基于基因表达谱识别人类疾病相关基因和功能
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摘要
DNA微阵列已经在功能基因组学研究中获得了广泛的应用。在人类疾病研究领域,分离出在疾病状态下特异的基因表达变化具有十分重要的研究意义和实用价值。如何从众多差异表达基因中分离出那些跟疾病形成与发展相关的表达变化,已经成为疾病基因表达谱数据分析的一个关键问题,目前的差异分析方法还不能完全解决这个问题。
为了解决这个问题,提出了系统的基因表达谱差异分析方法。通过整合基因水平差异分析和功能水平差异分析,从基因、功能两个层次上研究基因表达变化与疾病表型的关联。基因水平差异分析采用了修正的T检验算法来分析单个基因的表达变化,并用显著性p值来描述变化程度;功能水平差异分析采用了组合的S检验,通过整合基因的功能编目分类信息,利用单个基因差异表达的定量信息,研究一组功能相关基因的一致性表达变化。
基于上述的分析策略,开发了基因表达谱差异分析软件MageKey(v1.0)。利用MageKey分析一组人类肌萎缩性脊髓侧索硬化症疾病的基因表达谱数据,测试结果显示了这种分析策略能够有效地建立疾病表型与基因和功能之间的关联。
通过整合功能注释信息,从基因水平和功能水平研究基因表达变化与疾病表型的关联,这种分析策略能够快速地将疾病表型与功能失调及其表达变化关联起来,帮助生成一些关于疾病机制的生物学假设。同时本文的结果也说明了整合功能注释信息研究基因表达谱数据的重要性。
Genome-wide differential expression studies of human diseases using microarray technology usually produce long lists of genes with altered expression, therefore, the genes causally involved in a disease cannot be effectively separated from innocent bystanders. Existing methods for differential analysis of gene expression profiles seem unable to solve this problem successfully.
We present a systematic strategy that combines gene-wise and function-wise differential analysis of gene expression profiles to interrelate genes and functions with human diseases. The gene-wise analysis adopts a modified T-test to analyze the expression alteration of each single gene, and the alteration is represented by quantitative significant p-value. The function-wise analysis uses a new combined S-test to identify coordinate alterations of genes within each functional category.
A computational tool, MageKey, is developed based on this strategy, and its utility is demonstrated by the analysis results of gene expression dataset of human Amyotrophic Lateral Sclerosis (ALS) disease. MageKey is able to associate relevant biological functions with human disease.
The association between disease phenotype and gene expression change is investigated by integrating functional annotation information. Such analysis strategy is able to effectively interrelate genes and functions with human disease, and help generate biology hypothesis. Meanwhile, our results associating genes and functions with human disease illustrate the value of integrating functional annotation information in the analysis of gene expression profiles.
为了解决这个问题,提出了系统的基因表达谱差异分析方法。通过整合基因水平差异分析和功能水平差异分析,从基因、功能两个层次上研究基因表达变化与疾病表型的关联。基因水平差异分析采用了修正的T检验算法来分析单个基因的表达变化,并用显著性p值来描述变化程度;功能水平差异分析采用了组合的S检验,通过整合基因的功能编目分类信息,利用单个基因差异表达的定量信息,研究一组功能相关基因的一致性表达变化。
基于上述的分析策略,开发了基因表达谱差异分析软件MageKey(v1.0)。利用MageKey分析一组人类肌萎缩性脊髓侧索硬化症疾病的基因表达谱数据,测试结果显示了这种分析策略能够有效地建立疾病表型与基因和功能之间的关联。
通过整合功能注释信息,从基因水平和功能水平研究基因表达变化与疾病表型的关联,这种分析策略能够快速地将疾病表型与功能失调及其表达变化关联起来,帮助生成一些关于疾病机制的生物学假设。同时本文的结果也说明了整合功能注释信息研究基因表达谱数据的重要性。
Genome-wide differential expression studies of human diseases using microarray technology usually produce long lists of genes with altered expression, therefore, the genes causally involved in a disease cannot be effectively separated from innocent bystanders. Existing methods for differential analysis of gene expression profiles seem unable to solve this problem successfully.
We present a systematic strategy that combines gene-wise and function-wise differential analysis of gene expression profiles to interrelate genes and functions with human diseases. The gene-wise analysis adopts a modified T-test to analyze the expression alteration of each single gene, and the alteration is represented by quantitative significant p-value. The function-wise analysis uses a new combined S-test to identify coordinate alterations of genes within each functional category.
