Project No. 2446
PRIORITY STANDARD PROJECT
Dr Simon Mitchell- University of Sussex
Prof Sean Lim – University of Southampton
Dr Fabio Simoes – University of Sussex
B-cells within lymph nodes are exposed to cell-to-cell interactions, and mutliple cytokines.
These signals are interpreted by molecular signaling pathways such as the NF-κB signaling pathway, and in response B-cells can secrete cytokines and chemokines to potentially remodel their microenvionrment. The goal of this project is to understand this bi-directional relationship between B-cells and their microenvironment, and how mutations may impact signalling and cytokine secretion. Signal rewiring by mutations has important implications for cancer, immunotherapies, inflammation and auto-immunity.
This project will address this challenge using interdisciplinary systems biology approaches including computational modelling, experimental wet-lab work in cell lines, and validation in primary patient material and murine models.
Our computational/in vitro preliminary data suggests that mutations can re-wire the NF-kB signaling pathway, creating crosstalk between pro-survival/developmental signaling and pro-inflammatory signaling. One impact of this signaling crosstalk may be that mutated cells respond incorrectly to their microenvironment, and produce a pro-inflammatory response to pro-survival/developmental signals, resulting in microenvironment remodelling.
The student will perform computational modelling that predicts how mutations might alter the way B-cells cells interpret their environment. This work will leverage vast data available from cancer studies that spans the scales from mutations to outcomes. These predictions will then be tested in cell lines by measuring NF-κB activity and the cytokines expressed in mutated and unmutated cells using a combination of transfection/CRISPR-cas9 experiments to introduce mutated genes/knock-out genes respecitively. Exciting results from the lab will be carried forward to lymph node biopsy samples, and cell line-derived xenograft models where cell lines with/without cross-talk driving mutations will be introduced and immune infiltration will be assayed using spectral flow cytometry.
Computational modelling experience is not required, but familiarity with coding would be preferred along with a desire to learn computational techniques. Knowledge of cell biology/signalling is required. Hands-on wet lab experience/training is desireable.