Comprehensive prediction in 78 human cell lines reveals rigidity and compactness of transcription factor dimers

The binding of transcription factors (TFs) to their specific motifs in genomic regulatory regions is commonly studied in isolation. However, in order to elucidate the mechanisms of transcriptional regulation, it is essential to determine which TFs bind DNA cooperatively as dimers and to infer the precise nature of these interactions. So far, only a small number of such dimeric complexes are known. Here, we present an algorithm for predicting cell-type-specific TF-TF dimerization on DNA on a large scale, using DNase I hypersensitivity data from 78 human cell lines. We represented the universe of possible TF complexes by their corresponding motif complexes, and analyzed their occurrence at cell-type-specific DNase I hypersensitive sites. Based on ~1.4 billion tests for motif complex enrichment, we predicted 603 highly significant celltype- specific TF dimers, the vast majority of which are novel. Our predictions included 76% (19/25) of the known dimeric complexes and showed significant overlap with an e xperimental database of protein-protein interactions. They were also independently supported by evolutionary conservation, as well as quantitative variation in DNase I digestion patterns. Notably, the known and predicted TF dimers were almost always highly compact and rigidly spaced, suggesting that TFs dimerize in close proximity to their partners, which results in strict constraints on the structure of the DNA-bound complex. Overall, our results indicate that chromatin openness profiles are highly predictive of cell-type-specific TF-TF interactions. Moreover, cooperative TF dimerization seems to be a widespread phenomenon, with multiple TF complexes predicted in most cell types. © 2013, Published by Cold Spring Harbor Laboratory Press.Link_to_subscribed_fulltex

Equivalence of space and time-bins in DPS-QKD

We set up differential phase shift quantum key distribution (DPS-QKD), over 105 km of single-mode optical fiber, with a quantum bit error rate of less than 15% at a secure key rate of 2 kbps. The testbed was first used to investigate the effect of excess bias voltage and hold-off time on the temporal distribution of photons within a gate window of an InGaAs single-photon detector (SPD) and quantified the effects of afterpulsing. The key generation efficiency, and security, in DPS-QKDimprove with an increase in the number of path delays or time-bin superpositions. We finally demonstrate the implementation of superposition states using a time-bin approach, and establish equivalence with the path-based superposition approach, thus yielding a simpler approach to implementing superposition states for use in DPS-QKD.Comment: 7 pages, 14 figure

SOXE transcription factors form selective dimers on non-compact DNA motifs through multifaceted interactions between dimerization and high-mobility group domains

The SOXE transcription factors SOX8, SOX9 and SOX10 are master regulators of mammalian development directing sex determination, gliogenesis, pancreas specification and neural crest development. We identified a set of palindromic SOX binding sites specifically enriched in regulatory regions of melanoma cells. SOXE proteins homodimerize on these sequences with high cooperativity. In contrast to other transcription factor dimers, which are typically rigidly spaced, SOXE group proteins can bind cooperatively at a wide range of dimer spacings. Using truncated forms of SOXE proteins, we show that a single dimerization (DIM) domain, that precedes the DNA binding high mobility group (HMG) domain, is sufficient for dimer formation, suggesting that DIM:HMG rather than DIM:DIM interactions mediate the dimerization. All SOXE members can also heterodimerize in this fashion, whereas SOXE heterodimers with SOX2, SOX4, SOX6 and SOX18 are not supported. We propose a structural model where SOXE-specific intramolecular DIM:HMG interactions are allosterically communicated to the HMG of juxtaposed molecules. Collectively, SO XE factors evolved a unique mode to combinatorially regulate their target genes that relies on a multifaceted interplay between the HMG and DIM domains. This property potentially extends further the diversity of target genes and cell-specific functions that are regulated by SOXE proteins.Link_to_subscribed_fulltex

Machine Learning Based Classification Model for Network Traffic Anomaly Detection

