Arttu Jolma

DNA-binding specificities of human transcription factors.

Although the proteins that read the gene regulatory code, transcription factors (TFs), have been largely identified, it is not well known which sequences TFs can recognize. We have analyzed the sequence-specific binding of human TFs using high-throughput SELEX and ChIP sequencing. A total of 830 binding profiles were obtained, describing 239 distinctly different binding specificities. The models represent the majority of human TFs, approximately doubling the coverage compared to existing systematic studies. Our results reveal additional specificity determinants for a large number of factors for which a partial specificity was known, including a commonly observed A- or T-rich stretch that flanks the core motifs. Global analysis of the data revealed that homodimer orientation and spacing preferences, and base-stacking interactions, have a larger role in TF-DNA binding than previously appreciated. We further describe a binding model incorporating these features that is required to understand binding of TFs to DNA.

Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo

Members of the large ETS family of transcription factors (TFs) have highly similar DNA‐binding domains (DBDs)—yet they have diverse functions and activities in physiology and oncogenesis. Some differences in DNA‐binding preferences within this family have been described, but they have not been analysed systematically, and their contributions to targeting remain largely uncharacterized. We report here the DNA‐binding profiles for all human and mouse ETS factors, which we generated using two different methods: a high‐throughput microwell‐based TF DNA‐binding specificity assay, and protein‐binding microarrays (PBMs). Both approaches reveal that the ETS‐binding profiles cluster into four distinct classes, and that all ETS factors linked to cancer, ERG, ETV1, ETV4 and FLI1, fall into just one of these classes. We identify amino‐acid residues that are critical for the differences in specificity between all the classes, and confirm the specificities in vivo using chromatin immunoprecipitation followed by sequencing (ChIP‐seq) for a member of each class. The results indicate that even relatively small differences in in vitro binding specificity of a TF contribute to site selectivity in vivo.

Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities

The genetic code-the binding specificity of all transfer-RNAs--defines how protein primary structure is determined by DNA sequence. DNA also dictates when and where proteins are expressed, and this information is encoded in a pattern of specific sequence motifs that are recognized by transcription factors. However, the DNA-binding specificity is only known for a small fraction of the approximately 1400 human transcription factors (TFs). We describe here a high-throughput method for analyzing transcription factor binding specificity that is based on systematic evolution of ligands by exponential enrichment (SELEX) and massively parallel sequencing. The method is optimized for analysis of large numbers of TFs in parallel through the use of affinity-tagged proteins, barcoded selection oligonucleotides, and multiplexed sequencing. Data are analyzed by a new bioinformatic platform that uses the hundreds of thousands of sequencing reads obtained to control the quality of the experiments and to generate binding motifs for the TFs. The described technology allows higher throughput and identification of much longer binding profiles than current microarray-based methods. In addition, as our method is based on proteins expressed in mammalian cells, it can also be used to characterize DNA-binding preferences of full-length proteins or proteins requiring post-translational modifications. We validate the method by determining binding specificities of 14 different classes of TFs and by confirming the specificities for NFATC1 and RFX3 using ChIP-seq. Our results reveal unexpected dimeric modes of binding for several factors that were thought to preferentially bind DNA as monomers.

Transcription Factor Binding in Human Cells Occurs in Dense Clusters Formed around Cohesin Anchor Sites

During cell division, transcription factors (TFs) are removed from chromatin twice, during DNA synthesis and during condensation of chromosomes. How TFs can efficiently find their sites following these stages has been unclear. Here, we have analyzed the binding pattern of expressed TFs in human colorectal cancer cells. We find that binding of TFs is highly clustered and that the clusters are enriched in binding motifs for several major TF classes. Strikingly, almost all clusters are formed around cohesin, and loss of cohesin decreases both DNA accessibility and binding of TFs to clusters. We show that cohesin remains bound in S phase, holding the nascent sister chromatids together at the TF cluster sites. Furthermore, cohesin remains bound to the cluster sites when TFs are evicted in early M phase. These results suggest that cohesin-binding functions as a cellular memory that promotes re-establishment of TF clusters after DNA replication and chromatin condensation.

DNA-dependent formation of transcription factor pairs alters their binding specificity.

Gene expression is regulated by transcription factors (TFs), proteins that recognize short DNA sequence motifs. Such sequences are very common in the human genome, and an important determinant of the specificity of gene expression is the cooperative binding of multiple TFs to closely located motifs. However, interactions between DNA-bound TFs have not been systematically characterized. To identify TF pairs that bind cooperatively to DNA, and to characterize their spacing and orientation preferences, we have performed consecutive affinity-purification systematic evolution of ligands by exponential enrichment (CAP-SELEX) analysis of 9,400 TF–TF–DNA interactions. This analysis revealed 315 TF–TF interactions recognizing 618 heterodimeric motifs, most of which have not been previously described. The observed cooperativity occurred promiscuously between TFs from diverse structural families. Structural analysis of the TF pairs, including a novel crystal structure of MEIS1 and DLX3 bound to their identified recognition site, revealed that the interactions between the TFs were predominantly mediated by DNA. Most TF pair sites identified involved a large overlap between individual TF recognition motifs, and resulted in recognition of composite sites that were markedly different from the individual TF’s motifs. Together, our results indicate that the DNA molecule commonly plays an active role in cooperative interactions that define the gene regulatory lexicon.

