Advertisement

Journal of Molecular Neuroscience

, Volume 51, Issue 1, pp 28–36 | Cite as

Pathway Analysis of the Human Brain Transcriptome in Disease

  • Tomas Kavanagh
  • James D. Mills
  • Woojin S. Kim
  • Glenda M. Halliday
  • Michael JanitzEmail author
Article

Abstract

Pathway analysis is a powerful method for discerning differentially regulated genes and elucidating their biological importance. It allows for the identification of perturbed or aberrantly expressed genes within a biological context from extensive data sets and offers a simplistic approach for interrogating such data sets. With the growing use of microarrays and RNA-Seq, data for genome-wide studies are growing at an alarming rate, and the use of deep sequencing is revealing elements of the genome previously uncharacterised. Through the employment of pathway analysis, mechanisms in complex diseases may be explored and novel causatives found primarily through differentially regulated genes. Further, with the implementation of next generation sequencing, a deeper resolution may be attained, particularly in identification of isoform diversity and SNPs. Here, we look at a broad overview of pathway analysis in the human brain transcriptome and its relevance in teasing out underlying causes of complex diseases. We will outline processes in data gathering and analysis of particular diseases in which these approaches have been successful.

Keywords

Transcriptome Pathway analysis Human brain Brain disorders Gene expression RNA-Seq 

Notes

Acknowledgments

This work was supported by the National Health and Medical Research Council of Australia (1022325 to WSK, 630434 to GMH).

