32 research outputs found
Ontological representation, integration, and analysis of LINCS cell line cells and their cellular responses
Abstract
Background
Aiming to understand cellular responses to different perturbations, the NIH Common Fund Library of Integrated Network-based Cellular Signatures (LINCS) program involves many institutes and laboratories working on over a thousand cell lines. The community-based Cell Line Ontology (CLO) is selected as the default ontology for LINCS cell line representation and integration.
Results
CLO has consistently represented all 1097 LINCS cell lines and included information extracted from the LINCS Data Portal and ChEMBL. Using MCF 10A cell line cells as an example, we demonstrated how to ontologically model LINCS cellular signatures such as their non-tumorigenic epithelial cell type, three-dimensional growth, latrunculin-A-induced actin depolymerization and apoptosis, and cell line transfection. A CLO subset view of LINCS cell lines, named LINCS-CLOview, was generated to support systematic LINCS cell line analysis and queries. In summary, LINCS cell lines are currently associated with 43 cell types, 131 tissues and organs, and 121 cancer types. The LINCS-CLO view information can be queried using SPARQL scripts.
Conclusions
CLO was used to support ontological representation, integration, and analysis of over a thousand LINCS cell line cells and their cellular responses.https://deepblue.lib.umich.edu/bitstream/2027.42/140390/1/12859_2017_Article_1981.pd
The cell line ontology-based representation, integration and analysis of cell lines used in China
Abstract
Background
The Chinese National Infrastructure of Cell Line stores and distributes cell lines for biomedical research in China. This study aims to represent and integrate the information of NICR cell lines into the community-based Cell Line Ontology (CLO).
Results
We have aligned, represented, and added all identified 2704 cell line cells in NICR to CLO. We also proposed new ontology design patterns to represent the usage of cell line cells as disease models by inducing tumor formation in model organisms, and the relations between cell line cells and their expressed or overexpressed genes or proteins. The resulting CLO-NICR ontology also includes the Chinese representation of the NICR cell line information. CLO-NICR was merged into the general CLO. To serve the cell research community in China, the Chinese version of CLO-NICR was also generated and deposited in the OntoChina ontology repository. The usage of CLO-NICR was demonstrated by DL query and knowledge extraction.
Conclusions
In summary, all identified cell lines from NICR are represented by the semantics framework of CLO and incorporated into CLO as a most recent update. We also generated a CLO-NICR and its Chinese view (CLO-NICR-Cv). The development of CLO-NICR and CLO-NIC-Cv allows the integration of the cell lines from NICR into the community-based CLO ontology and provides an integrative platform to support different applications of CLO in China.https://deepblue.lib.umich.edu/bitstream/2027.42/148821/1/12859_2019_Article_2724.pd
CLO: The cell line ontology
Abstract
Background
Cell lines have been widely used in biomedical research. The community-based Cell Line Ontology (CLO) is a member of the OBO Foundry library that covers the domain of cell lines. Since its publication two years ago, significant updates have been made, including new groups joining the CLO consortium, new cell line cells, upper level alignment with the Cell Ontology (CL) and the Ontology for Biomedical Investigation, and logical extensions.
Construction and content
Collaboration among the CLO, CL, and OBI has established consensus definitions of cell line-specific terms such as âcell lineâ, âcell line cellâ, âcell line culturingâ, and âmortalâ vs. âimmortal cell line cellâ. A cell line is a genetically stable cultured cell population that contains individual cell line cells. The hierarchical structure of the CLO is built based on the hierarchy of the in vivo cell types defined in CL and tissue types (from which cell line cells are derived) defined in the UBERON cross-species anatomy ontology. The new hierarchical structure makes it easier to browse, query, and perform automated classification. We have recently added classes representing more than 2,000 cell line cells from the RIKEN BRC Cell Bank to CLO. Overall, the CLO now contains ~38,000 classes of specific cell line cells derived from over 200 in vivo cell types from various organisms.
