated with endotoxaemia. Diabetologia 50(11), 2374-2383. 21 Cerullo, M.A., Eliassen, J.C., Smith, C.T., Fleck, D.E., Nelson, E.B., Strawn, J.R., et al., 2014. Bipolar I disorder and major depressive disorder show similar brain activation during depression. Bipolar Disord 16(7), 703-712. 22 Chen, J.J., Zheng, P., Liu, Y.Y., Zhong, X.G., Wang, H.Y., Guo, Y.J., et al., 2018. Sex differences in gut microbiota in patients with major depressive disorder. Neuropsychiatr Dis Treat 14, 647-655. 23 Chen, Z., Li, J., Gui, S., Zhou, C., Chen, J., Yang, C., et al., 2018. Comparative metaproteomics analysis shows altered fecal microbiota signatures in patients with major depressive disorder. Neuroreport 29(5), 417-425. 24 Chou, Y.C., Chu, C.H., Wu, M.H., Hsu, G.C., Yang, T., Chou, W.Y., et al., 2011. Dietary intake of vitamin B(6) and risk of breast cancer in Taiwanese women. J Epidemiol 21(5), 329-336. 25 Chung, Y.C.E., Chen, H.C., Chou, H.C.L., Chen, I.M., Lee, M.S., Chuang, L.C., et al., 2019. Exploration of microbiota targets for major depressive disorder and mood related traits. J Psychiatr Res 111, 74-82. 26 Clarke, G., Grenham, S., Scully, P., Fitzgerald, P., Moloney, R.D., Shanahan, F., et al., 2013. The microbiome-gut-brain axis during early life regulates the hippocampal serotonergic system in a sex-dependent manner. Mol Psychiatry 18(6), 666-673. 27 Clemente, J.C., Ursell, L.K., Parfrey, L.W., Knight, R., 2012. The impact of the gut microbiota on human health: an integrative view. Cell 148(6), 1258-1270. 28 Collins, S.M., Surette, M., Bercik, P., 2012. The interplay between the intestinal microbiota and the brain. Nat Rev Microbiol 10(11), 735-742. 29 Cook, S., Sellin, J., 1998. Review article: short chain fatty acids in health and disease. Aliment Pharm Ther 12(6), 499-507. 30 Cryan, J.F., Dinan, T.G., 2012. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat Rev Neurosci 13(10), 701-712. 31 Cryan, J.F., O'Riordan, K.J., Cowan, C.S.M., Sandhu, K.V., Bastiaanssen, T.F.S., Boehme, M., et al., 2019. The Microbiota-Gut-Brain Axis. Physiol Rev 99(4), 1877-2013. 32 De Filippis, F., Pellegrini, N., Laghi, L., Gobbetti, M., Ercolini, D., 2016. Unusual sub-genus associations of faecal Prevotella and Bacteroides with specific dietary patterns. Microbiome 4(1), 57. 33 De Filippo, C., Cavalieri, D., Di Paola, M., Ramazzotti, M., Poullet, J.B., Massart, S., et al., 2010. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc Natl Acad Sci U S A 107(33), 14691-14696. 34 DeCastro, M., Nankova, B.B., Shah, P., Patel, P., Mally, P.V., Mishra, R., et al., 2005. Short chain fatty acids regulate tyrosine hydroxylase gene expression through a cAMP-dependent signaling pathway. Mol Brain Res 142(1), 28-38. 35 Desbonnet, L., Garrett, L., Clarke, G., Kiely, B., Cryan, J.F., Dinan, T.G., 2010. Effects of the probiotic Bifidobacterium infantis in the maternal separation model of depression. Neuroscience 170(4), 1179-1188. 36 Detka, J., Kurek, A., Kucharczyk, M., Glombik, K., Basta-Kaim, A., Kubera, M., et al., 2015. Brain glucose metabolism in an animal model of depression. Neuroscience 295, 198-208. 37 DiGiulio, D.B., 2012. Diversity of microbes in amniotic fluid, Semin Fetal Neonat M. Elsevier, pp. 2-11. 38 Faith, D.P., 1992. Conservation evaluation and phylogenetic diversity. Biol Conserv 61(1), 1-10. 39 Ferrari, A.J., Somerville, A.J., Baxter, A.J., Norman, R., Patten, S.B., Vos, T., et al., 2013. Global variation in the prevalence and incidence of major depressive disorder: a systematic review of the epidemiological literature. Psychol Med 43(3), 471-481. 40 Flowers, S.A., Evans, S.J., Ward, K.M., McInnis, M.G., Ellingrod, V.L., 2017. Interaction Between Atypical Antipsychotics and the Gut Microbiome in a Bipolar Disease Cohort. Pharmacotherapy 37(3), 261-267. 41 Fujimura, K.E., Slusher, N.A., Cabana, M.D., Lynch, S.V., 2010. Role of the gut microbiota in defining human health. Expert Rev Anti Infect Ther 8(4), 435-454. 42 Gangwisch, J.E., Hale, L., Garcia, L., Malaspina, D., Opler, M.G., Payne, M.E., et al., 2015. High glycemic index diet as a risk factor for depression: analyses from the Women's Health Initiative. Am J Clin Nutr 102(2), 454-463. 