Impact of cytosine methylation on DNA binding specificities of human transcription factors.

Positives and negatives of methylated CpG When the DNA bases cytosine and guanine are next to each other, a methyl group is generally added to the pyrimidine, generating a mCpG dinucleotide. This modification alters DNA structure but can also affect function by inhibiting transcription factor (TF) binding. Yin et al. systematically analyzed the effect of CpG methylation on the binding of 542 human TFs (see the Perspective by Hughes and Lambert). In addition to inhibiting binding of some TFs, they found that mCpGs can promote binding of others, particularly TFs involved in development, such as homeodomain proteins. Science, this issue p. eaaj2239; see also p. 489 Genome-scale analysis reveals positive and negative binding of transcription factors to methylated CpG dinucleotides. INTRODUCTION Nearly all cells in the human body share the same primary genome sequence consisting of four nucleotide bases. One of the bases, cytosine, is commonly modified by methylation of its 5 position in CpG dinucleotides (mCpG). Most CpG dinucleotides in the human genome are methylated, but the level of CpG methylation varies with genetic location (promoter versus gene body), whether genes are active versus silenced, and cell type. Research has shown that the maintenance of a particular cellular state after cell division is dependent on faithful transmission of methylated CpGs, as well as inheritance of the mother cells’ repertoire of transcription factors by the daughter cells. These two mechanisms of epigenetic inheritance are linked to each other; the binding of transcription factors can be affected by cytosine methylation, and cytosine methylation can, in turn, be added or removed by proteins that associate with transcription factors. RATIONALE The genetic and epigenetic language, which imparts when and where genes are expressed, is understood at a conceptual level. However, a more detailed understanding is needed of the genomic regulatory mechanism by which methylated cytosines affect transcription factor binding. Because cytosine methylation changes DNA structure, it has the potential to affect binding of all transcription factors. However, a systematic analysis of binding of a large collection of transcription factors to all possible DNA sequences has not previously been conducted. RESULTS To globally characterize the effect of cytosine methylation on transcription factor binding, we systematically analyzed binding specificities of full-length transcription factors and extended DNA binding domains to unmethylated and CpG-methylated DNA by using methylation-sensitive SELEX (systematic evolution of ligands by exponential enrichment). We evaluated binding of 542 transcription factors and identified a large number of previously uncharacterized transcription factor recognition motifs. Binding of most major classes of transcription factors, including bHLH, bZIP, and ETS, was inhibited by mCpG. In contrast, transcription factors such as homeodomain, POU, and NFAT proteins preferred to bind methylated DNA. This class of binding was enriched in factors with central roles in embryonic and organismal development. The observed binding preferences were validated using several orthogonal methods, including bisulfite-SELEX and protein-binding microarrays. In addition, the preference of the pluripotency factor OCT4 to bind to a mCpG-containing motif was confirmed by chromatin immunoprecipitation analysis in mouse embryonic stem cells with low or high levels of CpG methylation (due to deficiency in all enzymes that methylate cytosines or contribute to their removal, respectively). Crystal structure analysis of the homeodomain proteins HOXB13, CDX1, CDX2, and LHX4 revealed three key residues that contribute to the preference of this developmentally important family of transcription factors for mCpG. The preference for binding to mCpG was due to direct hydrophobic interactions with the 5-methyl group of methylcytosine. In contrast, inhibition of binding of other transcription factors to methylated sequences was found to be caused by steric hindrance. CONCLUSION Our work constitutes a global analysis of the effect of cytosine methylation on DNA binding specificities of human transcription factors. CpG methylation can influence binding of most transcription factors to DNA—in some cases negatively and in others positively. Our finding that many developmentally important transcription factors prefer to bind to mCpG sites can inform future analyses of the role of DNA methylation on cell differentiation, chromatin reprogramming, and transcriptional regulation. Systematic analysis of the impact of CpG methylation on transcription factor binding. The bottom left panel shows the fraction of transcription factors that prefer methylated (orange) or unmethylated (teal) CpG sites, are affected in multiple ways (yellow), are not affected (green), or do not have a CpG in their motifs (gray), as determined by methylation-sensitive SELEX (top left). The structure and logos on the right highlight how HOXB13 recognizes mCpG (blue shading indicates a CpG affected by methylation). The majority of CpG dinucleotides in the human genome are methylated at cytosine bases. However, active gene regulatory elements are generally hypomethylated relative to their flanking regions, and the binding of some transcription factors (TFs) is diminished by methylation of their target sequences. By analysis of 542 human TFs with methylation-sensitive SELEX (systematic evolution of ligands by exponential enrichment), we found that there are also many TFs that prefer CpG-methylated sequences. Most of these are in the extended homeodomain family. Structural analysis showed that homeodomain specificity for methylcytosine depends on direct hydrophobic interactions with the methylcytosine 5-methyl group. This study provides a systematic examination of the effect of an epigenetic DNA modification on human TF binding specificity and reveals that many developmentally important proteins display preference for mCpG-containing sequences.

