SUMMARY 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 study, 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 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families.

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 crossvalidation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vitro selection combined with deep sequencing (SELEX-seq). Our results showed that shapeaugmented models compared favorably to sequence-based models. Although both k-mer and DNA shape features can encode interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space. In addition, analyzing the feature weights of DNA shape-augmented models uncovered TF family-specific structural readout mechanisms that were not revealed by the DNA sequence. As such, this work combines knowledge from structural biology and genomics, and suggests a new path toward understanding TF binding and genome function.protein−DNA recognition | statistical machine learning | support vector regression | protein binding microarray | DNA structure

Prospects for specific immune intervention in T cell-mediated autoimmune disease via anti-idiotypic regulation depend on the degree of diversity of the responder cell antigen receptor repertoire. A highly heterogenous response against self epitopes offers little chance for such regulation. We report here that the Lewis rat autoimmune disease experimental allergic encephalomyelitis, generally considered to be a model of human multiple sclerosis, is caused by T cells that use a limited set of TCR V genes. We have cloned the rat TCR alpha and beta chain cDNAs from the Lewis rat x mouse T cell hybridoma 510, which retains the rat specificity for the encephalitogenic determinant of myelin basic protein (MBP). Using Northern blot analysis of T cell RNA with the cloned V region probes, we have found a specific, and near perfect, correlation between expression of TCR message hybridizing to the V alpha 510 and VB510 probes and specificity for the encephalitogenic determinant of MBP in both T cell hybridomas and encephalitogenic T cell clones. This restricted V gene usage provides a basis for observed idiotypic regulation of auto-reactive T cells, and possible therapy for autoimmune disease. A curious and unexplained observation is that the Lewis rat V alpha/V beta combination that dominates the encephalitogenic response to the 68-88 peptide of MBP is precisely the same V alpha/V beta combination used by the B10.PL mouse response to the encephalitogenic response to the 1-9 peptide of MBP.

The relationship between amyloid-β (Aβ) species and tau pathology in Alzheimer’s disease (AD) is not fully understood. Here, we provide direct evidence that Aβ42/40 ratio, not total Aβ level, plays a critical role in inducing neurofibrillary tangles (NTFs) in human neurons. Using 3D-differentiated clonal human neural progenitor cells (hNPCs) expressing varying levels of amyloid β precursor protein (APP) and presenilin 1 (PS1) with AD mutations, we show that pathogenic tau accumulation and aggregation are tightly correlated with Aβ42/40 ratio. Roles of Aβ42/40 ratio on tau pathology are also confirmed with APP transmembrane domain (TMD) mutant hNPCs, which display differential Aβ42/40 ratios without mutant PS1. Moreover, naïve hNPCs co-cultured with APP TMD I45F (high Aβ42/40) cells, not with I47F cells (low Aβ42/40), develop robust tau pathology in a 3D non-cell autonomous cell culture system. These results emphasize the importance of reducing the Aβ42/40 ratio in AD therapy.