43 Gloor, G., 2015. ALDEx2: ANOVA-Like Differential Expression tool for compositional data. ALDEX manual modular 20, 1-11. 44 Goldberger, C., Guelfi, J.D., Sheehan, D.V., 2011. Assessment of Anxiety in Clinical Trials with Depressed Patients Using the Hamilton Depression Rating Scale. Psychopharmacol Bull 44(3), 34-50. 45 Huang, C.B., Alimova, Y., Myers, T.M., Ebersole, J.L., 2011. Short- and medium-chain fatty acids exhibit antimicrobial activity for oral microorganisms. Arch Oral Biol 56(7), 650-654. 46 Huang, S.Y., Lin, W.W., Ko, H.C., Lee, J.F., Wang, T.J., Chou, Y.H., et al., 2004. Possible interaction of alcohol dehydrogenase and aldehyde dehydrogenase genes with the dopamine D2 receptor gene in anxiety-depressive alcohol dependence. Alcohol Clin Exp Res 28(3), 374-384. 47 Jain, M., Olsen, H.E., Paten, B., Akeson, M., 2016. The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community. Genome Biol 17(1), 239. 48 Javurek, A.B., Spollen, W.G., Johnson, S.A., Bivens, N.J., Bromert, K.H., Givan, S.A., et al., 2017. Consumption of a high-fat diet alters the seminal fluid and gut microbiomes in male mice. Reprod Fertil Dev 29(8), 1602-1612. 49 Jiang, H., Ling, Z., Zhang, Y., Mao, H., Ma, Z., Yin, Y., et al., 2015. Altered fecal microbiota composition in patients with major depressive disorder. Brain Behav Immun 48, 186-194. 50 Jiménez, E., Fernández, L., Marín, M.L., Martín, R., Odriozola, J.M., Nueno-Palop, C., et al., 2005. Isolation of commensal bacteria from umbilical cord blood of healthy neonates born by cesarean section. Curr Microbiol 51(4), 270-274. 51 Judd, L.L., 1997. The clinical course of unipolar major depressive disorders. Arch Gen Psychiatry 54(11), 989-991. 52 Kelly, J.R., Borre, Y., C, O.B., Patterson, E., El Aidy, S., Deane, J., et al., 2016. Transferring the blues: Depression-associated gut microbiota induces neurobehavioural changes in the rat. J Psychiatr Res 82, 109-118. 53 Keylock, C., 2005. Simpson diversity and the Shannon–Wiener index as special cases of a generalized entropy. Oikos 109(1), 203-207. 54 Khambadkone, S.G., Cordner, Z.A., Dickerson, F., Severance, E.G., Prandovszky, E., Pletnikov, M., et al., 2018. Nitrated meat products are associated with mania in humans and altered behavior and brain gene expression in rats. Mol Psychiatry. 55 Kim, K.A., Gu, W., Lee, I.A., Joh, E.H., Kim, D.H., 2012. High fat diet-induced gut microbiota exacerbates inflammation and obesity in mice via the TLR4 signaling pathway. PloS one 7(10), e47713. 56 Kirk, R.G., 2012. ' Life in a Germ-Free World':: Isolating Life from the Laboratory Animal to the Bubble Boy. Bull Hist Med 86(2), 237-275. 57 Kishi, T., Kitajima, T., Ikeda, M., Yamanouchi, Y., Kinoshita, Y., Kawashima, K., et al., 2009. Association study of clock gene (CLOCK) and schizophrenia and mood disorders in the Japanese population. Eur Arch Psy Clin N 259(5), 293. 58 Kongsakon, R., Bhatanaprabhabhan, D., 2005. Validity and reliability of the Young Mania Rating Scale: Thai version. J Med Assoc Thai 88(11), 1598-1604. 59 Kuo, P.H., Chung, Y.C.E., 2018. Moody microbiome: Challenges and chances. J Formos Med Assoc 118, S42-S54. 60 Lang, U.E., Borgwardt, S., 2013. Molecular mechanisms of depression: perspectives on new treatment strategies. Cell Physiol Biochem 31(6), 761-777. 61 Langille, M.G., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., et al., 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol 31(9), 814-821. 62 Lee, M.S., Pan, W.H., Liu, K.L., Yu, M.S., 2006. Reproducibility and validity of a Chinese food frequency questionnaire used in Taiwan. Asia Pac J Clin Nutr 15(2), 161-169. 63 Lee, S.H., Ripke, S., Neale, B.M., Faraone, S.V., Purcell, S.M., Perlis, R.H., et al., 2013. Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs. Nat Genet 45(9), 984-994. 64 Leone, V., Gibbons, S.M., Martinez, K., Hutchison, A.L., Huang, E.Y., Cham, C.M., et al., 2015. Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism. Cell Host Microbe 17(5), 681-689. 