A prostate cancer susceptibility allele at 6q22 increases RFX6 expression by modulating HOXB13 chromatin binding

Genome-wide association studies have identified thousands of SNPs associated with predisposition to various diseases, including prostate cancer. However, the mechanistic roles of these SNPs remain poorly defined, particularly for noncoding polymorphisms. Here we find that the prostate cancer risk-associated SNP rs339331 at 6q22 lies within a functional HOXB13-binding site. The risk-associated T allele at rs339331 increases binding of HOXB13 to a transcriptional enhancer, conferring allele-specific upregulation of the rs339331-associated gene RFX6. Suppression of RFX6 diminishes prostate cancer cell proliferation, migration and invasion. Clinical data indicate that RFX6 upregulation in human prostate cancers correlates with tumor progression, metastasis and risk of biochemical relapse. Finally, we observe a significant association between the risk-associated T allele at rs339331 and increased RFX6 mRNA levels in human prostate tumors. Together, our results suggest that rs339331 affects prostate cancer risk by altering RFX6 expression through a functional interaction with the prostate cancer susceptibility gene HOXB13.

Conservation of transcription factor binding specificities across 600 million years of bilateria evolution

Divergent morphology of species has largely been ascribed to genetic differences in the tissue-specific expression of proteins, which could be achieved by divergence in cis-regulatory elements or by altering the binding specificity of transcription factors (TFs). The relative importance of the latter has been difficult to assess, as previous systematic analyses of TF binding specificity have been performed using different methods in different species. To address this, we determined the binding specificities of 242 Drosophila TFs, and compared them to human and mouse data. This analysis revealed that TF binding specificities are highly conserved between Drosophila and mammals, and that for orthologous TFs, the similarity extends even to the level of very subtle dinucleotide binding preferences. The few human TFs with divergent specificities function in cell types not found in fruit flies, suggesting that evolution of TF specificities contributes to emergence of novel types of differentiated cells. DOI: http://dx.doi.org/10.7554/eLife.04837.001

The Human Transcription Factors

Transcription factors (TFs) recognize specific DNA sequences to control chromatin and transcription, forming a complex system that guides expression of the genome. Despite keen interest in understanding how TFs control gene expression, it remains challenging to determine how the precise genomic binding sites of TFs are specified and how TF binding ultimately relates to regulation of transcription. This review considers how TFs are identified and functionally characterized, principally through the lens of a catalog of over 1,600 likely human TFs and binding motifs for two-thirds of them. Major classes of human TFs differ markedly in their evolutionary trajectories and expression patterns, underscoring distinct functions. TFs likewise underlie many different aspects of human physiology, disease, and variation, highlighting the importance of continued effort to understand TF-mediated gene regulation.

Myt1l safeguards neuronal identity by actively repressing many non-neuronal fates

Normal differentiation and induced reprogramming require the activation of target cell programs and silencing of donor cell programs. In reprogramming, the same factors are often used to reprogram many different donor cell types. As most developmental repressors, such as RE1-silencing transcription factor (REST) and Groucho (also known as TLE), are considered lineage-specific repressors, it remains unclear how identical combinations of transcription factors can silence so many different donor programs. Distinct lineage repressors would have to be induced in different donor cell types. Here, by studying the reprogramming of mouse fibroblasts to neurons, we found that the pan neuron-specific transcription factor Myt1-like (Myt1l) exerts its pro-neuronal function by direct repression of many different somatic lineage programs except the neuronal program. The repressive function of Myt1l is mediated via recruitment of a complex containing Sin3b by binding to a previously uncharacterized N-terminal domain. In agreement with its repressive function, the genomic binding sites of Myt1l are similar in neurons and fibroblasts and are preferentially in an open chromatin configuration. The Notch signalling pathway is repressed by Myt1l through silencing of several members, including Hes1. Acute knockdown of Myt1l in the developing mouse brain mimicked a Notch gain-of-function phenotype, suggesting that Myt1l allows newborn neurons to escape Notch activation during normal development. Depletion of Myt1l in primary postmitotic neurons de-repressed non-neuronal programs and impaired neuronal gene expression and function, indicating that many somatic lineage programs are actively and persistently repressed by Myt1l to maintain neuronal identity. It is now tempting to speculate that similar ‘many-but-one’ lineage repressors exist for other cell fates; such repressors, in combination with lineage-specific activators, would be prime candidates for use in reprogramming additional cell types.