T-Reg Comparator: an analysis tool for the comparison of position weight matrices

T-Reg Comparator is a novel software tool designed to support research into transcriptional regulation. Sequence motifs representing transcription factor binding sites are usually encoded as position weight matrices. The user inputs a set of such weight matrices or binding site sequences and our program matches them against the T-Reg database, which is presently built on data from the Transfac [E. Wingender (2004) In Silico Biol., 4, 55–61] and Jaspar [A. Sandelin, W. Alkema, P. Engstrom, W. W. Wasserman and B. Lenhard (2004) Nucleic Acids Res., 32, D91–D94]. Our tool delivers a detailed report on similarities between user-supplied motifs and motifs in the database. Apart from simple one-to-one relationships, T-Reg Comparator is also able to detect similarities between submatrices. In addition, we provide a user interface to a program for sequence scanning with weight matrices. Typical areas of application for T-Reg Comparator are motif and regulatory module finding and annotation of regulatory genomic regions. T-Reg Comparator is available at

Modelling and analysis of offshore energy hubs

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Comparative promoter region analysis powered by CORG

BACKGROUND: Promoters are key players in gene regulation. They receive signals from various sources (e.g. cell surface receptors) and control the level of transcription initiation, which largely determines gene expression. In vertebrates, transcription start sites and surrounding regulatory elements are often poorly defined. To support promoter analysis, we present CORG , a framework for studying upstream regions including untranslated exons (5' UTR). DESCRIPTION: The automated annotation of promoter regions integrates information of two kinds. First, statistically significant cross-species conservation within upstream regions of orthologous genes is detected. Pairwise as well as multiple sequence comparisons are computed. Second, binding site descriptions (position-weight matrices) are employed to predict conserved regulatory elements with a novel approach. Assembled EST sequences and verified transcription start sites are incorporated to distinguish exonic from other sequences. As of now, we have included 5 species in our analysis pipeline (man, mouse, rat, fugu and zebrafish). We characterized promoter regions of 16,127 groups of orthologous genes. All data are presented in an intuitive way via our web site. Users are free to export data for single genes or access larger data sets via our DAS server . The benefits of our framework are exemplarily shown in the context of phylogenetic profiling of transcription factor binding sites and detection of microRNAs close to transcription start sites of our gene set. CONCLUSION: The CORG platform is a versatile tool to support analyses of gene regulation in vertebrate promoter regions. Applications for CORG cover a broad range from studying evolution of DNA binding sites and promoter constitution to the discovery of new regulatory sequence elements (e.g. microRNAs and binding sites)

Smoking and High-Sensitivity Troponin I Levels in Young and Healthy Adults from the General Population

Lower troponin concentrations measured in smokers in a healthy population raise the question of whether a lower troponin threshold should be considered for tobacco users. We aim to evaluate differences in troponin levels according to the smoking status in healthy young adults. Participants aged 25–41 years were enrolled in a population-based observational study. The smoking status was self-assessed, and participants were classified as never-, past-, and current smokers. Pack-years of smoking were calculated. High-sensitivity cardiac troponin I (hs-cTnI) concentrations were measured from thawed blood samples, and associations were assessed using multivariable linear regression analyses. We included 2155 subjects in this analysis. The mean (SD) age was 35.4 ± 5.22 years; 53% were women. The median hs-cTnI levels across smoking status categories were 0.70 (interquartile range 0.43–1.23) ng/L in never smokers (n = 1174), 0.69 (interquartile range 0.43–1.28) ng/L in past smokers (n = 503), and 0.67 (interquartile range 0.41–1.04) ng/L in current smokers (n = 478), p = 0.04. The troponin levels remained significantly lower in current smokers after adjustment for potential confounders (β-coefficient [95%CI] of −0.08 [−0.25; −0.08], p < 0.001). Our results confirm that current smokers have lower hs-cTnI levels than past or never smokers, with a significant dose–response relationship among current smokers. The absolute differences in hs-cTnI levels were small

Identification of an H-Ras nanocluster disrupting peptide.

peer reviewedHyperactive Ras signalling is found in most cancers. Ras proteins are only active in membrane nanoclusters, which are therefore potential drug targets. We previously showed that the nanocluster scaffold galectin-1 (Gal1) enhances H-Ras nanoclustering via direct interaction with the Ras binding domain (RBD) of Raf. Here, we establish that the B-Raf preference of Gal1 emerges from the divergence of the Raf RBDs at their proposed Gal1-binding interface. We then identify the L5UR peptide, which disrupts this interaction by binding with low micromolar affinity to the B- and C-Raf-RBDs. Its 23-mer core fragment is sufficient to interfere with H-Ras nanoclustering, modulate Ras-signalling and moderately reduce cell viability. These latter two phenotypic effects may also emerge from the ability of L5UR to broadly engage with several RBD- and RA-domain containing Ras interactors. The L5UR-peptide core fragment is a starting point for the development of more specific reagents against Ras-nanoclustering and -interactors

