Biology subjects

Kai, F.

Publications and source records attributed to Kai, F..

5 recordsLinked to original sources

bioRxiv2025

The Short Isoform of the Mouse Actin Adaptor Protein Synaptopodin-2 Activates Actin-Responsive Transcription Factors and Enhances Myoblast Fusion

Expression of synaptopodin-2 (SYNPO2), an actin cytoskeleton adaptor protein, is up-regulated following myoblast differentiation into myocytes and subsequently myotubes but no functional role in muscle development has been attributed to SYNPO2. We now show that of the three known mouse SYNPO2 isoforms, only the shortest isoform, SYNPO2As, is upregulated following differentiation of C2C12 myoblasts and primary mouse muscle satellite cells. Consistent with the differentiation-dependent expression pattern of these different isoforms, ectopic expression of SYNPO2As significantly increased myotube formation while the two large SYNPO2 isoforms (SYNPO2A or SYNPO2B) inhibited myocyte fusion into multinucleated myotubes. Irrespective of the fusion phenotype, all three isoforms enhanced migration of differentiating myoblasts. Knockdown studies using shRNA confirmed a pro-myogenic role for the short SYNPO2As isoform. Interestingly, SYNPO2As-transduced cells increased the transcript levels of late myogenic differentiation markers in the STARS (striated muscle activator of Rho signaling) and SRF (serum response factor) pathways that correlated with the enhanced fusion phenotype. These results identify the actin adaptor protein SYNPO2As as a new pro-myogenic factor that upregulates late myogenic differentiation markers and myotube formation.

developmental biology↗

bioRxiv2022

Mechanosensitive hormone signaling promotes mammary progenitor expansion and breast cancer progression

Tissue stem-progenitor cell frequency has been implicated in tumor risk and progression. Tissue-specific factors linking stem-progenitor cell frequency to cancer risk and progression remain ill defined. Using a genetically engineered mouse model that promotes integrin mechanosignaling with syngeneic manipulations, spheroid models, and patient-derived xenografts we determined that a stiff extracellular matrix and high integrin mechanosignaling increase stem-progenitor cell frequency to enhance breast tumor risk and progression. Studies revealed that high integrin-mechanosignaling expands breast epithelial stem-progenitor cell number by potentiating progesterone receptor-dependent RANK signaling. Consistently, we observed that the stiff breast tissue from women with high mammographic density, who exhibit an increased lifetime risk for breast cancer, also have elevated RANK signaling and a high frequency of stem-progenitor epithelial cells. The findings link tissue fibrosis and integrin mechanosignaling to stem-progenitor cell frequency and causally implicate hormone signaling in this phenotype. Accordingly, inhibiting RANK signaling could temper the tumor promoting impact of fibrosis on breast cancer and reduce the elevated breast cancer risk exhibited by women with high mammographic density. SummaryElevated mechano-signaling and matrix stiffness promote progesterone and RANK mediated expansion of mammary progenitors and breast cancer risk and progression.

cancer biology↗

bioRxiv2021

ECM dimensionality tunes actin tension to modulate the endoplasmic reticulum and spheroid phenotype

Primary tissue organoids and cell spheroids recapitulate tissue physiology with remarkable fidelity. We investigated how engagement with a three dimensional laminin-rich extracellular matrix supports the polarized, stress resilient spheroid phenotype of mammary epithelial cells. Cells within a three dimensional laminin-rich extracellular matrix decreased and redistributed the actin crosslinker filamin to reduce their cortical actin tension. Cells with low cortical actin tension had increased plasma membrane protrusions that promoted negative plasma membrane curvature and fostered protein associations with the plasma membrane, consistent with efficient protein secretion. By contrast, cells engaging a laminin-rich extracellular matrix in two dimensions had high filamin-dependent cortical actin tension, exhibited compromised endoplasmic reticulum function including increased expression of PKR-like Endoplasmic Reticulum Kinase signaling effectors, and had compromised protein secretion. Cells with low filamin-mediated cortical actin tension and reduced endoplasmic reticulum stress response signaling secreted, and assembled, a polarized endogenous basement membrane and survived better, and their spheroids were more resistant to exogenous stress. The findings implicate filamin-dependent cortical actin tension in endoplasmic reticulum function and highlight a role for mechanics in organoid homeostasis.

cell biology↗

bioRxiv2019

Massive computational acceleration by using neural networks to emulate mechanism-based biological models

Mechanism-based mathematical models are the foundation for diverse applications. It is often critical to explore the massive parametric space for each model. However, for many applications, such as agent-based models, partial differential equations, and stochastic differential equations, this practice can impose a prohibitive computational demand. To overcome this limitation, we present a fundamentally new framework to improve computational efficiency by orders of magnitude. The key concept is to train an artificial neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training of the neural network. The trained neural network can then be used to explore the system dynamics of a much larger parametric space. We demonstrate this notion by training neural networks to predict self-organized pattern formation and stochastic gene expression. With this framework, we can predict not only the 1-D distribution in space (for partial differential equation models) and probability density function (for stochastic differential equation models) of variables of interest with high accuracy, but also novel system dynamics not present in the training sets. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can potentially be a platform for faster parametric space screening of biological models with user defined objectives.

systems biology↗

bioRxiv2017

A Bulky Glycocalyx Fosters Metastasis Formation by Promoting G1 Cell Cycle Progression

Metastasis depends upon cancer cell growth and survival within the metastatic niche. Tumors which remodel their glycocalyces, by overexpressing bulky glycoproteins like mucins, exhibit a higher predisposition to metastasize, but the role of mucins in oncogenesis remains poorly understood. Here we report that a bulky glycocalyx promotes the expansion of disseminated tumor cells in vivo by fostering integrin adhesion assembly to permit G1 cell cycle progression. We engineered tumor cells to display glycocalyces of various thicknesses by coating them with synthetic mucin-mimetic glycopolymers. Cells adorned with longer glycopolymers showed increased metastatic potential, enhanced cell cycle progression, and greater levels of integrin-FAK mechanosignaling and Akt signaling in a syngeneic mouse model of metastasis. These effects were mirrored by expression of the ectodomain of cancer-associated mucin MUC1. These findings functionally link mucinous proteins with tumor aggression, and offer a new view of the cancer glycocalyx as a major driver of disease progression.

cancer biology↗