Brain-Derived Neurotrophic Factor/Tropomyosin Receptor Kinase B Signaling Controls Excitability and Long-Term Depression in Oval Nucleus of the BNST.

Dysregulation of proteins involved in synaptic plasticity is associated with pathologies in the CNS, including psychiatric disorders. The bed nucleus of the stria terminalis (BNST), a brain region of the extended amygdala circuit, has been identified as the critical hub responsible for fear responses related to stress coping and pathologic systems states. Here, we report that one particular nucleus, the oval nucleus of the BNST (ovBNST), is rich in brain-derived neurotrophic factor (BDNF) and tropomyosin receptor kinase B (TrkB) receptor. Whole-cell patch-clamp recordings of neurons from male mouse ovBNST in vitro showed that the BDNF/TrkB interaction causes a hyperpolarizing shift of the membrane potential from resting value, mediated by an inwardly rectifying potassium current, resulting in reduced neuronal excitability in all major types of ovBNST neurons. Furthermore, BDNF/TrkB signaling mediated long-term depression (LTD) at postsynaptic sites in ovBNST neurons. LTD of ovBNST neurons was prevented by a BDNF scavenger or in the presence of TrkB inhibitors, indicating the contribution to LTD induction. Our data identify BDNF/TrkB signaling as a critical regulator of synaptic activity in ovBNST, which acts at postsynaptic sites to dampen excitability at short and long time scales. Given the central role of ovBNST in mediating maladaptive behaviors associated with stress exposure, our findings suggest a synaptic entry point of the BDNF/TrkB system for adaptation to stressful environmental encounters.

Induction of BDNF Expression in Layer II/III and Layer V Neurons of the Motor Cortex Is Essential for Motor Learning.

Motor learning depends on synaptic plasticity between corticostriatal projections and striatal medium spiny neurons. Retrograde tracing from the dorsolateral striatum reveals that both layer II/III and V neurons in the motor cortex express BDNF as a potential regulator of plasticity in corticostriatal projections in male and female mice. The number of these BDNF-expressing cortical neurons and levels of BDNF protein are highest in juvenile mice when adult motor patterns are shaped, while BDNF levels in the adult are low. When mice are trained by physical exercise in the adult, BDNF expression in motor cortex is reinduced, especially in layer II/III projection neurons. Reduced expression of cortical BDNF in 3-month-old mice results in impaired motor learning while space memory is preserved. These findings suggest that activity regulates BDNF expression differentially in layers II/III and V striatal afferents from motor cortex and that cortical BDNF is essential for motor learning.SIGNIFICANCE STATEMENT Motor learning in mice depends on corticostriatal BDNF supply, and regulation of BDNF expression during motor learning is highest in corticostriatal projection neurons in cortical layer II/III.

An open source tool for automatic spatiotemporal assessment of calcium transients and local 'signal-close-to-noise' activity in calcium imaging data.

Local and spontaneous calcium signals play important roles in neurons and neuronal networks. Spontaneous or cell-autonomous calcium signals may be difficult to assess because they appear in an unpredictable spatiotemporal pattern and in very small neuronal loci of axons or dendrites. We developed an open source bioinformatics tool for an unbiased assessment of calcium signals in x,y-t imaging series. The tool bases its algorithm on a continuous wavelet transform-guided peak detection to identify calcium signal candidates. The highly sensitive calcium event definition is based on identification of peaks in 1D data through analysis of a 2D wavelet transform surface. For spatial analysis, the tool uses a grid to separate the x,y-image field in independently analyzed grid windows. A document containing a graphical summary of the data is automatically created and displays the loci of activity for a wide range of signal intensities. Furthermore, the number of activity events is summed up to create an estimated total activity value, which can be used to compare different experimental situations, such as calcium activity before or after an experimental treatment. All traces and data of active loci become documented. The tool can also compute the signal variance in a sliding window to visualize activity-dependent signal fluctuations. We applied the calcium signal detector to monitor activity states of cultured mouse neurons. Our data show that both the total activity value and the variance area created by a sliding window can distinguish experimental manipulations of neuronal activity states. Notably, the tool is powerful enough to compute local calcium events and 'signal-close-to-noise' activity in small loci of distal neurites of neurons, which remain during pharmacological blockade of neuronal activity with inhibitors such as tetrodotoxin, to block action potential firing, or inhibitors of ionotropic glutamate receptors. The tool can also offer information about local homeostatic calcium activity events in neurites.

Structural changes at the myrtenol backbone reverse its positive allosteric potential into inhibitory GABAA receptor modulation.

GABAA receptors are ligand-gated anion channels that form pentameric arrangements of various subunits. Positive allosteric modulators of GABAA receptors have been reported as being isolated either from plants or synthesized analogs of known GABAA receptor targeting drugs. Recently, we identified monoterpenes, e.g. myrtenol as a positive allosteric modulator at α1ß2 GABAA receptors. Here, along with pharmacophore-based virtual screening studies, we demonstrate that scaffold modifications of myrtenol resulted in the loss of modulatory activity. Two independent approaches, fluorescence-based compound analysis and electrophysiological recordings in whole-cell configurations were used for analysis of transfected cells. C-atoms 1 and 2 of the myrtenol backbone were identified as crucial to preserve positive allosteric potential. A modification at C-atom 2 and lack of the hydroxyl group at C-atom 1 exhibited significantly reduced GABAergic currents at α1ß2, α1ß2γ, α2ß3, α2ß3γ and α4ß3δ receptors. This effect was independent of the γ2 subunit. A sub-screen with side chain length and volume differences at the C-atom 1 identified two compounds that inhibited GABAergic responses but without receptor subtype specificity. Our combined approach of pharmacophore-based virtual screening and functional readouts reveals that side chain modifications of the bridged six-membered ring structure of myrtenol are crucial for its modulatory potential at GABAA receptors.

