摘要
背景
计算神经科学是使用数学分析和计算机模拟的方法在不同水平上对神经系统进行模拟和研究的科学,其主要内容之一就是探索神经系统中脑发育的过程及其分子机制。神经系统分中枢神经系统和周围神经系统,中枢神经系统包括脑和脊髓。脑的发育受到很多因素的影响,其中神经元内成千上万基因的调控起着关键的作用。研究表明,这些基因在以下三个层面上发挥着重要的调控功能:(1)大量基因按照特定的时空顺序表达,在胎儿发育的不同时期控制着不同脑区的发育;(2)这些基因分为若干簇团,它们彼此影响、相互调控,形成一个非常复杂的关联网络;(3)这些基因和基因簇团在时空上相互关联和作用,其最终结果是指导脑的正确发育。这三个层面上的基因调控关系,对应着基因信息在不同尺度空间上的复杂关联过程。因此,我们可以从多尺度分析的角度研究脑发育过程。
本研究的前期工作已经获得了人类胎儿脑发育过程三个部位(小脑、大脑皮质、海马)的基因芯片数据,每个部位包含10080个基因、7个时间点的信息。本文根据这些基因表达的数据信息,利用多尺度分析和恰当的数学模型预测了这三个部位的基因簇团及其调控网络。
方法
本文的研究方法集中在以下几个方面。
(1)放宽数据预处理的条件。按照基因表达有意义的基本要求和本研究的需要,只要每个部位的基因在7个时间点的ratio>0,我们认为该基因可能在基因调控过程中有作用,由此进行基因的初步筛选,并在进一步的模型求解中剔除与调控无关的基因。
(2)建立基因芯片的y ~ n曲线数学模型。基因芯片上的基因及其表达水平之间没有构成一个稳定的函数关系,为此,我们创立了基因芯片的y ~ n曲线模型,并通过基因芯片降噪来评判y ~ n曲线模型的优越性,还进一步分析了该模型用于多尺度分析的作用及适用于各芯片数据的最佳小波函数。
(3)构建多尺度聚类模型获得基因簇团。传统的基因数据分析用聚类将基因归类,我们是在y ~ n曲线的基础上进行多尺度分析,并在每个尺度聚类,只有当基因在各尺度上都被聚为同一类时,我们才将它们归为同一个基因簇团。
(4)建立基于客观赋权的整数非线性规划模型预测基因簇团的调控网络模型。我们将基因簇团的表达均值作为该簇团的表达水平,将小脑、大脑皮质和海马的基因簇团分别作为一个整体,用熵对每个基因簇团进行客观赋权,并建立基于加权矩阵的整数非线性规划和相关系数模型进行结果比较,仅当结果一致才被认为调控关系成立。
结果
聚类分析用SPSS求解、优化模型用LINGO编程求解,其余模型均用MATLAB R2007a编程求解,得到的主要结果如下。
(1)数据预处理后小脑组织有1153个基因、大脑皮质有956个基因、海马组织有1106个基因在7个时间点的表达强度。
(2) y ~ n曲线用16种小波函数对基因数据进行降噪处理,得到小脑组织、大脑皮质、海马组织最好降噪效果的小波函数分别为Daubechies小波系列的db7、db4和db4,且y ~ n曲线对三个部位的基因芯片的信噪比均高于原始芯片数据。
(3)在基因簇团的分析中,小脑组织共有402个基因构成34个基因簇团,大脑皮质有304个基因构成23个基因簇团,海马组织共有384个基因构成25个基因簇团。有49个基因同时属于小脑、大脑皮质和海马的基因簇团。
(4)小脑组织中有30个基因簇团参与调控网络,共有39个正调控、5个负调控关系;大脑皮质中有15个基因簇团参与调控网络,共有个6正调控、11个负调控关系;海马组织中有16个基因簇团参与调控网络,共有20个正调控、5个负调控关系。(5)IFITM3、H2AFY、SSRP1、SCAP和CD59这5个基因同时参与了小脑、大脑皮质和海马的基因簇团调控网络。
结论
(1)利用y ~ n曲线不但可以更少地损失基因芯片的数据信息,还能有比原始数据更好的降噪效果,是基因芯片数据分析的一种有效手段。
(2)多尺度聚类作为一种基因芯片的新型聚类方法,将对基因芯片处理方法带来一种有意义的创新,能更加准确地聚类,是一种有应用前景的统计方法,可以推广到其他领域。
(3)人类胎儿大脑的发育和功能受到基因的调控,其基因簇团及其调控关系十分复杂但有研究价值。
(4)本文建立的整数非线性规划模型比加权矩阵的权重选择更加客观合理,在人类胎儿脑发育过程的基因簇团的调控研究中得到比较理想的结果,该模型可以推广应用于其它组织、其他物种的基因调控网络研究。
(5)IFITM3、H2AFY、SSRP1、SCAP和CD59这5个基因在人类胎儿脑发育过程的基因调控中起重要的作用,和疾病(II型高脂蛋白血、糖尿病引发的淋巴管病、前列腺瘤等)的发生有密切联系。
Background
Computational neuroscience is a science using mathematical analysis and computer simulation at different levels to simulate and study the nervous system, one of whose main studies is to explore the process of brain development and its molecular mechanism in nervous system. Nervous system includes central nervous system and peripheral nervous system, and central nervous system includes brain and spinal cord. Development of the brain is affected by many factors, where the regulation of thousands of genes in neurons plays a key role. Studies have shown that these genes play important regulatory functions in the following three scales. (1) A large number of genes in accordance with specific spatial and temporal expression control the development of different brain regions in different fetal stages. (2) These genes are divided into some gene clusters, which are affected and regulated each other, and form very complex associated networks. (3) These genes and gene clusters are associated and affected in time and space, whose ultimate result is that the correct guidance of brain development. The gene regulations at three levels are corresponded to the complex associated process of genetic information in different scales. Therefore, we can study the process of brain development by multiscale analysis.
