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Neurogenetics

, Volume 7, Issue 2, pp 67–80 | Cite as

Gene expression analyses reveal molecular relationships among 20 regions of the human CNS

  • Richard B. RothEmail author
  • Peter Hevezi
  • Jerry Lee
  • Dorian Willhite
  • Sandra M. Lechner
  • Alan C. Foster
  • Albert Zlotnik
Original Article

Abstract

Transcriptional profiling was performed to survey the global expression patterns of 20 anatomically distinct sites of the human central nervous system (CNS). Forty-five non-CNS tissues were also profiled to allow for comparative analyses. Using principal component analysis and hierarchical clustering, we were able to show that the expression patterns of the 20 CNS sites profiled were significantly different from all non-CNS tissues and were also similar to one another, indicating an underlying common expression signature. By focusing our analyses on the 20 sites of the CNS, we were able to show that these 20 sites could be segregated into discrete groups with underlying similarities in anatomical structure and, in many cases, functional activity. These findings suggest that gene expression data can help define CNS function at the molecular level. We have identified subsets of genes with the following patterns of expression: (1) across the CNS, suggesting homeostatic/housekeeping function; (2) in subsets of functionally related sites of the CNS identified by our unsupervised learning analyses; and (3) in single sites within the CNS, indicating their participation in distinct site-specific functions. By performing network analyses on these gene sets, we identified many pathways that are upregulated in particular sites of the CNS, some of which were previously described in the literature, validating both our dataset and approach. In summary, we have generated a database of gene expression that can be used to gain valuable insight into the molecular characterization of functions carried out by different sites of the human CNS.

Keywords

Central nervous system Human Transcriptional profiling Microarray Network analysis 

Abbreviations

ADORA2A

Adenosine A2a receptor

AMPH

Amphiphysin (Stiff–Man syndrome with breast cancer 128 kDa autoantigen)

CACNA1A

Calcium channel, voltage-dependent, P/Q type, alpha 1A subunit

CACNA1B

Calcium channel, voltage-dependent, L type, alpha 1B subunit

CNS

Central nervous system

CRTAM

Class I MHC-restricted T cell-associated molecule

CTP

Cytosine triphosphate

DLG2

Discs, large homologue 2, chapsyn-110 (Drosophila)

DLG4

Discs, large homologue 4 (Drosophila)

DLGAP1

Discs, large (Drosophila) homologue-associated protein 1

DNM1

Dynamin 1

DRD2

Dopamine receptor D2EDNRB-endothelin receptor type B

EPS15

Epidermal growth factor receptor pathway substrate 15

FOSB

FBJ murine osteosarcoma viral oncogene homologue B

FYN

FYN oncogene related to SRC, FGR, and YES

GABRA6

Gamma-aminobutyric acid (GABA) A receptor, alpha 6

GPCR

G-protein coupled receptor

GRIK2

Glutamate receptor, ionotropic, kainate 2

GRIN1

Glutamate receptor, ionotropic, N-methyl D-aspartate 1

GRM3

Glutamate receptor, metabotropic 3

IPKB

Ingenuity Pathways Knowledge Base

IVT

In vitro transcription

MBP

Myelin basic protein

PCA

Principal component analysis

PCR

Polymerase chain reaction

PMCH

Pro-melanin-concentrating hormone

PMI

Postmortem interval

PNS

Peripheral nervous system

PTPN5

Protein tyrosine phosphatase, nonreceptor type 5

qPCR

Quantitative polymerase chain reaction

RAB3A

RAB3A, member RAS oncogene family

RMA

Robust multiarray analysis; robust multichip average

RNA

Ribonucleic acid

SFRP4

Secreted frizzled-related protein 4

SLC1A3

Solute carrier family 1 (glial high affinity glutamate transporter), member 3

SNAP25

Synaptosomal-associated protein, 25 kDa

SYT1

Synaptotagmin I

SYT3

Synaptotagmin III

SYT4

Synaptotagmin IV

TH

Tyrosine hydroxylase

TOI

Target of interest

UTP

Uridine triphosphate

VAMP2

Vesicle-associated membrane protein 2 (synaptobrevin 2)

Notes

Acknowledgement

We thank Dr. Richard A. Maki for helpful discussion and critical reading of the manuscript.

