Accounting for dimensional differences in stochastic domain invasion with applications to precancerous cell removal

KRASG12D Cells Override Homeostatic Cell Elimination Mechanisms in Adult Pancreas Via Wnt5a and Cell Dormancy

BACKGROUND & AIMS

The adult pancreas protects against cancer by actively expelling genetically mutated cells. Pancreatic cancer starts with cells carrying KRAS mutations; however, it is not clear how some KRAS mutant cells override cell elimination mechanisms to survive in tissues.

METHODS

An in vivo mouse model of sporadic tumorigenesis was used to induce Kras and/or Tp53 mutations in low numbers of cells in the adult pancreas. The mutant cell fate was monitored over time using quantitative fluorescence imaging. Gene signatures of noneliminated mutant cell populations were identified using bulk RNA sequencing. Differential gene expression was overlapped with publicly available datasets. Key molecular pathways were validated in murine pancreas using immunofluorescence and functionally tested using inhibitor studies in vivo and epithelial coculture systems in vitro.

RESULTS

Although most genetically mutant cells are eliminated from the adult pancreas, a population of KRASG12D- or p53R172H-expressing cells are stably retained. Wnt5a signaling, cell dormancy, and stemness were identified as key features of surviving KrasG12D cells in vivo. Wnt5a specifically inhibits apical extrusion of RasV12 cells by promoting stable E-cadherin-based cell-cell adhesions at RasV12: normal cell-cell boundaries in vitro. In the pancreas, Wnt signaling, E-cadherin, and β-catenin are increased at cell-cell contacts between noneliminated KrasG12D cells and normal neighbors. Active Wnt signaling is a general mechanism required to promote KrasG12D and p53R172H cell retention and cell survival in vivo.

CONCLUSIONS

RAS mutant cells activate Wnt5a and cell dormancy to avoid cell expulsion and to survive in the adult pancreas.

A simple and fast method for estimating bat roost locations

Bats play a pivotal role in pest control, pollination and seed dispersal. Despite their ecological significance, locating bat roosts remains a challenging task for ecologists. Traditional field surveys are time-consuming, expensive and may disturb sensitive bat populations. In this article, we combine data from static audio detectors with a bat movement model to facilitate the detection of bat roosts. Crucially, our technique not only provides a point prediction for the most likely location of a bat roost, but because of the algorithm's speed, it can be applied over an entire landscape, resulting in a likelihood map, which provides optimal searching regions. To illustrate the success of the algorithm and highlight limitations, we apply our technique to greater horseshoe bat (Rhinolophus ferrumequinum) acoustic data acquired from six surveys from four different UK locations and over six different times in the year. Furthermore, we investigate what happens to the accuracy of our predictions in the case that the roost is not contained within the area spanned by the detectors. This innovative approach to searching rural environments holds the potential to greatly reduce the labour required for roost finding, and, hence, enhance the conservation efforts of bat populations and their habitats.

Bat Motion can be Described by Leap Frogging

We present models of bat motion derived from radio-tracking data collected over 14 nights. The data presents an initial dispersal period and a return to roost period. Although a simple diffusion model fits the initial dispersal motion we show that simple convection cannot provide a description of the bats returning to their roost. By extending our model to include non-autonomous parameters, or a leap frogging form of motion, where bats on the exterior move back first, we find we are able to accurately capture the bat's motion. We discuss ways of distinguishing between the two movement descriptions and, finally, consider how the different motion descriptions would impact a bat's hunting strategy.