Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2012
Candida albicans, a major fungal pathogen in human, can grow in a variety of morphological forms ... more Candida albicans, a major fungal pathogen in human, can grow in a variety of morphological forms ranging from budding yeast to pseudohyphae and hyphae, and its ability to transition to true hyphae is critical for virulence in various types of C. albicans infections. Here, we identify 17 putative hyphal genes whose expression peaks during the S/G2 transition of the cell cycle in C. albicans . These genes are Candida-specific (i.e., they do not have orthologs in S.cerevisiae, a related fungal species that does not exhibit hyphal growth and is primarily non-pathogenic), and their promoters are enriched for the DNA binding site motifs of Tec1 and Rfg1, two transcription factors (TFs) known to play important roles in hyphal growth and virulence. For 5 of the 17 genes we found strong evidence in the literature that confirms our hypothesis that these genes are involved in hyphal growth and/or virulence, for 5 additional genes we found suggestive (albeit weak) evidence, while the other gene...
Proceedings of the National Academy of Sciences, 2014
Until now, it has been reasonably assumed that specific base-pair recognition is the only mechani... more Until now, it has been reasonably assumed that specific base-pair recognition is the only mechanism controlling the specificity of transcription factor (TF)−DNA binding. Contrary to this assumption, here we show that nonspecific DNA sequences possessing certain repeat symmetries, when present outside of specific TF binding sites (TFBSs), statistically control TF−DNA binding preferences. We used highthroughput protein−DNA binding assays to measure the binding levels and free energies of binding for several human TFs to tens of thousands of short DNA sequences with varying repeat symmetries. Based on statistical mechanics modeling, we identify a new protein−DNA binding mechanism induced by DNA sequence symmetry in the absence of specific base-pair recognition, and experimentally demonstrate that this mechanism indeed governs protein−DNA binding preferences.
The human neocortex differs from that of other great apes in several notable regards, including a... more The human neocortex differs from that of other great apes in several notable regards, including altered cell cycle, pro- longed corticogenesis, and increased size [1–5]. Although these evolutionary changes most likely contributed to the origin of distinctively human cognitive faculties, their ge- netic basis remains almost entirely unknown. Highly conserved non-coding regions showing rapid sequence changes along the human lineage are candidate loci for the development and evolution of uniquely human traits. Several studies have identified human-accelerated en- hancers [6–14], but none have linked an expression differ- ence to a specific organismal trait. Here we report the discovery of a human-accelerated regulatory enhancer (HARE5) of FZD8, a receptor of the Wnt pathway implicated in brain development and size [15, 16]. Using transgenic mice, we demonstrate dramatic differences in human and chimpanzee HARE5 activity, with human HARE5 driving early and robust expression at the onset of corticogenesis. Similar to HARE5 activity, FZD8 is expressed in neural pro- genitors of the developing neocortex [17–19]. Chromosome conformation capture assays reveal that HARE5 physically and specifically contacts the core Fzd8 promoter in the mouse embryonic neocortex. To assess the phenotypic consequences of HARE5 activity, we generated transgenic mice in which Fzd8 expression is under control of ortholo- gous enhancers (Pt-HARE5::Fzd8 and Hs-HARE5::Fzd8). In comparison to Pt-HARE5::Fzd8, Hs-HARE5::Fzd8 mice showed marked acceleration of neural progenitor cell cycle and increased brain size. Changes in HARE5 function unique to humans thus alter the cell-cycle dynamics of a critical population of stem cells during corticogenesis and may underlie some distinctive anatomical features of the human brain.
As an increasing number of eukaryotic genomes are being sequenced, comparative studies aimed at d... more As an increasing number of eukaryotic genomes are being sequenced, comparative studies aimed at detecting regulatory elements in intergenic sequences are becoming more prevalent. Most comparative methods for transcription factor (TF) binding site discovery make use of global or local alignments of orthologous regulatory regions to assess whether a particular DNA site is conserved across related organisms, and thus more likely to be functional. Since binding sites are usually short, sometimes degenerate, and often independent of orientation, alignment algorithms may not align them correctly. Here, we present a novel, alignment-free approach for incorporating conservation information into TF motif discovery. We relax the definition of conserved sites: we consider a DNA site within a regulatory region to be conserved in an orthologous sequence if it occurs anywhere in that sequence, irrespective of orientation. We use this definition to derive informative priors over DNA sequence positions, and incorporate these priors into a Gibbs sampling algorithm for motif discovery. Our approach is simple and fast. It does not require sequence alignments, nor the phylogenetic relationships between the orthologous sequences, and yet it is more effective on real biological data than methods that do.
