Biology subjects

Chung, C.

Publications and source records attributed to Chung, C..

3 recordsLinked to original sources

bioRxiv2018

Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/

systems biology↗

bioRxiv2018

Swedish Nerve Growth Factor Mutation (NGFR100W) Defines a Role for TrkA and p75NTR in Nociception

Nerve growth factor (NGF) exerts multiple functions on target neurons throughout development. The recent discovery of a point mutation leading to a change from arginine to tryptophan at residue 100 in the mature NGF{beta} sequence (NGFR100W) in patients with hereditary sensory and autonomic neuropathy, type V (HSAN V), made it possible to distinguish the signaling mechanisms that lead to two functionally different outcomes of NGF: trophic versus nociceptive. We performed extensive biochemical, cellular and live imaging experiments to examine the binding and signaling properties of NGFR100W. Our results show that, similar to the wildtype NGF (wtNGF), the naturally occurring NGFR100W mutant was capable of binding to and activating the TrkA receptor and its downstream signaling pathways to support neuronal survival and differentiation. However, NGFR100W failed to bind and stimulate the 75kD neurotrophic factor receptor (p75NTR)-mediated signaling cascades (i.e. the RhoA-Cofilin pathway). Intraplantar injection of NGFR100W into adult rats induced neither TrkA-mediated thermal nor mechanical acute hyperalgesia, but retained the ability to induce chronic hyperalgesia based on agonism for TrkA signaling. Taken together, our studies provide evidence that NGFR100W retains trophic support capability through TrkA and one aspect of its nociceptive signaling, but fails to engage p75NTR signaling pathways. Our findings suggest that wtNGF acts through TrkA to regulate the delayed priming of nociceptive responses. The integration of both TrkA and p75NTR signaling thus appears to regulate neuroplastic effects of NGF in peripheral nociception.\n\nSignificance StatementIn the present study, we characterized the naturally occurring NGFR100W mutant that is associated with hereditary sensory and autonomic neuropathy, type V. We have demonstrated for the first time that NGFR100W retains trophic support capability through TrkA but fails to engage p75NTR signaling pathways. Furthermore, following Intraplantar injection into adult rats, NGFR100W induced neither thermal nor mechanical acute hyperalgesia, but retained the ability to induce chronic hyperalgesia. We have also provided evidence that the integration of both TrkA-and p75NTR-mediated signaling thus appears to regulate neuroplastic effects of NGF in peripheral nociception. Our study with NGFR100W suggests that it is possible to uncouple trophic effect from nociceptive function, both induced by wildtype NGF.\n\nAbbreviations

neuroscience↗

bioRxiv2017

Drug Target Ontology to Classify and Integrate Drug Discovery Data

BackgroundOne of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome.\n\nResultsAs part of that effort, we have been developing a framework to integrate, navigate, and analyze drug discovery data based on formalized and standardized classifications and annotations of druggable protein targets, the Drug Target Ontology (DTO). DTO was constructed by extensive curation and consolidation of various resources. DTO classifies the four major drug target protein families, GPCRs, kinases, ion channels and nuclear receptors, based on phylogenecity, function, target development level, disease association, tissue expression, chemical ligand and substrate characteristics, and target-family specific characteristics. The formal ontology was built using a new software tool to auto-generate most axioms from a database while also supporting manual knowledge acquisition. A modular, hierarchical implementation facilitates development and maintenance and makes use of various external ontologies, thus integrating the DTO into the ecosystem of biomedical ontologies. As a formal OWL-DL ontology, DTO contains asserted and inferred axioms. Modeling data from the Library of Integrated Network-based Cellular Signatures (LINCS) program illustrates the potential of DTO for contextual data integration and nuanced definition of important drug target characteristics. DTO has been implemented in the IDG user interface Portal, Pharos and the TIN-X explorer of protein target disease relationships.\n\nConclusionsDTO was built based on the need for a formal semantic model for druggable targets including various related information such as protein, gene, protein domain, protein structure, binding site, small molecule drug, mechanism of action, protein tissue localization, disease association, and many other types of information. DTO will further facilitate the otherwise challenging integration and formal linking to biological assays, phenotypes, disease models, drug poly-pharmacology, binding kinetics and many other processes, functions and qualities that are at the core of drug discovery. The first version of DTO is publically available via the website http://drugtargetontology.org/, Github (https://github.com/DrugTargetOntology/DTO), and the NCBO Bioportal (https://bioportal.bioontology.org/ontologies/DTO). The long-term goal of DTO is to provide such an integrative framework and to populate the ontology with this information as a community resource.

bioinformatics↗