The Role of Intrinsically Unstructured Proteins in Neurodegenerative Diseases

The number and importance of intrinsically disordered proteins (IUP), known to be involved in various human disorders, are growing rapidly. To test for the generalized implications of intrinsic disorders in proteins involved in Neurodegenerative diseases, disorder prediction tools have been applied to three datasets comprising of proteins involved in Huntington Disease (HD), Parkinson's disease (PD), Alzheimer's disease (AD). Results show, in general, proteins in disease datasets possess significantly enhanced intrinsic unstructuredness. Most of these disordered proteins in the disease datasets are found to be involved in neuronal activities, signal transduction, apoptosis, intracellular traffic, cell differentiation etc. Also these proteins are found to have more number of interactors and hence as the proportion of disorderedness (i.e., the length of the unfolded stretch) increased, the size of the interaction network simultaneously increased. All these observations reflect that, “Moonlighting” i.e. the contextual acquisition of different structural conformations (transient), eventually may allow these disordered proteins to act as network “hubs” and thus they may have crucial influences in the pathogenecity of neurodegenerative diseases

Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT

Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment

A Comprehensive Resource of Interacting Protein Regions for Refining Human Transcription Factor Networks

Large-scale data sets of protein-protein interactions (PPIs) are a valuable resource for mapping and analysis of the topological and dynamic features of interactome networks. The currently available large-scale PPI data sets only contain information on interaction partners. The data presented in this study also include the sequences involved in the interactions (i.e., the interacting regions, IRs) suggested to correspond to functional and structural domains. Here we present the first large-scale IR data set obtained using mRNA display for 50 human transcription factors (TFs), including 12 transcription-related proteins. The core data set (966 IRs; 943 PPIs) displays a verification rate of 70%. Analysis of the IR data set revealed the existence of IRs that interact with multiple partners. Furthermore, these IRs were preferentially associated with intrinsic disorder. This finding supports the hypothesis that intrinsically disordered regions play a major role in the dynamics and diversity of TF networks through their ability to structurally adapt to and bind with multiple partners. Accordingly, this domain-based interaction resource represents an important step in refining protein interactions and networks at the domain level and in associating network analysis with biological structure and function

Inhibition of Neuroblastoma Tumor Growth by Targeted Delivery of MicroRNA-34a Using Anti-Disialoganglioside GD2 Coated Nanoparticles

Neuroblastoma is one of the most challenging malignancies of childhood, being associated with the highest death rate in paediatric oncology, underlining the need for novel therapeutic approaches. Typically, patients with high risk disease undergo an initial remission in response to treatment, followed by disease recurrence that has become refractory to further treatment. Here, we demonstrate the first silica nanoparticle-based targeted delivery of a tumor suppressive, pro-apoptotic microRNA, miR-34a, to neuroblastoma tumors in a murine orthotopic xenograft model. These tumors express high levels of the cell surface antigen disialoganglioside GD2 (GD(2)), providing a target for tumor-specific delivery.Nanoparticles encapsulating miR-34a and conjugated to a GD(2) antibody facilitated tumor-specific delivery following systemic administration into tumor bearing mice, resulted in significantly decreased tumor growth, increased apoptosis and a reduction in vascularisation. We further demonstrate a novel, multi-step molecular mechanism by which miR-34a leads to increased levels of the tissue inhibitor metallopeptidase 2 precursor (TIMP2) protein, accounting for the highly reduced vascularisation noted in miR-34a-treated tumors.These novel findings highlight the potential of anti-GD(2)-nanoparticle-mediated targeted delivery of miR-34a for both the treatment of GD(2)-expressing tumors, and as a basic discovery tool for elucidating biological effects of novel miRNAs on tumor growth

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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