Loss of RhoA Exacerbates,Rather Than Dampens,Oncogenic K-Ras Induced Lung Adenoma Formation in Mice

Numerous cellular studies have indicated that RhoA signaling is required for oncogenic Ras-induced transformation, suggesting that RhoA is a useful target in Ras induced neoplasia. However, to date very limited data exist to genetically attribute RhoA function to Ras-mediated tumorigenesis in mammalian models. In order to assess whether RhoA is required for K-Ras-induced lung cancer initiation, we utilized the K-Ras^G12D Lox-Stop-Lox murine lung cancer model in combination with a conditional RhoA^flox/flox and RhoC^-/- knockout mouse models. Deletion of the floxed Rhoa gene and expression of K-Ras^G12D was achieved by either CCSP-Cre or adenoviral Cre, resulting in simultaneous expression of K-Ras^G12D and deletion of RhoA from the murine lung. We found that deletion of RhoA, RhoC or both did not adversely affect normal lung development. Moreover, we found that deletion of either RhoA or RhoC alone did not suppress K-Ras^G12D induced lung adenoma initiation. Rather, deletion of RhoA alone exacerbated lung adenoma formation, whereas dual deletion of RhoA and RhoC together significantly reduced K-Ras^G12D induced adenoma formation. Deletion of RhoA appears to induce a compensatory mechanism that exacerbates adenoma formation. The compensatory mechanism is at least partly mediated by RhoC. This study suggests that targeting of RhoA alone may allow for compensation and a paradoxical exacerbation of neoplasia, while simultaneous targeting of both RhoA and RhoC is likely to lead to more favorable outcomes.     

Functional Connectivity and Tuning Curves in Populations of Simultaneously Recorded Neurons

How interactions between neurons relate to tuned neural responses is a longstanding question in systems neuroscience. Here we use statistical modeling and simultaneous multi-electrode recordings to explore the relationship between these interactions and tuning curves in six different brain areas. We find that, in most cases, functional interactions between neurons provide an explanation of spiking that complements and, in some cases, surpasses the influence of canonical tuning curves. Modeling functional interactions improves both encoding and decoding accuracy by accounting for noise correlations and features of the external world that tuning curves fail to capture. In cortex, modeling coupling alone allows spikes to be predicted more accurately than tuning curve models based on external variables. These results suggest that statistical models of functional interactions between even relatively small numbers of neurons may provide a useful framework for examining neural coding.