Search bioRxiv⌕ Search

Biology subjects

Pasani, S.

Publications and source records attributed to Pasani, S..

2 recordsLinked to original sources

The molecular architecture of the desmosomal outer dense plaque by integrative structural modeling

Desmosomes mediate cell-cell adhesion and are prevalent in tissues under mechanical stress. However, their detailed structural characterization is not available. Here, we characterized the molecular architecture of the desmosomal outer dense plaque (ODP) using Bayesian integrative structural modeling via the Integrative Modeling Platform. Starting principally from the structural interpretation of an electron cryo-tomogram, we integrated information from X-ray crystallography, an immuno-electron microscopy study, biochemical assays, in-silico predictions of transmembrane and disordered regions, homology modeling, and stereochemistry information. The integrative structure was validated by information from imaging, tomography, and biochemical studies that were not used in modeling. The ODP resembles a densely packed cylinder with a PKP layer and a PG layer; the desmosomal cadherins and PKP span these two layers. Our integrative approach allowed us to localize disordered regions, such as N-PKP and PG-C. We refined previous protein-protein interactions between desmosomal proteins and provided possible structural hypotheses for defective cell-cell adhesion in several diseases by mapping disease-related mutations on the structure. Finally, we point to features of the structure that could confer resilience to mechanical stress. Our model provides a basis for generating experimentally verifiable hypotheses on the structure and function of desmosomal proteins in normal and disease states. Significance statementDesmosomes are cell-cell junctions that possess a hyper-adhesive property and are prevalent in tissues under mechanical stress. However, their detailed structural characterization has eluded experimental structural biologists so far. Here, we use an integrative approach that allows us to rigorously combine biochemical, biophysical, and cell biological data at multiple scales in order to determine the molecular architecture of the outer dense plaque region of desmosomes. We validate the structural model by several pieces of information not used to compute it. The model allows us to generate hypotheses on the desmosomal proteins in normal and disease states.

biophysics↗

Hybrid E/M phenotype(s) and stemness: a mechanistic connection embedded in network topology

Metastasis remains an unsolved clinical challenge. Two crucial features of metastasizing cancer cells are a) their ability to dynamically move along the epithelial-hybrid-mesenchymal spectrum and b) their tumor-initiation potential or stemness. With increasing functional characterization of hybrid epithelial/mesenchymal (E/M) phenotypes along the spectrum, recent in vitro and in vivo studies have suggested an increasing association of hybrid E/M phenotypes with stemness. However, the mechanistic underpinnings enabling this association remain unclear. Here, we develop a mechanism-based mathematical modeling framework that interrogates the emergent nonlinear dynamics of the coupled network modules regulating E/M plasticity (miR-200/ZEB) and stemness (LIN28/let-7). Simulating the dynamics of this coupled network across a large ensemble of parameter sets, we observe that hybrid E/M phenotype(s) are more likely to acquire stemness relative to pure epithelial or mesenchymal states. We also integrate multiple phenotypic stability factors (PSFs) that have been shown to stabilize hybrid E/M phenotypes both in silico and in vitro - such as OVOL1/2, GRHL2, and NRF2 - with this network, and demonstrate that the enrichment of hybrid E/M phenotype(s) with stemness is largely conserved in the presence of these PSFs. Thus, our results offer mechanistic insights into recent experimental observations of hybrid E/M phenotype(s) being essential for tumor-initiation and highlight how this feature is embedded in the underlying topology of interconnected EMT and stemness networks.

cancer biology↗