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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 張孟基 | |
dc.contributor.author | Yu-Yuan Chen | en |
dc.contributor.author | 陳鈺元 | zh_TW |
dc.date.accessioned | 2021-06-15T13:42:36Z | - |
dc.date.available | 2018-02-19 | |
dc.date.copyright | 2016-02-19 | |
dc.date.issued | 2015 | |
dc.date.submitted | 2015-12-28 | |
dc.identifier.citation | Amrine K.C., Blanco-Ulate B., Cantu D. (2015) Discovery of core biotic stress responsive genes in Arabidopsis by weighted gene co-expression network analysis. PLoS ONE, 10: e0118731.
Anuradha S., and Rao S. S. R. (2003) Application of brassinosteroids to rice seeds (Oryza sativa L.) reduced the impact of salt stress on growth, prevented photosynthetic pigment loss and increased nitrate reductase activity. Plant Growth Regulation, 40: 29-32. Barabási A.L.& Oltvai Z. N. (2004) Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5: 101-113. Basnet R.K., Moreno-Pachon N., Lin K., Bucher J., Visser R.G., Maliepaard C., Bonnema G. (2013) Genome-wide analysis of coordinated transcript abundance during seed development in different Brassica rapa morphotypes. BMC Genomics, 14:840. Cheng S., Huang Y., Zhu N., Zhao Y. (2014) The rice WUSCHEL-related homeobox genes are involved in reproductive organ development, hormone signaling and abiotic stress response. Gene, 549: 266–274. Chandler P.M., Robertson, M. (1994) Gene expression regulated by abscisic acid and its relation to stress tolerance. Annu Rev Plant Biol., 45: 113–141. Childs K.L., Davidson R.M., Buell C.R. (2011) Gene coexpression network analysis as a source of functional annotation for rice genes. PLoS ONE, 6: e22196. Choi J., Huh SU, Kojima M, Sakakibara H, Paek K-H, Hwang I (2010). The cytokinin-activated transcription factor ARR2 promotes plant immunity via TGA3/NPR1-dependent salicylic acid signaling in arabidopsis. Developmental Cell, 19: 284–295. Cho HY, Lee C, Hwang SG, Park YC, Lim HL, Jang CS. (2014) Overexpression of the OsChI1 gene, encoding a putative laccase precursor, increases tolerance to drought and salinity stress in transgenic Arabidopsis. Gene, 552: 98-105. Clarke C., Madden S. F., Doolan P., Aherne S. T., Joyce H., Lorraine O’D., Gallagher W. M., Hennessy B. T., Moriarty M., Crown J., Kennedy S. and Clynes M. (2013) Correlating transcriptional networks to breast cancer survival: a large-scale coexpression analysis. Carcinogenesis, 34: 2300–2308. Clouse, S.D.; Sasse J.M. (1998). Brassinosteroids: Essential regulators of plant growth and development. Annu Rev Plant Physiol Plant Mol Biol, 49: 427–51. Coneva V., Simopoulos C., Casaretto J.A., El-kereamy A., Guevara D.R., Cohn J., Zhu T., Guo L., Alexander D.C., Bi Y.M., McNicholas P.D. and Rothstein S.J. (2014) Metabolic and co-expression network-based analyses associated with nitrate response in rice. BMC Genomics, 15:1056. Dai M.Q., Hu Y.F., Zhao Y., Liu H.F., Zhou D.X. (2007) A WUSCHEL-like homeobox gene represses a YABBY gene expression required for rice leaf development. Plant Physiol, 144:380–390. Deinlein U., Stephan A.B., Horie T., Luo