Rodriguez-Brenes, I. A. | bioRxiv Science

bioRxiv2024

Surface remodeling and inversion of cell-matrix interactions underlies community recognition and dispersal in Vibrio cholerae biofilms

Biofilms are ubiquitous surface-associated bacterial communities embedded in an extracellular matrix. While it is commonly assumed that biofilm-dwelling cells are glued together by the matrix, how the cell-matrix interaction depends on the specific biochemistry of the matrix components and how this interaction varies during biofilm growth remains unclear. Here, we investigated cell-matrix interactions in Vibrio cholerae (Vc), the causative agent of cholera. We combine genetics, microscopy, simulation, and biochemical tools to show that Vc cells are not attractive to Vibrio polysaccharide (VPS), the main matrix component, but they can be bridged with each other and to the VPS network through crosslinking by Bap1. Downregulation of VPS and surface trimming by the polysaccharide lyase RbmB cause surface remodeling as biofilms age, shifting the nature of cell-matrix interactions from attractive to repulsive and facilitating cell dispersal as aggregated groups. Our results suggest a new conceptual model in understanding the intricate cell-matrix interaction as the major driver for biofilm development, which is potentially generalizable to certain other biofilm-forming species and exopolysaccharides.

biophysics↗

bioRxiv2017

The role of telomere shortening in carcinogenesis: A hybrid stochastic-deterministic approach

Genome instability is a characteristic of most cancers, contributing to the acquisition of genetic alterations that drive tumor progression. One important source of genome instability is linked to telomere dysfunction in cells with critically short telomeres that lack p53-mediated surveillance of genomic integrity. Here we research the probability that cancer emerges through an evolutionary pathway that includes a telomere-induced phase of genome instability. To implement our models we use a hybrid stochastic-deterministic approach, which allows us to perform large numbers of simulations using biologically realistic population sizes and mutation rates, circumventing the traditional limitations of fully stochastic algorithms. The hybrid methodology should be easily adaptable to a wide range of evolutionary problems. In particular, we model telomere shortening and the acquisition of two mutations: Telomerase activation and p53 inactivation. We find that the death rate of unstable cells, and the number of cell divisions that p53 mutants can sustain beyond the normal senescence setpoint determine the likelihood that the first double mutant originates in a cell with telomere-induced instability. The model has applications to an influential telomerase-null mouse model and p16 silenced human cells. We end by discussing algorithmic performance and a measure for the accuracy of the hybrid approximation.

cancer biology↗