Biology subjects

Faeder, J. R.

Publications and source records attributed to Faeder, J. R..

5 recordsLinked to original sources

bioRxiv2026

Intercellular Communication via Mitotic Nanotubes is Influenced by Connexin-43 Trafficking and Actin Remodeling

Gap junction communication is reduced during mitosis as the junction protein connexin-43 (Cx43) is redistributed from gap junction plaques on the plasma membrane to cytoplasmic annular vesicles and actin-based mitotic nanotubes that transiently connect mitotic cells to neighboring cells. However, the dynamic details of Cx43 redistribution during cell entry into and exit from mitosis, and the roles of mitotic nanotubes and associated Cx43 in intercellular communication, remain poorly understood. Here, using confocal live-cell imaging, we show that as cells enter mitosis, plaque-derived Cx43 structures are transferred to mitotic nanotubes. Over time, these structures fragment and migrate along the length of the nanotubes, either being transferred to the cytoplasm of adjacent cells or being positioned at the nanotube ends where they could potentially enable communication. Functionally, mitotic nanotubes indeed facilitate gap junction-dependent intercellular communication, though at reduced rates compared interphase cells. Interestingly, knockdown of Cx43 resulted in impaired nanotube formation and intercellular communication while inhibition of Rho kinase (ROCK) with Y-27632 prevented mitotic cell rounding and nanotube elongation, and increased cell-cell communication during mitosis, suggesting that nanotube function is influenced by Cx43 expression and trafficking as well as actin remodeling via ROCK. Overall, these findings provide valuable insights into the mechanisms that regulate Cx43 and mitotic nanotube dynamics and reveal a novel role for mitotic nanotubes in facilitating cell-cell communication during cell division.

cell biology↗

bioRxiv2024

A detailed kinetic model of Eastern equine encephalitis virus replication in a susceptible host cell

Eastern equine encephalitis virus (EEEV) is an arthropod-borne, positive-sense RNA alphavirus posing a substantial threat to public health. Unlike similar viruses such as SARS-CoV-2, EEEV replicates efficiently in neurons, producing progeny viral particles as soon as 3-4 hours post-infection. EEEV infection, which can cause severe encephalitis with a human mortality rate surpassing 30%, has no licensed, targeted therapies, leaving patients to rely on supportive care. Although the general characteristics of EEEV infection within the host cell are well-studied, it remains unclear how these interactions lead to rapid production of progeny viral particles, limiting development of antiviral therapies. Here, we present a novel rule-based model that describes attachment, entry, uncoating, replication, assembly, and export of both infectious virions and virus-like particles within mammalian cells. Additionally, it quantitatively characterizes host ribosome activity in EEEV replication via a model parameter defining ribosome density on viral RNA. To calibrate the model, we performed experiments to quantify viral RNA, protein, and infectious particle production during acute infection. We used Bayesian inference to calibrate the model, discovering in the process that an additional constraint was required to ensure consistency with previous experimental observations of a high ratio between the amounts of full-length positive-sense viral genome and negative-sense template strand. Overall, the model recapitulates the experimental data and predicts that EEEV rapidly concentrates host ribosomes densely on viral RNA. Dense packing of host ribosomes was determined to be critical to establishing the characteristic positive to negative RNA strand ratio because of its role in governing the kinetics of transcription. Sensitivity analysis identified viral transcription as the critical step for infectious particle production, making it a potential target for future therapeutic development. Author SummaryEastern equine encephalitis virus (EEEV) is a positive-sense RNA virus transmitted via mosquitoes. In humans, it can cause lethal disease in humans with a high mortality rate, exceeding 30%. There are no licensed targeted treatments or vaccines currently available. We constructed a rule-based model that describes the mechanisms and the resulting dynamics of EEEV replication inside a mammalian cell. With a novel experimental dataset that measures the concentrations of EEEV RNA, proteins, and infectious viral particles over time in combination with a biological constraint based on known replication characteristics, we calibrated the model rate parameters with a Bayesian inference method that estimates parameter distributions and quantifies the confidence of model predictions. The resulting calibrated model captures key features of the experimental dataset. Model analyses identified a tight constraints in the RNA replication dynamics among the genome, the negative-sense template, and the subgenome, which is used for structural protein synthesis. The calibrated model demonstrates the potential for EEEV to rapidly recruit and densely pack host ribosomes on its viral RNA to accelerate replication. Sensitivity analysis found that parameters involving viral transcription, particularly of the genome and subgenome, are most critical for infectious viral particle production.

systems biology↗

bioRxiv2023

Predicting gene level sensitivity to JAK-STAT signaling perturbation using a mechanistic-to-machine learning framework

The JAK-STAT pathway integrates complex cytokine signals via a limited number of molecular components, inspiring numerous efforts to clarify the diversity and specificity of STAT transcription factor function. We developed a computational workflow to make global cytokine-induced gene predictions from STAT phosphorylation dynamics, modeling macrophage responses to IL-6 and IL-10, which signal through common STATs, but with distinct temporal dynamics and contrasting functions. Our mechanistic-to-machine learning model identified select cytokine-induced gene sets associated with late pSTAT3 timeframes and a preferential pSTAT1 reduction upon JAK2 inhibition. We predicted and validated the impact of JAK2 inhibition on gene expression, identifying dynamically regulated genes that were sensitive or insensitive to JAK2 variation. Thus, we successfully linked STAT signaling dynamics to gene expression to support future efforts targeting pathology-associated STAT-driven gene sets. This serves as a first step in developing multi-level prediction models to understand and perturb gene expression outputs from signaling systems.

systems biology↗

bioRxiv2022

WEBNG: A templating tool for weighted ensemble sampling of rule-based models

Time scales for biological processes span many orders of magnitude, forcing modelers to tackle coupled processes that have large time scale gaps. This results in rare events, which take longer to occur than the fastest processes in the model. Efficient generation of rare events has been a focus of modelers for a long time and multiple software packages implement various rare event sampling algorithms. However, these packages frequently require expertise to get started with, making it harder for researchers to start using them. WEBNG (short for Weighted Ensemble-BioNetGen) is an open source software framwework that bridges the open source software packages WESTPA, which implements the weighted ensemble method for sampling rare events, and BioNetGen, which facilitates the specification and simulation of biochemical reaction network models following a rule-based approach. WEBNG simplifies rare event sampling in simulations of rule-based models by taking a model specified in the BioNetGen language (BNGL) and generating a WESTPA simulation folder ready to simulate with default parameters selected to match model observables. WEBNG is written in Python with dependencies only on proven, open-source packages that are in active development, which makes WEBNG easy to install and maintain. Here, we describe the architecture and features of WEBNG and demonstrate its capabilities through application to a two-gene model of cell fate transitions.

systems biology↗

bioRxiv2019

Parallel Tempering with Lasso for Model Reduction in Systems Biology

Systems Biology models reveal relationships between signaling inputs and observable molecular or cellular behaviors. The complexity of these models, however, often obscures key elements that regulate emergent properties. We use a Bayesian model reduction approach that combines Parallel Tempering with Lasso regularization to identify minimal subsets of reactions in a signaling network that are sufficient to reproduce experimentally observed data. The Bayesian approach finds distinct reduced models that fit data equivalently. A variant of this approach based on Group Lasso is applied to the NF-{kappa}B signaling network to test the necessity of feedback loops for responses to pulsatile and continuous pathway stimulation. Taken together, our results demonstrate that Bayesian parameter estimation combined with regularization can isolate and reveal core motifs sufficient to explain data from complex signaling systems.

systems biology↗