Supplementary material

10048_2006_32_MOESM1_ESM.pdf (57 kb)
Table S1 (PDF 58 kb)
10048_2006_32_MOESM2_ESM.pdf (63 kb)
Table S2 (PDF 65 kb)
10048_2006_32_MOESM3_ESM.pdf (68 kb)
Table S3 (PDF 70 kb)

References

  1. 1.
    Bittner M, Meltzer P, Chen Y, Jiang Y, Seftor E, Hendrix M, Radmacher M, Simon R, Yakhini Z, Ben-Dor A, Sampas N, Dougherty E, Wang E, Marincola F, Gooden C, Lueders J, Glatfelter A, Pollock P, Carpten J, Gillanders E, Leja D, Dietrich K, Beaudry C, Berens M, Alberts D, Sondak V (2000) Molecular classification of cutaneous malignant melanoma by gene expression profiling. Nature 406:536–540PubMedCrossRefGoogle Scholar
  2. 2.
    Perou CM, Sorlie T, Eisen MB, van de Rijn M, Jeffrey SS, Rees CA, Pollack JR, Ross DT, Johnsen H, Akslen LA, Fluge O, Pergamenschikov A, Williams C, Zhu SX, Lonning PE, Borresen-Dale AL, Brown PO, Botstein D (2000) Molecular portraits of human breast tumours. Nature 406:747–752PubMedCrossRefGoogle Scholar
  3. 3.
    van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW, Schreiber GJ, Peterse JL, Roberts C, Marton MJ, Parrish M, Atsma D, Witteveen A, Glas A, Delahaye L, van der Velde T, Bartelink H, Rodenhuis S, Rutgers ET, Friend SH, Bernards R (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009PubMedCrossRefGoogle Scholar
  4. 4.
    van’t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M, Peterse HL, van der Kooy K, Marton MJ, Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C, Linsley PS, Bernards R, Friend SH (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536CrossRefGoogle Scholar
  5. 5.
    Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A, Martiat P, Fox SB, Harris AL, Liu ET (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci U S A 100:10393–10398PubMedCrossRefGoogle Scholar
  6. 6.
    Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, Hastie T, Eisen MB, van de Rijn M, Jeffrey SS, Thorsen T, Quist H, Matese JC, Brown PO, Botstein D, Eystein Lonning P, Borresen-Dale AL (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 98:10869–10874PubMedCrossRefGoogle Scholar
  7. 7.
    Bertucci F, Finetti P, Rougemont J, Charafe-Jauffret E, Nasser V, Loriod B, Camerlo J, Tagett R, Tarpin C, Houvenaeghel G, Nguyen C, Maraninchi D, Jacquemier J, Houlgatte R, Birnbaum D, Viens P (2004) Gene expression profiling for molecular characterization of inflammatory breast cancer and prediction of response to chemotherapy. Cancer Res 64:8558–8565PubMedCrossRefGoogle Scholar
  8. 8.
    Brennan DJ, O’Brien SL, Fagan A, Culhane AC, Higgins DG, Duffy MJ, Gallagher WM (2005) Application of DNA microarray technology in determining breast cancer prognosis and therapeutic response. Expert Opin Biol Ther 5:1069–1083PubMedCrossRefGoogle Scholar
  9. 9.
    Dhanasekaran SM, Barrette TR, Ghosh D, Shah R, Varambally S, Kurachi K, Pienta KJ, Rubin MA, Chinnaiyan AM (2001) Delineation of prognostic biomarkers in prostate cancer. Nature 412:822–826PubMedCrossRefGoogle Scholar
  10. 10.
    Welsh JB, Sapinoso LM, Su AI, Kern SG, Wang-Rodriguez J, Moskaluk CA, Frierson HF Jr, Hampton GM (2001) Analysis of gene expression identifies candidate markers and pharmacological targets in prostate cancer. Cancer Res 61:5974–5978PubMedGoogle Scholar
  11. 11.
    Heighway J, Knapp T, Boyce L, Brennand S, Field JK, Betticher DC, Ratschiller D, Gugger M, Donovan M, Lasek A, Rickert P (2002) Expression profiling of primary non-small cell lung cancer for target identification. Oncogene 21:7749–7763PubMedCrossRefGoogle Scholar
  12. 12.
    Velasco AM, Gillis KA, Li Y, Brown EL, Sadler TM, Achilleos M, Greenberger LM, Frost P, Bai W, Zhang Y (2004) Identification and validation of novel androgen-regulated genes in prostate cancer. Endocrinology 145:3913–3924PubMedCrossRefGoogle Scholar
  13. 13.
    Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, Wiltshire T, Orth AP, Vega RG, Sapinoso LM, Moqrich A, Patapoutian A, Hampton GM, Schultz PG, Hogenesch JB (2002) Large-scale analysis of the human and mouse transcriptomes. Proc Natl Acad Sci U S A 99:4465–4470PubMedCrossRefGoogle Scholar
  14. 14.
    Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, Zhang J, Soden R, Hayakawa M, Kreiman G, Cooke MP, Walker JR, Hogenesch JB (2004) A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A 101:6062–6067PubMedCrossRefGoogle Scholar
  15. 15.