Identification of an H-Ras nanocluster disrupting peptide

AbstractThe Ras-MAPK pathway is critical to regulate cell proliferation and differentiation. Its dysregulation is implicated in the onset and progression of numerous types of cancers. To be active, Ras proteins are membrane anchored and organized into nanoclusters, which realize high-fidelity signal transmission across the plasma membrane. Nanoclusters therefore represent potential drug targets. However, targetable protein components of signalling nanoclusters are poorly established.We previously proposed that the nanocluster scaffold galectin-1 (Gal1) enhances H-Ras nanoclustering by stabilizing stacked dimers of H-Ras and Raf via a direct interaction of dimeric Gal1 with the Ras binding domain (RBD) in particular of B-Raf. Here, we provide further supportive evidence for this model. We establish that the B-Raf preference emerges from divergent regions of the Raf RBDs that were proposed to interact with Gal1. We then identify the L5UR peptide, which disrupts this interaction by binding with low micromolar affinity to the B-Raf-RBD. Its 23-mer core fragment is thus sufficient to interfere with Gal1-enhanced H-Ras nanocluster, reduce MAPK-output and cell viability inHRAS-mutant cancer cell lines.Our data therefore suggest that the interface between Gal1 and the RBD of B-Raf can be targeted to disrupt Gal1-enhanced H-Ras nanoclustering. Collectively, our results support that Raf-proteins are integral components of active Ras nanoclusters

Large-amplitude driving of a superconducting artificial atom: Interferometry, cooling, and amplitude spectroscopy

Superconducting persistent-current qubits are quantum-coherent artificial atoms with multiple, tunable energy levels. In the presence of large-amplitude harmonic excitation, the qubit state can be driven through one or more of the constituent energy-level avoided crossings. The resulting Landau-Zener-Stueckelberg (LZS) transitions mediate a rich array of quantum-coherent phenomena. We review here three experimental works based on LZS transitions: Mach-Zehnder-type interferometry between repeated LZS transitions, microwave-induced cooling, and amplitude spectroscopy. These experiments exhibit a remarkable agreement with theory, and are extensible to other solid-state and atomic qubit modalities. We anticipate they will find application to qubit state-preparation and control methods for quantum information science and technology.Comment: 13 pages, 5 figure

A Novel murine model identifies cooperating mutations and therapeutic targets critical for chronic myeloid leukemia progression

The introduction of highly selective ABL-tyrosine kinase inhibitors (TKIs) has revolutionized therapy for chronic myeloid leukemia (CML). However, TKIs are only efficacious in the chronic phase of the disease and effective therapies for TKI-refractory CML, or after progression to blast crisis (BC), are lacking. Whereas the chronic phase of CML is dependent on BCR-ABL, additional mutations are required for progression to BC. However, the identity of these mutations and the pathways they affect are poorly understood, hampering our ability to identify therapeutic targets and improve outcomes. Here, we describe a novel mouse model that allows identification of mechanisms of BC progression in an unbiased and tractable manner, using transposon-based insertional mutagenesis on the background of chronic phase CML. Our BC model is the first to faithfully recapitulate the phenotype, cellular and molecular biology of human CML progression. We report a heterogeneous and unique pattern of insertions identifying known and novel candidate genes and demonstrate that these pathways drive disease progression and provide potential targets for novel therapeutic strategies. Our model greatly informs the biology of CML progression and provides a potent resource for the development of candidate therapies to improve the dismal outcomes in this highly aggressive disease.Work in the Huntly laboratory is funded by CRUK, The European Research Council (ERC), Leukaemia Lymphoma Research, the Kay Kendall Leukaemia Fund, Wellcome Trust, the Medical Research Council (UK), the Leukemia Lymphoma Society America and the Cambridge NIHR Biomedical Research centre. David Adams is funded by Cancer Research UK and Wellcome Trust. Steffen Koschmieder has received funding from Deutsche José Carreras Leukämie-Stiftung (DJCLS; grant 10/23).This is the final published version. It first appeared at http://dx.doi.org/10.1084/jem.2014166