65 Lewis, S., Heaton, K., 1997. Stool form scale as a useful guide to intestinal transit time. Scand J Gastroenterol 32(9), 920-924. 66 Li, Y., Hao, Y., Fan, F., Zhang, B., 2018. The Role of Microbiome in Insomnia, Circadian Disturbance and Depression. Front Psychiatry 9, 669. 67 Lin, P., Ding, B., Feng, C., Yin, S., Zhang, T., Qi, X., et al., 2017. Prevotella and Klebsiella proportions in fecal microbial communities are potential characteristic parameters for patients with major depressive disorder. J Affect Disord 207, 300-304. 68 Liskiewicz, P., Pelka-Wysiecka, J., Kaczmarczyk, M., Loniewski, I., Wronski, M., Baba-Kubis, A., et al., 2019. Fecal Microbiota Analysis in Patients Going through a Depressive Episode during Treatment in a Psychiatric Hospital Setting. J Clin Med 8(2), 164. 69 Liu, Y., Zhang, L., Wang, X., Wang, Z., Zhang, J., Jiang, R., et al., 2016. Similar Fecal Microbiota Signatures in Patients With Diarrhea-Predominant Irritable Bowel Syndrome and Patients With Depression. Clin Gastroenterol Hepatol 14(11), 1602-1611 e1605. 70 Lloyd-Price, J., Arze, C., Ananthakrishnan, A.N., Schirmer, M., Avila-Pacheco, J., Poon, T.W., et al., 2019. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569(7758), 655-662. 71 Lohoff, F.W., 2010. Overview of the genetics of major depressive disorder. Current psychiatry reports 12(6), 539-546. 72 Lozupone, C.A., Knight, R., 2008. Species divergence and the measurement of microbial diversity. FEMS Microbiol Rev 32(4), 557-578. 73 Malla, M.A., Dubey, A., Kumar, A., Yadav, S., Hashem, A., Abd Allah, E.F., 2018. Exploring the Human Microbiome: The Potential Future Role of Next-Generation Sequencing in Disease Diagnosis and Treatment. Front Immunol 9, 2868. 74 Mandal, S., Van Treuren, W., White, R.A., Eggesbo, M., Knight, R., Peddada, S.D., 2015. Analysis of composition of microbiomes: a novel method for studying microbial composition. Microb Ecol Health Dis 26, 27663. 75 Marin-Valencia, I., Cho, S.K., Rakheja, D., Hatanpaa, K.J., Kapur, P., Mashimo, T., et al., 2012. Glucose metabolism via the pentose phosphate pathway, glycolysis and Krebs cycle in an orthotopic mouse model of human brain tumors. NMR Biomed 25(10), 1177-1186. 76 Martin, D., Rybicki, E., 2000. RDP: detection of recombination amongst aligned sequences. Bioinformatics 16(6), 562-563. 77 Maurer, M.H., Canis, M., Kuschinsky, W., Duelli, R., 2004. Correlation between local monocarboxylate transporter 1 (MCT1) and glucose transporter 1 (GLUT1) densities in the adult rat brain. Neurosci Lett 355(1), 105-108. 78 McDonald, D., Price, M.N., Goodrich, J., Nawrocki, E.P., DeSantis, T.Z., Probst, A., et al., 2012. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 6(3), 610. 79 Merikangas, K.R., Jin, R., He, J.P., Kessler, R.C., Lee, S., Sampson, N.A., et al., 2011. Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Arch Gen Psychiatry 68(3), 241-251. 80 Messaoudi, M., Lalonde, R., Violle, N., Javelot, H., Desor, D., Nejdi, A., et al., 2011. Assessment of psychotropic-like properties of a probiotic formulation (Lactobacillus helveticus R0052 and Bifidobacterium longum R0175) in rats and human subjects. Br J Nutr 105(5), 755-764. 81 Mitchell, P.B., Wilhelm, K., Parker, G., Austin, M.-P., Rutgers, P., Malhi, G.S., 2001. The clinical features of bipolar depression: a comparison with matched major depressive disorder patients. J Clin Psychiatry 62(3), 212–216. 82 Mitsou, E.K., Kakali, A., Antonopoulou, S., Mountzouris, K.C., Yannakoulia, M., Panagiotakos, D.B., et al., 2017. Adherence to the Mediterranean diet is associated with the gut microbiota pattern and gastrointestinal characteristics in an adult population. Br J Nutr 117(12), 1645-1655. 83 Nakayama, J., Watanabe, K., Jiang, J., Matsuda, K., Chao, S.H., Haryono, P., et al., 2015. Diversity in gut bacterial community of school-age children in Asia. Sci Rep 5, 8397. 84 Naseribafrouei, A., Hestad, K., Avershina, E., Sekelja, M., Linlokken, A., Wilson, R., et al., 2014. Correlation between the human fecal microbiota and depression. Neurogastroenterol Motil 26(8), 1155-1162. 