Lineage-specific interface proteins match up the cell cycle and differentiation in embryo stem cells

The shortage of molecular information on cell cycle changes along embryonic stem cell (ESC) differentiation prompts an in silico approach, which may provide a novel way to identify candidate genes or mechanisms acting in coordinating the two programs. We analyzed germ layer specific gene expression changes during the cell cycle and ESC differentiation by combining four human cell cycle transcriptome profiles with thirteen in vitro human ESC differentiation studies. To detect cross-talk mechanisms we then integrated the transcriptome data that displayed differential regulation with protein interaction data. A new class of non-transcriptionally regulated genes was identified, encoding proteins which interact systematically with proteins corresponding to genes regulated during the cell cycle or cell differentiation, and which therefore can be seen as interface proteins coordinating the two programs. Functional analysis gathered insights in fate-specific candidates of interface functionalities. The non-transcriptionally regulated interface proteins were found to be highly regulated by post-translational ubiquitylation modification, which may synchronize the transition between cell proliferation and differentiation in ESCs

Lysyl oxidase drives tumour progression by trapping EGF receptors at the cell surface

Lysyl oxidase (LOX) remodels the tumour microenvironment by cross-linking the extracellular matrix. LOX overexpression is associated with poor cancer outcomes. Here, we find that LOX regulates the epidermal growth factor receptor (EGFR) to drive tumour progression. We show that LOX regulates EGFR by suppressing TGFβ1 signalling through the secreted protease HTRA1. This increases the expression of Matrilin2 (MATN2), an EGF-like domain-containing protein that traps EGFR at the cell surface to facilitate its activation by EGF. We describe a pharmacological inhibitor of LOX, CCT365623, which disrupts EGFR cell surface retention and delays the growth of primary and metastatic tumour cells in vivo. Thus, we show that LOX regulates EGFR cell surface retention to drive tumour progression, and we validate the therapeutic potential of inhibiting this pathway with the small molecule inhibitor CCT365623

Strong interface-induced spin-orbit coupling in graphene on WS2

Interfacial interactions allow the electronic properties of graphene to be modified, as recently demonstrated by the appearance of satellite Dirac cones in the band structure of graphene on hexagonal boron nitride (hBN) substrates. Ongoing research strives to explore interfacial interactions in a broader class of materials in order to engineer targeted electronic properties. Here we show that at an interface with a tungsten disulfide (WS2) substrate, the strength of the spin-orbit interaction (SOI) in graphene is very strongly enhanced. The induced SOI leads to a pronounced low-temperature weak anti-localization (WAL) effect, from which we determine the spin-relaxation time. We find that spin-relaxation time in graphene is two-to-three orders of magnitude smaller on WS2 than on SiO2 or hBN, and that it is comparable to the intervalley scattering time. To interpret our findings we have performed first-principle electronic structure calculations, which both confirm that carriers in graphene-on-WS2 experience a strong SOI and allow us to extract a spin-dependent low-energy effective Hamiltonian. Our analysis further shows that the use of WS2 substrates opens a possible new route to access topological states of matter in graphene-based systems.Comment: Originally submitted version in compliance with editorial guidelines. Final version with expanded discussion of the relation between theory and experiments to be published in Nature Communication

Analysis of jak2 catalytic function by peptide microarrays: The role of the JH2 domain and V617F mutation

Janus kinase 2 (JAK2) initiates signaling from several cytokine receptors and is required for biological responses such as erythropoiesis. JAK2 activity is controlled by regulatory proteins such as Suppressor of Cytokine Signaling (SOCS) proteins and protein tyrosine phosphatases. JAK2 activity is also intrinsically controlled by regulatory domains, where the pseudokinase (JAK homology 2, JH2) domain has been shown to play an essential role. The physiological role of the JH2 domain in the regulation of JAK2 activity was highlighted by the discovery of the acquired missense point mutation V617F in myeloproliferative neoplasms (MPN). Hence, determining the precise role of this domain is critical for understanding disease pathogenesis and design of new treatment modalities. Here, we have evaluated the effect of inter-domain interactions in kinase activity and substrate specificity. By using for the first time purified recombinant JAK2 proteins and a novel peptide micro-array platform, we have determined initial phosphorylation rates and peptide substrate preference for the recombinant kinase domain (JH1) of JAK2, and two constructs comprising both the kinase and pseudokinase domains (JH1-JH2) of JAK2. The data demonstrate that (i) JH2 drastically decreases the activity of the JAK2 JH1 domain, (ii) JH2 increased the Kmfor ATP (iii) JH2 modulates the peptide preference of JAK2 (iv) the V617F mutation partially releases this inhibitory mechanism but does not significantly affect substrate preference or Kmfor ATP. These results provide the biochemical basis for understanding the interaction between the kinase and the pseudokinase domain of JAK2 and identify a novel regulatory role for the JAK2 pseudokinase domain. Additionally, this method can be used to identify new regulatory mechanisms for protein kinases that provide a better platform for designing specific strategies for therapeutic approaches

