Analysis of the potential of cancer cell lines to release tissue factor-containing microvesicles: correlation with tissue factor and PAR2 expression

BackgroundDespite the association of cancer-derived circulating tissue factor (TF)-containing microvesicles and hypercoagulable state, correlations with the incidence of thrombosis remain unclear.MethodsIn this study the upregulation of TF release upon activation of various cancer cell lines, and the correlation with TF and PAR2 expression and/or activity was examined. Microvesicle release was induced by PAR2 activation in seventeen cell lines and released microvesicle density, microvesicle-associated TF activity, and phoshpatidylserine-mediated activity were measured. The time-course for TF release was monitored over 90 min in each cell line. In addition, TF mRNA expression, cellular TF protein and cell-surface TF activities were quantified. Moreover, the relative expression of PAR2 mRNA and cellular protein were analysed. Any correlations between the above parameters were examined by determining the Pearson’s correlation coefficients.ResultsTF release as microvesicles peaked between 30–60 min post-activation in the majority of cell lines tested. The magnitude of the maximal TF release positively correlated with TF mRNA (c = 0.717; p

Paroxysmal and cognitive phenotypes in Prrt2 mutant mice

Mutations in proline-rich transmembrane protein 2 (PRRT2) cause a range of episodic disorders that include paroxysmal kinesigenic dyskinesia and benign familial infantile epilepsy. Mutations are generally loss of function and include the c649dupC frameshifting mutation that is present in around 80% of affected individuals. To investigate how Prrt2 loss of function mutations causes disease, we performed a phenotypic investigation of a transgenic Prrt2 knockout (Prrt2 KO) mouse. We observed spontaneous paroxysmal episodes with behavioural features of both seizure and movement disorders, as well as unexplained deaths in KO and HET animals. KO mice showed spatial learning deficits in the Morris water maze, as well as gait abnormalities in the quantitative Digigait analysis; both of which may be representative of the more severe phenotypes experienced by homozygous patients. These findings extend the described phenotypes of Prrt2 mutant mice, further confirming their utility for in vivo investigation of the role of Prrt2 mutations in episodic diseases.Louise Robertson, Travis Featherby, Stuart Howell, James Hughes, Paul Thoma

Separating Probability and Reversal Learning in a Novel Probabilistic Reversal Learning Task for Mice

The exploration/exploitation tradeoff – pursuing a known reward vs. sampling from lesser known options in the hope of finding a better payoff – is a fundamental aspect of learning and decision making. In humans, this has been studied using multi-armed bandit tasks. The same processes have also been studied using simplified probabilistic reversal learning (PRL) tasks with binary choices. Our investigations suggest that protocols previously used to explore PRL in mice may prove beyond their cognitive capacities, with animals performing at a no-better-than-chance level. We sought a novel probabilistic learning task to improve behavioral responding in mice, whilst allowing the investigation of the exploration/exploitation tradeoff in decision making. To achieve this, we developed a two-lever operant chamber task with levers corresponding to different probabilities (high/low) of receiving a saccharin reward, reversing the reward contingencies associated with levers once animals reached a threshold of 80% responding at the high rewarding lever. We found that, unlike in existing PRL tasks, mice are able to learn and behave near optimally with 80% high/20% low reward probabilities. Altering the reward contingencies towards equality showed that some mice displayed preference for the high rewarding lever with probabilities as close as 60% high/40% low. Additionally, we show that animal choice behavior can be effectively modelled using reinforcement learning (RL) models incorporating learning rates for positive and negative prediction error, a perseveration parameter, and a noise parameter. This new decision task, coupled with RL analyses, advances access to investigate the neuroscience of the exploration/exploitation tradeoff in decision making