Utility and discussion
The CLO has been applied to different biomedical research studies. Example case studies include annotation and analysis of EBI ArrayExpress data, bioassays, and host-vaccine/pathogen interaction. CLOâs utility goes beyond a catalogue of cell line types. The alignment of the CLO with related ontologies combined with the use of ontological reasoners will support sophisticated inferencing to advance translational informatics development.http://deepblue.lib.umich.edu/bitstream/2027.42/109554/1/13326_2013_Article_185.pd
Cells in ExperimentaL Life Sciences (CELLS-2018): capturing the knowledge of normal and diseased cells with ontologies
Abstract
Cell cultures and cell lines are widely used in life science experiments. In conjunction with the 2018 International Conference on Biomedical Ontology (ICBO-2018), the 2nd International Workshop on Cells in ExperimentaL Life Science (CELLS-2018) focused on two themes of knowledge representation, for newly-discovered cell types and for cells in disease states. This workshop included five oral presentations and a general discussion session. Two new ontologies, including the Cancer Cell Ontology (CCL) and the Ontology for Stem Cell Investigations (OSCI), were reported in the workshop. In another representation, the Cell Line Ontology (CLO) framework was applied and extended to represent cell line cells used in China and their Chinese representation. Other presentations included a report on the application of ontologies to cross-compare cell types and marker patterns used in flow cytometry studies, and a presentation on new experimental findings about novel cell types based on single cell RNA sequencing assay and their corresponding ontological representation. The general discussion session focused on the ontology design patterns in representing newly-discovered cell types and cells in disease states.https://deepblue.lib.umich.edu/bitstream/2027.42/148823/1/12859_2019_Article_2721.pd
Network-driven strategies to integrate and exploit biomedical data
[eng] In the quest for understanding complex biological systems, the scientific community has been delving into protein, chemical and disease biology, populating biomedical databases with a wealth of data and knowledge. Currently, the field of biomedicine has entered a Big Data era, in which computational-driven research can largely benefit from existing knowledge to better understand and characterize biological and chemical entities. And yet, the heterogeneity and complexity of biomedical data trigger the need for a proper integration and representation of this knowledge, so that it can be effectively and efficiently exploited.
In this thesis, we aim at developing new strategies to leverage the current biomedical knowledge, so that meaningful information can be extracted and fused into downstream applications. To this goal, we have capitalized on network analysis algorithms to integrate and exploit biomedical data in a wide variety of scenarios, providing a better understanding of pharmacoomics experiments while helping accelerate the drug discovery process. More specifically, we have (i) devised an approach to identify functional gene sets associated with drug response mechanisms of action, (ii) created a resource of biomedical descriptors able to anticipate cellular drug response and identify new drug repurposing opportunities, (iii) designed a tool to annotate biomedical support for a given set of experimental observations, and (iv) reviewed different chemical and biological descriptors relevant for drug discovery, illustrating how they can be used to provide solutions to current challenges in biomedicine.[cat] En la cerca dâuna millor comprensiĂł dels sistemes biolĂČgics complexos, la comunitat cientĂfica ha estat aprofundint en la biologia de les proteĂŻnes, fĂ rmacs i malalties, poblant les bases de dades biomĂšdiques amb un gran volum de dades i coneixement. En lâactualitat, el camp de la biomedicina es troba en una era de âdades massivesâ (Big Data), on la investigaciĂł duta a terme per ordinadors seân pot beneficiar per entendre i caracteritzar millor les entitats quĂmiques i biolĂČgiques. No obstant, la heterogeneĂŻtat i complexitat de les dades biomĂšdiques requereix que aquestes sâintegrin i es representin dâuna manera idĂČnia, permetent aixĂ explotar aquesta informaciĂł dâuna manera efectiva i eficient.