We have got gene chip data in three parts of the developing human fetal brain (cerebellum, cerebral cortex and hippocampus) in the preliminary work of this study, each part of which contains information of 10,080 genes at seven point times. In this paper, we used multiscale analysis and appropriate mathematical models to predict the gene clusters and their regulatory networks in the three parts based on the information of these gene expression data.
Methods
The research methods of this thesis mostly focused on in the following areas.
(1) Broadening the conditions for data preprocessing. According to the basic requirements of gene expression and the needs of this study, as long as the gene ratio> 0 at the seven time points, we believe that the gene may have a role in the gene regulatory process, which makes an preliminary gene selection and then we eliminate the genes which has nothing to do with the gene regulations by other models.
(2) Establishment of gene chip model named y ~ n curve. There is no stable function between genes and their expressions in gene chip; therefore, we created a model y ~ n curve of gene chip, whose advantages were proved by using it to denoise gene chip data. We further analyzed the role of the model by using multiscale analysis and the best wavelet function of every gene chip.
(3) Construction of multiscale cluster model to get gene clusters. One of traditional analysis methods of gene data is clustering. We made multiscale analysis on the basis of the y ~ n curve and clustered on each scale. Only when the genes on every scale were clustered into the same category, we incorporated them into a gene cluster.
(4) Establishment of integer nonlinear programming model of regulatory networks based on objective weight to predict gene clusters. We took the average expression level of every cluster as its expression, and weighed every cluster with entropy, respectively taking the cerebellum, cerebral cortex and hippocampus as a whole. Then, we established the model of integer nonlinear programming based on weight matrix whose results were compared with that of the model of correlation coefficient. Only when their results were accordant, the regulatory relationships were admitted.
Results
We used SPSS to solve cluster analysis, used LINGO to solve optimization model and used MATLAB R2007a to solve the other models.Then, we got the following main results.
(1) There were gene expressions of 1153 genes in cerebellum, 956 genes in cerebral cortex and 1106 genes in hippocampus at 7 time points after data preprocessed.
(2) When gene data were denoised by y ~ n curve with 16 wavelet functions, we got the best effect were respectively db7 in cerebellum, db4 in cerebral cortex and db4 in hippocampus, and all of the signal-to-noises of y ~ n curves in the three parts were higher than that of the original data.
(3) In the gene cluster analysis, there are 34 clusters of 402 genes in cerebellum, 23 clusters of 304 genes in cerebral cortex, 25 clusters of 384 genes in hippocampus and 49 genes in the three parts.
(4) There are 30 gene clusters to be concerned with regulatory network which has 39 relations of positive regulation and 5 relations of negative regulation in cerebellum, 15 gene clusters to be concerned with regulatory network which has 6 relations of positive regulation and 11 relations of negative regulation in cerebral cortex, and 16 gene clusters to be concerned with regulatory network which has 20 relations of positive regulation and 5 relations of negative regulation in hippocampus.
(5) The five genes named IFITM3, H2AFY, SSRP1, SCAP and CD59 are participated in the gene clusters regulatory networks in cerebellum, cerebral cortex and hippocampus at the same time.
Conclusion
(1) The use of y ~ n curves can not only less lose of the information of gene chip data, but also has better denoising effect than the raw data, so it is an effective tool of gene chip data analysis.
(2) As a new clustering method of gene chip, multiscale clustering will bring about a meaningful innovation, which can cluster more accurately, therefore it is a useful and novel statistical method, and can be extended to other areas.
(3) The development and function of developing human fetal brain are regulated by genes, whose gene clusters and their regulations are very complex but valuable.
(4) The nonlinear integer optimization model of this paper is more objective and reasonable in weight than matrix, which is used to study the regulation of gene clusters in the developing human fetal brain and good results have been got. The model can be used in the study of gene regulatory networks in other organizations and other species.
(5) The five genes of IFITM3, H2AFY, SSRP1, SCAP and CD59 play a very important role in the gene regulatory process of the developing human fetal brain, which have been tested for association to diseases (hyperlipoproteinemia type II, diabetic angiopathies and prostatic neoplasms etc.).
引文
1. Human Genome Project Information. http://www.ornl. gov/ sci/ techresources/ Human_Genome/ home. shtml.
2. Eisen M., Spellman P., Brown P., Botstein D. Cluster analysis and display of genome-wide expression patterns[J].pnas, 1998, 95:14863-14868.
3. Kauffman SA. Metabolic stability and epigenesist in randomly constructed genetic nets [J]. J Theor Biol, 1969, 22:437-467.
4. Kauffman S.The larger scale structure and dynamics of gene control circuits: an ensemble approach [J]. J Theor Biol, 1974, 44:167-190.
5. Friedman N, Linial M, Nachman, et al. Using Bayesian networks to analyze expression data. J Comput Biol [J], 2000,7:601-620.