85 Neufeld, K.M., Kang, N., Bienenstock, J., Foster, J.A., 2011. Reduced anxiety-like behavior and central neurochemical change in germ-free mice. Neurogastroenterol Motil 23(3), 255-264, e119. 86 Nguyen, T.L., Vieira-Silva, S., Liston, A., Raes, J., 2015. How informative is the mouse for human gut microbiota research? Dis Model Mech 8(1), 1-16. 87 Nishi, D., Su, K.P., Usuda, K., Pei-Chen Chang, J., Chiang, Y.J., Chen, H.T., et al., 2019. The Efficacy of Omega-3 Fatty Acids for Depressive Symptoms among Pregnant Women in Japan and Taiwan: A Randomized, Double-Blind, Placebo-Controlled Trial (SYNCHRO; NCT01948596). Psychother Psychosom 88(2), 122-124. 88 Nouwen, A., Winkley, K., Twisk, J., Lloyd, C.E., Peyrot, M., Ismail, K., et al., 2010. Type 2 diabetes mellitus as a risk factor for the onset of depression: a systematic review and meta-analysis. Diabetologia 53(12), 2480-2486. 89 O'Mahony, S.M., Marchesi, J.R., Scully, P., Codling, C., Ceolho, A.-M., Quigley, E.M., et al., 2009. Early life stress alters behavior, immunity, and microbiota in rats: implications for irritable bowel syndrome and psychiatric illnesses. Biol Psychiat 65(3), 263-267. 90 Ohayon, M.M., Roth, T., 2003. Place of chronic insomnia in the course of depressive and anxiety disorders. J Psychiatr Res 37(1), 9-15. 91 Palmer, A., 1999. The activity of the pentose phosphate pathway is increased in response to oxidative stress in Alzheimer's disease. J Neural Transm 106(3-4), 317-328. 92 Pan, W.H., Sommer, F., Falk-Paulsen, M., Ulas, T., Best, P., Fazio, A., et al., 2018. Exposure to the gut microbiota drives distinct methylome and transcriptome changes in intestinal epithelial cells during postnatal development. Genome Med 10(1), 27. 93 Pellerin, L., 2005. How astrocytes feed hungry neurons. Mol Neurobiol 32(1), 59-72. 94 Pellerin, L., Halestrap, A.P., Pierre, K., 2005. Cellular and subcellular distribution of monocarboxylate transporters in cultured brain cells and in the adult brain. Journal of neuroscience research 79(1‐2), 55-64. 95 Perl, A., Hanczko, R., Telarico, T., Oaks, Z., Landas, S., 2011. Oxidative stress, inflammation and carcinogenesis are controlled through the pentose phosphate pathway by transaldolase. Trends Mol Med 17(7), 395-403. 96 Poroyko, V.A., Carreras, A., Khalyfa, A., Khalyfa, A.A., Leone, V., Peris, E., et al., 2016. Chronic sleep disruption alters gut microbiota, induces systemic and adipose tissue inflammation and insulin resistance in mice. Sci Rep 6(1), 1-11. 97 Prakash, T., Taylor, T.D., 2012. Functional assignment of metagenomic data: challenges and applications. Brief Bioinform 13(6), 711-727. 98 Prideaux, L., Kang, S., Wagner, J., Buckley, M., Mahar, J.E., De Cruz, P., et al., 2013. Impact of ethnicity, geography, and disease on the microbiota in health and inflammatory bowel disease. Inflamm Bowel Dis 19(13), 2906-2918. 99 Pruitt, K.D., Tatusova, T., Maglott, D.R., 2007. NCBI reference sequences (RefSeq): a curated non-redundant sequence database of genomes, transcripts and proteins. Nucleic Acids Res 35(suppl_1), D61-D65. 100 Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., et al., 2012. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41(D1), D590-D596. 101 Rawdin, B., Mellon, S., Dhabhar, F., Epel, E., Puterman, E., Su, Y., et al., 2013. Dysregulated relationship of inflammation and oxidative stress in major depression. Brain Behav Immun 31, 143-152. 102 Resende, W.R., Valvassori, S.S., Réus, G.Z., Varela, R.B., Arent, C.O., Ribeiro, K.F., et al., 2013. Effects of sodium butyrate in animal models of mania and depression: implications as a new mood stabilizer. Behav Pharmacol 24(7), 569-579. 103 Rhoads, A., Au, K.F., 2015. PacBio sequencing and its applications. Genom Proteom Bioinf 13(5), 278-289. 104 Rong, H., Xie, X.H., Zhao, J., Lai, W.T., Wang, M.B., Xu, D., et al., 2019. Similarly in depression, nuances of gut microbiota: Evidences from a shotgun metagenomics sequencing study on major depressive disorder versus bipolar disorder with current major depressive episode patients. J Psychiatr Res 113, 90-99. 