Positional weight matrices have sufficient prediction power for analysis of noncoding variants

The commonly accepted model to quantify the specificity of transcription factor binding to DNA is the position weight matrix, also called the position-specific scoring matrix. Position weight matrices are used in thousands of projects and computational tools in regulatory genomics, including prediction of the regulatory potential of single-nucleotide variants. Yet, recently Yan et al. presented new experimental method for analysis of regulatory variants and, based on its results, reported that "the position weight matrices of most transcription factors lack sufficient predictive power". Here, we re-analyze the rich experimental dataset obtained by Yan et al. and show that appropriately selected position weight matrices in fact can successfully quantify transcription factor binding to alternative alleles.

Functional Genomics Analysis to Disentangle the Role of Genetic Variants in Major Depression

Background: Major Depression is the leading cause of impairment worldwide. The understanding of its molecular underpinnings is key to identifying new potential biomarkers and drug targets to alleviate its burden in society. Leveraging available GWAS data and functional genomic tools to assess regulatory variation could help explain the role of Major Depression associated genetic variants in disease pathogenesis. We have conducted a fine-mapping analysis of genetic variants associated with Major Depression and applied a pipeline focused on gene expression regulation by using two complementary approaches: cis-eQTL colocalization analysis using GTEx data and alteration of transcription factor binding sites with pattern matching approaches and chromatin accessibility data.Results: The fine-mapping of major depression genetic variants uncovered putative causally associated variants whose proximal genes were linked with Major Depression pathophysiology. Four genetic variants altering the expression of 5 genes were found by colocalization analysis, highlighting the role of SLC12A5, involved in chlorine homeostasis in neurons, and MYRF, related with central nervous system myelination and oligodendrocyte differentiation. The transcription factor binding analysis revealed the potential role of the genomic variant rs62259947 in modulating the expression of P4HTM through the alteration of YY1 binding site, altogether regulating hypoxia response.Conclusions: The combination of GWAS signals, cis-eQTL, transcription factor binding site information and active regulatory regions in the chromatin, enabled the prioritization of putative causal genetic variants in Major Depression. Importantly, our pipeline can be applied when only index genetic variants are available. Finally, the presented approach enabled the proposal of mechanistic hypotheses of these genetic variants and their role in disease pathogenesis.

Pronounced sequence specificity of the TET enzyme catalytic domain guides its cellular function

TET (ten-eleven translocation) enzymes catalyze the oxidation of 5-methylcytosine bases in DNA, thus driving active and passive DNA demethylation. Here, we report that the catalytic cores of mammalian TET enzymes favor CpGs embedded within bHLH and bZIP transcription factor binding sites, with 250-fold preference in vitro. Crystal structures and molecular dynamics calculations show that sequence preference is caused by intra-substrate interactions and CpG flanking sequence indirectly affecting enzyme conformation. TET sequence preferences are physiologically relevant as they explain the rates of DNA demethylation in TET-rescue experiments in culture and in vivo within the zygote and germline. Most and least favorable TET motifs represent DNA sites that are bound by methylation-sensitive immediate-early transcription factors and OCT4, respectively, illuminating TET function in transcriptional responses and pluripotency support. One-Sentence Summary: The catalytic domains of the enzymes that facilitate passive and drive active DNA demethylation have intrinsic sequence preferences that target DNA demethylation to bHLH and bZIP transcription factor binding sites.

Systematic analysis of low-affinity transcription factor binding site clusters in vitro and in vivo establishes their functional relevance

Transcription factor binding to a single binding site and its functional consequence in a promoter context are beginning to be relatively well understood. However, binding to clusters of sites has yet to be characterized in depth, and the functional relevance of binding site clusters remains uncertain.We employed a high-throughput biochemical method to characterize transcription factor binding to clusters varying across a range of affinities and configurations. We found that transcription factors can bind concurrently to overlapping sites, challenging the notion of binding exclusivity. Further-more, compared to an individual high-affinity binding site, small clusters with binding sites an order of magnitude lower in affinity give rise to higher mean occupancies at physiologically-relevant transcription factor concentrations in vitro. To assess whether the observed in vitro occupancies translate to transcriptional activation in vivo, we tested low-affinity binding site clusters by inserting them into a synthetic minimal CYC1 and the native PHO5 S. cerevisiae promoter. In the minCYC1 promoter, clusters of low-affinity binding sites can generate transcriptional output comparable to a promoter containing three consensus binding sites. In the PHO5 promoter, replacing the native Pho4 binding sites with clusters of low-affinity binding sites recovered activation of these promoters as well. This systematic characterization demonstrates that clusters of low-affinity binding sites achieve substantial occupancies, and that this occupancy can drive expression in eukaryotic promoters

ATAC-STARR-seq v1

Transcriptional enhancers control cell-type specific gene expression in humans and dysfunction can lead to debilitating diseases, including cancer. Identifying bona-fide enhancers is difficult due to a lack of spatial or sequence constraints. In addition, only a small percentage of the genome is accessible in matured cell types; and therefore, most enhancers are inactive due to their chromatin context rather than intrinsic properties of the DNA sequence itself. For this reason, we decided to assay regulatory activity exclusively within accessible chromatin. To do this, we combined assay for transposase-accessible chromatin using sequencing (ATAC-seq) with self-transcribing active regulatory region sequencing (STARR-seq); we call this method ATAC-STARR-seq. With ATAC-STARR-seq, we identify both active and silent regulatory elements in GM12878 B cells; these active and silent elements are enriched for transcription factor motifs and histone modifications associated with activating and repressing regulation, respectively. We also show that ATAC-STARR-seq quantifies chromatin accessibility and transcription factor binding. We integrate this information and subset active regions based on transcription factor binding profiles. Depending on the transcription factors bound, subsets are enriched for distinct reactome pathways. Altogether, this highlights the power of ATAC-STARR-seq to investigate the transcriptional regulatory landscape of the human genome.

Constructing gene regulatory networks using epigenetic data

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell’s epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER’s predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.