Lowering levels of reelin in entorhinal cortex layer II-neurons results in lowered levels of intracellular amyloid-ß.

Projection neurons in the anteriolateral part of entorhinal cortex layer II are the predominant cortical site for hyper-phosphorylation of tau and formation of neurofibrillary tangles in prodromal Alzheimer's disease. A majority of layer II projection neurons in anteriolateral entorhinal cortex are unique among cortical excitatory neurons by expressing the protein reelin. In prodromal Alzheimer's disease, these reelin-expressing neurons are prone to accumulate intracellular amyloid-ß, which is mimicked in a rat model that replicates the spatio-temporal cascade of the disease. Two important findings in relation to this are that reelin-signalling downregulates tau phosphorylation, and that oligomeric amyloid-ß interferes with reelin-signalling. Taking advantage of this rat model, we used proximity ligation assay to assess whether reelin and intracellular amyloid-ß directly interact during early, pre-plaque stages in anteriolateral entorhinal cortex layer II reelin-expressing neurons. We next made a viral vector delivering micro-RNA against reelin, along with a control vector, and infected reelin-expressing anteriolateral entorhinal cortex layer II-neurons to test whether reelin levels affect levels of intracellular amyloid-ß and/or amyloid precursor protein. We analysed 25.548 neurons from 24 animals, which results in three important findings. First, in reelin-expressing anteriolateral entorhinal cortex layer II-neurons, reelin and intracellular amyloid-ß engage in a direct protein-protein interaction. Second, injecting micro-RNA against reelin lowers reelin levels in these neurons, amounting to an effect size of 1.3-4.5 (Bayesian estimation of Cohen's d effect size, 95% credible interval). This causes a concomitant reduction of intracellular amyloid-ß ranging across three levels of aggregation, including a reduction of Aß42 monomers/dimers amounting to an effect size of 0.5-3.1, a reduction of Aß prefibrils amounting to an effect size of 1.1-3.5 and a reduction of protofibrils amounting to an effect size of 0.05-2.1. Analysing these data using Bayesian estimation of mutual information furthermore reveals that levels of amyloid-ß are dependent on levels of reelin. Third, the reduction of intracellular amyloid-ß occurs without any substantial associated changes in levels of amyloid precursor protein. We conclude that reelin and amyloid-ß directly interact at the intracellular level in the uniquely reelin-expressing projection neurons in anteriolateral entorhinal cortex layer II, where levels of amyloid-ß are dependent on levels of reelin. Since amyloid-ß is known to impair reelin-signalling causing upregulated phosphorylation of tau, our findings are likely relevant to the vulnerability for neurofibrillary tangle-formation of this entorhinal neuronal population.

Enhancer-Driven Gene Expression (EDGE) Enables the Generation of Viral Vectors Specific to Neuronal Subtypes.

Although a variety of remarkable molecular tools for studying neural circuits have recently been developed, the ability to deploy them in particular neuronal subtypes is limited by the fact that native promoters are almost never specific enough. We recently showed that one can generate transgenic mice with anatomical specificity surpassing that of native promoters by combining enhancers uniquely active in particular brain regions with a heterologous minimal promoter, an approach we call EDGE (Enhancer-Driven Gene Expression). Here we extend this strategy to the generation of viral (rAAV) vectors, showing that some EDGE rAAVs can recapitulate the specificity of the corresponding transgenic lines in wild-type animals, even of another species. This approach thus holds the promise of enabling circuit-specific manipulations in wild-type animals, not only enhancing our understanding of brain function, but perhaps one day even providing novel therapeutic avenues to approach disorders of the brain.

Enhancer-Driven Gene Expression (EDGE) enables the generation of cell type specific tools for the analysis of neural circuits.

