Neuronal activity enhances the elaboration of newborn neurons as they integrate into the synaptic circuitry of the adult brain. The role microRNAs play in the transduction of neuronal activity into growth and synapse formation is largely unknown. MicroRNAs can influence the expression of hundreds of genes and thus could regulate gene assemblies during processes like activity-dependent integration. Here, we developed viral-based methods for the in vivo detection and manipulation of the activity-dependent microRNA, miR-132, in the mouse hippocampus. We find, using lentiviral and retroviral reporters of miR-132 activity, that miR-132 is expressed at the right place and right time to influence the integration of newborn neurons. Retroviral knockdown of miR-132 using a specific 'sponge' containing multiple target sequences impaired the integration of newborn neurons into the excitatory synaptic circuitry of the adult brain. To assess potential miR-132 targets, we used a whole-genome microarray in PC12 cells, which have been used as a model of neuronal differentiation. miR-132 knockdown in PC12 cells resulted in the increased expression of hundreds of genes. Functional grouping indicated that genes involved in inflammatory/immune signaling were the most enriched class of genes induced by miR-132 knockdown. The correlation of miR-132 knockdown to increased proinflammatory molecular expression may indicate a mechanistic link whereby miR-132 functions as an endogenous mediator of activity-dependent integration in vivo.

Brain oscillations play a critical role in information processing and may, therefore, be essential to uncovering the mechanisms of cognitive impairment in neurological disease. In Dravet syndrome (DS), a mutation in SCN1A, coding for the voltage-gated sodium channel Nav1.1, is associated with severe cognitive impairment and seizures. While seizure frequency and severity do not correlate with the extent of impairment, the slowing of brain rhythms may be involved. Here we investigate the role of Nav1.1 on brain rhythms and cognition using RNA interference. We demonstrate that knockdown of Nav1.1 impairs fast- and burst-firing properties of neurons in the medial septum in vivo. The proportion of neurons that fired phase-locked to hippocampal theta oscillations was reduced, and medial septal regulation of theta rhythm was disrupted. During a working memory task, this deficit was characterized by a decrease in theta frequency and was negatively correlated with performance. These findings suggest a fundamental role for Nav1.1 in facilitating fast-firing properties in neurons, highlight the importance of precise temporal control of theta frequency for working memory, and imply that Nav1.1 deficits may disrupt information processing in DS via a dysregulation of brain rhythms.

Neurogenesis is a highly-regulated process occurring in the dentate gyrus that has been linked to learning, memory, and antidepressant efficacy. MicroRNAs (miRNAs) have been previously shown to play an important role in the regulation of neuronal development and neurogenesis in the dentate gyrus via modulation of gene expression. However, this mode of regulation is both incompletely described in the literature thus far and highly multifactorial. In this study, we designed sensors and detected relative levels of expression of 10 different miRNAs and found miR-338-3p was most highly expressed in the dentate gyrus. Comparison of miR-338-3p expression with neuronal markers of maturity indicates miR-338-3p is expressed most highly in the mature neuron. We also designed a viral "sponge" to knock down in vivo expression of miR-338-3p. When miR-338-3p is knocked down, neurons sprout multiple primary dendrites that branch off of the soma in a disorganized manner, cellular proliferation is upregulated, and neoplasms form spontaneously in vivo. Additionally, miR-338-3p overexpression in glioblastoma cell lines slows their proliferation in vitro. Further, low miR-338-3p expression is associated with increased mortality and disease progression in patients with glioblastoma. These data identify miR-338-3p as a clinically relevant tumor suppressor in glioblastoma.

Glioblastoma multiforme (GBM), the most common and aggressive primary brain malignancy, is incurable despite the best combination of current cancer therapies. For the development of more effective therapies, discovery of novel candidate tumor drivers is urgently needed. Here, we report that peroxiredoxin 4 (PRDX4) is a putative tumor driver. PRDX4 levels were highly increased in a majority of human GBMs as well as in a mouse model of GBM. Reducing PRDX4 expression significantly decreased GBM cell growth and radiation resistance in vitro with increased levels of ROS, DNA damage, and apoptosis. In a syngenic orthotopic transplantation model, Prdx4 knockdown limited GBM infiltration and significantly prolonged mouse survival. These data suggest that PRDX4 can be a novel target for GBM therapies in the future.

A conventional study design among medical and biological experimentalists involves collecting multiple measurements from a study subject. For example, experiments utilizing mouse models in neuroscience often involve collecting multiple neuron measurements per mouse to increase the number of observations without requiring a large number of mice. This leads to a form of statistical dependence referred to as clustering. Inappropriate analyses of clustered data have resulted in several recent critiques of neuroscience research that suggest the bar for statistical analyses within the field is set too low. We compare naïve analytical approaches to marginal, fixed-effect, and mixed-effect models and provide guidelines for when each of these models is most appropriate based on study design. We demonstrate the influence of clustering on a between-mouse treatment effect, a within-mouse treatment effect, and an interaction effect between the two. Our analyses demonstrate that these statistical approaches can give substantially different results, primarily when the analyses include a between-mouse treatment effect. In a novel analysis from a neuroscience perspective, we also refine the mixed-effect approach through the inclusion of an aggregate mouse-level counterpart to a within-mouse (neuron level) treatment as an additional predictor by adapting an advanced modeling technique that has been used in social science research and show that this yields more informative results. Based on these findings, we emphasize the importance of appropriate analyses of clustered data, and we aim for this work to serve as a resource for when one is deciding which approach will work best for a given study.