A complete understanding of transcriptional regulatory processes in the cell requires identificat... more A complete understanding of transcriptional regulatory processes in the cell requires identification of transcription factor binding sites on a genomewide scale. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known transcription factor binding sites occur in the genome than are actually functional. Chromatin structure is known to play an important role in guiding transcription factors to those sites that are functional. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling transcription factors to bind DNA in those regions . In this paper, we describe a novel algorithm which employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy; the nucleosome occupancy information comes from a recently published computational model . When a Gibbs sampling algorithm with our informative prior is applied to yeast sequencesets identified by , the correct motif is found in 50% more cases than with an uninformative uniform prior. Moreover, if nucleosome occupancy information is not available, our informative prior reduces to a new kind of prior that can exploit discriminative information in a purely generative setting.
Supplementary . TFBSshape database flowchart. I n p u t d a t a a r e n u c l e o t i d e s e q u... more Supplementary . TFBSshape database flowchart. I n p u t d a t a a r e n u c l e o t i d e s e q u e n c e s derived from the motif databases JASPAR and UniPROBE, which are stored and managed using a MySQL database (yellow). Following this pre-processing, TFBSs are assembled, DNA shape features are predicted "on the fly", and an Apache server provides the user interface (blue). Supplementary Figure S3. DNA shape analysis of human bHLH TFBSs. Heat maps illustrate Roll selections of (A) Mad, (B) Max, and (C) Myc. (D-F) Roll preferences of the three TFs were compared using box plots. Boxes represent the median (line inside the box), 1 st and 3 rd quartiles (edges of the box), and the whiskers define the furthest data points within 1.5 x inter-quartile range from the edges of the box. Asterisks indicate nucleotide positions where differences in Roll distributions selected by (D) Mad vs. Max, (E) Mad vs. Myc, and (F) Max vs.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2008
Transcription factor (TF) binding site discovery is an important step in understanding transcript... more Transcription factor (TF) binding site discovery is an important step in understanding transcriptional regulation. Many computational tools have already been developed, but their success in detecting TF motifs is still limited. We believe one of the main reasons for the low accuracy of current methods is that they do not take into account the structural aspects of TF-DNA interaction. We have previously shown that knowledge about the structural class of the TF and information about nucleosome occupancy can be used to improve motif discovery. Here, we demonstrate the benefits of using information about the DNA double-helical stability for motif discovery. We notice that, in general, the energy needed to destabilize the DNA double helix is higher at TF binding sites than at random DNA sites. We use this information to derive informative positional priors that we incorporate into a motif finding algorithm. When applied to yeast ChIP-chip data, the new informative priors improve the perf...
Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a cr... more Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a crucial step in understanding transcriptional regulation. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known TF motifs occur in the genome than are actually functional. However, information about chromatin structure may help to identify the functional sites. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling TFs to bind DNA in those regions. Here, we describe a novel motif discovery algorithm that employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy. When a Gibbs sampling algorithm is applied to yeast sequence-sets identified by ChIP-chip, the correct motif is found in 52% more cases with our informative prior than with the commonly used uniform prior. This is the first demons...
Proceedings of the National Academy of Sciences of the United States of America, Jan 9, 2015
DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory p... more DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF-DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included cross-validation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vit...
... Both Ligand-and Cell-Specific Parameters Control Ligand Agonism in a Kinetic Model of G Prote... more ... Both Ligand-and Cell-Specific Parameters Control Ligand Agonism in a Kinetic Model of G Protein?Coupled Receptor Signaling Alternate Title:Cell-Specific Parameters Control Ligand Agonism. Tamara L Kinzer-Ursem1? ... Kukkonen JP,Nasman J,Akerman KEO. ...
Transcription factors (TFs) regulate gene expression by binding to specific DNA sites in cis regu... more Transcription factors (TFs) regulate gene expression by binding to specific DNA sites in cis regulatory regions of genes. Most eukaryotic TFs are members of protein families that share a common DNA binding domain and often recognize highly similar DNA sequences. Currently, it is not well understood why closely related TFs are able to bind different genomic regions in vivo, despite having the potential to interact with the same DNA sites. Here, we use the Myc/Max/Mad family as a model system to investigate whether interactions with additional proteins (co-factors) can explain why paralogous TFs with highly similar DNA binding preferences interact with different genomic sites in vivo. We use a classification approach to distinguish between targets of c-Myc versus Mad2, using features that reflect the DNA binding specificities of putative co-factors. When applied to c-Myc/Mad2 DNA binding data, our algorithm can distinguish between genomic regions bound uniquely by c-Myc versus Mad2 with 87% accuracy.
