BACKGROUND
G protein-coupled receptors (GPCRs) are the largest class of membrane-bound receptors and are emerging as targets for the effective treatment of cancer. The role of orphan GPCR GPR52 in cancer has not been characterized. Low mRNA expression of GPR52 in breast tumours correlates with reduced overall survival, leading to the hypothesis that loss of GPR52 supports breast cancer progression.
METHODS
CRISPR-Cas9 was used to knock out GPR52 in the human triple-negative breast cancer cell lines MDA-MB-468 and MDA-MB-231. 2D and 3D in vitro studies, electron microscopy, and a zebrafish xenograft model were used to assess the morphology and behaviour of GPR52 KO cells.
RESULTS
Loss of GPR52 was associated with elevated levels of cAMP, increased cell-cell interaction in 2D cultures, more spindle-like morphology on collagen, altered 3D spheroid morphology, and increased propensity to organize and invade collectively. Zebrafish injected with GPR52 KO cells developed a greater total cancer area than control. RNA sequencing and proteomic analyses of GPR52-null cells revealed an increased cAMP signalling signature. Re-expression of GPR52 and inhibition of cAMP production rescued some GPR52 KO phenotypes.
CONCLUSIONS
GPR52 loss is a potential mechanism by which breast cancer progression may occur and supports the investigation of GPR52 agonism as a therapeutic option for breast cancer.
STATEMENT OF SIGNIFICANCE
Loss of the orphan GPCR GPR52 in human breast cell lines leads to increased cell clustering, hybrid/partial EMT, and increased tumour burden in zebrafish, further expanding our understanding of mechanisms driving cancer progression and opening the door to novel therapeutic approaches.
Sarah Z. Hanif, Caleb Kutz, C. C. Au et al.· British Journal of Cancer· 0 citations
A key feature of the Precision Nutrition and Health approach is the ability to tailor interventions to individual variability using multimodal data from large-scale biobanks and cohorts. Artificial intelligence (AI) and machine learning (ML) models offer new potential to model complex data but remain constrained by challenges related to data quality, interpretability, validation, and causal inference. This Perspective synthesizes current AI/ML methodologies in PN, elucidates their interplay with the distinctive features of multi-omic and nutritional data, such as being compositional, episodic, context-dependent, and error-prone, and delineates nutrition-specific best practices for achieving robust, interpretable, and clinically actionable AI integration in research and practice. In this Perspective, the authors highlight critical challenges, knowledge gaps, and opportunities for robust, equitable, rigorous, reproducible, and actionable artificial intelligence integration in research and practice, and provide a roadmap and checklist for enabling the use of artificial intelligence in precision nutrition.
Paraskevi Massara, J. Kirkland, I. Pagani et al.· Nature Communications· 2 citations