跳到主要內容

臺灣博碩士論文加值系統

(3.236.124.56) 您好!臺灣時間:2021/07/30 06:34
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

我願授權國圖
: 
twitterline
研究生:李孟霜
研究生(外文):Meng-Shuang Lee
論文名稱:線蟲、果蠅、人類之MicroRNA調控蛋白質交互作用網路比較分析研究
論文名稱(外文):Comparison of microRNA-regulated Protein Interaction Networks among Worm, Fly and Human
指導教授:巫坤品黃宣誠
指導教授(外文):Kun-Pin WuHsuan-Cheng Huang
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:生物醫學資訊研究所
學門:工程學門
學類:生醫工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:英文
論文頁數:77
中文關鍵詞:蛋白質交互作用網路
外文關鍵詞:Protein-protein interaction network
相關次數:
  • 被引用被引用:0
  • 點閱點閱:157
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
蛋白質是執行生物體功能的最小單位,而且蛋白質交互作用在大部分生物現象中扮演著關鍵性的角色,近年來高產量 (high-throughput) 實驗方法的推陳出新,累積了許多模式生物 (model organism) 的蛋白質交互作用資料,透過蛋白質交互作用網路,可以幫助我們深入了解其生物意義。miRNA 則在後轉錄階段 (post-transcription) 調控基因的表現量,間接影響後續蛋白質的產量。然而,miRNA 調控與蛋白質交互作用網路間的關係仍然有待釐清。我們針對線蟲、果蠅、人類,藉由拓樸分析的方法,說明了在 miRNA 的調控與蛋白質交互作用網路之間整體的相關性,並結合三個物種保留的資訊 (conserved miRNAs and proteins),試圖找出三個物種蛋白質交互作用網路在拓樸性質上的變化趨勢。分析結果顯示,在人類物種上,受到單一 miRNA 所調控的基因傾向於扮演整個網路中具有高度連結或中心位置的角色;此外,受到同一個 miRNA 所調控的蛋白質在整個網路中具有比較靠近彼此或是有緊密連結的特性,但我們並沒有觀察到果蠅或線蟲有類似人類蛋白質作用網路的拓樸特性。結合保留資訊之分析結果顯示,從線蟲到人類,大部份蛋白質與網路中其他蛋白質的連結度有越來越高的趨勢;此外,受到保留 miRNA 所調控的蛋白質在整個網路中具有越來越緊密連結的趨勢。對於每一個 miRNA 所調控的基因,三個物種間皆有保留的基因比非保留的基因有較突顯的拓樸性質。我們的發現提供了 miRNA 是如何調控蛋白質交互作用網路以及其調控機制如何演化的線索。
Protein-protein interactions are critical to most biological processes. Available high throughput experiments on protein-protein interactions allow us to build the interaction network giving more insight. Protein-protein interactions (PPI) are regarded as building blocks of life. MicroRNAs (miRNAs) are small, endogenously expressed RNAs that regulate the expression of protein-encoding genes at the post-transcriptional level. We aim to reveal the relationships between miRNA regulation and PPI networks among different organisms including worm, fly, and human. PPI networks can be characterized by topological properties such as degree, clustering coefficient, betweenness centrality, closeness centrality, characteristic path length, and others. We performed topological analysis to investigate correlation between miRNA regulation and protein-protein interaction network of their target-gene products globally and focused on the characterization of regulated PPI subnetworks of individual miRNAs in worm, fly, and human, respectively. In addition, we investigated the characterization of target proteins regulated by conserved and non-conserved miRNAs, respectively, and also the conserved target proteins across three species to reveal their importance in PPI networks. Our analysis showed that miRNA targets tend to be network hubs or centers, and tend to form network modules in human, but not in fly or worm. The results indicated that miRNA regulation become much more complex from worm and fly to human. Our findings shed light on possible mechanisms of miRNA regulation in protein networks as well as their roles in evolution.
Contents
致謝………………………………………………………………………………..i
Abstract………………………………………………………………………….. ii
摘要……………………………………………………………………………….iii
1 Introduction 1
1.1 Protein-Protein Interaction Network . . . . . . . . . . . . . . . . . . . . . . . . ……..1
1.2 MicroRNA Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ………...2
1.3 Motivation and Aims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . …………4
2 Materials andMethods 5
2.1 Human, Fly andWorm Protein-Protein Interaction Networks . . . . . . . . . 5
2.2 MicroRNA Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...................5
2.3 Conserved MicroRNA Information . . . . . . . . . . . . . . . . . . . . . . . . ………6
2.4 Conserved Targets Information . . . . . . . . . . . . . . . . . . . . . . . . . . . ………8
2.5 Topological Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ………..10
2.5.1 Node properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . …………10
2.5.2 Subnetwork properties . . . . . . . . . . . . . . . . . . . . . . . . . . ……….. 13
2.6 Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ……….15
2.6.1 Random sampling for targets of each miRNA . . . . . . . . . . . . . …15
2.6.2 Standard normal distribution . . . . . . . . . . . . . . . . . . . . . . . ……...15
2.6.3 Independent samples t-test . . . . . . . . . . . . . . . . . . . . . . . . . ……..17
2.6.4 Analysis of variance (ANOVA) . . . . . . . . . . . . . . . . . . . . . . ……17
3 Results 18
3.1 Target Proteins Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ………. 18
3.2 Target Subnetworks Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . ……..21
3.3 Target Number of miRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ……...21
3.4 Comparison Analysis for Each miRNA and Target . . . . . . . . . . . . . . . . 31
3.5 ProteinsWere Regulated by Conserved miRNAs or Non-conserved miRNAs
3.5.1 Individual Proteins Were Regulated by Conserved miRNAs or Non- conserved miRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . …………….34
3.5.2 Subnetwork Proteins Were Regulated by Conserved miRNAs or Non- conserved miRNAs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . …………….41
3.6 Node Properties of Conserved and Non-Conserved Proteins . . . . . . . . . 46
3.7 Different Protein Groups Regulated by_e Same miRNA . . . . . . . . . . . ……..47
3.7.1 Conserved Extent Investigation . . . . . . . . . . . . . . . . . . . . . . ………………47
3.7.2 Proteinswere divided into conserved and non-conserved protein groups that regulated by the same miRNA . . . . . . . . . . . . . . . . . . . . ………………………..49
4 Discussion 55
5 Conclusion 56
6 Bibliography 57
7 Appendix 61
Aittokallio, T., and Schwikowski, B. (2006). Graph-based methods for analysing networks in cell biology. Briefings in Bioinformatics 7, 243.