Most transcription factors (TFs) belong to protein families that share a common DNA binding domai... more Most transcription factors (TFs) belong to protein families that share a common DNA binding domain and have very similar DNA binding preferences. However, many paralogous TFs (i.e. members of the same TF family) perform different regulatory functions and interact with different genomic regions in the cell. A potential mechanism for achieving this differential in vivo specificity is through interactions with protein co-factors. Computational tools for studying the genomic binding profiles of paralogous TFs and identifying their putative co-factors are currently lacking. Here, we present an interactive web implementation of COUGER, a classification-based framework for identifying protein co-factors that might provide specificity to paralogous TFs. COUGER takes as input two sets of genomic regions bound by paralogous TFs, and it identifies a small set of putative co-factors that best distinguish the two sets of sequences. To achieve this task, COUGER uses a classification approach, with features that reflect the DNA-binding specificities of the putative co-factors. The identified co-factors are presented in a userfriendly output page, together with information that allows the user to understand and to explore the contributions of individual co-factor features. COUGER can be run as a stand-alone tool or through a web interface: http://couger.oit.duke.edu.
Background: Transcription factors (TFs) play a central role in regulating gene expression by inte... more Background: Transcription factors (TFs) play a central role in regulating gene expression by interacting with cisregulatory DNA elements associated with their target genes. Recent surveys have examined the DNA binding specificities of most Saccharomyces cerevisiae TFs, but a comprehensive evaluation of their data has been lacking.
DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for... more DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein-binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case studies, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the threedimensional structure of DNA binding sites. Thus, the local shape of target sites might play a widespread role in achieving regulatory specificity within TF families.
Transcription factors (TFs) influence cell fate by interpreting the regulatory DNA within a genom... more Transcription factors (TFs) influence cell fate by interpreting the regulatory DNA within a genome. TFs recognize DNA in a specific manner; the mechanisms underlying this specificity have been identified for many TFs based on 3D structures of protein-DNA complexes. More recently, structural views have been complemented with data from high-throughput in vitro and in vivo explorations of the DNA-binding preferences of many TFs. Together, these approaches have greatly expanded our understanding of TF-DNA interactions. However, the mechanisms by which TFs select in vivo binding sites and alter gene expression remain unclear. Recent work has highlighted the many variables that influence TF-DNA binding, while demonstrating that a biophysical understanding of these many factors will be central to understanding TF function.
Identification of binding sites of transcription factors (TFs) on a genome-wide scale is an impor... more Identification of binding sites of transcription factors (TFs) on a genome-wide scale is an important part of understanding transcriptional regulatory processes in the cell. A common approach is to search for a motif that is statistically overrepresented in a set of promoters of co-regulated genes. However, TF binding sites are short and often degenerate, posing a significant statistical challenge to
Background: The Myc-Max heterodimer is a transcription factor that regulates expression of a larg... more Background: The Myc-Max heterodimer is a transcription factor that regulates expression of a large number of genes. Genome occupancy of Myc-Max is thought to be driven by Enhancer box (E-box) DNA elements, CACGTG or variants, to which the heterodimer binds in vitro. Results: By analyzing ChIP-Seq datasets, we demonstrate that the positions occupied by Myc-Max across the human genome correlate with the RNA polymerase II, Pol II, transcription machinery significantly better than with E-boxes. Metagene analyses show that in promoter regions, Myc is uniformly positioned about 100 bp upstream of essentially all promoter proximal paused polymerases with Max about 15 bp upstream of Myc. We re-evaluate the DNA binding properties of full length Myc-Max proteins. Electrophoretic mobility shift assay results demonstrate Myc-Max heterodimers display significant sequence preference, but have high affinity for any DNA. Quantification of the relative affinities of Myc-Max for all possible 8-mers using universal protein-binding microarray assays shows that sequences surrounding core 6-mers significantly affect binding. Compared to the in vitro sequence preferences, Myc-Max genomic occupancy measured by ChIP-Seq is largely, although not completely, independent of sequence specificity.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2012
Candida albicans, a major fungal pathogen in human, can grow in a variety of morphological forms ... more Candida albicans, a major fungal pathogen in human, can grow in a variety of morphological forms ranging from budding yeast to pseudohyphae and hyphae, and its ability to transition to true hyphae is critical for virulence in various types of C. albicans infections. Here, we identify 17 putative hyphal genes whose expression peaks during the S/G2 transition of the cell cycle in C. albicans . These genes are Candida-specific (i.e., they do not have orthologs in S.cerevisiae, a related fungal species that does not exhibit hyphal growth and is primarily non-pathogenic), and their promoters are enriched for the DNA binding site motifs of Tec1 and Rfg1, two transcription factors (TFs) known to play important roles in hyphal growth and virulence. For 5 of the 17 genes we found strong evidence in the literature that confirms our hypothesis that these genes are involved in hyphal growth and/or virulence, for 5 additional genes we found suggestive (albeit weak) evidence, while the other gene...