6. Friedman N, Inferring cellular networks using probabilistic graphical models [J]. Science. 2004,303:799-805.
7. Elowitz M B, Leiber S. A synthetic oscillatory network of transcriptional regulators[J]. Nature, 2000,403: 335-338.
8. Quackenbush J. Computational analysis of microarray data [J]. Nat Reviews Genetics, 2001, 2: 418-427.
9. Butte AJ, Kohane IS. Mutual information relevance networks: Functional genomic clustering using pairwise entropy Ineasurements[J]. PSB,2000,5: 415-426.
10. McAdams HH,Arkin A. Stochastic mechanisms in gene expression[J]. Proc Natl. Acad. Sci. USA, 1997,94: 814-819.
11. Lamb TM,Knecht AK,Smith WC ,et al. Neural induction by the secreted polypeptide Noggin[J]. Science ,1993 ,262(5134) :713-718.
12. McMahon JA , Takada S , Zimmerman LB ,et al . Noggin2mediated antago2 nismof BMP signaling is required for growth and patterning of the neural tube and somite[J] . Genes Dev ,1998 ,12(10) :1438-1452.
13. Finley MF ,Devata S ,Huettner JE.BMP24 inhibits neural differentiation of murine embryonic stem cells[J]. J Neurobiol . 1999 ,40 (3) :271-287.
14. TZANG Chi Huang,Lawrence.Study the gene expression profiles of developing humanfetal central nervous system suing high density cDNA microarrays.City University of Hong Kong.2003.
15.罗万春,易东,刘恩,龚利红.整数非线性规划模型在乳腺癌转移相关基因表达调控网络建立中的应用[J].北京生物医学工程,2008 ,27(2):138-141.
16.李瑶.基因芯片数据分析与处理[M].北京:化学工业出版社,2006.
17. Kanehisa M. Post-Genome Informatics[M]. Oxford University Press. 2001.
18. Ilya Shmalevich, Olli YliHarja, Jaakko Astola.Inference of genetic regulatory networks under the best-fit extension paradigm, Computational and Statistical Approaches to Genomics[M] . Springer US,2003: 197-210.
19. Patrik D’haeseleer, Shoudan Liang and Roland Somogyi. Genetic network inference: from co-expression clustering to reverse engineering[J]. Bioinformatics, 2000, 16(8):707-726.
20. L. F. A. Wessels, E. P. Van Someren and M.J.T. Reinders, A comparison of genetic network models[J]. Pacific Symposium on Biocomputing 2001,6:508-519.
21. Wagner A.How to reconstruct a large genetic network from n gene perturbations in fewer than n2 easy steps[J]. Bioinformatics. 2001, 17(12):1183-1197.
22.雷耀山,史定华,王翼飞等.基因调控网络的生物信息学研究.自然杂志,2003,26(1):7-12
23. Ilya Shm ulevich, Edward R Dougherty, Wei Zhang. From Boolean to probabilistic Boolean networks as models 0f genetic regulatory networks[J]. Proceedings of the IEEE,2002,90(11):l778-179l.
24. Weaver DC, Workman CT, Stormo GD. Modeling regulatory networks with weight matrices[J]. Pacific Symp Biocomp,1999, 4:112-123.
25. Chen T, He HL, Church GM. Modeling gene expression with differential equations[J]. Pacific Syrup Biocomp,1999, 4:29-40.
26. Mackay D J C. A practical Bayesian framework for backpropagation neiworks[J]. Neural Computation. 1992, 4(3):448-472.
27. Spirtes, P., Glymour, C., Scheines, R., et al..Constructing Bayesian Network Models of Gene Expression Networks from Microarray Data. Proceedings of the Atlantic Symposium on Computational Biology, Genome Information Systems & Technology, Duke University, March 2000.
28. Meyer Y.. Principe d'incertitude, bases hilbertiennes et algèbres d'opérateur. In, Bourbaki seminar, 1985–1986, 662.
29. Daubechies I.. Ten Lecture on Wavlets[M]. Society for Industrial and Applied Mathematics Philadelphia PA,1992.
30. Mallat S.. A theory for multiresolution signal decomposition: The wavelet representation [J]. IEEE trans Patt Anal Machine Intell, 1989, 11 (7):674～693
31.崔锦泰[美]著,程正兴译.小波分析导论[M].西安:西安交通大学出版社,1995.
32.刘培德.泛函分析基础[M].武汉:武汉大学出版社,2002.
33.程其襄,张奠宙,魏国强等.实变函数与泛函分析基础[M].北京:高等教育出版社,1992.
34.文成林.多尺度动态建模理论及其应用[M].北京:科学出版社,2008.
35.潘泉,张磊,崔培玲等.动态多尺度系统估计理论与应用[M].北京:科学出版社,2007.
36. N.Bellomo, A,Bellouquid, M.Delitala. From the mathematical kinetic theory of active particles to multiscale modelling of complex biological system [J].Mathematical and Computer Modelling, 47(2008):687-698.
37. George E. Kapellos, Terpsichori S.Alexiou, Alkiviades C.Payatakes. A multiscale theoretical model for diffusive mass transfer in cellular biological madia[J]. Mathematical Biosciences,210(2007):177-237.
38. Marco Viceconti, Fulvia Taddei, Serge Van Sint Jan, et al. Multiscale modelling of the skeleton for the prediction of the risk of fracture[J]. Clinical Biomechanics, 23(2008):845-852.