A computational tool, MageKey, is developed based on this strategy, and its utility is demonstrated by the analysis results of gene expression dataset of human Amyotrophic Lateral Sclerosis (ALS) disease. MageKey is able to associate relevant biological functions with human disease.
The association between disease phenotype and gene expression change is investigated by integrating functional annotation information. Such analysis strategy is able to effectively interrelate genes and functions with human disease, and help generate biology hypothesis. Meanwhile, our results associating genes and functions with human disease illustrate the value of integrating functional annotation information in the analysis of gene expression profiles.
引文
[1] Persidis A. Biochips. Nat Biotechnol, 1998, 16(10): 981~983
[2] Jain K K. Tech.Sight. Biochips for gene spotting. Science, 2001, 294(5542): 621~623
[3] Ng J H, Ilag L L. Biochips beyond DNA: technologies and applications. Biotechnol Annu Rev, 2003, 9: 1~149
[4] Stears R L, Martinsky T, Schena M. Trends in microarray analysis. Nat Med, 2003, 9(1): 140~145
[5] Schena M, Shalon D, Davis R W, et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 1995, 270(5235): 467~470
[6] Lockhart D J, Dong H, Byrne M C, et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol, 1996, 14(13): 1675~1680
[7] Brown P O, Botstein D. Exploring the new world of the genome with DNA microarrays. Nat Genet, 1999, 21(1 Suppl): 33~37
[8] Lipshutz R J, Fodor S P, Gingeras T R, et al. High density synthetic oligonucleotide arrays. Nat Genet, 1999, 21(1 Suppl): 20~24
[9] Schulze A, Downward J. Navigating gene expression using microarrays--a technology review. Nat Cell Biol, 2001, 3(8): E190~E195
[10] Young R A. Biomedical discovery with DNA arrays. Cell, 2000, 102(1): 9~15
[11] Xiang Z, Yang Y, Ma X, et al. Microarray expression profiling: analysis and applications. Curr Opin Drug Discov Devel, 2003, 6(3): 384~395
[12] Bals R, Jany B. Identification of disease genes by expression profiling. Eur Respir J, 2001, 18(5): 882~889
[13] McCarthy M I, Smedley D, Hide W. New methods for finding disease-susceptibility genes: impact and potential. Genome Biol, 2003, 4(10): 119
[14] Meltzer P S. Spotting the target: microarrays for disease gene discovery. Curr Opin Genet Dev, 2001, 11(3): 258~263
[15] Lyons P A. Gene-expression profiling and the genetic dissection of complex disease. Curr Opin Immunol, 2002, 14(5): 627~630
[16] Brazma A, Parkinson H, Sarkans U, et al. ArrayExpress--a public repository for microarray gene expression data at the EBI. Nucleic Acids Res, 2003, 31(1): 68~71
[17] Edgar R, Domrachev M, Lash A E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res, 2002, 30(1): 207~210
[18] Gollub J, Ball C A, Binkley G, et al. The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Res, 2003, 31(1): 94~96
[19] Brazma A, Vilo J. Gene expression data analysis. FEBS Lett, 2000, 480(1): 17~24
[20] Altman R B, Raychaudhuri S. Whole-genome expression analysis: challenges beyond clustering. Curr Opin Struct Biol, 2001, 11(3): 340~347
[21] Quackenbush J. Computational analysis of microarray data. Nat Rev Genet, 2001, 2(6): 418~427
[22] Butte A. The use and analysis of microarray data. Nat Rev Drug Discov, 2002, 1(12): 951~960
[23] Nadon R, Shoemaker J. Statistical issues with microarrays: processing and analysis. Trends Genet, 2002, 18(5): 265~271
[24] Quackenbush J. Microarray data normalization and transformation. Nat Genet, 2002, 32 Suppl: 496~501
[25] Leung Y F, Cavalieri D. Fundamentals of cDNA microarray data analysis. Trends Genet, 2003, 19(11): 649~659
[26] Stolovitzky G. Gene selection in microarray data: the elephant, the blind men and our algorithms. Curr Opin Struct Biol, 2003, 13(3): 370~376
[27] Zhang L, Zhou W, Velculescu V E, et al. Gene expression profiles in normal and cancer cells. Science, 1997, 276(5316): 1268~1272
[28] Baldi P, Long A D. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics, 2001, 17(6): 509~519
