Abstract Aging is considered as one of the main factors promoting the risk for Parkinson's disease (PD), and common mechanisms of dopamine neuron degeneration in aging and PD have been proposed in ...recent years. Here, we use a statistical meta-analysis of human brain transcriptomics data to investigate potential mechanistic relationships between adult brain aging and PD pathogenesis at the pathway and network level. The analyses identify statistically significant shared pathway and network alterations in aging and PD and an enrichment in PD-associated sequence variants from genome-wide association studies among the jointly deregulated genes. We find robust discriminative patterns for groups of functionally related genes with potential applications as combined risk biomarkers to detect aging- and PD-linked oxidative stress, e.g., a consistent over-expression of metallothioneins matching with findings in previous independent studies. Interestingly, analyzing the regulatory network and mouse knockout expression data for NR4A2, a transcription factor previously associated with rare mutations in PD and here found as the most significantly under-expressed gene in PD among the jointly altered genes, suggests that aging-related NR4A2 expression changes may increase PD risk via downstream effects similar to disease-linked mutations and to expression changes in sporadic PD. Overall, the analyses suggest mechanistic explanations for the age-dependence of PD risk and reveal significant and robust shared process alterations with potential applications in biomarker development for pre-symptomatic risk assessment or early stage diagnosis.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation ...and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).
Elderly surgical patients frequently experience postoperative delirium (POD) and the subsequent development of postoperative cognitive dysfunction (POCD). Clinical features include deterioration in ...cognition, disturbance in attention and reduced awareness of the environment and result in higher morbidity, mortality and greater utilization of social financial assistance. The aging Western societies can expect an increase in the incidence of POD and POCD. The underlying pathophysiological mechanisms have been studied on the molecular level albeit with unsatisfying small research efforts given their societal burden. Here, we review the known physiological and immunological changes and genetic risk factors, identify candidates for further studies and integrate the information into a draft network for exploration on a systems level. The pathogenesis of these postoperative cognitive impairments is multifactorial; application of integrated systems biology has the potential to reconstruct the underlying network of molecular mechanisms and help in the identification of prognostic and diagnostic biomarkers.
Silicon is widely regarded as one of the most promising anode materials for lithium ion and next-generation lithium batteries because of its high theoretical specific capacity. However, major issues ...arise from the large volume changes during alloying with lithium. In recent years, much effort has been spent on preparing nanostructured silicon and optimizing various aspects of material processing with the goal of preserving the electrode integrity upon lithiation/delithiation. The performance of silicon anodes is known to depend on a large number of parameters and, thus, the general definition of a “standard” is virtually impossible. In this work, we conduct a comparative performance study of silicon anode tapes prepared from commercially available materials while using both a well-defined electrode configuration and cycling method. Our results demonstrate that the polymer binder has a profound effect on the cell performance. Furthermore, we show that key parameters such as specific capacity, capacity retention, rate capability, and so forth can be strongly affected by the choice of silicon material, polymer binder and electrolyte system – even the formation of metastable crystalline Li15Si4 is found to depend on the electrode composition and low potential exposure time. Overall, the use of either poly(acrylic acid) with a viscosity-average molecular weight of 450.000 or poly(vinyl alcohol) Selvol 425 in combination with both silicon nanopowder containing a native oxide surface layer of ∼1 nm in diameter and with a monofluoroethylene carbonate-based electrolyte led to improved cycling stability at high loadings.
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IJS, KILJ, NUK, PNG, UL, UM
Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale ...functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized.
To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins.
EnrichNet is freely available at http://www.enrichnet.org.
Natalio.Krasnogor@nottingham.ac.uk, reinhard.schneider@uni.lu or avalencia@cnio.es
Supplementary data are available at Bioinformatics Online.
PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple ...sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein-protein binding sites (ISIS2), protein-polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org.
We report a facile strategy to synthesize water-soluble, fluorescent gold nanoclusters (AuNCs) in one step by using a mild reductant, tetrakis(hydroxymethyl)phosphonium chloride (THPC). A ...zwitterionic functional ligand, D-penicillamine (DPA), as a capping agent endowed the AuNCs with excellent stability in aqueous solvent over the physiologically relevant pH range. The DPA-capped AuNCs displayed excitation and emission bands at 400 and 610 nm, respectively; the fluorescence quantum yield was 1.3%. The effect of borohydride reduction on the optical spectra and X-ray photoelectron spectroscopy (XPS) results indicated that the AuNC luminescence is closely related to the presence of Au(I) on their surfaces. In a first optical imaging application, we studied internalization of the AuNCs by live HeLa cells using confocal microscopy with two-photon excitation. A cell viability assay revealed good biocompatibility of these AuNCs. Our studies demonstrate a great potential of DPA-stabilized AuNCs as fluorescent nanoprobes in bioimaging and related applications.
Despite our rapidly growing knowledge about the human genome, we do not know all of the genes required for some of the most basic functions of life. To start to fill this gap we developed a ...high-throughput phenotypic screening platform combining potent gene silencing by RNA interference, time-lapse microscopy and computational image processing. We carried out a genome-wide phenotypic profiling of each of the approximately 21,000 human protein-coding genes by two-day live imaging of fluorescently labelled chromosomes. Phenotypes were scored quantitatively by computational image processing, which allowed us to identify hundreds of human genes involved in diverse biological functions including cell division, migration and survival. As part of the Mitocheck consortium, this study provides an in-depth analysis of cell division phenotypes and makes the entire high-content data set available as a resource to the community.
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DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD ...pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at
http://minerva.uni.lu/pd_map
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover ...the molecular mechanisms underlying Parkinson's disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlights the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.
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