References

  1. Ackermann M, Strimmer K (2009) A general modular framework for gene set enrichment analysis. BMC Bioinforma 10:47CrossRefGoogle Scholar
  2. Ameur A, Zaghlool A, Halvardson J, Wetterbom A, Gyllensten U, Cavelier L, Feuk L (2011) Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain. Nat Struct Mol Biol 18:1435–1440PubMedCrossRefGoogle Scholar
  3. Bauer-Mehren A, Furlong LI, Sanz F (2009) Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Mol Syst Biol 5:290PubMedCrossRefGoogle Scholar
  4. Bertram L, Lill CM, Tanzi RE (2010) The genetics of Alzheimer disease: back to the future. Neuron 68:270–281PubMedCrossRefGoogle Scholar
  5. Blalock EM, Buechel HM, Popovic J, Geddes JW, Landfield PW (2011) Microarray analyses of laser-captured hippocampus reveal distinct gray and white matter signatures associated with incipient Alzheimer’s disease. J Chem Neuroanat 42:118–126PubMedCrossRefGoogle Scholar
  6. Blalock EM, Geddes JW, Chen KC, Porter NM, Markesbery WR, Landfield PW (2004) Incipient Alzheimer's disease: microarray correlation analyses reveal major transcriptional and tumor suppressor responses. Proc Natl Acad Sci USA 101:2173–2178PubMedCrossRefGoogle Scholar
  7. Breitling R, Amtmann A, Herzyk P (2004) Iterative Group Analysis (iGA): a simple tool to enhance sensitivity and facilitate interpretation of microarray experiments. BMC Bioinforma 5:34CrossRefGoogle Scholar
  8. Brown PO, Botstein D (1999) Exploring the new world of the genome with DNA microarrays. Nat Genet 21:33–37PubMedCrossRefGoogle Scholar
  9. Catts VS, Weickert CS (2012) Gene expression analysis implicates a death receptor pathway in schizophrenia pathology. PLoS One 7:e35511PubMedCrossRefGoogle Scholar
  10. Chan SL, Kim WS, Kwok JB, Hill AF, Cappai R, Rye KA, Garner B (2008) ATP-binding cassette transporter A7 regulates processing of amyloid precursor protein in vitro. J Neurochem 106:793–804PubMedCrossRefGoogle Scholar
  11. Chen G, Yin K, Shi L, Fang Y, Qi Y, Li P, Luo J, He B, Liu M, Shi T (2011) Comparative analysis of human protein-coding and noncoding RNAs between brain and 10 mixed cell lines by RNA-Seq. PLoS One 6:e28318PubMedCrossRefGoogle Scholar
  12. Chen Y, Dougherty ER, Bittner ML (1997) Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed Opt 2:364–374PubMedCrossRefGoogle Scholar
  13. Chu TT, Liu Y, Kemether E (2009) Thalamic transcriptome screening in three psychiatric states. J Hum Genet 54:665–675PubMedCrossRefGoogle Scholar
  14. Costa V, Angelini C, De Feis I, Ciccodicola A (2010) Uncovering the complexity of transcriptomes with RNA-Seq. J Biomed Biotechnol 2010:853916PubMedCrossRefGoogle Scholar
  15. Courtney E, Kornfeld S, Janitz K, Janitz M (2010) Transcriptome profiling in neurodegenerative disease. J Neurosci Methods 193:189–202PubMedCrossRefGoogle Scholar
  16. Cruts M, Gijselinck I, van der Zee J, Engelborghs S, Wils H, Pirici D, Rademakers R, Vandenberghe R, Dermaut B, Martin JJ, van Duijn C, Peeters K, Sciot R, Santens P, De Pooter T, Mattheijssens M, Van den Broeck M, Cuijt I, Vennekens K, De Deyn PP, Kumar-Singh S, Van Broeckhoven C (2006) Null mutations in progranulin cause ubiquitin-positive frontotemporal dementia linked to chromosome 17q21. Nature 442:920–924PubMedCrossRefGoogle Scholar
  17. Dracheva S, Davis KL, Chin B, Woo DA, Schmeidler J, Haroutunian V (2006) Myelin-associated mRNA and protein expression deficits in the anterior cingulate cortex and hippocampus in elderly schizophrenia patients. Neurobiol Dis 21:531–540PubMedCrossRefGoogle Scholar