Proceedings of the National Academy of Sciences, 2014
Until now, it has been reasonably assumed that specific base-pair recognition is the only mechani... more Until now, it has been reasonably assumed that specific base-pair recognition is the only mechanism controlling the specificity of transcription factor (TF)−DNA binding. Contrary to this assumption, here we show that nonspecific DNA sequences possessing certain repeat symmetries, when present outside of specific TF binding sites (TFBSs), statistically control TF−DNA binding preferences. We used highthroughput protein−DNA binding assays to measure the binding levels and free energies of binding for several human TFs to tens of thousands of short DNA sequences with varying repeat symmetries. Based on statistical mechanics modeling, we identify a new protein−DNA binding mechanism induced by DNA sequence symmetry in the absence of specific base-pair recognition, and experimentally demonstrate that this mechanism indeed governs protein−DNA binding preferences.
The human neocortex differs from that of other great apes in several notable regards, including a... more The human neocortex differs from that of other great apes in several notable regards, including altered cell cycle, pro- longed corticogenesis, and increased size [1–5]. Although these evolutionary changes most likely contributed to the origin of distinctively human cognitive faculties, their ge- netic basis remains almost entirely unknown. Highly conserved non-coding regions showing rapid sequence changes along the human lineage are candidate loci for the development and evolution of uniquely human traits. Several studies have identified human-accelerated en- hancers [6–14], but none have linked an expression differ- ence to a specific organismal trait. Here we report the discovery of a human-accelerated regulatory enhancer (HARE5) of FZD8, a receptor of the Wnt pathway implicated in brain development and size [15, 16]. Using transgenic mice, we demonstrate dramatic differences in human and chimpanzee HARE5 activity, with human HARE5 driving early and robust expression at the onset of corticogenesis. Similar to HARE5 activity, FZD8 is expressed in neural pro- genitors of the developing neocortex [17–19]. Chromosome conformation capture assays reveal that HARE5 physically and specifically contacts the core Fzd8 promoter in the mouse embryonic neocortex. To assess the phenotypic consequences of HARE5 activity, we generated transgenic mice in which Fzd8 expression is under control of ortholo- gous enhancers (Pt-HARE5::Fzd8 and Hs-HARE5::Fzd8). In comparison to Pt-HARE5::Fzd8, Hs-HARE5::Fzd8 mice showed marked acceleration of neural progenitor cell cycle and increased brain size. Changes in HARE5 function unique to humans thus alter the cell-cycle dynamics of a critical population of stem cells during corticogenesis and may underlie some distinctive anatomical features of the human brain.
As an increasing number of eukaryotic genomes are being sequenced, comparative studies aimed at d... more As an increasing number of eukaryotic genomes are being sequenced, comparative studies aimed at detecting regulatory elements in intergenic sequences are becoming more prevalent. Most comparative methods for transcription factor (TF) binding site discovery make use of global or local alignments of orthologous regulatory regions to assess whether a particular DNA site is conserved across related organisms, and thus more likely to be functional. Since binding sites are usually short, sometimes degenerate, and often independent of orientation, alignment algorithms may not align them correctly. Here, we present a novel, alignment-free approach for incorporating conservation information into TF motif discovery. We relax the definition of conserved sites: we consider a DNA site within a regulatory region to be conserved in an orthologous sequence if it occurs anywhere in that sequence, irrespective of orientation. We use this definition to derive informative priors over DNA sequence positions, and incorporate these priors into a Gibbs sampling algorithm for motif discovery. Our approach is simple and fast. It does not require sequence alignments, nor the phylogenetic relationships between the orthologous sequences, and yet it is more effective on real biological data than methods that do.