Chen, J., and Yuan, B. (2006). Detecting functional modules in the yeast protein-protein interaction network. Bioinformatics 22, 2283.

Coleman, T. F., and More, J. J. (1984). Estimation of sparse Hessian matrices and graph coloring problems. Mathematical Programming 28, 243-270.

Cui, Q., Yu, Z., Purisima, E. O., and Wang, E. (2006). Principles of microRNA regulation of a human cellular signaling network. Molecular Systems Biology 2.

Deane, C. M., Salwinski, L., Xenarios, I., and Eisenberg, D. (2002). Protein Interactions Two Methods for Assessment of the Reliability of High Throughput Observations*. Molecular & Cellular Proteomics 1, 349-356.

Graeber, T. G., and Eisenberg, D. (2001). Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles. nature genetics 29, 295-300.

Griffiths-Jones, S., Grocock, R. J., van Dongen, S., Bateman, A., and Enright, A. J. (2006). miRBase: microRNA sequences, targets and gene nomenclature. Nucleic acids research 34, D140.

Grimson, A., Farh, K. K. H., Johnston, W. K., Garrett-Engele, P., Lim, L. P., and Bartel, D. P. (2007). MicroRNA targeting specificity in mammals: determinants beyond seed pairing. Molecular cell 27, 91-105.

Hsu, C. W., Juan, H. F., and Huang, H. C. (2008). Characterization of microRNA-regulated protein-protein interaction network. Proteomics 8.

Ibanez-Ventoso, C., Vora, M., and Driscoll, M. (2008). Sequence Relationships among C. elegans, D. melanogaster and Human microRNAs Highlight the Extensive Conservation of microRNAs in Biology. PLoS One 3.


Jeong, H., Mason, S. P., Barabasi, A. L., and Oltvai, Z. N. (2001). Lethality and centrality in protein networks. Nature 411, 41-42.

Landgraf, P., Rusu, M., Sheridan, R., Sewer, A., Iovino, N., Aravin, A., Pfeffer, S., Rice, A., Kamphorst, A. O., and Landthaler, M. (2007). A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401-1414.

Lewis, B. P., Burge, C. B., and Bartel, D. P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15-20.

Lewis, B. P., Burge, C. B., and Bartel, D. P. (2005). Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15-20.

Marcotte, E. M., Xenarios, I., and Eisenberg, D. (2001). Mining literature for protein-protein interactions (Oxford Univ Press), pp. 359-363.

Prasad, T. S., Goel, R., Kandasamy, K., Keerthikumar, S., Kumar, S., Mathivanan, S., Telikicherla, D., Raju, R., Shafreen, B., and Venugopal, A. (2008). Human Protein Reference Database--2009 update. Nucleic Acids Research.

Robin, C. F., Kyle, K. F., Christopher, B. B., and David, P. B. (2009). Most Mammalian mRNAs Are Conserved Targets of MicroRNAs. Genome Research 19, 92-105

Rual, J. F., Venkatesan, K., Hao, T., Hirozane-Kishikawa, T., Dricot, A., Li, N., Berriz, G. F., Gibbons, F. D., Dreze, M., and Ayivi-Guedehoussou, N. (2005). Towards a proteome-scale map of the human protein–protein interaction network. Nature 437, 1173-1178.

Sabidussi, G. (1966). The centrality index of a graph. Psychometrika 31, 581-603.
Yu, H., Kim, P. M., Sprecher, E., Trifonov, V., and Gerstein, M. (2007). The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput Biol 3, e59.

Zhang, B., Pan, X., Cobb, G. P., and Anderson, T. A. (2007). MicroRNAs as oncogenes and tumor suppressors. Developmental biology 302, 1-12.
Zhang, S., Jin, G., Zhang, X. S., and Chen, L. (2007). Discovering functions and revealing mechanisms at molecular level from biological networks. Proteomics 7.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top