  18. Emmert-Streib F, Glazko GV (2011) Pathway analysis of expression data: deciphering functional building blocks of complex diseases. PLoS Comput Biol 7:e1002053PubMedCrossRefGoogle Scholar
  19. Fang Z, Cui X (2011) Design and validation issues in RNA-Seq experiments. Brief Bioinform 12:280–287PubMedCrossRefGoogle Scholar
  20. Faustino NA, Cooper TA (2003) Pre-mRNA splicing and human disease. Genes Dev 17:419–437PubMedCrossRefGoogle Scholar
  21. Fratiglioni L, De Ronchi D, Aguero-Torres H (1999) Worldwide prevalence and incidence of dementia. Drugs Aging 15:365–375PubMedCrossRefGoogle Scholar
  22. Golde TE, Estus S, Usiak M, Younkin LH, Younkin SG (1990) Expression of beta amyloid protein precursor mRNAs: recognition of a novel alternatively spliced form and quantitation in Alzheimer's disease using PCR. Neuron 4:253–267PubMedCrossRefGoogle Scholar
  23. Green ML, Karp PD (2006) The outcomes of pathway database computations depend on pathway ontology. Nucleic Acids Res 34:3687–3697PubMedCrossRefGoogle Scholar
  24. Harold D, Abraham R, Hollingworth P, Sims R, Gerrish A, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Williams A, Jones N, Thomas C, Stretton A, Morgan AR, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Rubinsztein DC, Gill M, Lawlor B, Lynch A, Morgan K, Brown KS, Passmore PA, Craig D, McGuinness B, Todd S, Holmes C, Mann D, Smith AD, Love S, Kehoe PG, Hardy J, Mead S, Fox N, Rossor M, Collinge J, Maier W, Jessen F, Schurmann B, van den Bussche H, Heuser I, Kornhuber J, Wiltfang J, Dichgans M, Frolich L, Hampel H, Hull M, Rujescu D, Goate AM, Kauwe JS, Cruchaga C, Nowotny P, Morris JC, Mayo K, Sleegers K, Bettens K, Engelborghs S, De Deyn PP, Van Broeckhoven C, Livingston G, Bass NJ, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Al-Chalabi A, Shaw CE, Tsolaki M, Singleton AB, Guerreiro R, Muhleisen TW, Nothen MM, Moebus S, Jockel KH, Klopp N, Wichmann HE, Carrasquillo MM, Pankratz VS, Younkin SG, Holmans PA, O'Donovan M, Owen MJ, Williams J (2009) Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer's disease. Nat Genet 41:1088–1093PubMedCrossRefGoogle Scholar
  25. Hashimoto T, Arion D, Unger T, Maldonado-Aviles JG, Morris HM, Volk DW, Mirnics K, Lewis DA (2008) Alterations in GABA-related transcriptome in the dorsolateral prefrontal cortex of subjects with schizophrenia. Mol Psychiatry 13:147–161PubMedCrossRefGoogle Scholar
  26. Hollingworth P, Harold D, Sims R, Gerrish A, Lambert JC, Carrasquillo MM, Abraham R, Hamshere ML, Pahwa JS, Moskvina V, Dowzell K, Jones N, Stretton A, Thomas C, Richards A, Ivanov D, Widdowson C, Chapman J, Lovestone S, Powell J, Proitsi P, Lupton MK, Brayne C, Rubinsztein DC, Gill M, Lawlor B, Lynch A, Brown KS, Passmore PA, Craig D, McGuinness B, Todd S, Holmes C, Mann D, Smith AD, Beaumont H, Warden D, Wilcock G, Love S, Kehoe PG, Hooper NM, Vardy ER, Hardy J, Mead S, Fox NC, Rossor M, Collinge J, Maier W, Jessen F, Rüther E, Schürmann B, Heun R, Kölsch H, van den Bussche H, Heuser I, Kornhuber J, Wiltfang J, Dichgans M, Frölich L, Hampel H, Gallacher J, Hüll M, Rujescu D, Giegling I, Goate AM, Kauwe JS, Cruchaga C, Nowotny P, Morris JC, Mayo K, Sleegers K, Bettens K, Engelborghs S, De Deyn PP, Van Broeckhoven C, Livingston G, Bass NJ, Gurling H, McQuillin A, Gwilliam R, Deloukas P, Al-Chalabi A, Shaw CE, Tsolaki M, Singleton AB, Guerreiro R, Mühleisen TW, Nöthen MM, Moebus S, Jöckel KH, Klopp N, Wichmann HE, Pankratz VS, Sando SB, Aasly