Targeting the hypoxic fraction of tumours using hypoxia activated prodrugs

The presence of a microenvironment within most tumours containing regions of low oxygen tension or hypoxia has profound biological and therapeutic implications. Tumour hypoxia is known to promote the development of an aggressive phenotype, resistance to both chemotherapy and radiotherapy and is strongly associated with poor clinical outcome. Paradoxically, it is recognised as a high priority target and one therapeutic strategies designed to eradicate hypoxic cells in tumours are a group of compounds known collectively as hypoxia activated prodrugs (HAPs) or bioreductive drugs. These drugs are inactive prodrugs that require enzymatic activation (typically by 1 or 2 electron oxidoreductases) to generate cytotoxic species with selectivity for hypoxic cells being determined by (i) the ability of oxygen to either reverse or inhibit the activation process and (ii) the presence of elevated expression of oxidoreductases in tumours. The concepts underpinning HAP development were established over 40 years ago and have been refined over the years to produce a new generation of HAPs that are under preclinical and clinical development. The purpose of this article is to describe current progress in the development of HAPs focusing on the mechanisms of action, preclinical properties and clinical progress of leading examples

Analysis of computational approaches for motif discovery

Recently, we performed an assessment of 13 popular computational tools for discovery of transcription factor binding sites (M. Tompa, N. Li, et al., "Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites", Nature Biotechnology, Jan. 2005). This paper contains follow-up analysis of the assessment results, and raises and discusses some important issues concerning the state of the art in motif discovery methods: 1. We categorize the objective functions used by existing tools, and design experiments to evaluate whether any of these objective functions is the right one to optimize. 2. We examine various features of the data sets that were used in the assessment, such as sequence length and motif degeneracy, and identify which features make data sets hard for current motif discovery tools. 3. We identify an important feature that has not yet been used by existing tools and propose a new objective function that incorporates this feature

The disruption of proteostasis in neurodegenerative diseases

Cells count on surveillance systems to monitor and protect the cellular proteome which, besides being highly heterogeneous, is constantly being challenged by intrinsic and environmental factors. In this context, the proteostasis network (PN) is essential to achieve a stable and functional proteome. Disruption of the PN is associated with aging and can lead to and/or potentiate the occurrence of many neurodegenerative diseases (ND). This not only emphasizes the importance of the PN in health span and aging but also how its modulation can be a potential target for intervention and treatment of human diseases.info:eu-repo/semantics/publishedVersio

rMotifGen: random motif generator for DNA and protein sequences

<p>Abstract</p> <p>Background</p> <p>Detection of short, subtle conserved motif regions within a set of related DNA or amino acid sequences can lead to discoveries about important regulatory domains such as transcription factor and DNA binding sites as well as conserved protein domains. In order to help assess motif detection algorithms on motifs with varying properties and levels of conservation, we have developed a computational tool, rMotifGen, with the sole purpose of generating a number of random DNA or protein sequences containing short sequence motifs. Each motif consensus can be user-defined, randomly generated, or created from a position-specific scoring matrix (PSSM). Insertions and mutations within these motifs are created according to user-defined parameters and substitution matrices. The resulting sequences can be helpful in mutational simulations and in testing the limits of motif detection algorithms.</p> <p>Results</p> <p>Two implementations of rMotifGen have been created, one providing a graphical user interface (GUI) for random motif construction, and the other serving as a command line interface. The second implementation has the added advantages of platform independence and being able to be called in a batch mode. rMotifGen was used to construct sample sets of sequences containing DNA motifs and amino acid motifs that were then tested against the Gibbs sampler and MEME packages.</p> <p>Conclusion</p> <p>rMotifGen provides an efficient and convenient method for creating random DNA or amino acid sequences with a variable number of motifs, where the instance of each motif can be incorporated using a position-specific scoring matrix (PSSM) or by creating an instance mutated from its corresponding consensus using an evolutionary model based on substitution matrices. rMotifGen is freely available at: <url>http://bioinformatics.louisville.edu/brg/rMotifGen/</url>.</p

Discriminative motif discovery in DNA and protein sequences using the DEME algorithm

<p>Abstract</p> <p>Background</p> <p>Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.</p> <p>Results</p> <p>We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.</p> <p>Conclusion</p> <p>Using artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at <url>http://bioinformatics.org.au/deme/</url></p

Incorporating background frequency improves entropy-based residue conservation measures

BACKGROUND: Several entropy-based methods have been developed for scoring sequence conservation in protein multiple sequence alignments. High scoring amino acid positions may correlate with structurally or functionally important residues. However, amino acid background frequencies are usually not taken into account in these entropy-based scoring schemes. RESULTS: We demonstrate that using a relative entropy measure that incorporates amino acid background frequency results in improved performance in identifying functional sites from protein multiple sequence alignments. CONCLUSION: Our results suggest that the application of appropriate background frequency information may lead to more biologically relevant results in many areas of bioinformatics