Biology subjects

Theriot, J.

Publications and source records attributed to Theriot, J..

5 recordsLinked to original sources

bioRxiv2026

Retrotransposons Promote Clonal Hematopoiesis Through Aging-Related Stromal Inflammation

Clonal hematopoiesis (CH) is an age-related phenomenon driven by the expansion of mutant hematopoietic stem cell (HSC) clones, most commonly harboring mutations in DNMT3A. While inflammation is known to promote CH, the upstream triggers of this inflammatory state remain unclear. We show that aging selectively upregulates retrotransposons in the non-hematopoietic cell compartment of the murine bone marrow, particularly in mesenchymal stromal cells. Using in vivo competitive transplant models, we demonstrate that retrotransposon-induced inflammation cell-extrinsically promotes Dnmt3a-mutant HSC expansion. This competitive advantage arises from mutant HSC resistance to inflammation-driven cell cycle perturbations. Mechanistically, we show that retrotransposon activation induces type I interferon signaling via viral mimicry, such that stromal knockdown of either Irf3 or Sting abrogates the competitive advantage of Dnmt3a-mutant HSCs. Our findings establish stromal-selective retrotransposon reactivation as a previously unrecognized, non-cell-autonomous source of inflammation that contributes to age-associated CH. Key PointsO_LIRetrotransposons remodel the aging niche through viral mimicry pathways to promote Dnmt3a-mutant clonal hematopoiesis. C_LIO_LIRetrotransposon-mediated inflammation impairs wild-type HSC fitness, while Dnmt3a-mutant HSCs resist inflammatory changes. C_LI

cancer biology↗

bioRxiv2026

RIC-3 Interacts Directly with the 5-HT3A Receptor to Mediate Trafficking Across Subcellular Compartments

The serotonin type 3A (5-HT3A) receptor is a pentameric ligand-gated ion channel (pLGIC) in central and peripheral neurons that conducts sodium and potassium ions upon serotonin binding. 5-HT3 receptors modulate neurotransmission and synaptic plasticity, influencing mood, sleep, appetite, and addiction. Disruptions in serotonin signaling are linked to central nervous system disorders, including schizophrenia, anxiety and depression. Clinically, these receptors are targeted by antagonists to treat chemotherapy-induced nausea and vomiting. The functional surface expression of these channels is regulated by the chaperone protein Resistant to Inhibitors of Cholinesterase 3 (RIC-3) that promotes plasma-membrane expression, maturation, and trafficking of 5-HT3A and nicotinic acetylcholine receptors. Our previous work identified a duplicated RIC-3 binding motif within the 5-HT3A intracellular domain (ICD). However, it was unclear whether this interaction reflected native conditions. Here, we used a recombinant 5-HT3A ICD peptide in peptide-resin pull-down assays to investigate RIC-3 Interactions in plasma membrane (PM) fractions from Xenopus oocytes, endoplasmic reticulum (ER) fractions from SH-SY5Y cells, and mouse brain tissue. Across all tested systems, the 5-HT3A ICD peptide specifically bound RIC-3. Furthermore, RIC-3 knockdown (RIC-3 KD) SH-SY5Y cells showed a marked reduction in peptide binding and decreased surface levels of nAChR7 and 5-HT3A receptors. These results demonstrate RIC-3-5-HT3A ICD interaction in native cellular contexts and support a role for RIC-3 in regulating receptor surface expression and neuronal signaling.

neuroscience↗

bioRxiv2021

A deep generative model of 3D single-cell organization

1 AbstractWe introduce a framework for end-to-end integrative modeling of 3D single-cell multi-channel fluorescent image data of diverse subcellular structures. We employ stacked conditional {beta}-variational autoencoders to first learn a latent representation of cell morphology, and then learn a latent representation of subcellular structure localization which is conditioned on the learned cell morphology. Our model is flexible and can be trained on images of arbitrary subcellular structures and at varying degrees of sparsity and reconstruction fidelity. We train our full model on 3D cell image data and explore design trade-offs in the 2D setting. Once trained, our model can be used to impute structures in cells where they were not imaged and to quantify the variation in the location of all subcellular structures by generating plausible instantiations of each structure in arbitrary cell geometries. We apply our trained model to a small drug perturbation screen to demonstrate its applicability to new data. We show how the latent representations of drugged cells differ from unperturbed cells as expected by on-target effects of the drugs. 2 Author summaryIts impossible to acquire all the information we want about every cell were interested in in a single experiment. Even just limiting ourselves to imaging, we can only image a small set of subcellular structures in each cell. If we are interested in integrating those images into a holistic picture of cellular organization directly from data, there are a number of approaches one might take. Here, we leverage the fact that of the three channels we image in each cell, two stay the same across the data set; these two channels assess the cells shape and nuclear morphology. Given these two reference channels, we learn a model of cell and nuclear morphology, and then use this as a reference frame in which to learn a representation of the localization of each subcellular structure as measured by the third channel. We use {beta}-variational autoencoders to learn representations of both the reference channels and representations of each subcellular structure (conditioned on the reference channels of the cell in which it was imaged). Since these models are both probabilistic and generative, we can use them to understand the variation in the data from which they were trained, to generate instantiations of new cell morphologies, and to generate imputations of structures in real cell images to create an integrated model of subcellular organization.

cell biology↗

bioRxiv2020

Modeling cell turning by mechanics at the cell rear

In this study, we explore a simulation of a mechanical model of the keratocyte lamellipodium as previously tested and calibrated for straight steady-state motility [1] and for the process of polarization and motility initiation [2]. In brief, this model uses the balance of three essential forces (myosin contraction, adhesive drag and actin network viscosity) to determine the cells mechanical behavior. Cell shape is set by the balance between the actin polymerization-driven protrusion at the cell boundary and myosin-driven retraction of the actin-myosin network. In the model, myosin acts to generate contractile stress applied to a viscous actin network with viscous resistance to actin flow created by adhesion to the substrate. Previous study [3] demonstrated that similar simple model with uniform constant adhesion predicts a rotating behavior of the cell; however, this behavior is idealized, and does not mimic observed features of the keratocytes turning behavior. Our goal is to explore what are the consequences of introducing mechanosensitive adhesions to the model.

biophysics↗

bioRxiv2019

A Bayesian framework for the detection of diffusive heterogeneity

Cells are crowded and spatially heterogeneous, complicating the transport of organelles, proteins and other substrates. One aspect of this complex physical environment, the mobility of passively transported substrates, can be quantitatively characterized by the diffusion coefficient: a descriptor of how rapidly substrates will diffuse in the cell, dependent on their size and effective local viscosity. The spatial dependence of diffusivity is challenging to quantitatively characterize, because temporally and spatially finite observations offer limited information about a spatially varying stochastic process. We present a Bayesian framework that estimates diffusion coefficients from single particle trajectories, and predicts our ability to distinguish differences in diffusion coefficient estimates, conditional on how much they differ and the amount of data collected. This framework is packaged into a public software repository, including a tutorial Jupyter notebook demonstrating implementation of our method for diffusivity estimation, analysis of sources of uncertainty estimation, and visualization of all results. This estimation and uncertainty analysis allows our framework to be used as a guide in experimental design of diffusivity assays.

biophysics↗