Aralar Sequesters GABA into Hyperactive Mitochondria, Causing Social Behavior Deficits.

Social impairment is frequently associated with mitochondrial dysfunction and altered neurotransmission. Although mitochondrial function is crucial for brain homeostasis, it remains unknown whether mitochondrial disruption contributes to social behavioral deficits. Here, we show that Drosophila mutants in the homolog of the human CYFIP1, a gene linked to autism and schizophrenia, exhibit mitochondrial hyperactivity and altered group behavior. We identify the regulation of GABA availability by mitochondrial activity as a biologically relevant mechanism and demonstrate its contribution to social behavior. Specifically, increased mitochondrial activity causes gamma aminobutyric acid (GABA) sequestration in the mitochondria, reducing GABAergic signaling and resulting in social deficits. Pharmacological and genetic manipulation of mitochondrial activity or GABA signaling corrects the observed abnormalities. We identify Aralar as the mitochondrial transporter that sequesters GABA upon increased mitochondrial activity. This study increases our understanding of how mitochondria modulate neuronal homeostasis and social behavior under physiopathological conditions.

A neural circuit mechanism integrating motivational state with memory expression in Drosophila.

Behavioral expression of food-associated memory in fruit flies is constrained by satiety and promoted by hunger, suggesting an influence of motivational state. Here, we identify a neural mechanism that integrates the internal state of hunger and appetitive memory. We show that stimulation of neurons that express neuropeptide F (dNPF), an ortholog of mammalian NPY, mimics food deprivation and promotes memory performance in satiated flies. Robust appetitive memory performance requires the dNPF receptor in six dopaminergic neurons that innervate a distinct region of the mushroom bodies. Blocking these dopaminergic neurons releases memory performance in satiated flies, whereas stimulation suppresses memory performance in hungry flies. Therefore, dNPF and dopamine provide a motivational switch in the mushroom body that controls the output of appetitive memory.

Regeneration of Dopaminergic Neurons in Adult Zebrafish Depends on Immune System Activation and Differs for Distinct Populations.

Adult zebrafish, in contrast to mammals, regenerate neurons in their brain, but the extent and variability of this capacity is unclear. Here we ask whether the loss of various dopaminergic neuron populations is sufficient to trigger their functional regeneration. Both sexes of zebrafish were analyzed. Genetic lineage tracing shows that specific diencephalic ependymo-radial glial (ERG) progenitor cells give rise to new dopaminergic [tyrosine hydroxylase-positive (TH+)] neurons. Ablation elicits an immune response, increased proliferation of ERG progenitor cells, and increased addition of new TH+ neurons in populations that constitutively add new neurons (e.g., diencephalic population 5/6). Inhibiting the immune response attenuates neurogenesis to control levels. Boosting the immune response enhances ERG proliferation, but not addition of TH+ neurons. In contrast, in populations in which constitutive neurogenesis is undetectable (e.g., the posterior tuberculum and locus ceruleus), cell replacement and tissue integration are incomplete and transient. This is associated with a loss of spinal TH+ axons, as well as permanent deficits in shoaling and reproductive behavior. Hence, dopaminergic neuron populations in the adult zebrafish brain show vast differences in regenerative capacity that correlate with constitutive addition of neurons and depend on immune system activation.SIGNIFICANCE STATEMENT Despite the fact that zebrafish show a high propensity to regenerate neurons in the brain, this study reveals that not all types of dopaminergic neurons are functionally regenerated after specific ablation. Hence, in the same adult vertebrate brain, mechanisms of successful and incomplete regeneration can be studied. We identify progenitor cells for dopaminergic neurons and show that activating the immune system promotes the proliferation of these cells. However, in some areas of the brain this only leads to insufficient replacement of functionally important dopaminergic neurons that later disappear. Understanding the mechanisms of regeneration in zebrafish may inform interventions targeting the regeneration of functionally important neurons, such as dopaminergic neurons, from endogenous progenitor cells in nonregenerating mammals.

Analysis of the expression patterns, subcellular localisations and interaction partners of Drosophila proteins using a pigP protein trap library.

