Engineering topics

Mcweeney, Shannon K.

Publications and source records attributed to Mcweeney, Shannon K..

3 records

DOE OSTI2021

The AML microenvironment catalyzes a step-wise evolution to gilteritinib resistance

This study reports a stepwise evolution of gilteritinib resistance in acute myeloid leukemia (AML) relapse, and identifies therapeutic vulnerabilities that could be exploited. Our in vitro model demonstrates that extrinsic microenvironmental factors within the bone marrow nurture the growth of residual cells that underlie early gilteritinib resistance. These early resistant cells exhibit a dependency upon Aurora B (AURKB) and undergo dynamic metabolic adaptations that fostered their transition to late resistance. In line with clinical observations of gilteritinib resistance, late resistant cells displayed an expansion of NRAS mutant subclones and a unique metabolic profile that originated during early resistance. Pharmacological inhibition of AURKB resensitized early resistant cells to gilteritinib. Early resistant cells from AML patients were found to display exquisite sensitivitye to AURKB inhibition. Together, our findings support that eradication of early resistant AML cells may increase the efficacy and durability of gilteritinib.

AML, Proteomics↗

DOE OSTI2014

A comprehensive collection of systems biology data characterizing the host response to viral infection

The Systems Biology for Infectious Diseases Research program was established by the U.S. National Institute of Allergy and Infectious Diseases to investigate host-pathogen interactions at a systems level. This program generated 47 transcriptomic and proteomic datasets from 30 studies that investigate in vivo and in vitro host responses to viral infections. Human pathogens in the Orthomyxoviridae and Coronaviridae families, especially pandemic H1N1 and avian H5N1 influenza A viruses and severe acute respiratory syndrome coronavirus (SARS-CoV), were investigated. Study validation was demonstrated via experimental quality control measures and meta-analysis of independent experiments performed under similar conditions. Primary assay results are archived at the GEO and PeptideAtlas public repositories, while processed statistical results together with standardized metadata are publically available at the Influenza Research Database (www.fludb.org) and the Virus Pathogen Resource (www.viprbrc.org). As a result, by comparing data from mutant versus wild-type virus and host strains, RNA versus protein differential expression, and infection with genetically similar strains, these data can be used to further investigate genetic and physiological determinants of host responses to viral infection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Protein Co-Expression Network Analysis (ProCoNA)

Biological networks are important for elucidating disease etiology due to their ability to model complex high dimensional data and biological systems. Proteomics provides a critical data source for such models, but currently lacks robust de novo methods for network construction, which could bring important insights in systems biology. We have evaluated the construction of network models using methods derived from weighted gene co-expression network analysis (WGCNA). We show that approximately scale-free peptide networks, composed of statistically significant modules, are feasible and biologically meaningful using two mouse lung experiments and one human plasma experiment. Within each network, peptides derived from the same protein are shown to have a statistically higher topological overlap and concordance in abundance, which is potentially important for inferring protein abundance. The module representatives, called eigenpeptides, correlate significantly with biological phenotypes. Furthermore, within modules, we find significant enrichment for biological function and known interactions (gene ontology and protein-protein interactions). Biological networks are important tools in the analysis of complex systems. In this paper we evaluate the application of weighted co-expression network analysis to quantitative proteomics data. Protein co-expression networks allow novel approaches for biological interpretation, quality control, inference of protein abundance, a framework for potentially resolving degenerate peptide-protein mappings, and a biomarker signature discovery.

Environmental Molecular Sciences Laboratory↗