A complete understanding of transcriptional regulatory processes in the cell requires identificat... more A complete understanding of transcriptional regulatory processes in the cell requires identification of transcription factor binding sites on a genomewide scale. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known transcription factor binding sites occur in the genome than are actually functional. Chromatin structure is known to play an important role in guiding transcription factors to those sites that are functional. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling transcription factors to bind DNA in those regions . In this paper, we describe a novel algorithm which employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy; the nucleosome occupancy information comes from a recently published computational model . When a Gibbs sampling algorithm with our informative prior is applied to yeast sequencesets identified by , the correct motif is found in 50% more cases than with an uninformative uniform prior. Moreover, if nucleosome occupancy information is not available, our informative prior reduces to a new kind of prior that can exploit discriminative information in a purely generative setting.
Supplementary . TFBSshape database flowchart. I n p u t d a t a a r e n u c l e o t i d e s e q u... more Supplementary . TFBSshape database flowchart. I n p u t d a t a a r e n u c l e o t i d e s e q u e n c e s derived from the motif databases JASPAR and UniPROBE, which are stored and managed using a MySQL database (yellow). Following this pre-processing, TFBSs are assembled, DNA shape features are predicted "on the fly", and an Apache server provides the user interface (blue). Supplementary Figure S3. DNA shape analysis of human bHLH TFBSs. Heat maps illustrate Roll selections of (A) Mad, (B) Max, and (C) Myc. (D-F) Roll preferences of the three TFs were compared using box plots. Boxes represent the median (line inside the box), 1 st and 3 rd quartiles (edges of the box), and the whiskers define the furthest data points within 1.5 x inter-quartile range from the edges of the box. Asterisks indicate nucleotide positions where differences in Roll distributions selected by (D) Mad vs. Max, (E) Mad vs. Myc, and (F) Max vs.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing, 2008
Transcription factor (TF) binding site discovery is an important step in understanding transcript... more Transcription factor (TF) binding site discovery is an important step in understanding transcriptional regulation. Many computational tools have already been developed, but their success in detecting TF motifs is still limited. We believe one of the main reasons for the low accuracy of current methods is that they do not take into account the structural aspects of TF-DNA interaction. We have previously shown that knowledge about the structural class of the TF and information about nucleosome occupancy can be used to improve motif discovery. Here, we demonstrate the benefits of using information about the DNA double-helical stability for motif discovery. We notice that, in general, the energy needed to destabilize the DNA double helix is higher at TF binding sites than at random DNA sites. We use this information to derive informative positional priors that we incorporate into a motif finding algorithm. When applied to yeast ChIP-chip data, the new informative priors improve the perf...
Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a cr... more Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a crucial step in understanding transcriptional regulation. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known TF motifs occur in the genome than are actually functional. However, information about chromatin structure may help to identify the functional sites. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling TFs to bind DNA in those regions. Here, we describe a novel motif discovery algorithm that employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy. When a Gibbs sampling algorithm is applied to yeast sequence-sets identified by ChIP-chip, the correct motif is found in 52% more cases with our informative prior than with the commonly used uniform prior. This is the first demons...
Proceedings of the National Academy of Sciences of the United States of America, Jan 9, 2015
DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory p... more DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF-DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included cross-validation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vit...
... Both Ligand-and Cell-Specific Parameters Control Ligand Agonism in a Kinetic Model of G Prote... more ... Both Ligand-and Cell-Specific Parameters Control Ligand Agonism in a Kinetic Model of G Protein?Coupled Receptor Signaling Alternate Title:Cell-Specific Parameters Control Ligand Agonism. Tamara L Kinzer-Ursem1? ... Kukkonen JP,Nasman J,Akerman KEO. ...
Transcription factors (TFs) regulate gene expression by binding to specific DNA sites in cis regu... more Transcription factors (TFs) regulate gene expression by binding to specific DNA sites in cis regulatory regions of genes. Most eukaryotic TFs are members of protein families that share a common DNA binding domain and often recognize highly similar DNA sequences. Currently, it is not well understood why closely related TFs are able to bind different genomic regions in vivo, despite having the potential to interact with the same DNA sites. Here, we use the Myc/Max/Mad family as a model system to investigate whether interactions with additional proteins (co-factors) can explain why paralogous TFs with highly similar DNA binding preferences interact with different genomic sites in vivo. We use a classification approach to distinguish between targets of c-Myc versus Mad2, using features that reflect the DNA binding specificities of putative co-factors. When applied to c-Myc/Mad2 DNA binding data, our algorithm can distinguish between genomic regions bound uniquely by c-Myc versus Mad2 with 87% accuracy.
