Locating mammalian transcription factor binding sites: a survey of computational and experimental techniques.

Genomic Functional Analysis Section, National Human Genome Research Institute, National Institutes of Health, Rockville, Maryland 20878, USA.
Genome Research (Impact Factor: 13.85). 01/2007; 16(12):1455-64. DOI: 10.1101/gr.4140006
Source: PubMed

ABSTRACT Fields such as genomics and systems biology are built on the synergism between computational and experimental techniques. This type of synergism is especially important in accomplishing goals like identifying all functional transcription factor binding sites in vertebrate genomes. Precise detection of these elements is a prerequisite to deciphering the complex regulatory networks that direct tissue specific and lineage specific patterns of gene expression. This review summarizes approaches for in silico, in vitro, and in vivo identification of transcription factor binding sites. A variety of techniques useful for localized- and high-throughput analyses are discussed here, with emphasis on aspects of data generation and verification.

  • [Show abstract] [Hide abstract]
    ABSTRACT: MicroRNAs (miRNAs) are short regulatory RNAs that negatively modulate protein expression at the post-transcriptional level. Additionally, they have been associated with the pathogenesis of a number of types of cancer. In the current study, two target sites for miR-150 were determined within the 3'-untranslated region of p27(Kip1) (hereafter referred to as p27) mRNA, and it was determined that ectopic overexpression of miR-150 led directly to p27 downregulation in cancer cells. These findings indicate that miR-150 may be a novel regulator of p27 expression. In the databases of the University of California, Santa Cruz (UCSC) and Match online, two common transcription factors were identified for miR-150 and p27: Cooperates with myogenic proteins 1 (COMP1) and hepatocyte nuclear factor-4 (HNF-4). Using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), it was determined that p27 is involved in pathways regulated by the target genes of miR-150. Therefore, these results suggest that there may be a regulatory loop between COMP1 and HNF-4-miR-150-p27. Additional functional studies are required to understand the molecular basis for the formation of this circuit loop, and provide an insight into the development of innovative therapies targeting specific tumor markers.
    Oncology letters 01/2015; 9(1):195-200. DOI:10.3892/ol.2014.2643 · 0.99 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The motif TMTCGCGANR (M being C or A, R being A or G, and N any nucleotide) called M8 was discovered as a putative cis-regulatory element present in 368 human gene promoters. Of these, 236 (64%) are conserved within promoter sequences of four related organisms: human, mouse, rat and dog. However, transcription factors (TFs) interacting with the M8 motif has not yet been described. We previously reported the use of quantitative proteomics coupled to one-step DNA affinity purification as a means of screening for TFs associated with given functional DNA elements. The procedure is performed in-vitro employing SILAC-labeled nuclear extracts and making use of a well-characterized cis-regulatory motifs. Building on that, in this study we have combined our method with statistical analysis to filter out false positive hits from the one-step DNA affinity pull-down experiments. This resulted in the identification of zinc finger BED domain-containing protein 1 (ZBED1), alpha globin transcription factor CP2 (TFCP2), upstream binding protein 1 (UBP) and transcription factor CP2 like 1(TFCP2L1), as specific M8 interacting factors. We validated our screen demonstrating the in vivo binding of alpha globin transcription factor TFCP2 to selected genes harboring M8-containing promoters using ChIP (chromatin immuno-precipitation) assays. This not only implicates a functional role of the above proteins in regulating M8 motif containing genes, but also suggests the potential use of our approach to decipher protein-DNA interactions occurring in living cells.
    Journal of Proteomics & Bioinformatics 03/2014; 7:082-087. DOI:10.4172/jpb.1000306
  • Source

Full-text (2 Sources)

Available from
Jun 1, 2014