39.程翼宇,瞿海斌.医药分析信息学及分析数据处理技术[M].北京:化学工业出版社,2006:48-49.
40. Shapiro J M. An embedded wavelet hierarchal image coder[J]. International Conference on ASSP, 1992, 4:657-660.
41. Shapiro J M. Embedded image coding using zerotrees of wavelet coefficients[J]. IEEE Trans. on signal Processing, 1993, 41(2):3445-3446.
42. Shapiro J M. Application of the embedded wavelet hierarchical image coder to very low bit rate image coding[J]. International Conference on ASSP, 1993,5:558-561.
43. Martucci S A, Sodagar I, Chiang I. A zerotree wavelet video coder[J]. IEEE Trans. onCircuits and systems for Video Technology, 1997, 7(1):109-118.
44. Martucci S A, Sodagar I. Zerotree entropy coding of wavelet coefficients for low bit rate video[J]. International Conference on ASSP, 1996, 2:533-535.
45. Goupillaud P., Grossman A., and Morlet J.. Cycle-Octave and Related Transforms in Seismic Signal Analysis[J]. Geoexploration, 23:85-102, 1984.
46. Daubechies I.. Orthonormal bases of compactly supported wavelets[J], Comm. Pure & Appl. Math., 1988, 41 (7): 909-996.
47. Daubechies I.. Orthonormal bases of wavelets with finite support - connection with discrete filters. Proceedings of the 1987 International Workshop on Wavelets and Applications[M], Marseille, France, eds. J.M. Combes, A. Grossmann, and Ph. Tchamitchian, Springer, Berlin, 1989.
48. Mallat S.. Multiresolution approximation and wavelet orthonormal bases of L2[J]. Transaction of the American Mathematical Society, 1989, 315: 69-87.
49. Mallat S.. Multifrequency channel decompositions of images and wavelet models[J]. IEEE Transaction in Acoustic Speech and Signal Processing, 1989, 37: 2091-2110.
50. Mallat S.. Zero-Crossings of a Wavelet Transform[J]. IEEE Transactions on Information Theory, 1991, 37: 1019-1033.
51.葛哲学,沙威.小波分析理论与MATLABR2007实现[M].北京:电子工业出版社,2007,10.
52.胡昌华,张军波,夏军,张伟.基于MATLAB的系统分析与设计——小波分析[M].西安:西安电子科技大学出版社,1999,12.
53. Willsky A S. Multiresolution Markov models for signal and image processing[J]. Proceedings of the IEEE, 2002,90(8):1396~1458.
54. Thakor. N.V. Wavelet and time frequency analysis of biomedical Signals[C], World congress medical physics and biomedical engineering. Rio Dejaneiro, Brazil, 1994:16-19.
55. Avci E., Avci D..The speaker identification by using genetic wavelet adaptive network based fuzzy inference system[J]. Expert Systems with Applications, 2009, 36(6):9928-9940.
56. Ansari M.A., Anand R.S.. Context based medical image compression for ultrasound images with contextual set partitioning in hierarchical trees algorithm[J]. Advances inEngineering Software, 2009, 40( 7): 487-496.
57. Elif Deryaübeyli. Statistics over features of ECG signals[J]. Expert Systems with Applications, 2009, 36(5):8758-8767.
58. Wei-Yen Hsua, Yung-Nien Sun. EEG-based motor imagery analysis using weighted wavelet transform features[J]. Journal of Neuroscience Methods, 2009, 176 (2): 310-318.
59. Thilo Hinterberger, Andrea Küblera, Jochen Kaisera,et al.. A brain–computer interface (BCI) for the locked-in: comparison of different EEG classifications for the thought translation device[J]. Clinical Neurophysiology, 2003,114(3):416-425 .
60. Zümray Dokur, Tamer ?lmez. Feature determination for heart sounds based on divergence analysis[J]. Digital Signal Processing, 2009,19(3) :521-531.
61. Kalayci T, OzdamarO. Wavelet processing for automated neural network detection of EEG spikes[J], IEEE Eng in Med and Biol, 1995, 2:160-166.
62. Thakor NV. Multiresolution wavelet analysis of evoked potentials[J]. IEEE Trans BME, 1993,40(11):1085-1094.
63. Liu W Q. Fast tracking of evoked potential variations using correlated scale function designed by multiresolution analysis [C] Procof 20th Annu Conf IEEE/EMBS, 1998, 1535-1538.
64. Raz J, Dickerson L A, Turetsky B. Wavelet packet model of evoked potentials[J]. Brain Lang, 1999, 66(1): 61-88.
65. Morlet D, Coudere J P, Touboul P. Wavelet analysis of high-resolution ECGs in post-infarction patients: role of the basic wavelet and of the analyzed lead[J]. Int J of Biomedical Comp, 1995, 39: 311-325.
66. Bradie B. Wavelet packet based compression of signal lead ECG[J]. IEEE Trans BME, 1994, 43(1): 49-60.
67. Sheng-Yun Wen,Chun-ting Zhang. Identification of isochore boundaries in the human genome using the technique of wavelet multiresolution analysis[J]. BBRC, 311(2003): 215-222.
68.李霖,吴凡.空间数据多尺度表达模型及其可视化[M].北京:科学出版社,2005:35-38.
69. Yang Bingru, Hou Wei, Zhou Zhun, Quan Huabin. KAAPRO: An approach of proteinsecondary structure prediction based on KDD in the compound pyramid prediction model[J]. Expert Systems with Applications, 2009, 36(5):9000-9006.
