Biomedical subjects

Dusica Vidovic

Publications and source records attributed to Dusica Vidovic.

3 recordsLinked to original sources

PubMed2025

Inclusion of Physical-Chemical Water Quality Measurements Can Improve Associations between SARS-CoV-2 RNA Levels in Wastewater and COVID-19 Cases within Smaller Sewersheds.

Measurements of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in wastewater can be used to understand the prevalence of COVID-19 cases within a community. Environmental conditions inclusive of physical-chemical water quality characteristics are known to impact wastewater SARS-CoV-2 signals, but they are rarely measured within the sewer infrastructure in areas upstream of wastewater treatment plants (WWTPs). The objectives of this study were to report on measurements of environmental parameters [flow and physical-chemical water quality (water temperature, pH, specific conductivity, dissolved oxygen, and turbidity)] upstream of a WWTP and to evaluate whether the inclusion of these environmental parameters improves correlations between SARS-CoV-2 RNA levels in wastewater, and COVID-19 prevalence in the sewershed community. Measurements of environmental parameters and SARS-CoV-2 RNA in wastewater spanned different time scales (minutes, hours and weeks) and population scales (building, campus, community). For short time scales, water quality parameters did not improve correlations between SARS-CoV-2 in wastewater and COVID-19 prevalence due to high variability of water quality and flows within the sewer system. When averaging data over weekly time scales, regressions showed that inclusion of pH improved correlations between RNA and COVID-19 prevalence. At the cluster scale, for the entire data set, the root mean square error decreased from 6.9 cases per week to 6.5 cases per week. At the community scale benefits were observed only for the delta wave with a decrease in root mean square error from 539 cases per week to 430 cases per week. The inclusion of pH improved correlations between wastewater SARS-CoV-2 and COVID-19 prevalence more frequently when evaluating the cluster sewershed scale (populations of a few thousand) in comparison to the community scale (populations of several 100,000). Given the simplicity of measuring pH and other physical-chemical water quality parameters, their inclusion should be considered as part of wastewater-based epidemiology programs.

COVID-19↗

PubMed2022

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

Epidermal Growth Factor↗

PubMed2017

Drug target ontology to classify and integrate drug discovery data.

BACKGROUND: One 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. RESULTS: As part of that effort, we have developed 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 supporting manual knowledge acquisition. A modular, hierarchical implementation facilitate ontology 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. CONCLUSIONS: DTO 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 ( http://github.com/DrugTargetOntology/DTO ), and the NCBO Bioportal ( http://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.

Biological Ontologies↗