Most transcription factors (TFs) belong to protein families that share a common DNA binding domai... more Most transcription factors (TFs) belong to protein families that share a common DNA binding domain and have very similar DNA binding preferences. However, many paralogous TFs (i.e. members of the same TF family) perform different regulatory functions and interact with different genomic regions in the cell. A potential mechanism for achieving this differential in vivo specificity is through interactions with protein co-factors. Computational tools for studying the genomic binding profiles of paralogous TFs and identifying their putative co-factors are currently lacking. Here, we present an interactive web implementation of COUGER, a classification-based framework for identifying protein co-factors that might provide specificity to paralogous TFs. COUGER takes as input two sets of genomic regions bound by paralogous TFs, and it identifies a small set of putative co-factors that best distinguish the two sets of sequences. To achieve this task, COUGER uses a classification approach, with features that reflect the DNA-binding specificities of the putative co-factors. The identified co-factors are presented in a userfriendly output page, together with information that allows the user to understand and to explore the contributions of individual co-factor features. COUGER can be run as a stand-alone tool or through a web interface: http://couger.oit.duke.edu.
Background: Transcription factors (TFs) play a central role in regulating gene expression by inte... more Background: Transcription factors (TFs) play a central role in regulating gene expression by interacting with cisregulatory DNA elements associated with their target genes. Recent surveys have examined the DNA binding specificities of most Saccharomyces cerevisiae TFs, but a comprehensive evaluation of their data has been lacking.
DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for... more DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein-binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case studies, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the threedimensional structure of DNA binding sites. Thus, the local shape of target sites might play a widespread role in achieving regulatory specificity within TF families.
Transcription factors (TFs) influence cell fate by interpreting the regulatory DNA within a genom... more Transcription factors (TFs) influence cell fate by interpreting the regulatory DNA within a genome. TFs recognize DNA in a specific manner; the mechanisms underlying this specificity have been identified for many TFs based on 3D structures of protein-DNA complexes. More recently, structural views have been complemented with data from high-throughput in vitro and in vivo explorations of the DNA-binding preferences of many TFs. Together, these approaches have greatly expanded our understanding of TF-DNA interactions. However, the mechanisms by which TFs select in vivo binding sites and alter gene expression remain unclear. Recent work has highlighted the many variables that influence TF-DNA binding, while demonstrating that a biophysical understanding of these many factors will be central to understanding TF function.
Identification of binding sites of transcription factors (TFs) on a genome-wide scale is an impor... more Identification of binding sites of transcription factors (TFs) on a genome-wide scale is an important part of understanding transcriptional regulatory processes in the cell. A common approach is to search for a motif that is statistically overrepresented in a set of promoters of co-regulated genes. However, TF binding sites are short and often degenerate, posing a significant statistical challenge to
Background: The Myc-Max heterodimer is a transcription factor that regulates expression of a larg... more Background: The Myc-Max heterodimer is a transcription factor that regulates expression of a large number of genes. Genome occupancy of Myc-Max is thought to be driven by Enhancer box (E-box) DNA elements, CACGTG or variants, to which the heterodimer binds in vitro. Results: By analyzing ChIP-Seq datasets, we demonstrate that the positions occupied by Myc-Max across the human genome correlate with the RNA polymerase II, Pol II, transcription machinery significantly better than with E-boxes. Metagene analyses show that in promoter regions, Myc is uniformly positioned about 100 bp upstream of essentially all promoter proximal paused polymerases with Max about 15 bp upstream of Myc. We re-evaluate the DNA binding properties of full length Myc-Max proteins. Electrophoretic mobility shift assay results demonstrate Myc-Max heterodimers display significant sequence preference, but have high affinity for any DNA. Quantification of the relative affinities of Myc-Max for all possible 8-mers using universal protein-binding microarray assays shows that sequences surrounding core 6-mers significantly affect binding. Compared to the in vitro sequence preferences, Myc-Max genomic occupancy measured by ChIP-Seq is largely, although not completely, independent of sequence specificity.
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Papers by Raluca Gordân