Genetic Control of Active Neural Circuits

The use of molecular tools to study the neurobiology of complex behaviors has been hampered by an inability to target the desired changes to relevant groups of neurons. Specific memories and specific sensory representations are sparsely encoded by a small fraction of neurons embedded in a sea of morphologically and functionally similar cells. In this review we discuss genetics techniques that are being developed to address this difficulty. In several studies the use of promoter elements that are responsive to neural activity have been used to drive long-lasting genetic alterations into neural ensembles that are activated by natural environmental stimuli. This approach has been used to examine neural activity patterns during learning and retrieval of a memory, to examine the regulation of receptor trafficking following learning and to functionally manipulate a specific memory trace. We suggest that these techniques will provide a general approach to experimentally investigate the link between patterns of environmentally activated neural firing and cognitive processes such as perception and memory

Natural Amyloid-Beta Oligomers Acutely Impair the Formation of a Contextual Fear Memory in Mice

Memory loss is one of the hallmark symptoms of Alzheimer's disease (AD). It has been proposed that soluble amyloid-beta (Abeta) oligomers acutely impair neuronal function and thereby memory. We here report that natural Abeta oligomers acutely impair contextual fear memory in mice. A natural Abeta oligomer solution containing Abeta monomers, dimers, trimers, and tetramers was derived from the conditioned medium of 7PA2 cells, a cell line that expresses human amyloid precursor protein containing the Val717Phe familial AD mutation. As a control we used 7PA2 conditioned medium from which Abeta oligomers were removed through immunodepletion. Separate groups of mice were injected with Abeta and control solutions through a cannula into the lateral brain ventricle, and subjected to fear conditioning using two tone-shock pairings. One day after fear conditioning, mice were tested for contextual fear memory and tone fear memory in separate retrieval trials. Three experiments were performed. For experiment 1, mice were injected three times: 1 hour before and 3 hours after fear conditioning, and 1 hour before context retrieval. For experiments 2 and 3, mice were injected a single time at 1 hour and 2 hours before fear conditioning respectively. In all three experiments there was no effect on tone fear memory. Injection of Abeta 1 hour before fear conditioning, but not 2 hours before fear conditioning, impaired the formation of a contextual fear memory. In future studies, the acute effect of natural Abeta oligomers on contextual fear memory can be used to identify potential mechanisms and treatments of AD associated memory loss

Experience-dependent resonance in amygdalocortical circuits supports fear memory retrieval following extinction

Theta range oscillations in the basolateral amygdala (BLA) and the medial prefrontal cortex (mPFC) are associated with conditioned fear. Here, the authors use exogenous oscillatory stimulation of the BLA and mPFC in mice to determine the dynamic roles of theta-range oscillatory states across conditioned fear and extinction learning

Characterization of NMDAR-Independent Learning in the Hippocampus

It is currently thought that memory formation requires the activation of NMDA receptors (NMDARs) in the hippocampus. However, recent studies indicate that these receptors are not necessary for all forms of learning. The current experiments examine this issue using context fear conditioning in mice. First, we show that context fear can be acquired without NMDAR activation in previously trained animals. Mice trained in one environment (context A) are subsequently able to learn about a second environment (context B) in the presence of NMDAR antagonists. Second, we demonstrate that NMDAR-independent learning requires the hippocampus and is dependent on protein synthesis. However, unlike NMDAR-dependent learning, it is not contingent on the expression of activity-regulated cytoskeleton-associated protein (Arc). Lastly, we present data that suggests NMDAR-independent learning is only observed when recently stimulated neurons are reactivated during conditioning. These data suggest that context fear conditioning modifies synaptic plasticity mechanisms in the hippocampus and allows subsequent learning to occur in the absence of NMDAR activation