70.王翼飞,史定华.生物信息学——智能化算法及其应用[M].北京:化学工业出版社,2006,7.
71. Stears RL, et al. Trends in microarray analysis[J]. NatMed, 2003,9(1): 140-145.
72. Wang X H,et al. Microarray image enhancement by denoising using stationary wavelet transform[J]. IEEE Trans. Nanobioscience, 2003(4): 176-183.
73. Van De Ville, Nachtegael M, Van dereken, et al. Noise reduction by fuzzy image filtering[J]. IEEE Transactions on Fuzzy Systems, 2003, 11(8): 429-436.
74.王黎明,陈颖,杨楠.应用回归分析[M].上海:复旦大学出版社,2008:120-121.
75.王岩,隋思涟,王爱青.数理统计与MATLAB工程数据分析[M].北京:清华大学出版社,2006.131-135.
76. Lingfeng Chen,Xusheng Zhanga, Jiaming Lina , Dingguo Sha. Signal-to-noise ratio evaluation of a CCD camera[J]. Optics & Laser Technology, 2009,41(5): 574-579.
77. L. Kausmann, D. Kramer, L.Crooks, D.Ortendahl. Measuring signal-to-noise ratios in MR-imaging [J]. Radiology,173 (1989): 265-267.
78. J. Frank, L. Al-Ali. Signal-to-noise ratio of electron micrographs obtained by cross correlation[J]. Nature, 1975 (256) :376-379.
79. MacQueen,J.B.“Some Methods for Classification and Analysis of Multivariate Observations.”Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability[M], 1,Berkeley,CA: University of California Press(1967):281-297.
80. Richard A.Johnson,Dean W. Wichern.Applied multivariate statistical analysis[M]. Prentice Hall, 2001.
81. Shai Ben-David. A framework for statistical clustering with constant time approximation algorithms for K-median and K-means clustering[J]. Machine Learning, 2007,66(2-3):243 - 257.
82. H. Zha, C. Ding, M. Gu, X. He, H.D. Simon. spectral relaxation for K-means clustering[J]. Neural Information Processing Systems,2001,14:1057-1064.
83.陈平雁.SPSS13.0统计软件应用教程[M].北京:人民卫生出版社,2005.
84.吕锋,王虹,刘皓春,苏扬.信息理论与编码[M].北京:人民邮电出版社,2004
85.罗万春,易东,刘恩,龚利红.整数非线性规划模型在乳腺癌转移相关基因表达调控网络建立中的应用[J].北京生物医学工程,2008 ,27(2):138-141.
86.谢金星,薛毅.优化建模与LINDO/LINGO软件[M].北京:清华大学出版社,2005.
87. Ashburner M,Ball C, Blake J et al. Gene Ontology:tool for the unification of biology[J]. Nat Genet 2000, 25:25-29.
88. Chen W, Rassidakis GZ, Medeiros LJ. Nucleophosmin gene mutations in acute myeloid leukemia[J]. Arch Pathol Lab Med. 2006, 130(11):1687-1692.
89. Chen W, Rassidakis GZ, Li J, et al.. High frequency of NPM1 gene mutations in acute myeloid leukemia with prominent nuclear invaginations (“cuplike”nuclei) [J]. Blood. 2006,108(5):1783-1784.
90. Oki Y, Jelinek J, Beran M, et al.. Mutations and promoter methylation status of NPM1 in myeloproliferative disorders[J]. Haematologica. 2006, 91(8): 1147- 1148.
91. Ruiz P, Weppler D, Tryphonopoulos P, et al.. CD55 and CD59 deficiency in transplant patient populations: possible association with paroxysmal nocturnal hemoglobinuria- like symptoms in Campath-treated patients[J]. Transplant Proc. 2006, 38(6): 1750- 1752.
92. Kinderlerer AR, Steinberg R, Johns M, et al..Statin-induced expression of CD59 on vascular endothelium in hypoxia: a potential mechanism for the anti-inflammatory actions of statins in rheumatoid arthritis[J]. Arthritis Res Ther. 2006, 8(4):130.
93. Omidvar N, Wang EC, Brennan P, et al.. Expression of glycosylphosphatidylinositol- anchored CD59 on target cells enhances human NK cell-mediated cytotoxicity[J]. J Immunol. 2006, 176(5): 2915- 2923.
94. de Krom M, van der Schouw YT, Hendriks J, et al.. Common genetic variations in CCK, leptin, and leptin receptor genes are associated with specific human eating patterns[J]. Diabetes. 2007, 56(1): 276-280.
95. Takimoto T, Terayama H, Waga C, et al.. Cholecystokinin (CCK) and the CCKA receptor gene polymorphism, and smoking behavior[J]. Psychiatry Res. 2005 , 133(2-3):123-128.
96. Yang SX, Hewitt SM, Steinberg SM, et al.. Expression levels of eIF4E, VEGF, and cyclin D1, and correlation of eIF4E with VEGF and cyclin D1 in multi-tumor tissue microarray[J]. Oncol Rep. 2007, 17(2): 281-287.
97. Culjkovic B, Topisirovic I, Skrabanek L, et al.. eIF4E is a central node of an RNAregulon that governs cellular proliferation[J]. J Cell Biol, 2006, 175(3):415-26.