105 Rush, A.J., Trivedi, M.H., Wisniewski, S.R., Nierenberg, A.A., Stewart, J.W., Warden, D., et al., 2006. Acute and longer-term outcomes in depressed outpatients requiring one or several treatment steps: a STAR*D report. Am J Psychiatry 163(11), 1905-1917. 106 Sartorius, N., Üstün, T.B., Lecrubier, Y., Wittchen, H.-U., 1996. Depression comorbid with anxiety: Results from the WHO study on' Psychological disorders in primary health care.'. Br J Psychiatry. 107 Sartorius, N., Ustun, T.B., Lecrubier, Y., Wittchen, H.U., 1996. Depression comorbid with anxiety: results from the WHO study on psychological disorders in primary health care. Br J Psychiatry Suppl(30), 38-43. 108 Sender, R., Fuchs, S., Milo, R., 2016. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol 14(8), e1002533. 109 Serretti, A., Benedetti, F., Mandelli, L., Lorenzi, C., Pirovano, A., Colombo, C., et al., 2003. Genetic dissection of psychopathological symptoms: insomnia in mood disorders and CLOCK gene polymorphism. Am J Med Genet B 121(1), 35-38. 110 Styczen, K., Sowa-Kucma, M., Dudek, D., Siwek, M., Reczynski, W., Szewczyk, B., et al., 2018. Zinc and copper concentration do not differentiate bipolar disorder from major depressive disorder. Psychiatr Pol 52(3), 449-457. 111 Sudo, N., Chida, Y., Aiba, Y., Sonoda, J., Oyama, N., Yu, X.N., et al., 2004. Postnatal microbial colonization programs the hypothalamic–pituitary–adrenal system for stress response in mice. J Physiol 558(1), 263-275. 112 Tillmann, S., Abildgaard, A., Winther, G., Wegener, G., 2019. Altered fecal microbiota composition in the Flinders sensitive line rat model of depression. Psychopharmacology 236(5), 1445-1457. 113 Tremaroli, V., Bäckhed, F., 2012. Functional interactions between the gut microbiota and host metabolism. Nature 489(7415), 242. 114 Valles-Colomer, M., Falony, G., Darzi, Y., Tigchelaar, E.F., Wang, J., Tito, R.Y., et al., 2019. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat Microbiol 4(4), 623-632. 115 Vatanen, T., Kostic, A.D., d'Hennezel, E., Siljander, H., Franzosa, E.A., Yassour, M., et al., 2016. Variation in Microbiome LPS Immunogenicity Contributes to Autoimmunity in Humans. Cell 165(4), 842-853. 116 Vich Vila, A., Collij, V., Sanna, S., Sinha, T., Imhann, F., Bourgonje, A.R., et al., 2020. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota. Nat Commun 11(1), 362. 117 Vigo, D., Thornicroft, G., Atun, R., 2016. Estimating the true global burden of mental illness. Lancet Psychiat 3(2), 171-178. 118 Vinolo, M.A., Rodrigues, H.G., Nachbar, R.T., Curi, R., 2011. Regulation of inflammation by short chain fatty acids. Nutrients 3(10), 858-876. 119 Vohringer, P.A., Perlis, R.H., 2016. Discriminating Between Bipolar Disorder and Major Depressive Disorder. Psychiatr Clin North Am 39(1), 1-10. 120 Walker, A.W., Ince, J., Duncan, S.H., Webster, L.M., Holtrop, G., Ze, X., et al., 2011. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. ISME J 5(2), 220. 121 Wang, X., Li, H., Bezemer, T.M., Hao, Z., 2015. Drivers of bacterial beta diversity in two temperate forests. Ecol Res 31(1), 57-64. 122 Whiteford, H.A., Degenhardt, L., Rehm, J., Baxter, A.J., Ferrari, A.J., Erskine, H.E., et al., 2013. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. The Lancet 382(9904), 1575-1586. 123 Whittaker, R.H., 1972. Evolution and measurement of species diversity. Taxon 21(2-3), 213-251. 124 World Health Organization, 2017. Depression and other common mental disorders: global health estimates. 125 Wray, N., Pergadia, M., Blackwood, D., Penninx, B., Gordon, S., Nyholt, D., et al., 2012. Genome-wide association study of major depressive disorder: new results, meta-analysis, and lessons learned. Mol Psychiatry 17(1), 36-48. 126 Xia, Y., Sun, J., 2017. Hypothesis Testing and Statistical Analysis of Microbiome. Genes Dis 4(3), 138-148. 127 Xu, J., Mahowald, M.A., Ley, R.E., Lozupone, C.A., Hamady, M., Martens, E.C., et al., 2007. Evolution of symbiotic bacteria in the distal human intestine. Plos Biol 5(7). 128 Young, R.C., Biggs, J.T., Ziegler, V.E., Meyer, D.A., 1978. