Divergent Transcription: A Driving Force for New Gene Origination?

David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Cell (Impact Factor: 31.96). 11/2013; 155(5):990-996. DOI: 10.1016/j.cell.2013.10.048
Source: PubMed

ABSTRACT The mammalian genome is extensively transcribed, a large fraction of which is divergent transcription from promoters and enhancers that is tightly coupled with active gene transcription. Here, we propose that divergent transcription may shape the evolution of the genome by new gene origination.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Only a small fraction of the mammalian genome codes for messenger RNAs destined to be translated into proteins, and it is generally assumed that a large portion of transcribed sequences—including introns and several classes of noncoding RNAs (ncRNAs)—do not give rise to peptide products. A systematic examination of translation and physiological regulation of ncRNAs has not been conducted. Here we use computational methods to identify the products of non-canonical translation in mouse neurons by analysing unannotated transcripts in combination with proteomic data. This study supports the existence of non-canonical translation products from both intragenic and extragenic genomic regions, including peptides derived from antisense transcripts and introns. Moreover, the studied novel translation products exhibit temporal regulation similar to that of proteins known to be involved in neuronal activity processes. These observations highlight a potentially large and complex set of biologically regulated translational events from transcripts formerly thought to lack coding potential.
    Nature Communications 11/2014; · 10.74 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: It is assumed that DNA sequences are conserved in the diverse cell types present in a multicellular organism like the human being. Thus, in order to compare the sequences in the genome of DNA from different individuals, nucleic acid is commonly isolated from a single tissue. In this regard, blood cells are widely used for this purpose because of their availability. Thus blood DNA has been used to study genetic familiar diseases that affect other tissues and organs, such as the liver, heart, and brain. While this approach is valid for the identification of familial diseases in which mutations are present in parental germinal cells and, therefore, in all the cells of a given organism, it is not suitable to identify sporadic diseases in which mutations might occur in specific somatic cells. This review addresses somatic DNA variations in different tissues or cells (mainly in the brain) of single individuals and discusses whether the dogma of DNA invariance between cell types is indeed correct. We will also discuss how single nucleotide somatic variations arise, focusing on the presence of specific DNA mutations in the brain.
    Front. Aging Neurosci. 01/2014; 6:323.
  • [Show abstract] [Hide abstract]
    ABSTRACT: A large fraction of microRNAs (miRNAs) are derived from intergenic non-coding loci and the identification of their promoters remains 'elusive'. Here, we present microTSS, a machine-learning algorithm that provides highly accurate, single-nucleotide resolution predictions for intergenic miRNA transcription start sites (TSSs). MicroTSS integrates high-resolution RNA-sequencing data with active transcription marks derived from chromatin immunoprecipitation and DNase-sequencing to enable the characterization of tissue-specific promoters. MicroTSS is validated with a specifically designed Drosha-null/conditional-null mouse model, generated using the conditional by inversion (COIN) methodology. Analyses of global run-on sequencing data revealed numerous pri-miRNAs in human and mouse either originating from divergent transcription at promoters of active genes or partially overlapping with annotated long non-coding RNAs. MicroTSS is readily applicable to any cell or tissue samples and constitutes the missing part towards integrating the regulation of miRNA transcription into the modelling of tissue-specific regulatory networks.
    Nature Communications 12/2014; 5:5700. · 10.74 Impact Factor