98. Murata T, Shimotohno K. Ubiquitination and proteasome-dependent degradation of human eukaryotic translation initiation factor 4E[J]. J Biol Chem, 2006 , 281(30): 20788- 20800.
99. Slepenkov SV, Darzynkiewicz E, Rhoads RE. Stopped-flow kinetic analysis of eIF4E and phosphorylated eIF4E binding to cap analogs and capped oligoribonucleotides: evidence for a one-step binding mechanism[J]. J Biol Chem, 2006 , 281(21): 14927- 14938.
100. Ahn J, Chung KS, Kim DU, et al.. Systematic identification of hepatocellular proteins interacting with NS5A of the hepatitis C virus[J]. J Biochem Mol Biol, 2004, 37(6): 741- 748.
101. Toussaint L, Bertrand L, Hue L, et al.. High-resolution X-ray structures of human apoferritin H-chain mutants correlated with their activity and metal-binding sites[J]. J Mol Biol. 2007,365(2): 440-452.
102. Inoue T, Kon T, Ajima R, et al.. MYO18B interacts with the proteasomal subunit Sug1 and is degraded by the ubiquitin-proteasome pathway[J]. Biochem Biophys Res Commun, 2006, 342(3): 829-834.
103. Tanaka SS, Yamaguchi YL, Tsoi B, et al.. IFITM/Mil/fragilis family proteins IFITM1 and IFITM3 play distinct roles in mouse primordial germ cell homing and repulsion[J]. Dev Cell, 2005,9(6): 745-756.
104. Suchanek M, Radzikowska A, Thiele C.Photo-leucine and photo-methionine allow identification of protein-protein interactions in living cells[J].Nat Methods, 2005,2(4):261-267.
105. Dobrosotskaya IY, Goldstein JL, Brown MS,et al.. Reconstitution of sterol-regulated endoplasmic reticulum-to-Golgi transport of SREBP-2 in insect cells by co-expression of mammalian SCAP and Insigs[J].J Biol Chem, 2003,278(37):35837-35843.
106. Durst R, Jansen A, Erez G, et al.. The discrete and combined effect of SREBP-2 and SCAP isoforms in the control of plasma lipids among familial hypercholesterolaemia patients[J]. Atherosclerosis, 2006,189(2):443-450.
107. Pati?o-Lopez G, Hevezi P, Lee J, et al.. Human class-I restricted T cell associated molecule is highly expressed in the cerebellum and is a marker for activated NKT andCD8+ T lymphocytes[J]. J Neuroimmunol, 2006, 171(1-2):145-155.
108. Boles KS, Barchet W, Diacovo T, et al..The tumor suppressor TSLC1/NECL-2 triggers NK-cell and CD8+ T-cell responses through the cell-surface receptor CRTAM[J]. Blood. 2005 ,106(3):779-786.
109. Shin S, Paul-Satyaseela M, Lee JS, et al.. Focal adhesion kinase is involved in type III group B streptococcal invasion of human brain microvascular endothelial cells[J]. Microb Pathog. 2006 , 41(4-5): 168-173.
110. Park SY, Li H, Avraham S. RAFTK/Pyk2 regulates EGF-induced PC12 cell spreading and movement[J]. Cell Signal. 2007, 19(2):289-300.
111. Urcuqui-Inchima S, Casta?o ME, Hernandez-Verdun D, et al..Nuclear Factor 90, a cellular dsRNA binding protein inhibits the HIV Rev-export function. Retrovirology, 2006,3:83.
112. Tao WA, Wollscheid B, O'Brien R, et al.. Quantitative phosphoproteome analysis using a dendrimer conjugation chemistry and tandem mass spectrometry. Nat Methods,2005;2(8):591-598.
113. Nie Y, Ding L, Kao PN,et al.. ADAR1 interacts with NF90 through double-stranded RNA and regulates NF90-mediated gene expression independently of RNA editing. Mol Cell Biol, 2005, 25(16): 6956-6963.
114. Xu YH, Leonova T, Grabowski GA,et al.. Cell cycle dependent intracellular distribution of two spliced isoforms of TCP/ILF3 proteins. Mol Genet Metab, 2003 ,80(4):426-36.
115. Trompezinski S, Berthier-Vergnes O, Denis A, et al.. Comparative expression of vascular endothelial growth factor family members, VEGF-B, -C and -D, by normal human keratinocytes and fibroblasts[J]. Exp Dermatol, 2004,13(2):98-105.
116. de Paulis A, Prevete N, Fiorentino I, et al.. Expression and functions of the vascular endothelial growth factors and their receptors in human basophils[J]. J Immunol, 2006,177(10):7322-7331.
117. Paul S, Dansithong W, Kim D,et al.. Interaction of muscleblind, CUG-BP1 and hnRNP H proteins in DM1-associated aberrant IR splicing. EMBO J,2006,25(18):4271-4283.
118. Moraes KC, Wilusz CJ, Wilusz J. CUG-BP binds to RNA substrates and recruits PARN deadenylase. RNA, 2006,12(6):1084-1091.
119. Jun KY, Xia Y, Han X,et al.. (1)H, (15)N and (13)C chemical shift assignments of RNA repeats binding protein—CUGBP1ab. J Biomol NMR, 2004,30(3):371-372.
120. Ghosh G, Li G, Myung K, Hendrickson EA. The lethality of Ku86 (XRCC5) loss-of-function mutations in human cells is independent of p53 (TP53). Radiat Res. 2007 ,167(1):66-79.