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry 133, 429-435. 129 Zhang, C., Zhang, M., Wang, S., Han, R., Cao, Y., Hua, W., et al., 2010. Interactions between gut microbiota, host genetics and diet relevant to development of metabolic syndromes in mice. ISME J 4(2), 232-241. 130 Zhang, J., Kobert, K., Flouri, T., Stamatakis, A., 2014. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30(5), 614-620. 131 Zheng, P., Zeng, B., Zhou, C., Liu, M., Fang, Z., Xu, X., et al., 2016. Gut microbiome remodeling induces depressive-like behaviors through a pathway mediated by the host's metabolism. Mol Psychiatry 21(6), 786-796. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/63572 | - |
dc.description.abstract | 情感性疾患主要包含重度憂鬱症以及雙極性疾患。兩種疾患在所有疾病負擔中佔有很大的比例。高疾病負擔部分來自情感性疾患患者症狀無法依賴現有藥物達到有效的緩解,部分來自於疾病反覆發作的特性,以致病患在生活上嚴重的失能。至今,我們仍對疾病的制病機制了解有限,為了降低疾病負擔,找出疾病致病機制對疾病的在治療以及介入預防相當重要。
許多的證據顯示微生物相-腸-腦軸影響著憂鬱情緒的調控,不管是微生物相多樣性的差異、各個分類學層級發現的微生物相標的或是微生物相的功能。過去幾篇文獻在比較重度憂鬱症患者與健康對照組時,找到數個與憂鬱相關的微生物相標的,然而這些研究並沒有很完善的控制飲食或者其他干擾因子,導致找到的微生物相標的可能受到干擾因子影響。此外雙極性疾患也會表現出憂鬱的症狀,過去研究表示重度憂鬱症與雙極性疾患不管在基因抑或是其他生物性的指標有著相似性,因此有些研究開始了解雙極性疾患內憂鬱與微生物相的相關性,目前已分別在重度憂鬱症以及雙極性疾患內發現數個與憂鬱相關的微生物相標的,然而仍缺乏比較重度憂鬱症與雙極性疾患共享的微生物相標的以及他們的微生物相功能。過去的文獻多利用病例對照研究找到與疾病相關的微生物相標的,疾病相關的微生物相標的可能參與疾病的制病機轉,然而作為生物標記而言,仍有侷限性,因此我們額外想像,究竟微生物相標的是否可能作為憂鬱嚴重度的標記,並且隨著疾病嚴重度不同改變,同時反映其他與憂鬱相關的特徵,例如:焦慮或壓力感知的程度。 結合上述,本研究有五個研究目標:(1) 在考慮到飲食以及其他干擾因子的影響,釐清重度憂鬱症與雙極性疾患各自與憂鬱相關的微生物相標的;(2) 比較重度憂鬱症與雙極性疾患內與憂鬱相關的微生物相標的是否共享;(3) 透過兩種研究設計探討微生物相是否與疾病嚴重度,以及疾病急性緩解狀態有顯著相關;(4) 探討前述找到的憂鬱標的是否額外與憂鬱相關的焦慮情緒、壓力感知程度的表現相關;(5) 利用現有微生物相功能分析軟體,剖析在重度憂鬱症以及雙極性疾患的微生物相功能。 本論文分為三個部分,第一部分利用病例對照設計探討重度憂鬱症內與憂鬱相關的微生物相標的;討論與憂鬱、焦慮以及壓力感知程度的相關性;以及剖析重度憂鬱症相關的微生物相功能。第二部分與第一部分呼應,同樣使用病例對照設計,在雙極性疾患患者內探討上述描述的標的、相關性以及功能,並且額外探討重度憂鬱症以及雙極性疾患是否有共享的微生物相標的以及功能。第三部分則採用追蹤研究設計,收集重度憂鬱症以及雙極性疾患患患者憂鬱急性發作以及緩解兩個時間點的資料,深入探討與憂鬱嚴重程度相關的微生物相標的。 本研究考量微生物相的干擾因子,排除兩個月內有服用益生菌或抗生素、進行腸胃道手術以及腸胃道感染的受試者。第一部分共收集36位重度憂鬱症患者以及37位健康對照組;第二部分共收集33位雙極性疾患、47位重度憂鬱症以及53位健康受試者,並且包含第一部分的27位重度憂鬱症患者,以及26位健康受試者;第三部分則額外收集11位憂鬱急性發作的情感性疾患患者。第一、二部分,每位受試者填寫貝氏憂鬱量表收集過去兩周的憂鬱情形,以及填寫壓力感知問卷以及貝氏焦慮量表。此外也訪問飲食頻率問卷並且收集糞便檢體。糞便檢體經過DNA萃取後進行16S核醣體定序 (16S ribosomal RNA gene sequencing)。在微生物相的分析,我們利用QIIME軟體 (Quantitative Insights Into Microbial Ecology),將定序後的序列進行微生物分類。分析策略中,我們利用ANCOM (Analysis of composition of microbiomes),在校正飲食資訊以及定序資訊後找到與重度憂鬱症相關之標的,再利用斯皮爾曼等級相關係數探討微生物相標的與焦慮、感知壓力以及憂鬱嚴重度的相關性,最後使用PICRUST軟體 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) 分析與憂鬱相關的功能。 第三部分,我們於受試者急性發作與緩解兩個時間點,使用漢氏憂鬱量表 (HAMD-17) 評估兩個時間點憂鬱症狀的嚴重程度,並且定序微生物相組成,分析憂鬱嚴重度相關的微生物相標的。此外我們將憂鬱症狀拆解成六個因子,利用斯皮爾曼等級相關係數釐清微生物相與憂鬱因子群的相關性。 比較重度憂鬱症患者與健康對照組後,發現12個與憂鬱相關的屬,而在比較雙極性疾患患者與健康對照組後,則發現11個與憂鬱相關的屬,其中巨單胞菌屬 (Megamonas)、普雷沃氏菌屬 (Prevotella)、布勞特氏菌屬 (Blautia) 以及薩特氏菌屬 (Sutterella) 皆在兩部分的比較中發現。額外將雙極性疾患患者合併重度憂鬱症患者與健康對照組比較,發現除了上述四個屬外,還有腸球菌屬 (Enterococcus)、小桿菌屬 (Dialister)、顫螺菌屬 (Oscillospira) 以及脫硫弧菌科 (Desulfovibrionaceae) 內一個無法分類的屬。與憂鬱、焦慮以及壓力感知程度進行相關性分析,在第一、二部分皆可發現薩特氏菌屬與憂鬱和壓力感知程度呈現顯著的負相關。第三部分,比較病患於急性與緩解的微生物相組成顯示有兩個屬別有顯著不同,分別為柔嫩梭菌屬 (Faecalibacterium) 以及鏈球菌屬 (Streptococcus),與憂鬱的六個因子進行相關性分析,柔嫩梭菌與¬失眠有高度的正相關。從PICRUST結果來看,我們也觀察到躁鬱症患者、重度憂鬱症患者與健康對照組的細菌組成在代謝相關的功能有顯著不同。 本研究顯示腸道微生物相標的不僅與情感性疾患相關,也與憂鬱嚴重程度相關,兩個部分找到的菌相標的不同,顯示致病與嚴重度牽涉的制病機轉可能不同。由於微生物相與憂鬱特徵的異質性皆高,因此未來需收集更大的樣本,並且結合更精密的霰彈槍定序法 (Shotgun sequencing) 、代謝體學抑或是動物實驗模型,找出比屬更精確的分類,如:種或者株,研究細菌的功能在憂鬱特徵的致病或者是嚴重度機轉中扮演的角色。 | zh_TW |
dc.description.abstract | Major depressive disorder (MDD) and bipolar disorder (BPD) are two major categories in affective disorder and obtain substantial disease burden. The high disease burden originated from the current situation that patients are not efficiently responding to the treatment regimen and the characteristics of repeated recurrence during the disease course. At present, we have limited knowledge regarding the etiology of affective disorder. In order to lower the disease burden, it is thus crucial to discover the disease mechanisms to prevent and treat the disease.