Neurobiology of local and intercellular BDNF signaling.

Brain-derived neurotrophic factor (BDNF) is a member of the neurotrophin family of secreted proteins. Signaling cascades induced by BDNF and its receptor, the receptor tyrosine kinase TrkB, link neuronal growth and differentiation with synaptic plasticity. For this reason, interference with BDNF signaling has emerged as a promising strategy for potential treatments in psychiatric and neurological disorders. In many brain circuits, synaptically released BDNF is essential for structural and functional long-term potentiation, two prototypical cellular models of learning and memory formation. Recent studies have revealed an unexpected complexity in the synaptic communication of mature BDNF and its precursor proBDNF, not only between local pre- and postsynaptic neuronal targets but also with participation of glial cells. Here, we consider recent findings on local actions of the BDNF family of ligands at the synapse and discuss converging lines of evidence which emerge from per se conflicting results.

Astrocytic microdomains from mouse cortex gain molecular control over long-term information storage and memory retention.

Memory consolidation requires astrocytic microdomains for protein recycling; but whether this lays a mechanistic foundation for long-term information storage remains enigmatic. Here we demonstrate that persistent synaptic strengthening invited astrocytic microdomains to convert initially internalized (pro)-brain-derived neurotrophic factor (proBDNF) into active prodomain (BDNFpro) and mature BDNF (mBDNF) for synaptic re-use. While mBDNF activates TrkB, we uncovered a previously unsuspected function for the cleaved BDNFpro, which increases TrkB/SorCS2 receptor complex at post-synaptic sites. Astrocytic BDNFpro release reinforced TrkB phosphorylation to sustain long-term synaptic potentiation and to retain memory in the novel object recognition behavioral test. Thus, the switch from one inactive state to a multi-functional one of the proBDNF provides post-synaptic changes that survive the initial activation. This molecular asset confines local information storage in astrocytic microdomains to selectively support memory circuits.

On the objectivity, reliability, and validity of deep learning enabled bioimage analyses.

Bioimage analysis of fluorescent labels is widely used in the life sciences. Recent advances in deep learning (DL) allow automating time-consuming manual image analysis processes based on annotated training data. However, manual annotation of fluorescent features with a low signal-to-noise ratio is somewhat subjective. Training DL models on subjective annotations may be instable or yield biased models. In turn, these models may be unable to reliably detect biological effects. An analysis pipeline integrating data annotation, ground truth estimation, and model training can mitigate this risk. To evaluate this integrated process, we compared different DL-based analysis approaches. With data from two model organisms (mice, zebrafish) and five laboratories, we show that ground truth estimation from multiple human annotators helps to establish objectivity in fluorescent feature annotations. Furthermore, ensembles of multiple models trained on the estimated ground truth establish reliability and validity. Our research provides guidelines for reproducible DL-based bioimage analyses.

Research in biology generates many image datasets, mostly from microscopy. These images have to be analyzed, and much of this analysis relies on a human expert looking at the images and manually annotating features. Image datasets are often large, and human annotation can be subjective, so automating image analysis is highly desirable. This is where machine learning algorithms, such as deep learning, have proven to be useful. In order for deep learning algorithms to work first they have to be 'trained'. Deep learning algorithms are trained by being given a training dataset that has been annotated by human experts. The algorithms extract the relevant features to look out for from this training dataset and can then look for these features in other image data. However, it is also worth noting that because these models try to mimic the annotation behavior presented to them during training as well as possible, they can sometimes also mimic an expert's subjectivity when annotating data. Segebarth, Griebel et al. asked whether this was the case, whether it had an impact on the outcome of the image data analysis, and whether it was possible to avoid this problem when using deep learning for imaging dataset analysis. For this research, Segebarth, Griebel et al. used microscopy images of mouse brain sections, where a protein called cFOS had been labeled with a fluorescent tag. This protein typically controls the rate at which DNA information is copied into RNA, leading to the production of proteins. Its activity can be influenced experimentally by testing the behaviors of mice. Thus, this experimental manipulation can be used to evaluate the results of deep learning-based image analyses. First, the fluorescent images were interpreted manually by a group of human experts. Then, their results were used to train a large variety of deep learning models. Models were trained either on the results of an individual expert or on the results pooled from all experts to come up with a consensus model, a deep learning model that learned from the personal annotation preferences of all experts. This made it possible to test whether training a model on multiple experts reduces the risk of subjectivity. As the training of deep learning models is random, Segebarth, Griebel et al. also tested whether combining the predictions from multiple models in a so-called model ensemble improves the consistency of the analyses. For evaluation, the annotations of the deep learning models were compared to those of the human experts, to ensure that the results were not influenced by the subjective behavior of one person. The results of all bioimage annotations were finally compared to the experimental results from analyzing the mice's behaviors in order to check whether the models were able to find the behavioral effect on cFOS. Segebarth, Griebel et al. concluded that combining the expert knowledge of multiple experts reduces the subjectivity of bioimage annotation by deep learning algorithms. Combining such consensus information in a group of deep learning models improves the quality of bioimage analysis, so that the results are reliable, transparent and less subjective.