Genome-wide analysis of protein-DNA interactions Arttu Jolma1,2, Jian Yan1, Thomas Whitington1, Martin Enge1, Kazuhiro Nitta1,3, Teemu Kivioja1,2, Mikko Taipale1, Juan M. Vaquerizas4, Nicholas M. Luscombe4, Minna Taipale1,2, Esko Ukkonen2, Yutaka Satou5, Patrick Lemaire3,6 and Jussi Taipale1,2 1Karolinska Institutet, Department of Biosciences and Nutrition; 2University of Helsinki, Finland;3IBDML, Marseilles, France; 4EMBL - European Bioinformatics Institute, Cambridge, UK; 5Department of Zoology, University of Kyoto; 6CRBM, MONTPELLIER, France Understanding the information encoded in the human genome requires two genetic codes, the first code specifies how mRNA sequence is converted to protein sequence, and the second code determines where and when the mRNAs are expressed. Although the proteins that read the second, regulatory code – transcription factors (TFs) – have been largely identified, the code is poorly understood as it is not known which sequences TFs can bind in the genome. To understand the regulatory code, we have analyzed the occupancy of the majority of all expressed TFs in human colorectal cancer cells, and analyzed the sequence-specific binding of all human TFs using high-throughput SELEX. Comparison of the human binding profiles with those of house mouse and an early chordate, Ciona intestinalis, revealed that the monomer binding specificity of TFs evolves very slowly, and has been almost completely fixed in the entire chordate lineage, despite complete lack of regulatory element conservation between Ciona and Human. However, factors that bind identical sequences as monomers form dimers with different spacing and orientation preferences, that and these preferences appear to evolve much faster than monomer binding preferences, indicating that changes in spacing and orientation preferences of TFs is a potential source of evolutionary novelty. A binding model that is required to understand binding of TFs to the genome, which incorporates information about protein-protein interactions induced by DNA, and inheritance of epigenetic states across cell division will be discussed.

Genome-wide analysis of protein-DNA interactions

Arttu Jolma1,2, Jian Yan1, Thomas Whitington1, Martin Enge1, Kazuhiro Nitta1,3, Teemu Kivioja1,2, Mikko Taipale1, Juan M. Vaquerizas4, Nicholas M. Luscombe4, Minna Taipale1,2, Esko Ukkonen2, Yutaka Satou5, Patrick Lemaire3,6 and Jussi Taipale1,2

1Karolinska Institutet, Department of Biosciences and Nutrition; 2University of Helsinki, Finland;3IBDML, Marseilles, France; 4EMBL - European Bioinformatics Institute, Cambridge, UK; 5Department of Zoology, University of Kyoto; 6CRBM, MONTPELLIER, France

Understanding the information encoded in the human genome requires two genetic codes, the first code specifies how mRNA sequence is converted to protein sequence, and the second code determines where and when the mRNAs are expressed. Although the proteins that read the second, regulatory code – transcription factors (TFs) – have been largely identified, the code is poorly understood as it is not known which sequences TFs can bind in the genome. To understand the regulatory code, we have analyzed the occupancy of the majority of all expressed TFs in human colorectal cancer cells, and analyzed the sequence-specific binding of all human TFs using high-throughput SELEX. Comparison of the human binding profiles with those of house mouse and an early chordate, Ciona intestinalis, revealed that the monomer binding specificity of TFs evolves very slowly, and has been almost completely fixed in the entire chordate lineage, despite complete lack of regulatory element conservation between Ciona and Human. However, factors that bind identical sequences as monomers form dimers with different spacing and orientation preferences, that and these preferences appear to evolve much faster than monomer binding preferences, indicating that changes in spacing and orientation preferences of TFs is a potential source of evolutionary novelty. A binding model that is required to understand binding of TFs to the genome, which incorporates information about protein-protein interactions induced by DNA, and inheritance of epigenetic states across cell division will be discussed.