Is human blood a good surrogate for brain tissue in transcriptional studies?
详细信息    查看全文
  • 作者:Chaochao Cai (1) (2)
    Peter Langfelder (1)
    Tova F Fuller (1)
    Michael C Oldham (3)
    Rui Luo (1)
    Leonard H van den Berg (4)
    Roel A Ophoff (4) (5)
    Steve Horvath (1) (2)
  • 刊名:BMC Genomics
  • 出版年:2010
  • 出版时间:December 2010
  • 年:2010
  • 卷:11
  • 期:1
  • 全文大小:1694KB
  • 参考文献:1. Jasinska AJ, Service S, Choi OW, DeYoung J, Grujic O, Kong SY, Jorgensen MJ, Bailey J, Breidenthal S, Fairbanks LA, / et al.: Identification of brain transcriptional variation reproduced in peripheral blood: an approach for mapping brain expression traits. / Hum Mol Genet 2009, 18 (22) : 4415鈥?427. CrossRef
    2. Sullivan PF, Fan C, Perou CM: Evaluating the comparability of gene expression in blood and brain. / Am J Med Genet B Neuropsychiatr Genet 2006, 141B (3) : 261鈥?68. CrossRef
    3. Gladkevich A, Kauffman HF, Korf J: Lymphocytes as a neural probe: potential for studying psychiatric disorders. / Prog Neuropsychopharmacol Biol Psychiatry 2004, 28 (3) : 559鈥?76. CrossRef
    4. Glatt SJ, Everall IP, Kremen WS, Corbeil J, Sasik R, Khanlou N, Han M, Liew CC, Tsuang MT: Comparative gene expression analysis of blood and brain provides concurrent validation of SELENBP1 up-regulation in schizophrenia. / Proc Natl Acad Sci USA 2005, 102 (43) : 15533鈥?5538. CrossRef
    5. Matigian NA, McCurdy RD, F茅ron Fo, Perry C, Smith H, Filippich C, McLean D, McGrath J, Mackay-Sim A, Mowry B, / et al.: Fibroblast and Lymphoblast Gene Expression Profiles in Schizophrenia: Are Non-Neural Cells Informative? / PLoS ONE 2008, 3 (6) : e2412. CrossRef
    6. Tsuang MT, Nossova N, Yager T, Tsuang MM, Guo SC, Shyu KG, Glatt SJ, Liew CC: Assessing the validity of blood-based gene expression profiles for the classification of schizophrenia and bipolar disorder: a preliminary report. / Am J Med Genet B Neuropsychiatr Genet 2005, 133B (1) : 1鈥?. CrossRef
    7. Saris CG, Horvath S, van Vught PW, van Es MA, Blauw HM, Fuller TF, Langfelder P, DeYoung J, Wokke JH, Veldink JH, / et al.: Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients. / BMC Genomics 2009, 10: 405. CrossRef
    8. Borovecki F, Lovrecic L, Zhou J, Jeong H, Then F, Rosas HD, Hersch SM, Hogarth P, Bouzou B, Jensen RV, / et al.: Genome-wide expression profiling of human blood reveals biomarkers for Huntington's disease. / Proc Natl Acad Sci USA 2005, 102 (31) : 11023鈥?1028. CrossRef
    9. Maes OC, Xu S, Yu B, Chertkow HM, Wang E, Schipper HM: Transcriptional profiling of Alzheimer blood mononuclear cells by microarray. / Neurobiol Aging 2007, 28 (12) : 1795鈥?809. CrossRef
    10. Presson A, Sobel E, Papp J, Suarez C, Whistler T, Rajeevan M, Vernon S, Horvath S: Integrated Weighted Gene Co-expression Network Analysis with an Application to Chronic Fatigue Syndrome. / BMC Systems Biology 2008, 2 (1) : 95. CrossRef
    11. Oldham MC, Horvath S, Geschwind DH: Conservation and evolution of gene coexpression networks in human and chimpanzee brains. / Proc Natl Acad Sci USA 2006, 103 (47) : 17973鈥?7978. CrossRef