W., Xu G., Schroeder J.I. (2014) Plant salt-tolerance mechanisms. Trends Plant Sci, 19:371-9. Downs G.S., Bi Y.M., Colasanti J., Wu W., Chen X., Zhu T., Rothstein S.J., Lukens L.N. (2013) A developmental transcriptional network for maize defines coexpression modules. Plant Physiol, 161:1830-43. Du H., Liu H., Xiong L. (2013) Endogenous auxin and jasmonic acid levels are differentially modulated by abiotic stresses in rice. Front Plant Sci, 4:397. Duan L., Dietrich D., Ng C.H., Chan P.M., Bhalerao R., Bennett M.J., Dinneny J.R. (2013). Endodermal ABA Signaling Promotes Lateral Root Quiescence during Salt Stress in Arabidopsis Seedlings. The Plant Cell, 25: 324–341. Ficklin S.P., Luo F., Feltus F.A. (2010) The association of multiple interacting genes with specific phenotypes in rice using gene coexpression networks. Plant Physiol, 154:13-24. Golldack D., Li C., Mohan H., Probst N. (2014) Tolerance to drought and salt stress in plants: Unraveling the signaling networks. Front Plant Sci, 5:151. Großkinsky D.K., Naseem M., Abdelmohsen U.R., Plickert N., Engelke T., Griebel T., Zeier J., Novák O., Strnad M., Pfeifhofer H., van der Graaff E., Simon U., Roitsch T. (2011) Cytokinins mediate resistance against Pseudomonas syringae in tobacco through increased antimicrobial phytoalexin synthesis independent of salicylic acid signaling. Plant Physiol, 157:815-30. Gururani M.A., Mohanta T.K., Bae H. (2015) Current Understanding of the Interplay between Phytohormones and Photosynthesis under Environmental Stress. Int J Mol Sci, 16:19055-85. Ha S., Vankova R., Yamaguchi-Shinozaki K., Shinozaki K., Tran L.S. (2012) Cytokinins: metabolism and function in plant adaptation to environmental stresses. Trends Plant Sci, 17:172-9. Hayat S., Ahmad A. (2007) Salicylic acid - A Plant Hormone. Springer. Hohm T., Preuten T., Fankhauser C. (2013) Phototropism: translating light into directional growth. Am J Bot. 100:47-59. Hu H., You J., Fang Y., Zhu X., Qi Z., Xiong L. (2008) Characterization of transcription factor gene SNAC2 conferring cold and salt tolerance in rice. Plant Mol Biol, 67:169-81. Kazan K. (2015) Diverse roles of jasmonates and ethylene in abiotic stress tolerance. Trends Plant Sci, 20:219-29. Kim Y.S., Lee M., Lee J.H., Lee H.J., Park C.M. (2015) The unified ICE-CBF pathway provides a transcriptional feedback control of freezing tolerance during cold acclimation in Arabidopsis. Plant Mol Biol, 89:187-201. Leal LG, López C, López-Kleine L. (2014) Construction and comparison of gene co-expression networks shows complex plant immune responses. PeerJ, 2: e610. MacLennan N.K., Dong J., Aten J.E., Horvath S., Rahib L., Ornelas L., Dipple K.M., McCabe E.R. (2009) Weighted gene co-expression network analysis identifies biomarkers in glycerol kinase deficient mice. Mol Genet Metab., 98:203-14. Moctezuma E. (2003) The peanut gynophore: a developmental and physiological perspective. Can J Bot, 81:183–190. Moctezuma E., Feldman L.J. (1999) The role of amyloplasts during gravity perception in gynophores of the peanut plant (Arachis hypogaea). Ann Bot, 84:709–714. Pires I.S., Negrão