In current days, cloud environments are facing a huge challenge from the attackers in terms of various attacks thrown to the cloud service providers. In both industry and academics, the problem of detection and mitigation of DDoS attacks is now a challenging issue. Detecting Distributed Denial of Service (DDos) threats is mainly a classification problem that can be addressed using data mining, machine learning and deep learning techniques. DDoS attacks can occur in any of the seven-layer OSI model's network. Hence, detecting the DDoS attacks is an important task for cloud service providers to overcome dangerous attacks and loss incurred to stake holders and also the provider

Genome wide binding (ChIP-Seq) of murine Bapx1 and Sox9 proteins in vivo and in vitro

AbstractThis work pertains to GEO submission GSE36672, in vivo and in vitro genome wide binding (ChIP-Seq) of Bapx1/Nkx3.2 and Sox9 proteins. We have previously shown that data from a genome wide binding assay combined with transcriptional profiling is an insightful means to divulge the mechanisms directing cell type specification and the generation of tissues and subsequent organs [1]. Our earlier work identified the role of the DNA-binding homeodomain containing protein Bapx1/Nkx3.2 in midgestation murine embryos. Microarray analysis of EGFP-tagged cells (both wildtype and null) was integrated using ChIP-Seq analysis of Bapx1/Nkx3.2 and Sox9 DNA-binding proteins in living tissue

Response to comment on "Human-specific gain of function in a developmental enhancer"

Duret and Galtier argue that human-specific sequence divergence and gain of function in the HACNS1 enhancer result from deleterious biased gene conversion (BGC) with no contribution from positive selection. We reinforce our previous conclusion by analyzing hypothesized BGC events genomewide and assessing the effect of recombination rates on human-accelerated conserved noncoding sequence ascertainment. We also provide evidence that AT → GC substitution bias can coexist with positive selection

Primate-specific Evolution of an LDLR Enhancer

Background: Sequence changes in regulatory regions have often been invoked to explain phenotypic divergence among species, but molecular examples of this have been difficult to obtain. Results: In this study we identified an anthropoid primate-specific sequence element that contributed to the regulatory evolution of the low-density lipoprotein receptor. Using a combination of close and distant species genomic sequence comparisons coupled with in vivo and in vitro studies, we found that a functional cholesterol-sensing sequence motif arose and was fixed within a pre-existing enhancer in the common ancestor of anthropoid primates. Conclusion: Our study demonstrates one molecular mechanism by which ancestral mammalian regulatory elements can evolve to perform new functions in the primate lineage leading to human

TherMos: Estimating protein-DNA binding energies from in vivo binding profiles

Accurately characterizing transcription factor (TF)-DNA affinity is a central goal of regulatory genomics. Although thermodynamics provides the most natural language for describing the continuous range of TF-DNA affinity, traditional motif discovery algorithms focus instead on classification paradigms that aim to discriminate 'bound' and 'unbound' sequences. Moreover, these algorithms do not directly model the distribution of tags in ChIP-seq data. Here, we present a new algorithm named Thermodynamic Modeling of ChIP-seq (TherMos), which directly estimates a positionspecific binding energy matrix (PSEM) from ChIPseq/exo tag profiles. In cross-validation tests on seven genome-wide TF-DNA binding profiles, one of which we generated via ChIP-seq on a complex developing tissue, TherMos predicted quantitative TF-DNA binding with greater accuracy than five well-known algorithms. We experimentally validated TherMos binding energy models for Klf4 and Esrrb, using a novel protocol to measure PSEMs in vitro. Strikingly, our measurements revealed strong nonadditivity at multiple positions within the two PSEMs. Among the algorithms tested, only TherMos was able to model the entire binding energy landscape of Klf4 and Esrrb. Our study reveals new insights into the energetics of TF-DNA binding in vivo and provides an accurate first-principles approach to binding energy inference from ChIP-seq and ChIP-exo data. © 2013 The Author(s).Link_to_subscribed_fulltex