As in all circuits, fully understanding how neural circuits operate requires the ability to specifically manipulate individual circuit elements, i.e. particular neuronal cell types. While recent years saw the development of molecular genetic tools allowing one to control and monitor neuronal activity, progress is limited by the ability to express such transgenes specifically enough. This goal is complicated by the fact that we are only beginning to understand how many cell types exist in the mammalian brain. Obtaining neuronal cell type-specific expression requires co-opting the genetic machinery which specifies their striking diversity, typically done by making transgenic animals using promoters expressing in neurons. However, while the vast majority of genes express in the brain, they almost always express in multiple cell types, meaning native promoters are not specific enough. We have recently taken a new approach to increase the specificity of transgene expression based upon identifying the distal cis-regulatory genomic elements (i.e. enhancers) uniquely active in a brain region and combining them with a heterologous minimal promoter. Termed Enhancer-Driven Gene Expression (EDGE), it allows for the generation of transgenic animals targeting the cell types of any brain region with far greater specificity than can be obtained with native promoters. Moreover, their small size allows for the generation of cell-specific viral vectors, conceivably enabling circuit-specific manipulations to any species.

Functional properties of stellate cells in medial entorhinal cortex layer II.

Layer II of the medial entorhinal cortex (MEC) contains two principal cell types: pyramidal cells and stellate cells. Accumulating evidence suggests that these two cell types have distinct molecular profiles, physiological properties, and connectivity. The observations hint at a fundamental functional difference between the two cell populations but conclusions have been mixed. Here, we used a tTA-based transgenic mouse line to drive expression of ArchT, an optogenetic silencer, specifically in stellate cells. We were able to optogenetically identify stellate cells and characterize their firing properties in freely moving mice. The stellate cell population included cells from a range of functional cell classes. Roughly one in four of the tagged cells were grid cells, suggesting that stellate cells contribute not only to path-integration-based representation of self-location but also have other functions. The data support observations suggesting that grid cells are not the sole determinant of place cell firing.

Marked Diversity of Unique Cortical Enhancers Enables Neuron-Specific Tools by Enhancer-Driven Gene Expression.

Understanding neural circuit function requires individually addressing their component parts: specific neuronal cell types. However, not only do the precise genetic mechanisms specifying neuronal cell types remain obscure, access to these neuronal cell types by transgenic techniques also remains elusive. Whereas most genes are expressed in the brain, the vast majority are expressed in many different kinds of neurons, suggesting that promoters alone are not sufficiently specific to distinguish cell types. However, there are orders of magnitude more distal genetic cis-regulatory elements controlling transcription (i.e., enhancers), so we screened for enhancer activity in microdissected samples of mouse cortical subregions. This identified thousands of novel putative enhancers, many unique to particular cortical subregions. Pronuclear injection of expression constructs containing such region-specific enhancers resulted in transgenic lines driving expression in distinct sets of cells specifically in the targeted cortical subregions, even though the parent gene's promoter was relatively non-specific. These data showcase the promise of utilizing the genetic mechanisms underlying the specification of diverse neuronal cell types for the development of genetic tools potentially capable of targeting any neuronal circuit of interest, an approach we call enhancer-driven gene expression (EDGE).

Generation of a synthetic memory trace.

We investigated the effect of activating a competing, artificially generated, neural representation on encoding of contextual fear memory in mice. We used a c-fos-based transgenic approach to introduce the hM(3)D(q) DREADD receptor (designer receptor exclusively activated by designer drug) into neurons naturally activated by sensory experience. Neural activity could then be specifically and inducibly increased in the hM(3)D(q)-expressing neurons by an exogenous ligand. When an ensemble of neurons for one context (ctxA) was artificially activated during conditioning in a distinct second context (ctxB), mice formed a hybrid memory representation. Reactivation of the artificially stimulated network within the conditioning context was required for retrieval of the memory, and the memory was specific for the spatial pattern of neurons artificially activated during learning. Similar stimulation impaired recall when not part of the initial conditioning.

Looking for cognition in the structure within the noise.

Neural activity in the mammalian CNS is determined by both observable processes, such as sensory stimuli or motor output, and covert, internal cognitive processes that cannot be directly observed. We propose methods to identify these cognitive processes by examining the covert structure within the apparent 'noise' in spike trains. Contemporary analyses of neural codes include encoding (tuning curves derived from spike trains and behavioral, sensory or motor variables), decoding (reconstructing behavioral, sensory or motor variables from spike trains and hypothesized tuning curves) and generative models (predicting the spike trains from hypothesized encoding models and decoded variables). We review examples of each of these processes in hippocampal activity, and propose a general methodology to examine cognitive processes via the identification of dynamic changes in covert variables.