Growing evidence suggests the regulation of microbiota-gut-brain axis in depressive mood. Previous studies revealed several microbiota targets related to depression by comparing dozens of MDD patients and healthy controls. However, these studies lack of reasonable control of the confounding factors. The targets revealed previously may be confounded. BPD also exhibits the depressive feature, where accumulating evidence showed the similarity between MDD and BPD in genetic correlation or other biology functions. Currently, several studies demonstrated the microbiota targets in depressive BPD patients. However, the comparisons between the microbiota targets in MDD and BPD are still limited. Additionally, with growing studies discovered the disease-related targets, another question emerges if the depressive severity and other mood-related traits, for instance, anxiety and perceived stress level, also associated with microbiota. There are five aims in the current research: (1) Explore microbiota targets while consider the confounding factors in patients with affective disorders; (2) Discover if MDD and BPD share common microbiota targets; (3) Discuss if the microbiota targets correlate with depressive severity via two study designs; (4) Explore if the microbiota targets correlate with mood-related traits; (5) Apply putative functional annotative software to analyze the microbiota functions in MDD and BPD. The current research has been separated into three parts. In part I, we apply a case-control design in 36 MDD and 37 healthy controls. After the DNA extraction, 16S ribosomal RNA gene sequencing was applied to obtain microbiota composition in the subjects. QIIME served as a vital tool to conduct the taxonomy classification. ANCOM was applied to discover the microbiota targets while adjusting for diet and sequencing information. The correlations between microbiota targets and symptom severities was conducted via Spearman's rank correlation. Finally, PICRUST was used to inference the microbial functions. In part II, a case-control design was also applied in 33 BPD patients, 47 MDD patients and 53 healthy controls and we also conducted the analysis mentioned above. We further compared if MDD and BPD share common targets via comparing microbiota composition between patients with affective disorder and healthy controls. In part III, a follow-up designed was applied. We collected the information from 11 patients with affective disorders during the acute phase and remission phase. The depressive severity was assessed via HAMD-17. ANCOM and Spearman's rank correlation was applied to discover depressive severity-related targets and the correlations with depressive symptoms. In the current study, 12 genera were discovered comparing MDD and controls, while 11 genera were explored comparing BPD and controls. Four genera, including Megamonas, Prevotella, Blautia, and Sutterella consistently showed up. After comparing microbiota composition between affective disorders and control, the 4 targets remain significant while the other 4 genera (Enterococcus, Dialister, Oscillospira, and an unclassified genus in Desulfovibrionaceae) emerge. For the correlation analysis, genus Sutterella consistently revealed negative correlations with depressive and perceived stress levels. While in part III, two genera (Faecalibacterium and Streptococcus) exhibited differential abundance in the acute and remission phase. Genus Faecalibacterium further exhibited a strong positive correlation with HAMD factor 1- insomnia. For the functional pathway analysis in PICRUST, the results revealed that pathways related to metabolism or biosynthesis process were different between depressive BPD, MDD and control groups. The current research suggested the involvement of microbiota in the depression mechanism and the depressive severity mechanism. Both depression and microbiota composition contain very high inter-individual variations; therefore, the future study requires a larger sample size to discover robust microbiota targets related to depression. Additionally, if we combine whole-genome shotgun sequencing, the metabolomics, and animal models might further shed light on the mechanism of microbiota targets related to depressive diagnosis and depressive severity. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T17:14:11Z (GMT). No. of bitstreams: 1 ntu-109-F02849026-1.pdf: 3601758 bytes, checksum: dc52b1f115ab8155e193288fd2910ac7 (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | 中文摘要 IV