121. Mari PO, Florea BI, Persengiev SP,et al.. Dynamic assembly of end-joining complexes requires interaction between Ku70/80 and XRCC4. Proc Natl Acad Sci U S A. 2006,103(49): 18597-18602.
122. Spagnolo L, Rivera-Calzada A, Pearl LH,et al..Three-dimensional structure of the human DNA-PKcs/Ku70/Ku80 complex assembled on DNA and its implications for DNA DSB repair. Mol Cell. 2006,22(4):511-519.
123. Savinainen KJ, Helenius MA, Lehtonen HJ,et al..Overexpression of EIF3S3 promotes cancer cell growth. Prostate. 2006 ,66(11):1144-1150.
124. Savinainen KJ, Linja MJ, Saram?ki OR,et al.. Expression and copy number analysis of TRPS1, EIF3S3 and MYC genes in breast and prostate cancer. Br J Cancer. 2004 ,90(5):1041-1046.
125. Lyons TE, Salih M, Tuana BS,et al..Activating E2Fs mediate transcriptional regulation of human E2F6 repressor. Am J Physiol Cell Physiol, 2006,290(1):189-199.
126. Giangrande PH, Zhu W, Schlisio S,et al..A role for E2F6 in distinguishing G1/S- and G2/M-specific transcription. Genes Dev, 2004,18(23):2941-2951.
127. Attwooll C, Oddi S, Cartwright P,et al..A novel repressive E2F6 complex containing the polycomb group protein, EPC1, that interacts with EZH2 in a proliferation-specific manner. J Biol Chem, 2005 ,280(2):1199-1208.
128. González L, Freije JM, Cal S,et al.. A functional link between the tumour suppressors ARF and p33ING1[J]. Oncogene. 2006,25(37):5173-5179.
129. Gong W, Russell M, Suzuki K,et al.. Subcellular targeting of p33ING1b by phosphorylation-dependent 14-3-3 binding regulates p21WAF1 expression[J]. Mol Cell Biol. 2006, 26(8): 2947-2954.
130. Okano T, Gemma A, Hosoya Y, et al.. Alterations in novel candidate tumor suppressor genes, ING1 and ING2 in human lung cancer.Oncol Rep[J]. 2006,15(3):545-549.
131. GhassibéM, Bernier V, Boon LM,et al.. A novel mutation in the MSX2 homeoboxgene of a family with foramina parietalia permagna, headache and vascular anomaly[J]. Eur J Pediatr, 2006, 165(10): 734- 735.
132. Morsczeck C.Gene expression of runx2, Osterix, c-fos, DLX-3, DLX-5, and MSX-2 in dental follicle cells during osteogenic differentiation in vitro.Calcif Tissue Int[J], 2006,78(2):98-102.
133. Hamada S, Satoh K, Kimura K,et al.. MSX2 overexpression inhibits gemcitabine- induced caspase-3 activity in pancreatic cancer cells[J].World J Gastroenterol, 2005, 11(43): 6867-6870.
134. Yu Y, Ji H, Doudna JA,et al.. Mass spectrometric analysis of the human 40S ribosomal subunit: native and HCV IRES-bound complexes[J]. Protein Sci, 2005, 14(6): 1438-1446.
135. Zhou ZD, Bao L, Liu DG,et al.. Low content of protein S29 in ribosomes of human lung cancer cell line a549: detected by two-dimensional electrophoresis[J]. Protein Pept Lett, 2003,10(1):91-97.
136. Mahmoudi T, Parra M, Vries RG,et al.. The SWI/SNF chromatin-remodeling complex is a cofactor for Tat transactivation of the HIV promoter[J].J Biol Chem, 2006, 281(29):19960-19968.
137. Ring HZ, Vameghi-Meyers V, Wang W, et al.. Five SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin (SMARC) genes are dispersed in the human genome[J]. Genomics. 1998,51(1):140-143.
138. Ho KK, Hurley TD, Weiner H.Selective alteration of the rate-limiting step in cytosolic aldehyde dehydrogenase through random mutagenesis[J]. Biochemistry, 2006, 45(31): 9445- 9453.
139. Ehlers CL, Spence JP, Wall TL,et al.. Association of ALDH1 promoter polymorphisms with alcohol-related phenotypes in southwest California Indians[J]. Alcohol Clin Exp Res, 2004, 28(10): 1481-1486.
140. Emingil G, Tervahartiala T, M?ntyl? P,et al.. Gingival crevicular fluid matrix metalloproteinase (MMP)-7, extracellular MMP inducer, and tissue inhibitor of MMP-1 levels in periodontal disease[J]. J Periodontol,2006, 77(12):2040-2050.
141. Jiang H, Zhu H, Chen X,et a.. TIMP-1 transgenic mice recover from diabetes induced by multiple low-dose streptozotocin. Diabetes[J],2007,56(1):49-56.
142. Hoffmann U, Bertsch T, Dvortsak E,et al.. Matrix-metalloproteinases and their inhibitors are elevated in severe sepsis: prognostic value of TIMP-1 in severe sepsis[J]. Scand J Infect Dis, 2006, 38(10): 867-872.
143. Pak BJ, Park H, Chang ER,ET AL.. Differential display analysis of oxygen-mediated changes in gene expression in first trimester human trophoblast cells[J]. Placenta,1998, 19(7):483-488.
