From Databases to Modelling of Functional Pathways

This short review comments on current informatics resources and methodologies in the study of functional pathways in cell biology. It highlights recent achievements in unveiling the structural design of protein and gene networks and discusses current approaches to model and simulate the dynamics of regulatory pathways in the cell

Molecular recognition in helix-loop-helix and helix-loop-helix-leucine zipper domains: Design of repertoires and selection of high affinity ligands for natural proteins

Helix-loop-helix (HLH) and helix-loop-helix-leucine zipper (HLHZip) are dimerization domains that mediate selective pairing among members of a large transcription factor family involved in cell fate determination. To investigate the molecular rules underlying recognition specificity and to isolate molecules interfering with cell proliferation and differentiation control, we assembled two molecular repertoires obtained by directed randomization of the binding surface in these two domains. For this strategy we selected the Heb HLH and Max Zip regions as molecular scaffolds for the randomization process and displayed the two resulting molecular repertoires on lambda phage capsids. By affinity selection, many domains were isolated that bound to the proteins Mad, Rox, MyoD, and Id2 with different levels of affinity. Although several residues along an extended surface within each domain appeared to contribute to dimerization, some key residues critically involved in molecular recognition could be identified. Furthermore, a number of charged residues appeared to act as switch points facilitating partner exchange. By successfully selecting ligands for four of four HLH or HLHZip proteins, we have shown that the repertoires assembled are rather general and possibly contain elements that bind with sufficient affinity to any natural HLH or HLHZip molecule. Thus they represent a valuable source of ligands that could be used as reagents for molecular dissection of functional regulatory pathways

Non coding RNA and brain

Small non coding RNAs are a group of very different RNA molecules, present in virtually all cells, with a wide spectrum of regulatory functions which include RNA modification and regulation of protein synthesis. They have been isolated and characterized in all organisms and tissues, from Archaeobacteria to mammals. In mammalian brain there are a number of these small molecules, which are involved in neuronal differentiation as well as, possibly, in learning and memory. In this manuscript, we analyze the present knowledge about the function of the most important groups of small non-coding RNA present in brain: small nucleolar RNAs, small cytoplasmic RNAs, and microRNAs. The last ones, in particular, appear to be critical for dictating neuronal cell identity during development and to play an important role in neurite growth, synaptic development and neuronal plasticity

Role of interface and morphology in the magnetic behaviour of perpendicular thin films based on L10 FePt

FePt L10 ordered alloy is a promising material for high-density magnetic recording, since it allows the ferromagnetic stability in particles of few nanometers. Here we present our recent studies on the correlation between magnetic and morphological/interfacial properties of FePt -based thin films, nanostructures, and nano-composite bilayers. L10 FePt (001) epitaxial thin films with high structural quality were grown on (100) MgO by sputtering r.f., using the alternate-layer deposition method. By playing with growth temperature on the one hand and post-annealing temperature and time on the other, we have been able to finely control epitaxy, structural order, and morphology from 3D laterally confined structures to continuous film, with desired grain size. In particular we have been able to decrease grain size and to optimise magnetic properties (increase of anisotropy/coercivity ratio) at the same time, by post-annealing in situ [1]. Laterally confined magnetic structures were also obtained by focused ion beam (FIB). We have shown that for suitable Ga+ doses (1?1014 ion/cm2), it is possible to transform the L10 ordered phase to the A1 disordered one, without affecting morphology, giving rise to substantial modifications of magnetic properties from hard to soft. Perpendicular 2D magnetic patterns (dots, stripes) in a soft easy-plane matrix were realized in films of continuous morphology [2]. FePt L10 has also been exploited as the hard layer of nanostructured hard-soft nanocomposite bilayers. The exploitation of the exchange-coupling between hard and soft layers in exchange-coupled media represents a possible approach to overcome the so-called "recording trilemma" [3]. The samples were prepared by growing a magnetically soft Fe layer (2 and 3.5 nm) over a hard FePt(001) layer (10 nm). Three bilayers series have been grown based on FePt epitaxial layers with high degree of chemical order (S≥0.76) and different morphologies, corresponding to different interface characteristics. The resulting hard layer anisotropy is high (K>1?107 erg/cm3), and the coercivity is increased by the grains separation (from 1.7 to 3 T). In the Fe/FePt bilayers the coercivity HC is strongly reduced compared to the hard layer value (HC/HChard down to 0.37), indicating that high anisotropy perpendicular systems with moderate coercivity can be obtained [4]. Moreover, the control of the interface morphology allows to modify the magnetic regime at fixed Fe thickness (Rigid Magnet to Exchange-Spring), due to the nanoscale structure effect on the hard/soft coupling, and to tailor the hysteresis loop characteristics

Myc inhibition is effective against glioma and reveals a role for Myc in proficient mitosis.