S., Oliveira M.M., Purugganan M.D. (2015) Comprehensive phenotypic analysis of rice (Oryza sativa) response to salinity stress. Physiol Plant., 155:43-54. Priest H.D., Fox S.E., Rowley E.R., Murray J.R., Michael T.P., Mockler T.C. (2014) Analysis of global gene expression in Brachypodium distachyon reveals extensive network plasticity in response to abiotic stress. PLoS ONE, 9: e87499. Renata P. da C., Sperotto R.A., Cargnelutti D., Adamski J.M., FreitasTerra T de and Fett J P. (2013) Avoiding damage and achieving cold tolerance in rice plants. Food and Energy Security, 2: 96–119. Sato Y., Takehisa H., Kamatsuki K., Minami H., Namiki N., Ikawa H., Ohyanagi H., Sugimoto K., Antonio B.A., Nagamura Y. (2013) RiceXPro version 3.0: expanding the informatics resource for rice transcriptome. Nucleic Acids Res 41 (Database issue): D1206-13. Sato Y., Namiki N., Takehisa H., Kamatsuki K., Minami H., Ikawa H., Ohyanagi H., Sugimoto K., Itoh J., Antonio B.A., Nagamura Y. (2013) RiceFREND: a platform for retrieving coexpressed gene networks in rice. Nucleic Acids Res 41 (Database issue): D1214-21. Shaik R., Ramakrishna W. (2013) Genes and co-expression modules common to drought and bacterial stress responses in Arabidopsis and rice. PLoS ONE, 8: e77261. Sharma P., Bhardwaj, R. (2007) Effects of 24-Epibrassinolide on growth and metal uptake in Brassica juncea L. under copper metal stress. Acta Physiologiae Plantarum. 2007, 29: 259–263. Steuer B., Thomas S; Heinrich P. F. (1988) The efficiency of water use in water stressed plants is increased due to ABA induced stomatal closure. Photosynthesis Research, 18: 327–336. Sun X., Xu L., Wang Y., Yu R., Zhu X., Luo X., Gong Y., Wang R., Limera C., Zhang K. and Liu L. (2015) Identification of novel and salt-responsive miRNAs to explore miRNA-mediated regulatory network of salt stress response in radish (Raphanus sativus L.). BMC Genomics 16:197. Usadel B., Obayashi T., Mutwil M., Giorgi F.M., Bassel G.W., Tanimoto M., Chow A., Steinhauser D., Persson S., Provart N.J. (2009) Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ, 32:1633-51. Verma S., Sinha R., Rahman M.H., Megha S., Deyholos M.K., Kav N.N.V. (2014) miRNA-Mediated posttranscriptional regulation of gene expression in ABR17-transgenic Arabidopsis thaliana under salt stress. Plant Mol Biol Rep, 32:1203–1218. Wang D, Pei K, Fu Y, Sun Z, Li S, Liu H, Tang K, Han B, Tao Y (2007) Genome-wide analysis of the auxin response factors (ARF) gene family in rice (Oryza sativa) Gene, 394 :13–24. Wang P., Li C., Li C., Zhao C., Xia H., Zhao S., Hou L., Gao C., Wan S., Wang S. (2015) Identification and expression dynamics of three WUSCHEL related homeobox 13 (WOX13) genes in peanut. Dev Genes Evol, 225:221–233 Wasternack C. (2007). Jasmonates: An update on biosynthesis, signal transduction and action in plant stress response, growth and development. Annals of Botany, 100: 681–697. Whippo C.W., Hangarter R.P. (2006) Phototropism: bending towards enlightenment. Plant Cell. 18: 1110–1119. Xia H., Zhao C., Hou L., Li A., Zhao S., Bi Y., An J., Zhao Y., Wan S., Wang J. (2013) Transcriptome