Although we now have a wealth of information on the transcription patterns of all the genes in the Drosophila genome, much less is known about the properties of the encoded proteins. To provide information on the expression patterns and subcellular localisations of many proteins in parallel, we have performed a large-scale protein trap screen using a hybrid piggyBac vector carrying an artificial exon encoding yellow fluorescent protein (YFP) and protein affinity tags. From screening 41 million embryos, we recovered 616 verified independent YFP-positive lines representing protein traps in 374 genes, two-thirds of which had not been tagged in previous P element protein trap screens. Over 20 different research groups then characterized the expression patterns of the tagged proteins in a variety of tissues and at several developmental stages. In parallel, we purified many of the tagged proteins from embryos using the affinity tags and identified co-purifying proteins by mass spectrometry. The fly stocks are publicly available through the Kyoto Drosophila Genetics Resource Center. All our data are available via an open access database (Flannotator), which provides comprehensive information on the expression patterns, subcellular localisations and in vivo interaction partners of the trapped proteins. Our resource substantially increases the number of available protein traps in Drosophila and identifies new markers for cellular organelles and structures.

Dynamics of elongation factor 2 kinase regulation in cortical neurons in response to synaptic activity.

The rapid regulation of cell signaling in response to calcium in neurons is essential for real-time processing of large amounts of information in the brain. A vital regulatory component, and one of the most energy-intensive biochemical processes in cells, is the elongation phase of mRNA translation, which is controlled by the Ca(2+)/CaM-dependent elongation factor 2 kinase (eEF2K). However, little is known about the dynamics of eEF2K regulation in neurons despite its established role in learning and synaptic plasticity. To explore eEF2K dynamics in depth, we stimulated synaptic activity in mouse primary cortical neurons. We find that synaptic activity results in a rapid, but transient, increase in eEF2K activity that is regulated by a combination of AMPA and NMDA-type glutamate receptors and the mitogen-activated protein kinase (MEK)/extracellular signal-regulated kinase (ERK) and mammalian target of rapamycin complex 1 (mTORC1) pathways. We then used computational modeling to test the hypothesis that considering Ca(2+)-coordinated MEK/ERK, mTORC1, and eEF2k activation is sufficient to describe the observed eEF2K dynamics. Although such a model could partially fit the empirical findings, it also suggested that a crucial positive regulator of eEF2K was also necessary. Through additional modeling and empirical evidence, we demonstrate that AMP kinase (AMPK) is also an important regulator of synaptic activity-driven eEF2K dynamics in neurons. Our combined modeling and experimental findings provide the first evidence that it is necessary to consider the combined interactions of Ca(2+) with MEK/ERK, mTORC1, and AMPK to adequately explain eEF2K regulation in neurons.

The gene regulatory cascade linking proneural specification with differentiation in Drosophila sensory neurons.

In neurogenesis, neural cell fate specification is generally triggered by proneural transcription factors. Whilst the role of proneural factors in fate specification is well studied, the link between neural specification and the cellular pathways that ultimately must be activated to construct specialised neurons is usually obscure. High-resolution temporal profiling of gene expression reveals the events downstream of atonal proneural gene function during the development of Drosophila chordotonal (mechanosensory) neurons. Among other findings, this reveals the onset of expression of genes required for construction of the ciliary dendrite, a key specialisation of mechanosensory neurons. We determine that atonal activates this cellular differentiation pathway in several ways. Firstly, atonal directly regulates Rfx, a well-known highly conserved ciliogenesis transcriptional regulator. Unexpectedly, differences in Rfx regulation by proneural factors may underlie variations in ciliary dendrite specialisation in different sensory neuronal lineages. In contrast, fd3F encodes a novel forkhead family transcription factor that is exclusively expressed in differentiating chordotonal neurons. fd3F regulates genes required for specialized aspects of chordotonal dendrite physiology. In addition to these intermediate transcriptional regulators, we show that atonal directly regulates a novel gene, dilatory, that is directly associated with ciliogenesis during neuronal differentiation. Our analysis demonstrates how early cell fate specification factors can regulate structural and physiological differentiation of neuronal cell types. It also suggests a model for how subtype differentiation in different neuronal lineages may be regulated by different proneural factors. In addition, it provides a paradigm for how transcriptional regulation may modulate the ciliogenesis pathway to give rise to structurally and functionally specialised ciliary dendrites.