144. Hussey DJ, Parker NJ, Hussey ND,et al.. Characterization of a KRAB family zinc finger gene, ZNF195, mapping to chromosome band 11p15.5[J].Genomics,1997, 45(2):451-455.
145. Shibata R, Mori T, Du W, Chuma M,et al.. Overexpression of cyclase-associated protein 2 in multistage hepatocarcinogenesis[J]. Clin Cancer Res, 2006,12(18): 5363- 5368.
146. Osawa Y, Xu D, Sternberg D,et al.. Functional expression of the GABAB receptor in human airway smooth muscle[J]. Am J Physiol Lung Cell Mol Physiol, 2006, 291(5): L923- 1931.
147. Brock C, Boudier L, Maurel D,et al.. Assembly-dependent surface targeting of the heterodimeric GABAB Receptor is controlled by COPI but not 14-3-3[J]. Mol Biol Cell, 2005, 16(12):5572-5578.
148. Wittmack EK, Rush AM, Craner MJ,et al.. Fibroblast growth factor homologous factor 2B: association with Nav1.6 and selective colocalization at nodes of Ranvier of dorsal root axons[J]. J Neurosci, 2004,24(30):6765-6775.
149. Knox S, Merry C, Stringer S,et al.. Not all perlecans are created equal: interactions with fibroblast growth factor (FGF) 2 and FGF receptors[J]. J Biol Chem, 2002, 277(17):14657-14665.
150. Lee HG, Ueda M, Miyamoto Y,et al.. Aberrant localization of importin alpha1 in hippocampal neurons in Alzheimer disease[J]. Brain Res, 2006,1124(1):1-4.
151. Tseng SF, Chang CY, Wu KJ, et al.. Importin KPNA2 is required for proper nuclear localization and multiple functions of NBS1[J]. J Biol Chem, 2005, 280(47): 39594- 39600.
152. Lee KY, Ahn YM, Joo EJ,et al.. The association of DUSP6 gene with schizophrenia and bipolar disorder: its possible role in the development of bipolar disorder[J]. MolPsychiatry, 2006, 11(5): 425-426.
153. Furukawa T, Fujisaki R, Yoshida Y,et al.. Distinct progression pathways involving the dysfunction of DUSP6/MKP-3 in pancreatic intraepithelial neoplasia and intraductal papillary-mucinous neoplasms of the pancreas[J]. Mod Pathol,2005, 18(8):1034-1042.
154. Xu S, Furukawa T, Kanai N,et al.. Abrogation of DUSP6 by hypermethylation in human pancreatic cancer[J]. J Hum Genet, 2005, 50(4):159-167.
155. Jiang CL, Jin SG, Pfeifer GP.MBD3L1 is a transcriptional repressor that interacts with methyl-CpG-binding protein 2 (MBD2) and components of the NuRD complex[J]. J Biol Chem, 2004,279(50):52456-52464.
156. Saito M, Ishikawa F.The mCpG-binding domain of human MBD3 does not bind to mCpG but interacts with NuRD/Mi2 components HDAC1 and MTA2[J]. J Biol Chem, 2002, 277(38): 35434-35439.
157. Yarden RI, Brody LC.BRCA1 interacts with components of the histone deacetylase complex[J]. Proc Natl Acad Sci U S A, 1999 ,96(9): 4983-4988.
158. Shimozawa N, Tsukamoto T, Nagase T, et al.. Identification of a new complementation group of the peroxisome biogenesis disorders and PEX14 as the mutated gene[J]. Hum Mutat, 2004, 23(6): 552-558.
159. Oliveira ME, Reguenga C, Gouveia AM, et al.. Mammalian Pex14p: membrane topology and characterisation of the Pex14p-Pex14p interaction[J]. Biochim Biophys Acta, 2002,1567(1-2):13-22.
160. Ban J, Siligan C, Kreppel M, et al.. EWS-FLI1 in Ewing's sarcoma: real targets and collateral damage[J]. Adv Exp Med Biol, 2006,587:41-52.
161. Kinsey M, Smith R, Lessnick SL.NR0B1 is required for the oncogenic phenotype mediated by EWS/FLI in Ewing's sarcoma[J]. Mol Cancer Res, 2006,4(11):851-859.
162. Hallor KH, Mertens F, Jin Y, Meis-Kindblom JM, et al.. Fusion of the EWSR1 and ATF1 genes without expression of the MITF-M transcript in angiomatoid fibrous histiocytoma[J]. Genes Chromosomes Cancer, 2005,44(1):97-102.
163. Jishage M, Fujino T, Yamazaki Y,et al.. Identification of target genes for EWS/ATF-1 chimeric transcription factor[J]. Oncogene, 2003, 22(1):41-49.
164. Weintrob N, Drouin J, Vallette-Kasic S,et al.. Low estriol levels in the maternal triple-marker screen as a predictor of isolated adrenocorticotropic hormone deficiencycaused by a new mutation in the TPIT gene[J]. Pediatrics, 2006,117(2):e322-327.
165. Vallette-Kasic S, Pulichino AM, Gueydan M,et al.. A neonatal form of isolated ACTH deficiency frequently associated with Tpit gene mutations[J]. Endocr Res, 2004, 30(4): 943- 944.
166. Vallette-Kasic S, Brue T, Pulichino AM,et al.. Congenital isolated adrenocorticotropin deficiency: an underestimated cause of neonatal death, explained by TPIT gene mutations[J]. J Clin Endocrinol Metab, 2005, 90(3):1323-1331.