JO, Barcikowska M, Wszolek ZK, Dickson DW, Graff-Radford NR, Petersen RC, Alzheimer's Disease Neuroimaging Initiative, van Duijn CM, Breteler MM, Ikram MA, DeStefano AL, Fitzpatrick AL, Lopez O, Launer LJ, Seshadri S, CHARGE consortium, Berr C, Campion D, Epelbaum J, Dartigues JF, Tzourio C, Alpérovitch A, Lathrop M, EADI1 consortium, Feulner TM, Friedrich P, Riehle C, Krawczak M, Schreiber S, Mayhaus M, Nicolhaus S, Wagenpfeil S, Steinberg S, Stefansson H, Stefansson K, Snaedal J, Björnsson S, Jonsson PV, Chouraki V, Genier-Boley B, Hiltunen M, Soininen H, Combarros O, Zelenika D, Delepine M, Bullido MJ, Pasquier F, Mateo I, Frank-Garcia A, Porcellini E, Hanon O, Coto E, Alvarez V, Bosco P, Siciliano G, Mancuso M, Panza F, Solfrizzi V, Nacmias B, Sorbi S, Bossù P, Piccardi P, Arosio B, Annoni G, Seripa D, Pilotto A, Scarpini E, Galimberti D, Brice A, Hannequin D, Licastro F, Jones L, Holmans PA, Jonsson T, Riemenschneider M, Morgan K, Younkin SG, Owen MJ, O'Donovan M, Amouyel P, Williams J (2011) Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer’s disease. Nat Genet 43:429–435PubMedCrossRefGoogle Scholar
  27. Homer N, Szelinger S, Redman M, Duggan D, Tembe W, Muehling J, Pearson JV, Stephan DA, Nelson SF, Craig DW (2008) Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays. PLoS Genet 4:e1000167PubMedCrossRefGoogle Scholar
  28. Hu X, Pickering E, Liu YC, Hall S, Fournier H, Katz E, Dechairo B, John S, Van Eerdewegh P, Soares H (2011) Meta-analysis for genome-wide association study identifies multiple variants at the BIN1 locus associated with late-onset Alzheimer's disease. PLoS One 6:e16616PubMedCrossRefGoogle Scholar
  29. Huang DW, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4:44–57CrossRefGoogle Scholar
  30. Huang R, Jaritz M, Guenzl P, Vlatkovic I, Sommer A, Tamir IM, Marks H, Klampfl T, Kralovics R, Stunnenberg HG, Barlow DP, Pauler FM (2011) An RNA-Seq strategy to detect the complete coding and non-coding transcriptome including full-length imprinted macro ncRNAs. PLoS One 6:e27288PubMedCrossRefGoogle Scholar
  31. Ikeda Y, Abe-Dohmae S, Munehira Y, Aoki R, Kawamoto S, Furuya A, Shitara K, Amachi T, Kioka N, Matsuo M, Yokohama S, Ueda K (2003) Posttranscriptional regulation of human ABCA7 and its function for the apoA-I-dependent lipid release. Biochem Biophys Res Commun 311:313–318PubMedCrossRefGoogle Scholar
  32. Iwamoto N, Abe-Dohmae S, Sato R, Yokoyama S (2006) ABCA7 expression is regulated by cellular cholesterol through the SREBP2 pathway and associated with phagocytosis. J Lipid Res 47:1915–1927PubMedCrossRefGoogle Scholar
  33. Jehle AW, Gardai SJ, Li S, Linsel-Nitschke P, Marimoto K, Janssen WJ, Vandivier RW, Wang N, Greenberg S, Dale BM, Qin C, Henson PM, Tall AR (2006) ATP-binding cassette transporter A7 enhances phagocytosis of apoptotic cells and associated ERK signaling in macrophages. J Cell Biol 174:547–556PubMedCrossRefGoogle Scholar
  34. Kanehisa M, Goto S (2000) KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28:27–30PubMedCrossRefGoogle Scholar
  35. Kapur K, Jiang H, Xing Y, Wong WH (2008) Cross-hybridization modeling on Affymetrix exon arrays. Bioinformatics 24:2887–2893PubMedCrossRefGoogle Scholar
  36. Kellett KA, Hooper NM (2009) Prion protein and Alzheimer disease. Prion 3:190–194PubMedCrossRefGoogle Scholar
  37. Kim WS, Guillemin GJ, Glaros EN, Lim CK, Garner B (2006) Quantitation of ATP-binding cassette subfamily-A transporter gene expression in primary human brain cells. Neuroreport 17:891–896PubMedCrossRefGoogle Scholar