    12. Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH: Functional organization of the transcriptome in human brain. / Nat Neurosci 2008, 11 (11) : 1271鈥?282. CrossRef
    13. Miller JA, Oldham MC, Geschwind DH: A systems level analysis of transcriptional changes in Alzheimer's disease and normal aging. / J Neurosci 2008, 28 (6) : 1410鈥?420. CrossRef
    14. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ, Chen RO, Brownstein BH, Cobb JP, Tschoeke SK, / et al.: A network-based analysis of systemic inflammation in humans. / Nature 2005, 437 (7061) : 1032鈥?037. CrossRef
    15. Hodges A, Strand AD, Aragaki AK, Kuhn A, Sengstag T, Hughes G, Elliston LA, Hartog C, Goldstein DR, Thu D, / et al.: Regional and cellular gene expression changes in human Huntington's disease brain. / Hum Mol Genet 2006, 15 (6) : 965鈥?77. CrossRef
    16. Iwamoto K, Bundo M, Kato T: Altered expression of mitochondria-related genes in postmortem brains of patients with bipolar disorder or schizophrenia, as revealed by large-scale DNA microarray analysis. / Hum Mol Genet 2005, 14 (2) : 241鈥?53. CrossRef
    17. Ryan MM, Lockstone HE, Huffaker SJ, Wayland MT, Webster MJ, Bahn S: Gene expression analysis of bipolar disorder reveals downregulation of the ubiquitin cycle and alterations in synaptic genes. / Mol Psychiatry 2006, 11 (10) : 965鈥?78. CrossRef
    18. van der Merwe PA, McNamee PN, Davies EA, Barclay AN, Davis SJ: Topology of the CD2-CD48 cell-adhesion molecule complex: implications for antigen recognition by T cells. / Curr Biol 1995, 5 (1) : 74鈥?4. CrossRef
    19. Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM, Laurance MF, Zhao W, Qi S, Chen Z, / et al.: Analysis of oncogenic signaling networks in glioblastoma identifies ASPM as a molecular target. / Proc Natl Acad Sci USA 2006, 103 (46) : 17402鈥?7407. CrossRef
    20. Langfelder P, Horvath S: WGCNA: an R package for weighted correlation network analysis. / BMC Bioinformatics 2008, 9: 559. CrossRef
    21. Zhang B, Horvath S: A general framework for weighted gene co-expression network analysis. / Stat Appl Genet Mol Biol 2005., 4: Article17
    22. Goring HH, Curran JE, Johnson MP, Dyer TD, Charlesworth J, Cole SA, Jowett JB, Abraham LJ, Rainwater DL, Comuzzie AG, / et al.: Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. / Nat Genet 2007, 39 (10) : 1208鈥?216. CrossRef
    23. Johnson WE, Li C, Rabinovic A: Adjusting batch effects in microarray expression data using empirical Bayes methods. / Biostatistics 2007, 8 (1) : 118鈥?27. CrossRef
    24. Ghazalpour A, Doss S, Zhang B, Plaisier C, Wang S, Schadt E, Drake TA, Lusis A, Horvath S: Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight. / PLoS Genet 2006, 2 (8) : e130. CrossRef
    25. Langfelder P, Horvath S: Eigengene networks for studying the relationships between co-expression modules. / BMC Syst Biol 2007, 1: 54. CrossRef
    26. Horvath S, Dong J: Geometric interpretation of gene coexpression network analysis. / PLoS Comput Biol 2008, 4 (8) : e1000117. CrossRef
    27. Torkamani A, Dean B, Schork NJ, Thomas EA: Coexpression network analysis of neural tissue reveals perturbations in developmental processes in schizophrenia. / Genome Research 20 (4) : 403鈥?12.
