Calhoun, J. C. | Search bioRxiv

bioRxiv2024

Mechanistic modeling of cell viability assays with in silico lineage tracing

Data from cell viability assays, which measure cumulative division and death events in a population and reflect substantial cellular heterogeneity, are widely available. However, interpreting such data with mechanistic computational models is hindered because direct model/data comparison is often muddled. We developed an algorithm that tracks simulated division and death events in mechanistically-detailed single-cell lineages to enable such a model/data comparison and suggest causes of cell-cell drug response variability. Using our previously developed model of mammalian single-cell proliferation and death signaling, we simulated drug dose response experiments for four targeted anti-cancer drugs (alpelisib, neratinib, trametinib and palbociclib) and compared them to experimental data. Simulations are consistent with data for strong growth inhibition by trametinib (MEK inhibitor) and overall lack of efficacy for alpelisib (PI-3K inhibitor), but are inconsistent with data for palbociclib (CDK4/6 inhibitor) and neratinib (EGFR inhibitor). Model/data inconsistencies suggest that (i) the importance of CDK4/6 for driving the cell cycle may be overestimated, and (ii) the cellular balance between basal (tonic) and ligand-induced signaling is a critical determinant of receptor inhibitor response. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions. Variations in mother cells prior to drug treatment impinging on ERK pathway activity are associated with the rapidly dividing phenotype and trametinib resistance. This work lays a foundation for the application of mechanistic modeling to large-scale cell viability datasets and better understanding determinants of cellular heterogeneity in drug response. Author SummaryThere is a large amount of data in the public domain for how a variety of cancer cell types respond to a multitude of anti-cancer drugs. However, interpreting such data mechanistically is hindered because of a lack of precise computational tools for model/data comparison. We developed an algorithm that tracks simulated division and death events in mechanistically detailed single-cell lineages to enable such a model/data comparison and suggest causes of drug response variability. We applied this tool to understand four targeted anti-cancer drugs better. Simulations are consistent with data for strong growth inhibition by trametinib and overall lack of efficacy for alpelisib, but are inconsistent with data for palbociclib and neratinib. Model/data inconsistencies suggest where biological understanding may be incomplete. Simulations show subpopulations of rapidly and slowly dividing cells in both control and drug-treated conditions, and analysis thereof suggest mechanisms potentially involved with acute drug resistance. This work lays a foundation for using large-scale datasets to better understand drug response.

systems biology↗

bioRxiv2023

Electron microscopy mapping of the DNA-binding sites of monomeric, dimeric, and multimeric KSHV RTA protein

Molecular interactions between viral DNA and viral-encoded protein are a prerequisite for successful herpesvirus replication and production of new infectious virions. Here, we examined how the essential Kaposis sarcoma-associated herpesvirus (KSHV) protein, RTA, binds to viral DNA using transmission electron microscopy (TEM). Previous studies using gel-based approaches to characterize RTA binding are important for studying the predominant form(s) of RTA within a population and identifying the DNA sequences that RTA binds with high affinity. However, using TEM we were able to examine individual protein-DNA complexes and capture the various oligomeric states of RTA when bound to DNA. Hundreds of images of individual DNA and protein molecules were collected and then quantified to map the DNA binding positions of RTA bound to the two KSHV lytic origins of replication encoded within the KSHV genome. The relative size of RTA or RTA bound to DNA were then compared to protein standards to determine whether RTA complexed with DNA was monomeric, dimeric, or formed larger oligomeric structures. We successfully analyzed a highly heterogenous dataset and identified new binding sites for RTA. This provides direct evidence that RTA forms dimers and high order multimers when bound to KSHV origin of replication DNA sequences. This work expands our understanding of RTA binding, and demonstrates the importance of employing methodologies that can characterize highly heterogenic populations of proteins. ImportanceKaposis sarcoma-associated herpesvirus (KSHV) is a human herpesvirus associated with several human cancers, typically in patients with compromised immune systems. Herpesviruses establish lifelong infections in hosts in part due to the two phases of infection: the dormant and active phases. Effective antiviral treatments to prevent the production of new viruses are needed to treat KSHV. A detailed microscopy-based investigation of the molecular interactions between viral protein and viral DNA revealed how protein-protein interactions play a role in DNA binding specificity. This analysis will lead to a more in depth understanding of KSHV DNA replication and serve as the basis for anti-viral therapies that disrupt and prevent the protein-DNA interactions, thereby decreasing spread to new hosts.

molecular biology↗