  38. Kim WS, Weickert CS, Garner B (2008) Role of ATP-binding cassette transporters in brain lipid transport and neurological disease. J Neurochem 104:1145–1166PubMedCrossRefGoogle Scholar
  39. Khatri P, Sirota M, Butte AJ (2012) Ten years of pathway analysis: current approaches and outstanding challenges. PLoS Comput Biol 8:e1002375PubMedCrossRefGoogle Scholar
  40. Kim KH, Moon M, Yu SB, Mook-Jung I, Kim JI (2012) RNA-Seq analysis of frontal cortex and cerebellum from 5XFAD mice at early stage of disease pathology. J Alzheimers Dis 29:793–808PubMedGoogle Scholar
  41. Konradi C, Eaton M, MacDonald ML, Walsh J, Benes FM, Heckers S (2004) Molecular evidence for mitochondrial dysfunction in bipolar disorder. Arch Gen Psychiatry 61:300–308PubMedCrossRefGoogle Scholar
  42. Lambert JC, Heath S, Even G, Campion D, Sleegers K, Hiltunen M, Combarros O, Zelenika D, Bullido MJ, Tavernier B, Letenneur L, Bettens K, Berr C, Pasquier F, Fievet N, Barberger-Gateau P, Engelborghs S, De Deyn P, Mateo I, Franck A, Helisalmi S, Porcellini E, Hanon O, de Pancorbo MM, Lendon C, Dufouil C, Jaillard C, Leveillard T, Alvarez V, Bosco P, Mancuso M, Panza F, Nacmias B, Bossu P, Piccardi P, Annoni G, Seripa D, Galimberti D, Hannequin D, Licastro F, Soininen H, Ritchie K, Blanche H, Dartigues JF, Tzourio C, Gut I, Van Broeckhoven C, Alperovitch A, Lathrop M, Amouyel P (2009) Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer's disease. Nat Genet 41:1094–1099PubMedCrossRefGoogle Scholar
  43. Lerch JK, Kuo F, Motti D, Morris R, Bixby JL, Lemmon VP (2012) Isoform diversity and regulation in peripheral and central neurons revealed through RNA-Seq. PLoS One 7:e30417PubMedCrossRefGoogle Scholar
  44. Li YY, Cui JG, Hill JM, Bhattacharjee S, Zhao Y, Lukiw WJ (2011) Increased expression of miRNA-146a in Alzheimer's disease transgenic mouse models. Neurosci Lett 487:94–98PubMedCrossRefGoogle Scholar
  45. Lin M, Pedrosa E, Shah A, Hrabovsky A, Maqbool S, Zheng D, Lachman HM (2011) RNA-Seq of human neurons derived from iPS cells reveals candidate long non-coding RNAs involved in neurogenesis and neuropsychiatric disorders. PLoS One 6:e23356PubMedCrossRefGoogle Scholar
  46. Lobo A, Launer LJ, Fratiglioni L, Andersen K, Di Carlo A, Breteler MM, Copeland JR, Dartigues JF, Jagger C, Martinez-Lage J, Soininen H, Hofman A (2000) Prevalence of dementia and major subtypes in Europe: a collaborative study of population-based cohorts. Neurologic Diseases in the Elderly Research Group. Neurology 54:S4–S9PubMedCrossRefGoogle Scholar
  47. Lu T, Pan Y, Kao SY, Li C, Kohane I, Chan J, Yankner BA (2004) Gene regulation and DNA damage in the ageing human brain. Nature 429:883–891PubMedCrossRefGoogle Scholar
  48. Martin JA, Wang Z (2011) Next-generation transcriptome assembly. Nat Rev Genet 12:671–682PubMedCrossRefGoogle Scholar
  49. Mills JD, Janitz M (2012) Alternative splicing of mRNA in the molecular pathology of neurodegenerative diseases. Neurobiol Aging 33:1012.e11–1012.e24CrossRefGoogle Scholar
  50. Pan Q, Shai O, Lee LJ, Frey BJ, Blencowe BJ (2008) Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet 40:1413–1415PubMedCrossRefGoogle Scholar
  51. Pavlidis P, Qin J, Arango V, Mann JJ, Sibille E (2004) Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex. Neurochem Res 29:1213–1222PubMedCrossRefGoogle Scholar
