Biology subjects

Spaulding, C.

Publications and source records attributed to Spaulding, C..

5 recordsLinked to original sources

bioRxiv2023

BET inhibition rescues ciliogenesis and ameliorates pancreatitis-driven phenotypic changes in mice with Par3 loss.

The apical-basal polarity of pancreatic acinar cells is essential for maintaining tissue architecture. However, the mechanisms by which polarity proteins regulate acinar pancreas tissue homeostasis are poorly understood. Here, we evaluate the role of Par3 in acinar pancreas injury and homeostasis. While Par3 loss in the mouse pancreas disrupts tight junctions, Par3 loss is dispensable for pancreatogenesis. However, with aging, Par3 loss results in low-grade inflammation, acinar degeneration, and pancreatic lipomatosis. Par3 loss also exacerbates pancreatitis-induced acinar cell loss, resulting in pronounced pancreatic lipomatosis and failure to regenerate. Moreover, Par3 loss in mice harboring mutant Kras causes extensive pancreatic intraepithelial neoplastic (PanIN) lesions and large pancreatic cysts. We also show that Par3 loss restricts injury-induced primary ciliogenesis. Significantly, targeting BET proteins enhances primary ciliogenesis during pancreatitis-induced injury and, in mice with Par3 loss, limits pancreatitis-induced acinar loss and facilitates acinar cell regeneration. Combined, this study demonstrates how Par3 restrains pancreatitis- and Kras-induced changes in the pancreas and identifies a potential role for BET inhibitors to attenuate pancreas injury and facilitate pancreas tissue regeneration.

pathology↗

bioRxiv2023

Theoretical understanding of evolutionary dynamics on inhomogeneous networks

Evolution is the main feature of all biological systems that allows populations to change their characteristics over successive generations. A powerful approach to understand evolutionary dynamics is to investigate fixation probabilities and fixation times of novel mutations on networks that mimic biological populations. It is now well established that the structure of such networks can have dramatic effects on evolutionary dynamics. In particular, there are population structures that might amplify the fixation probabilities while simultaneously delaying the fixation events. However, the microscopic origins of such complex evolutionary dynamics remain not well understood. We present here a theoretical investigation of the microscopic mechanisms of mutation fixation processes on inhomogeneous networks. It views evolutionary dynamics as a set of stochastic transitions between discrete states specified by different numbers of mutated cells. By specifically considering star networks, we obtain a comprehensive description of evolutionary dynamics. Our approach allows us to employ physics-inspired free-energy landscape arguments to explain the observed trends in fixation times and fixation probabilities, providing a better microscopic understanding of evolutionary dynamics in complex systems.

biophysics↗

bioRxiv2022

Optimal pathways control fixation of multiple mutations during cancer initiation

Cancer starts after initially healthy tissue cells accumulate several specific mutations or other genetic alterations. The dynamics of tumor formation is a very complex phenomenon due to multiple involved biochemical and biophysical processes. It leads to a very large number of possible pathways on the road to final fixation of all mutations that marks the beginning of the cancer, complicating the understanding of microscopic mechanisms of tumor formation. We present a new theoretical framework of analyzing the cancer initiation dynamics by exploring the properties of effective free-energy landscape of the process. It is argued that although there are many possible pathways for the fixation of all mutations in the system, there are only few dominating pathways on the road to tumor formation. The theoretical approach is explicitly tested in the system with only two mutations using analytical calculations and Monte Carlo computer simulations. Excellent agreement with theoretical predictions is found for a large range of parameters, supporting our hypothesis and allowing us to better understand the mechanisms of cancer initiation. Our theoretical approach clarifies some important aspects of microscopic processes that lead to tumor formation.

biophysics↗

bioRxiv2021

The Role of Extended Range of Interactions in the Dynamics of Interacting Molecular Motors

Motor proteins, also known as biological molecular motors, play important roles in various intracellular processes. Experimental investigations suggest that molecular motors interact with each other during the cellular transport, but the nature of such interactions remains not well understood. Stimulated by these observations, we present a theoretical study aimed to understand the effect of the range of interactions on dynamics of interacting molecular motors. For this purpose, we develop a new version of the totally asymmetric simple exclusion processes in which nearest-neighbor as well as the next nearest-neighbor interactions are taken into account in a thermodynamically consistent way. A theoretical framework based on a cluster mean-field approximation, which partially takes correlations into account, is developed to evaluate the stationary properties of the system. It is found that fundamental current-density relations in the system strongly depend on the strength and the sign of interactions, as well as on the range of interactions. For repulsive interactions stronger than some critical value, increasing the range of interactions leads to a change from unimodal to trimodal dependence in the flux-density fundamental diagram. Theoretical calculations are tested with extensive Monte Carlo computer simulations. Although in most ranges of parameters excellent agreement between theoretical predictions and computer simulations is observed, there are situations when the cluster mean-field approach fails to describe properly the dynamics in the system. Theoretical arguments to explain these observations are presented. Our theoretical analysis clarifies the microscopic picture of how the range of interactions influences the dynamics of interacting molecular motors.

biophysics↗

bioRxiv2021

Gα13 loss promotes tumor progression in the KPC transgenic mouse model of advanced pancreatic cancer

G13 transduces signals from G protein-coupled receptors. G13 is pro-tumorigenic in epithelial cancer cell lines, which contrasts with its tumor-suppressive function in transgenic mouse models of lymphomas. Here we show that while loss of G13 in pancreatic cell lines decreases tumor growth in vivo, G13 loss in the Kras-driven (KC) mouse model of pancreatic tumor initiation does not affect tumor development or survival. Instead, G13 loss in the Kras/Tp53 (KPC) transgenic mouse model of advanced pancreatic cancer promotes well-differentiated tumors with increased tumor burden and reduced survival. Mechanistically, G13 loss in the KPC mouse model enhances E-cadherin-mediated cell-cell junctions and mTOR signaling. Importantly, human pancreatic cancers with low G13 expression exhibit increased E-cadherin protein expression and mTOR signaling. This work establishes a context-dependent role of G13 in pancreatic tumorigenesis, demonstrating a tumor-suppressive role in transgenic mouse models of advanced pancreatic cancer.

cancer biology↗