The Virtual Fly Brain browser and query interface.

MOTIVATION: Sources of neuroscience data in Drosophila are diverse and disparate making integrated search and retrieval difficult. A major obstacle to this is the lack of a comprehensive and logically structured anatomical framework and an intuitive interface. RESULTS: We present an online resource that provides a convenient way to study and query fly brain anatomy, expression and genetic data. We extended the newly developed BrainName nomenclature for the adult fly brain into a logically structured ontology that relates a comprehensive set of published neuron classes to the brain regions they innervate. The Virtual Fly Brain interface allows users to explore the structure of the Drosophila brain by browsing 3D images of a brain with subregions displayed as coloured overlays. An integrated query mechanism allows complex searches of underlying anatomy, cells, expression and other data from community databases. AVAILABILITY: Virtual Fly Brain is freely available online at www.virtualflybrain.org CONTACT: jda@inf.ed.ac.uk.

A strategy for building neuroanatomy ontologies.

MOTIVATION: Advancing our understanding of how nervous systems work will require the ability to store and annotate 3D anatomical datasets, recording morphology, partonomy and connectivity at multiple levels of granularity from subcellular to gross anatomy. It will also require the ability to integrate this data with other data-types including functional, genetic and electrophysiological data. The web ontology language OWL2 provides the means to solve many of these problems. Using it, one can rigorously define and relate classes of anatomical structure using multiple criteria. The resulting classes can be used to annotate datasets recording, for example, gene expression or electrophysiology. Reasoning software can be used to automate classification and error checking and to construct and answer sophisticated combinatorial queries. But for such queries to give consistent and biologically meaningful results, it is important that both classes and the terms (relations) used to relate them are carefully defined. RESULTS: We formally define a set of relations for recording the spatial and connectivity relationships of neuron classes and brain regions in a broad range of species, from vertebrates to arthropods. We illustrate the utility of our approach via its application in the ontology that drives the Virtual Fly Brain web resource. AVAILABILITY AND IMPLEMENTATION: The relations we define are available from http://purl.obolibrary.org/obo/ro.owl. They are used in the Drosophila anatomy ontology (http://purl.obolibrary.org/obo/fbbt/2011-09-06/), which drives the web resource http://www.virtualflybrain.org

Consolidation and translation regulation.

mRNA translation, or protein synthesis, is a major component of the transformation of the genetic code into any cellular activity. This complicated, multistep process is divided into three phases: initiation, elongation, and termination. Initiation is the step at which the ribosome is recruited to the mRNA, and is regarded as the major rate-limiting step in translation, while elongation consists of the elongation of the polypeptide chain; both steps are frequent targets for regulation, which is defined as a change in the rate of translation of an mRNA per unit time. In the normal brain, control of translation is a key mechanism for regulation of memory and synaptic plasticity consolidation, i.e., the off-line processing of acquired information. These regulation processes may differ between different brain structures or neuronal populations. Moreover, dysregulation of translation leads to pathological brain function such as memory impairment. Both normal and abnormal function of the translation machinery is believed to lead to translational up-regulation or down-regulation of a subset of mRNAs. However, the identification of these newly synthesized proteins and determination of the rates of protein synthesis or degradation taking place in different neuronal types and compartments at different time points in the brain demand new proteomic methods and system biology approaches. Here, we discuss in detail the relationship between translation regulation and memory or synaptic plasticity consolidation while focusing on a model of cortical-dependent taste learning task and hippocampal-dependent plasticity. In addition, we describe a novel systems biology perspective to better describe consolidation.

Computational Pipeline for Analysis of Biomedical Networks with BioNAR.