Abstract VII List of Figures XIV List of Tables XV 1 General Background 1 1.1 Affective Disorder 1 1.1.1 The Global Burden Disease 1 1.1.2 Diagnosis Criteria 1 1.1.3 Methodologies of Symptom Assessments 3 1.1.4 Possible Mechanisms 4 1.2 Microbiota 5 1.2.1 Introduction of Microbiota 5 1.2.2 Factors Influencing Microbiota 6 1.2.3 Methodologies of Microbiota 7 1.2.3.1 Germ-Free (GF) Models 7 1.2.3.2 Fecal Microbiota Transplantation (FMT) 8 1.2.3.3 Next-Generation Sequencing (NGS) Technology 8 1.2.4 Bioinformatics Analysis of Sequence Data 9 1.2.4.1 Bioinformatics Process Management 10 1.2.4.2 16S rRNA Gene Reference Databases 11 1.2.4.3 Functional Annotations 12 1.2.5 Statistical Strategies in Microbiota Studies 13 1.2.5.1 Diversity Indexes 13 1.2.5.2 Differential Abundance Analysis 15 1.3 Microbiota-Gut-Brain Axis 16 1.3.1 Introduction 16 2 Aims 17 3 Project I: Exploration of Microbiota Targets for Major Depressive Disorder and Mood Related Traits 18 3.1 Background 18 3.2 Materials and Methods 21 3.2.1 Subject Recruitment 21 3.2.2 Mood Assessment 22 3.2.3 Dietary Assessment 22 3.2.4 Bio-Sample Collection and Stool DNA Extraction 23 3.2.5 16S Ribosomal RNA Gene Sequencing 24 3.2.6 Statistical Analysis 25 3.3 Results 27 3.3.1 Demographic Characteristics 27 3.3.2 Microbiota Composition Between MDD Patients and Controls 27 3.3.3 Correlations of Microbiota Abundance and Clinical Characteristics 28 3.3.4 Functional Analysis 28 3.4 Discussion 29 4 Project 2: Characterizing Microbiota Profiles Related to Depression Among Patients With Affective Disorders 34 4.1 Background 34 4.2 Materials and Methods 38 4.2.1 Subject Recruitment 38 4.2.2 Mood Assessment 38 4.2.3 Dietary Assessment 39 4.2.4 Stool Assessmsent 39 4.2.5 Bio-Sample Collection and Stool DNA extraction 39 4.2.6 16S rRNA gene sequencing 40 4.2.7 Bioinformatics Process 40 4.2.8 Statistical Analysis 41 4.3 Results 42 4.3.1 Demographic, Clinical and Dietary Characteristics 42 4.3.2 Microbiota Composition Between Affective patients and Controls 43 4.3.3 Correlations of Microbiota Abundance and Mood-Related Traits 44 4.3.4 The Association Between Psychiatric Drug Usage and Microbiota 44 4.3.5 The functional pathways between BPD, MDD and controls. 45 4.4 Discussion 45 5 Project 3: The Preliminary Follow-up Study of Microbiota Profiles in Affective Disorders During Acute and Remitted States 48 5.1 Background 49 5.2 Materials and methods 50 5.2.1 Subject Recruitment 50 5.2.2 Mood Assessment 50 5.2.3 Dietary Assessment 51 5.2.4 Bio-Sample Collection and Stool DNA Extraction 51 5.2.5 16S rRNA Gene Sequencing 51 5.2.6 Bioinformatics Process 52 5.2.7 Statistical Analysis 52 5.3 Results 53 5.3.1 Demographic Characteristics of Subjects 53 5.3.2 Microbiota Composition Differences in Acute and Remission Phases 54 5.3.3 The Correlation Between Microbial Candidates and Depressive Symptoms 54 5.4 Discussion 54 6 Conclusion and overall discussion 57 6.1 Conclusion 57 6.2 Overall Discussion 58 7 References 60 | |
dc.language.iso | zh-TW | |
dc.title | 探討情緒障礙之憂鬱特徵與腸道微生物相的相關性 | zh_TW |
dc.title | Exploration of the Relationship Between Depressive Features and Gut Microbiota in Affective Disorders | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 博士 | |
dc.contributor.coadvisor | 施惟量(Wei-Liang Shih) | |
dc.contributor.oralexamcommittee | 陳為堅(Wei J. Chen),倪衍玄(Yen-Hsuan Ni),高承源(Cheng-Yuan Kao),藍祚鴻(Tsuo-Hung Lan) | |
dc.subject.keyword | 微生物相-腸-腦軸,情緒障礙疾患,憂鬱特徵,腸道微生物相,16S核醣體定序,焦慮,壓力感知程度, | zh_TW |
dc.subject.keyword | Microbiota-gut-brain axis,Affective disorders,Depressive feature,Gut microbiota,16S rRNA gene sequencing,Anxiety,Perceived stress level, | en |
dc.relation.page | 104 | |
dc.identifier.doi | 10.6342/NTU202000740 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2020-04-10 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
顯示於系所單位: | 流行病學與預防醫學研究所 |
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