Neural mechanisms of social learning in the female mouse

Social interactions are often powerful drivers of learning. In female mice, mating creates a long-lasting sensory memory for the pheromones of the stud male that alters neuroendocrine responses to his chemosignals for many weeks. The cellular and synaptic correlates of pheromonal learning, however, remain unclear. We examined local circuit changes in the accessory olfactory bulb (AOB) using targeted ex vivo recordings of mating-activated neurons tagged with a fluorescent reporter. Imprinting led to striking plasticity in the intrinsic membrane excitability of projection neurons (mitral cells, MCs) that dramatically curtailed their responsiveness, suggesting a novel cellular substrate for pheromonal learning. Plasticity was selectively expressed in the MC ensembles activated by the stud male, consistent with formation of memories for specific individuals. Finally, MC excitability gained atypical activity-dependence whose slow dynamics strongly attenuated firing on timescales of several minutes. This unusual form of AOB plasticity may act to filter sustained or repetitive sensory signals.R21 DC013894 - NIDCD NIH HH

Understanding and classifying metabolite space and metabolite-likeness

While the entirety of 'Chemical Space' is huge (and assumed to contain between 10(63) and 10(200) 'small molecules'), distinct subsets of this space can nonetheless be defined according to certain structural parameters. An example of such a subspace is the chemical space spanned by endogenous metabolites, defined as 'naturally occurring' products of an organisms' metabolism. In order to understand this part of chemical space in more detail, we analyzed the chemical space populated by human metabolites in two ways. Firstly, in order to understand metabolite space better, we performed Principal Component Analysis (PCA), hierarchical clustering and scaffold analysis of metabolites and non-metabolites in order to analyze which chemical features are characteristic for both classes of compounds. Here we found that heteroatom (both oxygen and nitrogen) content, as well as the presence of particular ring systems was able to distinguish both groups of compounds. Secondly, we established which molecular descriptors and classifiers are capable of distinguishing metabolites from non-metabolites, by assigning a 'metabolite-likeness' score. It was found that the combination of MDL Public Keys and Random Forest exhibited best overall classification performance with an AUC value of 99.13%, a specificity of 99.84% and a selectivity of 88.79%. This performance is slightly better than previous classifiers; and interestingly we found that drugs occupy two distinct areas of metabolite-likeness, the one being more 'synthetic' and the other being more 'metabolite-like'. Also, on a truly prospective dataset of 457 compounds, 95.84% correct classification was achieved. Overall, we are confident that we contributed to the tasks of classifying metabolites, as well as to understanding metabolite chemical space better. This knowledge can now be used in the development of new drugs that need to resemble metabolites, and in our work particularly for assessing the metabolite-likeness of candidate molecules during metabolite identification in the metabolomics field.Analytical BioScience

Sub-Typing of Rheumatic Diseases Based on a Systems Diagnosis Questionnaire

The future of personalized medicine depends on advanced diagnostic tools to characterize responders and non-responders to treatment. Systems diagnosis is a new approach which aims to capture a large amount of symptom information from patients to characterize relevant sub-groups.49 patients with a rheumatic disease were characterized using a systems diagnosis questionnaire containing 106 questions based on Chinese and Western medicine symptoms. Categorical principal component analysis (CATPCA) was used to discover differences in symptom patterns between the patients. Two Chinese medicine experts where subsequently asked to rank the Cold and Heat status of all the patients based on the questionnaires. These rankings were used to study the Cold and Heat symptoms used by these practitioners.The CATPCA analysis results in three dimensions. The first dimension is a general factor (40.2% explained variance). In the second dimension (12.5% explained variance) 'anxious', 'worrying', 'uneasy feeling' and 'distressed' were interpreted as the Internal disease stage, and 'aggravate in wind', 'fear of wind' and 'aversion to cold' as the External disease stage. In the third dimension (10.4% explained variance) 'panting s', 'superficial breathing', 'shortness of breath s', 'shortness of breath f' and 'aversion to cold' were interpreted as Cold and 'restless', 'nervous', 'warm feeling', 'dry mouth s' and 'thirst' as Heat related. 'Aversion to cold', 'fear of wind' and 'pain aggravates with cold' are most related to the experts Cold rankings and 'aversion to heat', 'fullness of chest' and 'dry mouth' to the Heat rankings.This study shows that the presented systems diagnosis questionnaire is able to identify groups of symptoms that are relevant for sub-typing patients with a rheumatic disease