[29] Tusher V G, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A, 2001, 98(9): 5116~5121
[30] Wu T D. Analysing gene expression data from DNA microarrays to identify candidate genes. J Pathol, 2001, 195(1): 53~65
[31] Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics, 2002, 18(4): 546~554
[32] Troyanskaya O G, Garber M E, Brown P O, et al. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics, 2002, 18(11): 1454~1461
[33] Cui X, Churchill G A. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol, 2003, 4(4): 210
[34] Li X, Rao S, Wang Y, et al. Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. Nucleic Acids Res, 2004, 32(9): 2685~2694
[35] Miklos G L, Maleszka R. Microarray reality checks in the context of a complex disease. Nat Biotechnol, 2004, 22(5): 615~621
[36] Khatri P, Draghici S, Ostermeier G C, et al. Profiling gene expression using onto-express. Genomics, 2002, 79(2): 266~270
[37] Doniger S W, Salomonis N, Dahlquist K D, et al. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol, 2003, 4(1): R7
[38] Al Shahrour F, Diaz-Uriarte R, Dopazo J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics, 2004, 20(4): 578~580
[39] Cheng J, Sun S, Tracy A, et al. NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis. Bioinformatics, 2004, 20(9): 1462~1463
[40] Masseroli M, Martucci D, Pinciroli F. GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining. Nucleic Acids Res, 2004, 32(Web Server issue): W293~W300
[41] Zhang B, Schmoyer D, Kirov S, et al. GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics, 2004, 5(1): 16
[42] Draghici S, Khatri P, Martins R P, et al. Global functional profiling of gene expression. Genomics, 2003, 81(2): 98~104
[43] Volinia S, Evangelisti R, Francioso F, et al. GOAL: automated Gene Ontology analysis of expression profiles. Nucleic Acids Res, 2004, 32(Web Server issue): W492~W499
[44] Kanehisa M, Goto S, Kawashima S, et al. The KEGG resource for deciphering the genome. Nucleic Acids Res, 2004, 32(Database issue): D277~D280
[45] Kanehisa M, Goto S, Kawashima S, et al. The KEGG databases at GenomeNet. Nucleic Acids Res, 2002, 30(1): 42~46
[46] Ashburner M, Ball C A, Blake J A, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000, 25(1): 25~29
[47] Harris M A, Clark J, Ireland A, et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res, 2004, 32 Database issue: D258~D261
[48] Pruitt K D, Maglott D R. RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Res, 2001, 29(1): 137~140
[49] Liu G, Loraine A E, Shigeta R, et al. NetAffx: Affymetrix probesets and annotations. Nucleic Acids Res, 2003, 31(1): 82~86
[50] Malaspina A, de Belleroche J. Spinal cord molecular profiling provides a better understanding of amyotrophic lateral sclerosis pathogenesis. Brain Res Rev, 2004, 45(3): 213~229
[51] Dangond F, Hwang D, Camelo S, et al. Molecular signature of late-stage human ALS revealed by expression profiling of postmortem spinal cord gray matter. Physiol Genomics, 2004, 16(2): 229~239
[52] Mirnics K, Pevsner J. Progress in the use of microarray technology to study the neurobiology of disease. Nat Neurosci, 2004, 7(5): 434~439
[53] Casabona G. Intracellular signal modulation: a pivotal role for protein kinase C. Prog Neuropsychopharmacol Biol Psychiatry, 1997, 21(3): 407~425
[54] Bull N D, Barnett N L. Antagonists of protein kinase C inhibit rat retinal glutamate transport activity in situ. J Neurochem, 2002, 81(3): 472~480
[55] Lanius R A, Paddon H B, Mezei M, et al. A role for amplified protein kinase C activity in the pathogenesis of amyotrophic lateral sclerosis. J Neurochem, 1995, 65(2): 927~930
[56] Hu J H, Chernoff K, Pelech S, et al. Protein kinase and protein phosphataseexpression in the central nervous system of G93A mSOD over-expressing mice. J Neurochem, 2003, 85(2): 422~431
[57] Camon E, Magrane M, Barrell D, et al. The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro. Genome Res, 2003, 13(4): 662~672