    28. Davies MN, Lawn S, Whatley S, Fernandes C, Williams RW, Schalkwyk LC: To what extent is blood a reasonable surrogate for brain in gene expression studies: estimation from mouse hippocampus and spleen. / Frontiers in Neurogenomics 2009., 3 (54) :
    29. Keller MP, Choi Y, Wang P, Davis DB, Rabaglia ME, Oler AT, Stapleton DS, Argmann C, Schueler KL, Edwards S, / et al.: A gene expression network model of type 2 diabetes links cell cycle regulation in islets with diabetes susceptibility. / Genome Res 2008, 18 (5) : 706鈥?16. CrossRef
    30. Stuart JM, Segal E, Koller D, Kim SK: A gene-coexpression network for global discovery of conserved genetic modules. / Science 2003, 302 (5643) : 249鈥?55. CrossRef
    31. Dong J, Horvath S: Understanding network concepts in modules. / BMC Syst Biol 2007, 1: 24. CrossRef
    32. Mason M, Fan G, Plath K, Zhou Q, Horvath S: Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells. / BMC Genomics 2009, 10 (1) : 327. CrossRef
    33. Yip AM, Horvath S: Gene network interconnectedness and the generalized topological overlap measure. / BMC Bioinformatics 2007, 8: 22. CrossRef
    34. Li A, Horvath S: Network neighborhood analysis with the multi-node topological overlap measure. / Bioinformatics 2007, 23 (2) : 222鈥?31. CrossRef
    35. Langfelder P, Zhang B, Horvath S: Defining clusters from a hierarchical cluster tree: the Dynamic Tree Cut package for R. / Bioinformatics 2008, 24 (5) : 719鈥?20. CrossRef
  • 作者单位:Chaochao Cai (1) (2)
    Peter Langfelder (1)
    Tova F Fuller (1)
    Michael C Oldham (3)
    Rui Luo (1)
    Leonard H van den Berg (4)
    Roel A Ophoff (4) (5)
    Steve Horvath (1) (2)

    1. Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, 90095, Los Angeles, CA, USA
    2. Department of Biostatistics, David Geffen School of Medicine, University of California Los Angeles, 90095, Los Angeles, CA, USA
    3. Department of Neurology, The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, 94143, San Francisco, CA, USA
    4. Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, 3584, CX, Utrecht, The Netherlands
    5. UCLA Center for Neurobehavioral Genetics, Semel Institute of Neuroscience and Human Behavioral, School of Medicine, University of California Los Angeles, 90095, Los Angeles, CA, USA
文摘
Background Since human brain tissue is often unavailable for transcriptional profiling studies, blood expression data is frequently used as a substitute. The underlying hypothesis in such studies is that genes expressed in brain tissue leave a transcriptional footprint in blood. We tested this hypothesis by relating three human brain expression data sets (from cortex, cerebellum and caudate nucleus) to two large human blood expression data sets (comprised of 1463 individuals). Results We found mean expression levels were weakly correlated between the brain and blood data (r range: [0.24,0.32]). Further, we tested whether co-expression relationships were preserved between the three brain regions and blood. Only a handful of brain co-expression modules showed strong evidence of preservation and these modules could be combined into a single large blood module. We also identified highly connected intramodular "hub" genes inside preserved modules. These preserved intramodular hub genes had the following properties: first, their expression levels tended to be significantly more heritable than those from non-preserved intramodular hub genes (p < 10-90); second, they had highly significant positive correlations with the following cluster of differentiation genes: CD58, CD47, CD48, CD53 and CD164; third, a significant number of them were known to be involved in infection mechanisms, post-transcriptional and post-translational modification and other basic processes. Conclusions Overall, we find transcriptome organization is poorly preserved between brain and blood. However, the subset of preserved co-expression relationships characterized here may aid future efforts to identify blood biomarkers for neurological and neuropsychiatric diseases when brain tissue samples are unavailable.

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700