profiling of peanut gynophores revealed global reprogramming of gene expression during early pod development in darkness. BMC Genomics 14: 517. Xie W., Huang J., Liu Y., Rao J., Luo D., He M. (2015) Exploring potential new floral organ morphogenesis genes of Arabidopsis thaliana using systems biology approach. Front Plant Sci., 13:829 Yamauchi, Y.; Ogawa M., Kuwahara A., Hanada A., Kamiya Y., Yamaguchi S. (2004). Activation of gibberellin biosynthesis and response pathways by low temperature during imbibition of Arabidopsis thaliana seeds. The Plant Cell, 16: 367–378. Yang, S. F., and Hoffman N. E. (1984) Ethylene biosynthesis and its regulation in higher plants. Ann Rev Plant Physiol, 35: 155–89. Yang Y. W., Chen H. C., Wei F. J., Liu L. Y., Chang M. C. (2015) Comparative Transcriptome Analysis of Shoots and Roots of TNG67 and TCN1 Rice Seedlings under Cold Stress and Following Subsequent Recovery: Insights into Metabolic Pathways, Phytohormones, and Transcription Factors. PLoS ONE, 10: e0131391. Zhang L., Yu S., Zuo K., Luo L., Tang K. (2012) Identification of gene modules associated with drought response in rice by network-based analysis. PLoS ONE 7: e33748 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/51644 | - |
dc.description.abstract | 水稻為世界主要糧食作物之一,供養全球逾半之人口數。近年來氣候變遷影響水稻之產量與質量,因此提升水稻非生物性逆境之耐受性相當重要。為了解非生性逆境下水稻的調控機制與植物荷爾蒙所扮演的角色,本研究利用實驗室現有之低溫與鹽處理水稻的微陣列基因表現數據,並由NCBI之GEO(Gene Expression Omnibus)資料庫下載不同植物荷爾蒙處理水稻的基因表現資料,利用加權性基因共表現網絡分析(Weighted gene co-expression network analysis)此統計套件,依每個基因的表現模式,建構基因群模組(Module construction)並且找出埠基因(Hub gene)。以藉此系統性分析之生物資訊的方法,瞭解可能影響水稻非生物性逆境耐受性以及植物荷爾蒙反應之基因群組與可能生物調控路徑及重要基因。本研究由荷爾蒙資料組與非生物性逆境資料組個別篩選出5743與3130個差異表現基因(Differentially expressed genes, DEGs),並分出七個與五個模組,依序為模組1至7與模組I至V,其中在非生物性逆境資料組中,找到含有243個基因之模組V在對低溫鈍感(TNG67)與敏感(TCN1)之兩水稻品種皆受到低溫誘導,且TCN1之基因群受到低溫而擾動的幅度較高。模組V經基因富集分析顯示與基因表達與RNA生合成作用相關。含有725個基因之模組II 與1094個基因之模組IV與受到鹽處理誘導,模組II之基因分群為轉錄調控與蛋白質胺基酸之磷酸化作用。模組IV之基因參與RNA之生合成與代謝作用。對照至荷爾蒙之模組分群,與模組5具有最多之相同基因富集分群,而模組5為在生長素處理下之擾動幅度最高。依富集分群篩選出OsARF14, OsHOX13 和 OsWOX13三個基因同時受到生長素與高鹽誘導,且與皆與基因表達相關,進而推測此三個基因可能調控水稻受到高鹽下之下游基因表現,且生長素可能是其中一個負責之植物荷爾蒙,與鹽害之耐受機制有關。 | zh_TW |
dc.description.abstract | Rice is one of the major cereal crop which provides the main calories for more than 50% of the world population. Since the global weather change, abiotic stresses severely affect and reduce the productivity and yield of rice. Thus, to understand the mechanism of rice abiotic stress tolerance is extremely important. Meanwhile, various phtohormones participate in the regulation of rice growth and development. In particular, phytohormones can cross talk with different abiotic stresses and affect the final survival rate of rice. Hence, to further dissect the abiotic stress tolerance mechanism in rice must accompany with the knowledge regards to how phytohormones regulate rice growth and development. To reach this purpose, we took the available microarray data sets of cold- and salinity-treatment and downloaded the microarray data sets of various phtohormones treatments of rice seedlings from NCE (GEO), then using Weighted Gene Co-expression Network