Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE

Transcription factors are crucial regulators of gene expression. Accurate quantitative definition of their intrinsic DNA binding preferences is critical to understanding their biological function. High-throughput in vitro technology has recently been used to deeply probe the DNA binding specificity of hundreds of eukaryotic transcription factors, yet algorithms for analyzing such data have not yet fully matured. Here, we present a general framework (FeatureREDUCE) for building sequence-to-affinity models based on a biophysically interpretable and extensible model of protein-DNA interaction that can account for dependencies between nucleotides within the binding interface or multiple modes of binding. When training on protein binding microarray (PBM) data, we use robust regression and modeling of technology-specific biases to infer specificity models of unprecedented accuracy and precision. We provide quantitative validation of our results by comparing to gold-standard data when available.

Evolving insights on how cytosine methylation affects protein-DNA binding

Many anecdotal observations exist of a regulatory effect of DNA methylation on gene expression. However, in general, the underlying mechanisms of this effect are poorly understood. In this review, we summarize what is currently known about how this important, but mysterious, epigenetic mark impacts cellular functions. Cytosine methylation can abrogate or enhance interactions with DNA-binding proteins, or it may have no effect, depending on the context. Despite being only a small chemical change, the addition of a methyl group to cytosine can affect base readout via hydrophobic contacts in the major groove and shape readout via electrostatic contacts in the minor groove. We discuss the recent discovery that CpG methylation increases DNase I cleavage at adjacent positions by an order of magnitude through altering the local 3D DNA shape and the possible implications of this structural insight for understanding the methylation sensitivity of transcription factors (TFs). Additionally, 5-methylcytosines change the stability of nucleosomes and, thus, affect the local chromatin structure and access of TFs to genomic DNA. Given these complexities, it seems unlikely that the influence of DNA methylation on protein-DNA binding can be captured in a small set of general rules. Hence, data-driven approaches may be essential to gain a better understanding of these mechanisms.

ETO family protein Mtg16 regulates the balance of dendritic cell subsets by repressing Id2

Dendritic cells (DCs) comprise two major subsets, the interferon (IFN)-producing plasmacytoid DCs (pDCs) and antigen-presenting classical DCs (cDCs). The development of pDCs is promoted by E protein transcription factor E2-2, whereas E protein antagonist Id2 is specifically absent from pDCs. Conversely, Id2 is prominently expressed in cDCs and promotes CD8(+) cDC development. The mechanisms that control the balance between E and Id proteins during DC subset specification remain unknown. We found that the loss of Mtg16, a transcriptional cofactor of the ETO protein family, profoundly impaired pDC development and pDC-dependent IFN response. The residual Mtg16-deficient pDCs showed aberrant phenotype, including the expression of myeloid marker CD11b. Conversely, the development of cDC progenitors (pre-DCs) and of CD8(+) cDCs was enhanced. Genome-wide expression and DNA-binding analysis identified Id2 as a direct target of Mtg16. Mtg16-deficient cDC progenitors and pDCs showed aberrant induction of Id2, and the deletion of Id2 facilitated the impaired development of Mtg16-deficient pDCs. Thus, Mtg16 promotes pDC differentiation and restricts cDC development in part by repressing Id2, revealing a cell-intrinsic mechanism that controls subset balance during DC development.