Biology subjects

Bobak, C. A.

Publications and source records attributed to Bobak, C. A..

5 recordsLinked to original sources

bioRxiv2026

Host DNA repair factors empower a mechanism of antiviral nucleoside analog resistance

How host functions affect resistance to antiviral drugs is poorly understood. Ganciclovir, a chain-terminating nucleoside analog, is a first-line therapy against human cytomegalovirus, a widespread herpesvirus that causes life-threatening disease in immunocompromised individuals and newborns. Ganciclovir resistance, which is caused by mutations that affect the viral kinase, UL97 and/or the viral polymerase, UL54, can cause treatment failures. Among these mutations, those reducing the exonuclease activity of the viral DNA polymerase permit ganciclovir incorporation without chain termination. However, the fate of DNA strands containing the incorporated nucleotide analog is unknown. We show here that template DNA containing ganciclovir fails to support DNA synthesis of the complementary strand by exonuclease-mutant polymerase. Moreover, while DNA synthesis and ganciclovir incorporation are limited in drug-treated fibroblasts infected by virus with wild-type polymerase, an exonuclease-resistant mutant virus can better synthesize full-length genomes and incorporate substantially more ganciclovir into DNA. Notably, ganciclovir is lost from DNA when drug is removed, suggesting that ganciclovir-containing templates are repaired. We identify the host nucleotide excision repair component, XPA, and the repair enzyme, polymerase kappa, as each being necessary for mutant virus ganciclovir resistance and polymerase kappa as being required for the mutants cidofovir resistance, demonstrating a role for host DNA repair machinery in a mechanism of antiviral resistance. We propose a model for this mechanism, which has relevance for at least one other antiviral drug and likely other nucleoside analog therapeutics, and highlights the participation of host DNA repair machinery during human cytomegalovirus DNA replication. IMPORTANCENucleoside analogues such as ganciclovir, which is a leading drug for preventing and treating human cytomegalovirus, are a critical defense against viral diseases, but antiviral resistance often results in treatment failures. This study reveals a critical role for host DNA repair in a mechanism of resistance to ganciclovir, and identifies at least one specific repair pathway that permits viral DNA synthesis in the presence of ganciclovir, defining a mechanism by which cellular DNA repair pathways conspire to enable antiviral drug resistance. This mechanism is relevant to at least one other antiviral drug and may apply to other antiviral and anticancer agents. The study also showcases the participation of host DNA repair machinery during human cytomegalovirus DNA synthesis.

microbiology↗

bioRxiv2021

Improving the Virtual Trichrome Assessment through Bridge Category Models

Non-alcoholic steatohepatitis (NASH) is a liver disease characterized by excessive lipid accumulation and disease progression is typically assessed through inspection of a Trichrome stain for Fibrosis staging. As the public health burden of NASH worsens due to evolving lifestyle habits, pathology laboratory resources will become increasingly strained due to rising demand for specialized stains. Virtual staining processes, computational methods which can synthesize the application of chemical staining reagents, can potentially provide resource savings by obviating the need to acquire specialized stains. Virtual staining technologies are assessed by comparing virtual and real tissue stains for their realism and ability to stage. However, these assessment methods are rife with statistical mistreatment of observed phenomena that are difficult to account for. Bridge category ratings represent a phenomenon where a pathologist may assign two adjacent stages simultaneously, which may bias and/or reduce the power of research findings. Such stage assignments were frequently reported in a large-scale assessment of Virtual Trichrome technologies yet were unaccounted for since no statistical adjustment procedures existed. In this work, we provide an updated assessment of Virtual Trichrome technologies using Bridge Category Models, which account for these bridge ratings. We report that two of four pathologists tended to assign lower Fibrosis stages to virtually stained tissue while the other two pathologists assigned similar stages. These research findings differ when bridge ratings are not accounted for. While promising, these results indicate further room for algorithmic finetuning of Virtual Trichrome technologies.

pathology↗

bioRxiv2021

Bridge Category Models: Development of Bayesian Modelling Procedures to Account for Bridge Ordinal Ratings for Disease Staging

Disease grading and staging is accomplished through the assignment of an ordinal rating. Bridge ratings occur when a rater assigns two adjacent categories. Most statistical methodology necessitates the use of a single ordinal category. Consequently, bridge ratings often go unreported in clinical research studies. We propose three methodologies (Expanded, Mixture, and Collapsed) Bridge Category Models, to account for bridge ratings. We perform simulations to examine the impact of our approaches on detecting treatment effects, and comment on a real-world scenario of staging liver biopsies. Results indicate that if bridge ratings are not accounted for, disease staging models may exhibit significant bias and precision loss. All models worked well when they corresponded to the data generating mechanism.

pathology↗

bioRxiv2020

Estimating latent positions of actors using Neural Networks in R with GCN4R

Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While numerous methods have been developed for the R statistical software to analyze such data, deep learning methods have not been implemented in this language. Here, we introduce GCN4R, an R library for fitting graph neural networks on independent networks to aggregate actor covariate information to yield meaningful embeddings for a variety of network-based tasks (e.g. community detection, peer effects models, social influence). We provide an extensive overview of insights and methods utilized by the deep learning community on learning on social and biological networks, followed by a tutorial that demonstrates some of the capabilities of the GCN4R framework to make these methods more accessible to the R research community.

bioinformatics↗

bioRxiv2018

An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer

Recent advances in deep learning, particularly unsupervised approaches, have shown promise for furthering our biological knowledge through their application to gene expression datasets, though applications to epigenomic data are lacking. Here, we employ an unsupervised deep learning framework with variational autoencoders (VAEs) to learn latent representations of the DNA methylation landscape from three independent breast tumor datasets. Through interrogation of methylation-based learned latent dimension activation values, we demonstrate the feasibility of VAEs to track representative differential methylation patterns among clinical subtypes of tumors. CpGs whose methylation was most correlated VAE latent dimension activation values were significantly enriched for CpG sparse regulatory regions of the genome including enhancer regions. In addition, through comparison with LASSO, we show the utility of the VAE approach for revealing novel information about CpG DNA methylation patterns in breast cancer.

bioinformatics↗