[58] Camon E, Magrane M, Barrell D, et al. The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res, 2004, 32(Database issue): D262~D266
[2] Jain K K. Tech.Sight. Biochips for gene spotting. Science, 2001, 294(5542): 621~623
[3] Ng J H, Ilag L L. Biochips beyond DNA: technologies and applications. Biotechnol Annu Rev, 2003, 9: 1~149
[4] Stears R L, Martinsky T, Schena M. Trends in microarray analysis. Nat Med, 2003, 9(1): 140~145
[5] Schena M, Shalon D, Davis R W, et al. Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 1995, 270(5235): 467~470
[6] Lockhart D J, Dong H, Byrne M C, et al. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol, 1996, 14(13): 1675~1680
[7] Brown P O, Botstein D. Exploring the new world of the genome with DNA microarrays. Nat Genet, 1999, 21(1 Suppl): 33~37
[8] Lipshutz R J, Fodor S P, Gingeras T R, et al. High density synthetic oligonucleotide arrays. Nat Genet, 1999, 21(1 Suppl): 20~24
[9] Schulze A, Downward J. Navigating gene expression using microarrays--a technology review. Nat Cell Biol, 2001, 3(8): E190~E195
[10] Young R A. Biomedical discovery with DNA arrays. Cell, 2000, 102(1): 9~15
[11] Xiang Z, Yang Y, Ma X, et al. Microarray expression profiling: analysis and applications. Curr Opin Drug Discov Devel, 2003, 6(3): 384~395
[12] Bals R, Jany B. Identification of disease genes by expression profiling. Eur Respir J, 2001, 18(5): 882~889
[13] McCarthy M I, Smedley D, Hide W. New methods for finding disease-susceptibility genes: impact and potential. Genome Biol, 2003, 4(10): 119
[14] Meltzer P S. Spotting the target: microarrays for disease gene discovery. Curr Opin Genet Dev, 2001, 11(3): 258~263
[15] Lyons P A. Gene-expression profiling and the genetic dissection of complex disease. Curr Opin Immunol, 2002, 14(5): 627~630
[16] Brazma A, Parkinson H, Sarkans U, et al. ArrayExpress--a public repository for microarray gene expression data at the EBI. Nucleic Acids Res, 2003, 31(1): 68~71
[17] Edgar R, Domrachev M, Lash A E. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res, 2002, 30(1): 207~210
[18] Gollub J, Ball C A, Binkley G, et al. The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Res, 2003, 31(1): 94~96
[19] Brazma A, Vilo J. Gene expression data analysis. FEBS Lett, 2000, 480(1): 17~24
[20] Altman R B, Raychaudhuri S. Whole-genome expression analysis: challenges beyond clustering. Curr Opin Struct Biol, 2001, 11(3): 340~347
[21] Quackenbush J. Computational analysis of microarray data. Nat Rev Genet, 2001, 2(6): 418~427
[22] Butte A. The use and analysis of microarray data. Nat Rev Drug Discov, 2002, 1(12): 951~960
[23] Nadon R, Shoemaker J. Statistical issues with microarrays: processing and analysis. Trends Genet, 2002, 18(5): 265~271
[24] Quackenbush J. Microarray data normalization and transformation. Nat Genet, 2002, 32 Suppl: 496~501
[25] Leung Y F, Cavalieri D. Fundamentals of cDNA microarray data analysis. Trends Genet, 2003, 19(11): 649~659
[26] Stolovitzky G. Gene selection in microarray data: the elephant, the blind men and our algorithms. Curr Opin Struct Biol, 2003, 13(3): 370~376
[27] Zhang L, Zhou W, Velculescu V E, et al. Gene expression profiles in normal and cancer cells. Science, 1997, 276(5316): 1268~1272
[28] Baldi P, Long A D. A Bayesian framework for the analysis of microarray expression data: regularized t -test and statistical inferences of gene changes. Bioinformatics, 2001, 17(6): 509~519
[29] Tusher V G, Tibshirani R, Chu G. Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A, 2001, 98(9): 5116~5121
[30] Wu T D. Analysing gene expression data from DNA microarrays to identify candidate genes. J Pathol, 2001, 195(1): 53~65
[31] Pan W. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics, 2002, 18(4): 546~554
[32] Troyanskaya O G, Garber M E, Brown P O, et al. Nonparametric methods for identifying differentially expressed genes in microarray data. Bioinformatics, 2002, 18(11): 1454~1461