In a living cell, proteins interact to assemble both transient and constant molecular complexes, which transfer signals/information around internal pathways. Modern proteomic techniques can identify the constituent components of these complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. The Bioconductor package BioNAR combines a selection of existing R protocols for network analysis with newly designed original methodological features to support step-by-step analysis of biological/biomedical . Critically, BioNAR supports a pipeline approach whereby many networks and iterative analyses can be performed. Here we present a network analysis pipeline that starts from initiating a network model from a list of components/proteins and their interactions through to identifying its functional components based solely on network topology. We demonstrate that BioNAR can help users achieve a number of network analysis goals that are difficult to achieve anywhere else. This includes how users can choose the optimal clustering algorithm from a range of options based on independent annotation enrichment, and predict a protein's influence within and across multiple subcomplexes in the network and estimate the co-occurrence or linkage between metadata at the network level (e.g., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms). The package is freely available in Bioconductor release 3.17: https://bioconductor.org/packages/3.17/bioc/html/BioNAR.html. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating and annotating the network Support Protocol 1: Installing BioNAR from RStudio Support Protocol 2: Building the sample network from synaptome.db Basic Protocol 2: Network properties and centrality Basic Protocol 3: Network communities Basic protocol 4: Choosing the optimal clustering algorithm based on the enrichment with annotation terms Basic Protocol 5: Influencing network components and bridgeness Basic Protocol 6: Co-occurrence of the annotations.

Improved prediction of hiking speeds using a data driven approach.

Hikers and hillwalkers typically use the gradient in the direction of travel (walking slope) as the main variable in established methods for predicting walking time (via the walking speed) along a route. Research into fell-running has suggested further variables which may improve speed algorithms in this context; the gradient of the terrain (hill slope) and the level of terrain obstruction. Recent improvements in data availability, as well as widespread use of GPS tracking now make it possible to explore these variables in a walking speed model at a sufficient scale to test statistical significance. We tested various established models used to predict walking speed against public GPS data from almost 88,000 km of UK walking / hiking tracks. Tracks were filtered to remove breaks and non-walking sections. A new generalised linear model (GLM) was then used to predict walking speeds. Key differences between the GLM and established rules were that the GLM considered the gradient of the terrain (hill slope) irrespective of walking slope, as well as the terrain type and level of terrain obstruction in off-road travel. All of these factors were shown to be highly significant, and this is supported by a lower root-mean-square-error compared to existing functions. We also observed an increase in RMSE between the GLM and established methods as hill slope increases, further supporting the importance of this variable.

Longitudinal home-cage automated assessment of climbing behavior shows sexual dimorphism and aging-related decrease in C57BL/6J healthy mice and allows early detection of motor impairment in the N171-82Q mouse model of Huntington's disease.

Monitoring the activity of mice within their home cage is proving to be a powerful tool for revealing subtle and early-onset phenotypes in mouse models. Video-tracking, in particular, lends itself to automated machine-learning technologies that have the potential to improve the manual annotations carried out by humans. This type of recording and analysis is particularly powerful in objective phenotyping, monitoring behaviors with no experimenter intervention. Automated home-cage testing allows the recording of non-evoked voluntary behaviors, which do not require any contact with the animal or exposure to specialist equipment. By avoiding stress deriving from handling, this approach, on the one hand, increases the welfare of experimental animals and, on the other hand, increases the reliability of results excluding confounding effects of stress on behavior. In this study, we show that the monitoring of climbing on the wire cage lid of a standard individually ventilated cage (IVC) yields reproducible data reflecting complex phenotypes of individual mouse inbred strains and of a widely used model of neurodegeneration, the N171-82Q mouse model of Huntington's disease (HD). Measurements in the home-cage environment allowed for the collection of comprehensive motor activity data, which revealed sexual dimorphism, daily biphasic changes, and aging-related decrease in healthy C57BL/6J mice. Furthermore, home-cage recording of climbing allowed early detection of motor impairment in the N171-82Q HD mouse model. Integrating cage-floor activity with cage-lid activity (climbing) has the potential to greatly enhance the characterization of mouse strains, detecting early and subtle signs of disease and increasing reproducibility in preclinical studies.

Virtual Fly Brain-An interactive atlas of the Drosophila nervous system.

As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.

Mena regulates nesprin-2 to control actin-nuclear lamina associations, trans-nuclear membrane signalling and gene expression.

