Functional modelling of planar cell polarity: an approach for identifying molecular function.

BACKGROUND: Cells in some tissues acquire a polarisation in the plane of the tissue in addition to apical-basal polarity. This polarisation is commonly known as planar cell polarity and has been found to be important in developmental processes, as planar polarity is required to define the in-plane tissue coordinate system at the cellular level. RESULTS: We have built an in-silico functional model of cellular polarisation that includes cellular asymmetry, cell-cell signalling and a response to a global cue. The model has been validated and parameterised against domineering non-autonomous wing hair phenotypes in Drosophila. CONCLUSIONS: We have carried out a systematic comparison of in-silico polarity phenotypes with patterns observed in vivo under different genetic manipulations in the wing. This has allowed us to classify the specific functional roles of proteins involved in generating cell polarity, providing new hypotheses about their specific functions, in particular for Pk and Dsh. The predictions from the model allow direct assignment of functional roles of genes from genetic mosaic analysis of Drosophila wings.

Lipid raft association restricts CD44-ezrin interaction and promotion of breast cancer cell migration.

Cancer cell migration is an early event in metastasis, the main cause of breast cancer-related deaths. Cholesterol-enriched membrane domains called lipid rafts influence the function of many molecules, including the raft-associated protein CD44. We describe a novel mechanism whereby rafts regulate interactions between CD44 and its binding partner ezrin in migrating breast cancer cells. Specifically, in nonmigrating cells, CD44 and ezrin localized to different membranous compartments: CD44 predominantly in rafts, and ezrin in nonraft compartments. After the induction of migration (either nonspecific or CD44-driven), CD44 affiliation with lipid rafts was decreased. This was accompanied by increased coprecipitation of CD44 and active (threonine-phosphorylated) ezrin-radixin-moesin (ERM) proteins in nonraft compartments and increased colocalization of CD44 with the nonraft protein, transferrin receptor. Pharmacological raft disruption using methyl-ß-cyclodextrin also increased CD44-ezrin coprecipitation and colocalization, further suggesting that CD44 interacts with ezrin outside rafts during migration. Conversely, promoting CD44 retention inside lipid rafts by pharmacological inhibition of depalmitoylation virtually abolished CD44-ezrin interactions. However, transient single or double knockdown of flotillin-1 or caveolin-1 was not sufficient to increase cell migration over a short time course, suggesting complex crosstalk mechanisms. We propose a new model for CD44-dependent breast cancer cell migration, where CD44 must relocalize outside lipid rafts to drive cell migration. This could have implications for rafts as pharmacological targets to down-regulate cancer cell migration.

Circulating microRNA profiles reflect the presence of breast tumours but not the profiles of microRNAs within the tumours.

BACKGROUND: Extra-cellular microRNAs have been identified within blood and their profiles reflect various pathologies; therefore they have potential as disease biomarkers. Our aim was to investigate how circulating microRNA profiles change during cancer treatment. Our hypothesis was that tumour-related profiles are lost after tumour resection and therefore that comparison of profiles before and after surgery would allow identification of biomarker microRNAs. We aimed to examine whether these microRNAs were directly derived from tumours, and whether longitudinal expression monitoring could provide recurrence diagnoses. METHODS: Plasma was obtained from ten breast cancer patients before and at two time-points after resection. Tumour tissue was also obtained. Quantitative PCR were used to determine levels of 367 miRNAs. Relative expressions were determined after normalisation to miR-16, as is typical in the field, or to the mean microRNA level. RESULTS: 210 microRNAs were detected in at least one plasma sample. Using miR-16 normalisation, we found few consistent changes in circulating microRNAs after resection, and statistical analyses indicated that this normalisation was not justifiable. However, using data normalised to mean microRNA expression we found a significant bias for levels of individual circulating microRNAs to be reduced after resection. Potential biomarker microRNAs were identified, including let-7b, let-7g and miR-18b, with higher levels associated with tumours. These microRNAs were over-represented within the more highly expressed microRNAs in matched tumours, suggesting that circulating populations are tumour-derived in part. Longitudinal monitoring did not allow early recurrence detection. CONCLUSIONS: We concluded that specific circulating microRNAs may act as breast cancer biomarkers but methodological issues are critical.