Analysis (WGCNA) algorithm to identify co-expressed genes that are differentially expressed in response to low temperature, salt stresses and multiple phytohormones. We expected to use this bioinformatics approach with system biology to characterize the gene modules, possible biological processes and candidate genes that may involve in the abiotic stress tolerance of rice. From the genes expression data sets, first we identified 5743 and 3130 rice genes (differential expressed genes; DEGs) under cold/ salt treatments and phytohormone. After WGCNA analysis, 5 ( I to V) and 7 (1 to 7) modules were identified from cold/ or salt treatments and phytohormone data sets, respectively. We observed that module V consisting of 243 genes was significantly associated with cold response in both cold-tolerant (TNG 67) and cold-sensitive (TCN1) rice varieties. This module is enriched for genes involved in gene expression and RNA biosynthetic process. Two gene modules, II and IV, consisting of 725 and 1094 genes were significantly associated with salt response. Module II is enriched with genes involved in regulation of transcription and protein amino acid phosphorylation. Module IV is enriched with genes involved in RNA metabolic and biosynthetic process. In general, salt-responsive gene modules are shown with enrichments for genes in gene expression, gene expression, RNA metabolic process and cellular component biogenesis. Cold-responsive gene modules are shown with enrichments for genes in regulation of transcription, protein amino acid phosphorylation, regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolic process and regulation of nitrogen compound metabolic process. Phytohormone-responsive gene modules are shown with enrichments for genes in common functional categories, stress response changes, tissue-specific expression and transcription factor binding sites. Besides, module II and IV have a close or similar relationship with phytohormone module 5 which are highly associated with auxin treatment. These three modules share similar GO terms, including DNA-dependent regulation of transcription, regulation of RNA metabolic and biosynthetic process and gene expression. The above results suggested that genes in these 3 modules may play a vital role in phytohormone-dependent salt tolerant pathway, especially for auxin. Finally, we also found three genes that involved in RNA metabolic process, OsARF14, OsHOX13 and OsWOX13. The relationship between these genes may provide a key to understand the mechanism of salt tolerance in rice. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T13:42:36Z (GMT). No. of bitstreams: 1 ntu-104-R01621112-1.pdf: 1444835 bytes, checksum: 665de88268398a6fa237a68873371f86 (MD5) Previous issue date: 2015 | en |
dc.description.tableofcontents | 口試委員會審定書……………………………………………………………………....i
