title: Genome-wide transcriptomic variations of human lymphoblastoid cell lines: insights from pairwise gene-expression correlations.
abstract: OBJECTIVE
  Human lymphoblastoid cell lines (LCLs) are a rich resource of information on human interindividual genomic, transcriptomic, proteomic and phenomic variations, and are therefore gaining popularity for pharmacogenomic studies. In the present study we demonstrate that genome-wide transcriptomic data from a small LCL panel from unrelated individuals is sufficient for detecting pairs of genes that exhibit highly correlated expression levels and may thus convey insights about coregulated genes.
  
  
  METHODS
  RNA samples were prepared from LCLs representing 12 unrelated healthy adult female Caucasian donors. Transcript levels were determined with the Affymetrix Human Gene arrays. Expression-level correlations were searched using Partek(®) Genomics Suite™ and the R environment. Sequences of detected correlated gene pairs were compared for shared conserved 3´-UTR miRNA binding.
  
  
  RESULTS
  Most of the approximately 33,000 transcripts covered by the Affymetrix arrays showed closely similar expression levels in LCLs from unrelated donors. However, the expression levels of some transcripts showed large inter-individual variations. When comparing the expression levels of each of the top 1000 genes showing the largest interindividual expression variations against the others, two sets containing 156 and 4438 correlated gene pairs with false-discovery rates of 0.01 and 0.05 were detected, respectively. Similar analysis of another gene-expression data set from LCLs (GSE11582) indicated that 61 and 39% of identified pairs matched the pairs detected from our transcriptomic data, respectively. Shared conserved 3´-UTR miRNA binding sites were noted for 14-17% of the top 100 gene pairs, suggesting that regulation by miRNA may contribute to their coordinated expression.
  
  
  CONCLUSIONS
  Probing genome-wide transcriptomic data sets of LCLs from unrelated individuals may detect coregulated genes, adding insights on cellular regulation by miRNAs.
authors.affiliation: Université Paris Descartes, INSERM UMRS775, Paris, France.
authors.forename: Marc
authors.initials: M
authors.lastname: Vincent
authors.forename: Keren
authors.initials: K
authors.lastname: Oved
authors.forename: Ayelet
authors.initials: A
authors.lastname: Morag
authors.forename: Metsada
authors.initials: M
authors.lastname: Pasmanik-Chor
authors.forename: Varda
authors.initials: V
authors.lastname: Oron-Karni
authors.forename: Noam
authors.initials: N
authors.lastname: Shomron
authors.forename: David
authors.initials: D
authors.lastname: Gurwitz
mesh_terms: D020413:3' Untranslated Regions / Q000235:genetics; D002461:Cell Line, Transformed; D005260:Female; D015870:Gene Expression; D015894:Genome, Human; D006801:Humans; D035683:MicroRNAs / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis; D040901:Proteomics; D012313:RNA / Q000235:genetics*; D059467:Transcriptome / Q000235:genetics*
chemical_list: D020413:3' Untranslated Regions; D035683:MicroRNAs; D012313:RNA