[33] Cui X, Churchill G A. Statistical tests for differential expression in cDNA microarray experiments. Genome Biol, 2003, 4(4): 210
[34] Li X, Rao S, Wang Y, et al. Gene mining: a novel and powerful ensemble decision approach to hunting for disease genes using microarray expression profiling. Nucleic Acids Res, 2004, 32(9): 2685~2694
[35] Miklos G L, Maleszka R. Microarray reality checks in the context of a complex disease. Nat Biotechnol, 2004, 22(5): 615~621
[36] Khatri P, Draghici S, Ostermeier G C, et al. Profiling gene expression using onto-express. Genomics, 2002, 79(2): 266~270
[37] Doniger S W, Salomonis N, Dahlquist K D, et al. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol, 2003, 4(1): R7
[38] Al Shahrour F, Diaz-Uriarte R, Dopazo J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics, 2004, 20(4): 578~580
[39] Cheng J, Sun S, Tracy A, et al. NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis. Bioinformatics, 2004, 20(9): 1462~1463
[40] Masseroli M, Martucci D, Pinciroli F. GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysis, and mining. Nucleic Acids Res, 2004, 32(Web Server issue): W293~W300
[41] Zhang B, Schmoyer D, Kirov S, et al. GOTree Machine (GOTM): a web-based platform for interpreting sets of interesting genes using Gene Ontology hierarchies. BMC Bioinformatics, 2004, 5(1): 16
[42] Draghici S, Khatri P, Martins R P, et al. Global functional profiling of gene expression. Genomics, 2003, 81(2): 98~104
[43] Volinia S, Evangelisti R, Francioso F, et al. GOAL: automated Gene Ontology analysis of expression profiles. Nucleic Acids Res, 2004, 32(Web Server issue): W492~W499
[44] Kanehisa M, Goto S, Kawashima S, et al. The KEGG resource for deciphering the genome. Nucleic Acids Res, 2004, 32(Database issue): D277~D280
[45] Kanehisa M, Goto S, Kawashima S, et al. The KEGG databases at GenomeNet. Nucleic Acids Res, 2002, 30(1): 42~46
[46] Ashburner M, Ball C A, Blake J A, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000, 25(1): 25~29
[47] Harris M A, Clark J, Ireland A, et al. The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res, 2004, 32 Database issue: D258~D261
[48] Pruitt K D, Maglott D R. RefSeq and LocusLink: NCBI gene-centered resources. Nucleic Acids Res, 2001, 29(1): 137~140
[49] Liu G, Loraine A E, Shigeta R, et al. NetAffx: Affymetrix probesets and annotations. Nucleic Acids Res, 2003, 31(1): 82~86
[50] Malaspina A, de Belleroche J. Spinal cord molecular profiling provides a better understanding of amyotrophic lateral sclerosis pathogenesis. Brain Res Rev, 2004, 45(3): 213~229
[51] Dangond F, Hwang D, Camelo S, et al. Molecular signature of late-stage human ALS revealed by expression profiling of postmortem spinal cord gray matter. Physiol Genomics, 2004, 16(2): 229~239
[52] Mirnics K, Pevsner J. Progress in the use of microarray technology to study the neurobiology of disease. Nat Neurosci, 2004, 7(5): 434~439
[53] Casabona G. Intracellular signal modulation: a pivotal role for protein kinase C. Prog Neuropsychopharmacol Biol Psychiatry, 1997, 21(3): 407~425
[54] Bull N D, Barnett N L. Antagonists of protein kinase C inhibit rat retinal glutamate transport activity in situ. J Neurochem, 2002, 81(3): 472~480
[55] Lanius R A, Paddon H B, Mezei M, et al. A role for amplified protein kinase C activity in the pathogenesis of amyotrophic lateral sclerosis. J Neurochem, 1995, 65(2): 927~930
[56] Hu J H, Chernoff K, Pelech S, et al. Protein kinase and protein phosphataseexpression in the central nervous system of G93A mSOD over-expressing mice. J Neurochem, 2003, 85(2): 422~431
[57] Camon E, Magrane M, Barrell D, et al. The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro. Genome Res, 2003, 13(4): 662~672
[58] Camon E, Magrane M, Barrell D, et al. The Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology. Nucleic Acids Res, 2004, 32(Database issue): D262~D266
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