Lâobjectiu dâaquesta tesis doctoral Ă©s desenvolupar noves estratĂšgies que permetin explotar el coneixement biomĂšdic actual i aixĂ extreure informaciĂł rellevant per aplicacions biomĂšdiques futures. Per aquesta finalitat, em fet servir algoritmes de xarxes per tal dâintegrar i explotar el coneixement biomĂšdic en diferents tasques, proporcionant un millor enteniment dels experiments farmacoĂČmics per tal dâajudar accelerar el procĂ©s de descobriment de nous fĂ rmacs. Com a resultat, en aquesta tesi hem (i) dissenyat una estratĂšgia per identificar grups funcionals de gens associats a la resposta de lĂnies cel·lulars als fĂ rmacs, (ii) creat una col·lecciĂł de descriptors biomĂšdics capaços, entre altres coses, dâanticipar com les cĂšl·lules responen als fĂ rmacs o trobar nous usos per fĂ rmacs existents, (iii) desenvolupat una eina per descobrir quins contextos biolĂČgics corresponen a una associaciĂł biolĂČgica observada experimentalment i, finalment, (iv) hem explorat diferents descriptors quĂmics i biolĂČgics rellevants pel procĂ©s de descobriment de nous fĂ rmacs, mostrant com aquests poden ser utilitzats per trobar solucions a reptes actuals dins el camp de la biomedicina
Characterizing Migratory Signaling Pathways Of Transplantable Retinal Progenitor Cells And Photoreceptor Precursor Cells Toward Restoration Of Degenerative Retina \u27 A Systems Biology Approach
A common feature of several heterogeneous diseases that result in retinal degeneration (RD) is photoreceptor loss, leading to an irreversible decline in visual function [15-17]. There are no cell replacement treatments available for RD diseases such as age-related macular degeneration (AMD) and retinitis pigmentosa (RP). Although many RD cases are of a genetic origin, a promising strategy to treat diseased phenotypes is by replacing lost photoreceptor cells, for synaptic integration and restoration of visual function. To advance photoreceptor-replacement strategies as a practical therapy, in light of highly restricted integration rates reported across studies, this body of research focused on defining the molecular mechanisms facilitating migration of transplantable photoreceptor precursors in the retinal microenvironment. To accomplish this work we utilized bioinformatics, bioengineering and molecular biologic techniques for a systems level approach.
Guided by classic neuronal migration models, we hypothesized that transplanted photoreceptor precursors navigate to specific retinal lamina in part due to cell surface receptor expression and in response to spatially gradated directional ligand cues provided by the host retinal microenvironment. Given the neural origin of the mammalian retinal system, we also predicted that these chemotactic receptor-ligand pairs trigger intracellular signaling events in migrating photoreceptors analogous to canonical migration pathways exhibited by neuronal precursors. For a comprehensive account of these motility-deterministic biochemical interactions, we first performed in silico bioinformatics modeling of PPC transplantation into light-damaged retina by matching microarray datasets between PPC receptors and ligands in the light-damaged retinal microenvironment. We then refined the gene expression network data to focus on motility deterministic interactions at the interface of the PPC cell-surface receptors and extracellular ligands of the damaged retina. Our in silico network modeling generated a library of ligand-receptor pairs associated with cellular movement specific for this retinal transplantation paradigm and the intracellular signaling pathways induced by candidate chemotactic ligands.
Working from predicted interactions of in silico paired PPC receptors and retinal ligands, we then performed cell migration analysis to evaluate whether exposure to stromal derived factor-1α (SDF-1α) would guide the motility of PPCs and RPCs in vitro. We also assessed the chemotactic effects of epidermal growth factor (EGF) on RPCs. Cell surface expression of C-X-C chemokine receptor type 4 (CXCR4) receptors on PPCs and RPCs, and EGF receptor expression on RPCs were verified via immunocytochemical staining and validated by Western blot analysis. Boyden chamber analysis was used as an initial high-throughput screen to verify the motogenic effects of the ligands on PPCs and RPCs. We determined that RPC motility was optimally stimulated in these chambers by EGF concentrations in the range of 20-400ng/ml, with decreased stimulation at higher concentrations, suggesting concentration-dependence of EGF-induced motility. Both RPCs and PPCs also demonstrated a concentration-dependent chemotactic response to an optimal SDF-1α concentration of 100ng/ml.
