Date: Friday, 18 March 2022

Time: 5:00 p.m. - 6:00 p.m.

Via Zoom: https://hku.zoom.us/j/91690932997?pwd=ajc2RVUxYlRCV1BMc1JvVU1rd05hZz09

Meeting ID: 916 9093 2997

Password: 287171

5:00 p.m.

Presenter: Miss Wan Ning LEE, PhD candidate
Primary Supervisor: Dr. Cheng-Han YU
Presentation Title: Inside-out signal transduction regulates the assembly of integrin beta6-mediated adhesions
Abstract: Integrins are transmembrane receptors that mediate adhesion formation. Here, we report that the association of intracellular adapter proteins orchestrates force-dependent adhesion assembly of integrin beta6. When CHO cells adhere on the supported membrane with diffusive RGD ligands, integrin beta6 and RGD ligands promptly assemble micro-clusters (RGD-integrin beta6 clusters) at 15 minutes. However, these clusters gradually dissipate over 60 minutes while RGD-integrin beta3 clusters persist. We hypothesise that the activation and association of key adaptor protein, talin regulate force-dependent adhesion clustering of integrin beta6. Indeed, RGD-integrin beta6 clusters become persistent upon overexpressing vinculin T12 mutant, which constitutively activates talin. In addition, we apply domain-swapping approaches and find that integrin b6b3 chimera, rather than integrin b6b3-YA chimera where the key talin binding residues Y773 and Y785 are mutated, can assemble stable RGD clusters at 60 minutes. Collectively, traction force is critical for integrin beta6-mediated adhesions, however, can be bypassed by talin reinforcement.

5:30 p.m.

Presenter: Miss Huaping LI, PhD candidate
Primary Supervisor: Dr. Jason Wing Hon WONG
Presentation Title: Gene expression is a poor predictor of the metabolite abundance in cancer cells
Abstract: Metabolic reprogramming is a hallmark of cancer characterized by global changes in metabolite levels. However, compared with the study of gene expression, profiling of metabolites in cancer samples remains relatively understudied. We obtained metabolomic profiling and gene expression data from 454 human solid cancer cell lines across 24 cancer types from the Cancer Cell Line Encyclopedia (CCLE) database, to evaluate the feasibility of inferring metabolite levels from gene expression data. For each metabolite, we trained multivariable LASSO regression models to identify gene sets that are most predictive of the level of each metabolite profiled. Even when accounting for cell culture conditions or cell lineage in the models, few metabolites could be accurately predicted. In some cases, the inclusion of the upstream and downstream metabolites improved prediction accuracy, suggesting that gene expression is a poor predictor of steady-state metabolite levels. 

ALL ARE WELCOME

Should you have any enquiries, please feel free to contact Miss Cynthia Cheung at 3917 9748.