Gliomas are the most common primary tumours affecting the adult central nervous system and respond poorly to standard therapy. Myc is causally implicated in most human tumours and the majority of glioblastomas have elevated Myc levels. Using the Myc dominant negative Omomyc, we previously showed that Myc inhibition is a promising strategy for cancer therapy. Here, we preclinically validate Myc inhibition as a therapeutic strategy in mouse and human glioma, using a mouse model of spontaneous multifocal invasive astrocytoma and its derived neuroprogenitors, human glioblastoma cell lines, and patient-derived tumours both in vitro and in orthotopic xenografts. Across all these experimental models we find that Myc inhibition reduces proliferation, increases apoptosis and remarkably, elicits the formation of multinucleated cells that then arrest or die by mitotic catastrophe, revealing a new role for Myc in the proficient division of glioma cells

The Action Mechanism of the Myc Inhibitor Termed Omomyc May Give Clues on How to Target Myc for Cancer Therapy

Recent evidence points to Myc – a multifaceted bHLHZip transcription factor deregulated in the majority of human cancers – as a priority target for therapy. How to target Myc is less clear, given its involvement in a variety of key functions in healthy cells. Here we report on the action mechanism of the Myc interfering molecule termed Omomyc, which demonstrated astounding therapeutic efficacy in transgenic mouse cancer models in vivo. Omomyc action is different from the one that can be obtained by gene knockout or RNA interference, approaches designed to block all functions of a gene product. This molecule – instead – appears to cause an edge-specific perturbation that destroys some protein interactions of the Myc node and keeps others intact, with the result of reshaping the Myc transcriptome. Omomyc selectively targets Myc protein interactions: it binds c- and N-Myc, Max and Miz-1, but does not bind Mad or select HLH proteins. Specifically, it prevents Myc binding to promoter E-boxes and transactivation of target genes while retaining Miz-1 dependent binding to promoters and transrepression. This is accompanied by broad epigenetic changes such as decreased acetylation and increased methylation at H3 lysine 9. In the presence of Omomyc, the Myc interactome is channeled to repression and its activity appears to switch from a pro-oncogenic to a tumor suppressive one. Given the extraordinary therapeutic impact of Omomyc in animal models, these data suggest that successfully targeting Myc for cancer therapy might require a similar twofold action, in order to prevent Myc/Max binding to E-boxes and, at the same time, keep repressing genes that would be repressed by Myc