Using bioinformatics downstream signaling pathway analysis of the EGF and SDF-1α ligands in a retina-specific gene network, we predicted a chemotactic function for EGF involving the MAPK and JAK-STAT intracellular signaling pathways. Based on targeted inhibition studies, we show that ligand binding, phosphorylation of EGFR and activation of the intracellular STAT3 and PI3Kinase signaling pathways are necessary to drive RPC motility. The JAK-STAT pathway was also implicated in transducing similar motogenic effects on PPCs with SDF-1α induction.
To test our hypothesis of the gradated nature of ECM ligand effects on both ontogenetic retinal cell types, we employed engineered microfluidic devices to generate quantifiable steady-state gradients of EGF and SDF-1α coupled with live-cell tracking, and analyzed the dynamics of individual RPC and PPC motility. Microfluidic analysis, including center of mass and maximum accumulated distance, revealed that EGF induced motility is chemokinetic in EGFR expressing RPCs with optimal activity observed in response to low concentration gradients. On the other hand, PPCs and RPCs exhibited significant chemotaxis towards the source of SDF-1α with longer accumulated Euclidean distances and Center of Mass (COM) compared to controls. We also ascertained that receptor mediated signaling was requisite for ligand-induced motility by using the CXCR4 inhibitor, AMD 3100, to antagonize the SDF-1α receptor. CXCR4 receptor inhibition resulted in decreases of PPC and RPC movement in uniform and steady state gradients for a number of migration indices measured.
To advance translational application of the characterized chemotactic signaling potential of transplantable photoreceptor precursors, we performed computational drug analysis of our newly identified motility-deterministic networks, to develop a library of FDA approved drugs and small molecules predicted to potentially influence the expression of target motility signaling mechanisms in photoreceptor progenitor cells. Using the Expression2Kinases software and LINCS drug computational algorithm, we were able to identify pharmacological drug targets that modulate the biochemical activity of transcriptional regulatory genes which govern the expression of candidate receptor protein targets, and provide preliminary results validating the up-regulatory effect of candidate drug aminophenazone on SDF-1α receptor CXCR4 expression. Results from this study demonstrate the applicability of our systems level in silico modeling of matched transplantable cell surface-receptors and transplantation site ligands to predict molecular signaling guiding migration. Verification of in silico predictions, using molecular and microfluidic analysis provide important data for defining cell response properties to specific ligands present during transplantation into the retinal microenvironment. The drug computational analysis provides a translational perspective to our in silico modeling paradigms extending its applicability.
Future studies will validate the functionality of resolved ligand-receptor pairs from our in silico library and characterize down-stream signaling guiding motility and homing. This systems level paradigm can effectively be applied to defining the molecular basis of transplantable cell migration in vivo toward improved efficiency for repair of retina and other neural tissue types
Annotating Adverse Outcome Pathways to Organize Toxicological Information for Risk Assessment
The Adverse Outcome Pathway (AOP) framework connects molecular perturbations with organism and population level endpoints used for regulatory decision-making by providing a conceptual construct of the mechanistic basis for toxicity. Development of an AOP typically begins with the adverse outcome, and intermediate effects connect the outcome with a molecular initiating event amenable to high-throughput toxicity testing (HTT). Publicly available controlled vocabularies were used to provide terminology supporting AOPâs at all levels of biological organization. The resulting data model contains terms from 22 ontologies and controlled vocabularies annotating currently existing AOPâs. The model provides the ability to attach evidence in support of the AOP, supports data aggregation, and promotes the development of AOP networks. Long term, this structured description of the AOP will enable logical reasoning for hazard identification and for dose-response assessment. Case studies showcase how the model informs AOP development in the context of chemical risk assessment.Master of Scienc