  52. Peng Z, Cheng Y, Tan BC, Kang L, Tian Z, Zhu Y, Zhang W, Liang Y, Hu X, Tan X, Guo J, Dong Z, Liang Y, Bao L, Wang J (2012) Comprehensive analysis of RNA-Seq data reveals extensive RNA editing in a human transcriptome. Nat Biotechnol 30:253–260PubMedCrossRefGoogle Scholar
  53. Satoh J (2012) Molecular network of microRNA targets in Alzheimer's disease brains. Exp Neurol 235:436–446PubMedCrossRefGoogle Scholar
  54. Shendure J (2008) The beginning of the end for microarrays? Nat Methods 5:585–587PubMedCrossRefGoogle Scholar
  55. Stolzing A, Grune T (2004) Neuronal apoptotic bodies: phagocytosis and degradation by primary microglial cells. FASEB J 18:743–745PubMedGoogle Scholar
  56. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102:15545–15550PubMedCrossRefGoogle Scholar
  57. Sutherland GT, Janitz M, Kril JJ (2011) Understanding the pathogenesis of Alzheimer's disease: will RNA-Seq realize the promise of transcriptomics? J Neurochem 116:937–946PubMedCrossRefGoogle Scholar
  58. Tollervey JR, Wang Z, Hortobagyi T, Witten JT, Zarnack K, Kayikci M, Clark TA, Schweitzer AC, Rot G, Curk T, Zupan B, Rogelj B, Shaw CE, Ule J (2011) Analysis of alternative splicing associated with aging and neurodegeneration in the human brain. Genome Res 21:1572–1582PubMedCrossRefGoogle Scholar
  59. Toung JM, Morley M, Li M, Cheung VG (2011) RNA-sequence analysis of human B-cells. Genome Res 21:991–998PubMedCrossRefGoogle Scholar
  60. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25:1105–1111PubMedCrossRefGoogle Scholar
  61. Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L (2012) Differential gene and transcript expression analysis of RNA-Seq experiments with TopHat and Cufflinks. Nat Protoc 7:562–578PubMedCrossRefGoogle Scholar
  62. Twine NA, Janitz K, Wilkins MR, Janitz M (2011) Whole transcriptome sequencing reveals gene expression and splicing differences in brain regions affected by Alzheimer's disease. PLoS One 6:e16266PubMedCrossRefGoogle Scholar
  63. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH (2011) Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature 474:380–384PubMedCrossRefGoogle Scholar
  64. Wang S, Qaisar U, Yin X, Grammas P (2012) Gene expression profiling in Alzheimer's disease brain microvessels. J Alzheimers Dis 31:193–205PubMedGoogle Scholar
  65. Wang Z, Gerstein M, Snyder M (2009) RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet 10:57–63PubMedCrossRefGoogle Scholar
  66. Williams C, Mehrian Shai R, Wu Y, Hsu YH, Sitzer T, Spann B, McCleary C, Mo Y, Miller CA (2009) Transcriptome analysis of synaptoneurosomes identifies neuroplasticity genes overexpressed in incipient Alzheimer's disease. PLoS One 4:e4936PubMedCrossRefGoogle Scholar
  67. Xing Y, Stoilov P, Kapur K, Han A, Jiang H, Shen S, Black DL, Wong WH (2008) MADS: a new and improved method for analysis of differential alternative splicing by exon-tiling microarrays. RNA 14:1470–1479PubMedCrossRefGoogle Scholar
  68. Ziats MN, Rennert OM (2011) Expression profiling of autism candidate genes during human brain development implicates central immune signaling pathways. PLoS One 6:e24691PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Tomas Kavanagh
    • 1
  • James D. Mills
    • 1
  • Woojin S. Kim
    • 2
    • 3
  • Glenda M. Halliday
    • 2
    • 3
  • Michael Janitz
    • 1
    Email author
  1. 1.School of Biotechnology and Biomolecular SciencesUniversity of New South WalesSydneyAustralia
  2. 2.Neuroscience Research AustraliaSydneyAustralia
  3. 3.School of Medical SciencesUniversity of New South WalesSydneyAustralia

Personalised recommendations