Conference Review From databases to modelling of functional pathways

Abstract This short review comments on current informatics resources and methodologies in the study of functional pathways in cell biology. It highlights recent achievements in unveiling the structural design of protein and gene networks and discusses current approaches to model and simulate the dynamics of regulatory pathways in the cell. Understanding how genes interact to perform specific biological processes is a major challenge in biology. It is felt that this is becoming possible due to the large amount of information generated by genomic sequencing, protein interaction and gene expression studies, and stored in public databases (www.ncbi.nlm.nih.gov/GenBank; www.ncbi.nlm.nih.gov/LocusLink; www.ncbi.nlm.nih.gov/UniGene; http://us.expasy.org/sprot; www.ensembl.org; www.ebi.ac.uk; www.yeastgenome.org/; www.arabidopsis.org/; www.wormbase.org/; http://flybase.bio.indiana.edu/; www.informatics.jax.org; http://rgd.mcw.edu/; http://genome-www5.stanford.edu/MicroArray/ SMD/). Achieving this objective will require data to be organized in a more understandable structure. Data representation in the form of networks or functional pathways, and modelling their dynamic behaviour, is expected to give a better insight into the complex patterns of gene-protein interactions. At the same time, such models are expected to revolutionize drug screening, and the identification of functional pathways involved in pathogenesis will facilitate the rational design of therapies Databases The effort of creating biological pathway databases and providing informatics tools for their analysis has been undertaken by public and private initiatives, such as Transpath (www.biobase.de), Biocarta (www.biocarta.com), GenMAPP (www.genmapp.org), aMaze (www.amaze.ulb.ac.be) and the Alliance for Cellular Signaling (AfCS:www.afcs.org). The AfCS consortium, which is presently focused on lymphocyte and cardiac myocyte signalling, has the overall goal to understand the relationships between sets of inputs and outputs that vary both temporally and spatially. This will involve identification of all the proteins that comprise the various signalling systems, the assessment of information flow in both normal and pathological states, and the reduction of the data into a set of theoretical models. The aMaze project of an omni-comprehensive, object-orientated data model is implemented in both MySQL and Oracle languages. It aims at representing functional and physical interactions among biochemical entities mapped onto their cellular and tissue locations. It also attempts to provide a workbench for analysing networks of cellular processes, such as metabolic pathways, protein-protein interactions, gene regulation, transport and signal transduction. Most of the pathway data presently stored in the database relate to yeast and bacterial cells. A complication in pathway analysis results from network component compartmentalization in space and time, both at the cellular level Gene and protein network architecture Gene or protein networks are more easily understood when represented as graphs, in which nodes are genes or proteins, and arcs (edges) are relationships between nodes. Depending on the case, edges can have direction and weight. Data from highthroughput protein interaction screens and DNA microarray experiments, as well as tools for mining information in the scientific literature, have supported the elucidation of the structural design of networks, an important step towards modelling and understanding cellular control systems. By employing controlled vocabularies (www.geneontology. org) linked to gene symbols, it is possible to mine qualitative information: automatic query methods have been used to extract and structure knowledge from publicly available gene/protein and text databases. This allows the creation of a cocitation network Due to their importance in cell physiology, considerable efforts are being devoted to large-scale mapping of protein interaction networks by yeast two hybrid screens jsp). Microarray data analysis presents the challenge of revealing functional patterns in the chaos that is gene expression. The starting point is a gene expression data matrix, utilized by clustering algorithms to identify co-expressed genes, which are thought to be regulated by shared transcription factors (http://genexpress.stanford.edu). Although powerful for organizing data, such algorithms, by themselves, are unfit for model building since they do not relate gene expression values to a given functional state. Graph theory, supervised learning and other statistical and computational approaches have been adopted to make predictions and to reconstruct gene regulation networks from microarray data Although it might be possible in principle, network reconstruction based solely on microarray experiments proved very hard to achieve, pointing to the utility of incorporating information on transcription factor binding to gene promoters ([31] www.math.uah.edu/stat). Interaction between transcription factors and their DNA binding sites may be deduced from computational analysis of binding sites in promoter sequences Methods have been devised to extract regulatory information from binding data and to find synergistic motif combinations in the promoters of co-regulated genes ([19,22,23] http://web.wi.mit. edu/young/regulator network). More advanced methods, such as the genetic regulatory modules (GRAM Both protein and gene interaction networks appear to be scale-free, the connectivity of their nodes following a power law; therefore, they have small world properties like many other networks found in nature. Such global views, although fascinating, do not always appear of immediate utility for biologists, since they give only a general impression of the network operation and lack crucial details 182 S. Nasi Modelling of cellular pathways Depicting sets of molecular interactions as static graphs does not reveal the dynamics of events within cells. The myriad of data now available has stimulated attempts to design a computer replica of a living cell, by including everything that is known in one description of an entire cell biological network. Several projects aim to develop theoretical supports, technologies and software platforms for whole cell simulation

Myc beyond Cancer: Regulation of Mammalian Tissue Regeneration

Myc is one of the most well-known oncogenes driving tumorigenesis in a wide variety of tissues. From the brain to blood, its deregulation derails physiological pathways that grant the correct functioning of the cell. Its action is carried out at the gene expression level, where Myc governs basically every aspect of transcription. Indeed, in addition to its role as a canonical, chromatin-bound transcription factor, Myc rules RNA polymerase II (RNAPII) transcriptional pause–release, elongation and termination and mRNA capping. For this reason, it is evident that minimal perturbations of Myc function mirror malignant cell behavior and, consistently, a large body of literature mainly focuses on Myc malfunctioning. In healthy cells, Myc controls molecular mechanisms involved in pivotal functions, such as cell cycle (and proliferation thereof), apoptosis, metabolism and cell size, angiogenesis, differentiation and stem cell self-renewal. In this latter regard, Myc has been found to also regulate tissue regeneration, a hot topic in the research fields of aging and regenerative medicine. Indeed, Myc appears to have a role in wound healing, in peripheral nerves and in liver, pancreas and even heart recovery. Herein, we discuss the state of the art of Myc’s role in tissue regeneration, giving an overview of its potent action beyond cancer