title: A transcription factor map as revealed by a genome-wide gene expression analysis of whole-blood mRNA transcriptome in multiple sclerosis.
abstract: BACKGROUND
  Several lines of evidence suggest that transcription factors are involved in the pathogenesis of Multiple Sclerosis (MS) but complete mapping of the whole network has been elusive. One of the reasons is that there are several clinical subtypes of MS and transcription factors that may be involved in one subtype may not be in others. We investigate the possibility that this network could be mapped using microarray technologies and contemporary bioinformatics methods on a dataset derived from whole blood in 99 untreated MS patients (36 Relapse Remitting MS, 43 Primary Progressive MS, and 20 Secondary Progressive MS) and 45 age-matched healthy controls.
  
  
  RESULTS
  We have used two different analytical methodologies: a non-standard differential expression analysis and a differential co-expression analysis, which have converged on a significant number of regulatory motifs that are statistically overrepresented in genes that are either differentially expressed (or differentially co-expressed) in cases and controls (e.g., V$KROX_Q6, p-value <3.31E-6; V$CREBP1_Q2, p-value <9.93E-6, V$YY1_02, p-value <1.65E-5).
  
  
  CONCLUSIONS
  Our analysis uncovered a network of transcription factors that potentially dysregulate several genes in MS or one or more of its disease subtypes. The most significant transcription factor motifs were for the Early Growth Response EGR/KROX family, ATF2, YY1 (Yin and Yang 1), E2F-1/DP-1 and E2F-4/DP-2 heterodimers, SOX5, and CREB and ATF families. These transcription factors are involved in early T-lymphocyte specification and commitment as well as in oligodendrocyte dedifferentiation and development, both pathways that have significant biological plausibility in MS causation.
authors.affiliation: Centre for Bioinformatics, Biomarker Discovery & Information-Based Medicine, University of Newcastle, and Hunter Medical Research Institute, Newcastle, Australia.
authors.forename: Carlos
authors.initials: C
authors.lastname: Riveros
authors.forename: Drew
authors.initials: D
authors.lastname: Mellor
authors.forename: Kaushal S
authors.initials: KS
authors.lastname: Gandhi
authors.forename: Fiona C
authors.initials: FC
authors.lastname: McKay
authors.forename: Mathew B
authors.initials: MB
authors.lastname: Cox
authors.forename: Regina
authors.initials: R
authors.lastname: Berretta
authors.forename: S Yahya
authors.initials: SY
authors.lastname: Vaezpour
authors.forename: Mario
authors.initials: M
authors.lastname: Inostroza-Ponta
authors.forename: Simon A
authors.initials: SA
authors.lastname: Broadley
authors.forename: Robert N
authors.initials: RN
authors.lastname: Heard
authors.forename: Stephen
authors.initials: S
authors.lastname: Vucic
authors.forename: Graeme J
authors.initials: GJ
authors.lastname: Stewart
authors.forename: David W
authors.initials: DW
authors.lastname: Williams
authors.forename: Rodney J
authors.initials: RJ
authors.lastname: Scott
authors.forename: Jeanette
authors.initials: J
authors.lastname: Lechner-Scott
authors.forename: David R
authors.initials: DR
authors.lastname: Booth
authors.forename: Pablo
authors.initials: P
authors.lastname: Moscato
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D016022:Case-Control Studies; D015331:Cohort Studies; D005260:Female; D020869:Gene Expression Profiling; D055106:Genome-Wide Association Study; D006801:Humans; D008297:Male; D008875:Middle Aged; D009103:Multiple Sclerosis / Q000097:blood* / Q000378:metabolism; D009836:Oligodendroglia / Q000166:cytology; D012333:RNA, Messenger / Q000378:metabolism*; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D012333:RNA, Messenger; D014157:Transcription Factors