    Haverty PM, Weng Z, Best NL, Auerbach KR, Hsiao LL, Jensen RV, Gullans SR (2002) HugeIndex: a database with visualization tools for high-density oligonucleotide array data from normal human tissues. Nucleic Acids Res 30:214–217PubMedCrossRefGoogle Scholar
  16. 16.
    Son CG, Bilke S, Davis S, Greer BT, Wei JS, Whiteford CC, Chen QR, Cenacchi N, Khan J (2005) Database of mRNA gene expression profiles of multiple human organs. Genome Res 15:443–450PubMedCrossRefGoogle Scholar
  17. 17.
    Shyamsundar R, Kim YH, Higgins JP, Montgomery K, Jorden M, Sethuraman A, van de Rijn M, Botstein D, Brown PO, Pollack JR (2005) A DNA microarray survey of gene expression in normal human tissues. Genome Biol 6:R22PubMedCrossRefGoogle Scholar
  18. 18.
    Hsiao LL, Dangond F, Yoshida T, Hong R, Jensen RV, Misra J, Dillon W, Lee KF, Clark KE, Haverty P, Weng Z, Mutter GL, Frosch MP, Macdonald ME, Milford EL, Crum CP, Bueno R, Pratt RE, Mahadevappa M, Warrington JA, Stephanopoulos G, Gullans SR (2001) A compendium of gene expression in normal human tissues. Physiol Genomics 7:97–104PubMedGoogle Scholar
  19. 19.
    Saito-Hisaminato A, Katagiri T, Kakiuchi S, Nakamura T, Tsunoda T, Nakamura Y (2002) Genome-wide profiling of gene expression in 29 normal human tissues with a cDNA microarray. DNA Res 9:35–45PubMedCrossRefGoogle Scholar
  20. 20.
    Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95:14863–14868PubMedCrossRefGoogle Scholar
  21. 21.
    Misra J, Schmitt W, Hwang D, Hsiao LL, Gullans S, Stephanopoulos G (2002) Interactive exploration of microarray gene expression patterns in a reduced dimensional space. Genome Res 12:1112–1120PubMedCrossRefGoogle Scholar
  22. 22.
    Raychaudhuri S, Stuart JM, Altman RB (2000) Principal components analysis to summarize microarray experiments: application to sporulation time series. Pac Symp Biocomput 455–466Google Scholar
  23. 23.
    Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, Speed TP (2003) Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4:249–264PubMedCrossRefGoogle Scholar
  24. 24.
    Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP (2003) Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res 31:e15PubMedCrossRefGoogle Scholar
  25. 25.
    Nolte J (2002) The human brain. An introduction to its functional anatomy, 5th edn. Mosby, St. Louis, MissouriGoogle Scholar
  26. 26.
    Standaert DG (2003) Adenosine A2A receptor modulation of motor systems for symptomatic therapy in Parkinson’s disease. Neurology 61:S30–S31PubMedGoogle Scholar
  27. 27.
    Ferre S, Fredholm BB, Morelli M, Popoli P, Fuxe K (1997) Adenosine-dopamine receptor-receptor interactions as an integrative mechanism in the basal ganglia. Trends Neurosci 20:482–487PubMedCrossRefGoogle Scholar
  28. 28.
    Fredholm BB, Svenningsson P (2003) Adenosine–dopamine interactions: development of a concept and some comments on therapeutic possibilities. Neurology 61:S5–S9PubMedGoogle Scholar
  29. 29.
    Baum LW (2005) Sex, hormones, and Alzheimer’s disease. J Gerontol A Biol Sci Med Sci 60:736–743PubMedGoogle Scholar
  30. 30.
    Czlonkowska A, Ciesielska A, Gromadzka G, Kurkowska-Jastrzebska I (2005) Estrogen and cytokines production—the possible cause of gender differences in neurological diseases. Curr Pharm Des 11:1017–1030PubMedCrossRefGoogle Scholar
  31. 31.
    Duquette P, Pleines J, Girard M, Charest L, Senecal-Quevillon M, Masse C (1992) The increased susceptibility of women to multiple sclerosis. Can J Neurol Sci 19:466–471PubMedGoogle Scholar
  32. 32.
    McGrath J, Saha S, Welham J, El Saadi O, MacCauley C, Chant D (2004) A systematic review of the incidence of schizophrenia: the distribution of rates and the influence of sex, urbanicity, migrant status and methodology. BMC Med 2:13PubMedCrossRefGoogle Scholar
  33. 33.
    Nelson LM (1995) Epidemiology of ALS. Clin Neurosci 3:327–331PubMedGoogle Scholar

Copyright information

© Springer-Verlag 2006

Authors and Affiliations

  • Richard B. Roth
    • 1
    Email author
  • Peter Hevezi
    • 1
  • Jerry Lee
    • 1
  • Dorian Willhite
    • 1
  • Sandra M. Lechner
    • 2
  • Alan C. Foster
    • 2
  • Albert Zlotnik
    • 1
  1. 1.Department of Molecular Medicine, Neurocrine Biosciences, IncorporatedSan DiegoUSA
  2. 2.Department of Neuroscience, Neurocrine Biosciences, IncorporatedSan DiegoUSA

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