誌謝……………………………………………………………………………………...ii 中文摘要………………………………………………………………………………..iii Abstract…………………………………………………………………………………..v 目錄…………………………………………………………………………………….vii 表目錄…………………………………………………………………………………...x 附表目錄………………………………………………………………………………...x 圖目錄…………………………………………………………………………………..xi 附錄目錄………………………………………………………………………………..xi 縮寫字對照…………………………………………………………………………….xii 第一章 前言………………………………………………………………………….....1 第二章 前人研究…………………………………………………………………….....2 一. 植物在非生物性逆境下之反應……………………………………………...2 1. 溫度逆境……………………………………………………………………2 2. 滲透壓逆境…………………………………………………………………2 二. 植物荷爾蒙在生長發育與逆境下之角色………………………………….....3 三. 生物統計研究………………………………………………………………….4 四. 共表現網絡分析之發展……………………………………………………… 5 五. 加權性基因共表現網絡分析之發展………………………………………….5 1.WGCNA在醫學領域中之應用……………………………………………..6 2.WGCNA應用於植物生長發育之研究……………………………………..6 3.WGCNA於植物逆境研究之應用…………………………………………..8 六. 研究目的與試驗架構………………………………………………………...10 第三章 材料與方法……………………………………………………………….......12 一. 樣本收集……………………………………………………………………...12 1. 植物荷爾蒙處理資料組…………………………………………………..12 2. 低溫處理與高鹽處理資料組……………………………………………..12 二. 使用程式語言及公用/商業軟體……………………………………………..13 1. 變方分析…………………………………….…………………………….13 2. 加權性基因共表現網絡分析…………………………………….……….13 三. 差異表現基因之選取………………………………………………………...14 四. 共關聯網絡之建構…………………………………………………………...14 五. 建構共表現基因分群………………………………………………………...14 六. 基因分群與不同荷爾蒙處理與低溫高鹽處理之相關性熱圖(Heat map) …15 七. 基因富集分析(Gene ontology enrichment analysis)………………………....15 第四章 結果………………………………………………………………………...17 一. 植物荷爾蒙資料組之統計分析……………………………………………...17 1.1植物荷爾蒙組之篩選差異表現基因數(DEGs) …………………………17 1.2植物荷爾蒙處理資料組之基因分群…………………………………….17 1.3植物荷爾蒙處理資料組之基因分群與各荷爾蒙處理之相關性……….17 1.4植物荷爾蒙處理資料組之基因分群之基因富集分析……..……..…….18 二. 非生物性逆境資料組之統計分析…………………………………………...18 2.1 非生物性逆境資料組之篩選差異表現基因數(DEGs) ………………...18 2.2 非生物性逆境資料組之基因分群………………………………………18 2.3 非生物性逆境資料組之基因分群與各荷爾蒙處理之相關性…………19 2.4非生物性逆境資料組之基因分群與兩不同品種之相關性…………….19 2.5 非生物性逆境資料組之基因分群之基因富集分析………………..…..19 第五章 討論…………………………………………………………………………...21 一. 經由不同植物荷爾蒙處理之模組之分析結果……………………………...21 二. 不同品種經低溫處理與高鹽處理之模組差異……………………………...21 三. 經由低溫處理與高鹽處理模組之基因富集分析結果……………………...22 四. 荷爾蒙處理組與低溫處理與高鹽處理模組之基因富集分析對照………...22 五. 冷鹽處理與荷爾蒙處理共同調控或誘導之遺傳因子……………………...23 六. 結語…………………………………………………………………………...24 參考文獻……………………………………………………………………………….26表目錄 表一. 植物荷爾蒙處理組與低溫高鹽處理組一覽……………..……………………34 表二. 低溫或高鹽處理之基因富集分析結果(部分)…………………..……………..35 附表目錄 附表一. 植物荷爾蒙資料之基因富集分析結果…………...………………………...41 附表二. 非生物逆境處理模組之基因富集形式…………………..…………………49圖目錄 圖一. 植物荷爾蒙組之浮動閾值分佈……………………………………………….36 圖二. 植物荷爾蒙處理之基因分群分析結果……………………………………… 37 圖三. 低溫高鹽資料組之浮動閾值分佈…………………………………………….38 圖四. (A)低溫或高鹽處理之基因分群分析結果(B)兩品種(TNG67與TCN1)植物荷 爾蒙處理之基因分群分析結果……………………………………………...39 附錄目錄 附圖ㄧ. 試驗架構…………………………..…………………………..……………..11 附錄一. 變方分析之R code(以非生物性逆境作為範例) …………………………...51 附錄二. 關聯圖表之R code (以植物荷爾蒙資料組作為範例) …………………….53 | |
dc.language.iso | zh-TW | |
dc.title | 以加權性基因共表現網絡分析確認與水稻低溫、高鹽逆境及植物荷爾蒙反應相關之基因模組 | zh_TW |
dc.title | Identification of Gene Modules Associated with Chilling, High Salinity Stresses and Phytohormones Response in Rice by Weighted Gene Co-expression Network Analysis (WGCNA) | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 劉力瑜 | |
dc.contributor.oralexamcommittee | 洪傳揚 | |
dc.subject.keyword | 非生物性逆境,低溫逆境,植物荷爾蒙,鹽害逆境,加權性基因共表現網絡分析, | zh_TW |
dc.subject.keyword | Abiotic stress,Cold,Phytohormone,Salinity,WGCNA, | en |
dc.relation.page | 55 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2015-12-28 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 農藝學研究所 | zh_TW |
顯示於系所單位: | 農藝學系 |
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