Interactions between cells and the extracellular matrix, mediated by integrin adhesion complexes, play key roles in fundamental cellular processes, including the sensing and transduction of mechanical cues. Here, we investigate systems-level changes in the integrin adhesome in patient-derived cutaneous squamous cell carcinoma cells and identify the actin regulatory protein Mena as a key node in the adhesion complex network. Mena is connected within a subnetwork of actin-binding proteins to the LINC complex component nesprin-2, with which it interacts and co-localises at the nuclear envelope. Moreover, Mena potentiates the interactions of nesprin-2 with the actin cytoskeleton and the nuclear lamina. CRISPR-mediated Mena depletion causes altered nuclear morphology, reduces tyrosine phosphorylation of the nuclear membrane protein emerin and downregulates expression of the immunomodulatory gene PTX3 via the recruitment of its enhancer to the nuclear periphery. We uncover an unexpected role for Mena at the nuclear membrane, where it controls nuclear architecture, chromatin repositioning and gene expression. Our findings identify an adhesion protein that regulates gene transcription via direct signalling across the nuclear envelope.

Simple Neurite Tracer: open source software for reconstruction, visualization and analysis of neuronal processes.

MOTIVATION: Advances in techniques to sparsely label neurons unlock the potential to reconstruct connectivity from 3D image stacks acquired by light microscopy. We present an application for semi-automated tracing of neurons to quickly annotate noisy datasets and construct complex neuronal topologies, which we call the Simple Neurite Tracer. AVAILABILITY: Simple Neurite Tracer is open source software, licensed under the GNU General Public Licence (GPL) and based on the public domain image processing software ImageJ. The software and further documentation are available via http://fiji.sc/Simple_Neurite_Tracer as part of the package Fiji, and can be used on Windows, Mac OS and Linux. Documentation and introductory screencasts are available at the same URL. CONTACT: longair@ini.phys.ethz.ch; longair@ini.phys.ethz.ch.

Biologically inspired EM image alignment and neural reconstruction.

MOTIVATION: Three-dimensional reconstruction of consecutive serial-section transmission electron microscopy (ssTEM) images of neural tissue currently requires many hours of manual tracing and annotation. Several computational techniques have already been applied to ssTEM images to facilitate 3D reconstruction and ease this burden. RESULTS: Here, we present an alternative computational approach for ssTEM image analysis. We have used biologically inspired receptive fields as a basis for a ridge detection algorithm to identify cell membranes, synaptic contacts and mitochondria. Detected line segments are used to improve alignment between consecutive images and we have joined small segments of membrane into cell surfaces using a dynamic programming algorithm similar to the Needleman-Wunsch and Smith-Waterman DNA sequence alignment procedures. A shortest path-based approach has been used to close edges and achieve image segmentation. Partial reconstructions were automatically generated and used as a basis for semi-automatic reconstruction of neural tissue. The accuracy of partial reconstructions was evaluated and 96% of membrane could be identified at the cost of 13% false positive detections. AVAILABILITY: An open-source reference implementation is available in the Supplementary information. CONTACT: seymour.kb@ed.ac.uk; douglas.armstrong@ed.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A model of non-elemental olfactory learning in Drosophila.

The pathways for olfactory learning in the fruitfly Drosophila have been extensively investigated, with mounting evidence that that the mushroom body is the site of the olfactory associative memory trace (Heisenberg, Nature 4:266-275, 2003; Gerber et al., Curr Opin Neurobiol 14:737-744, 2004). Heisenberg's description of the mushroom body as an associative learning device is a testable hypothesis that relates the mushroom body's function to its neural structure and input and output pathways. Here, we formalise a relatively complete computational model of the network interactions in the neural circuitry of the insect antennal lobe and mushroom body, to investigate their role in olfactory learning, and specifically, how this might support learning of complex (non-elemental; Giurfa, Curr Opin Neuroethol 13:726-735, 2003) discriminations involving compound stimuli. We find that the circuit is able to learn all tested non-elemental paradigms. This does not crucially depend on the number of Kenyon cells but rather on the connection strength of projection neurons to Kenyon cells, such that the Kenyon cells require a certain number of coincident inputs to fire. As a consequence, the encoding in the mushroom body resembles a unique cue or configural representation of compound stimuli (Pearce, Psychol Rev 101:587-607, 1994). Learning of some conditions, particularly negative patterning, is strongly affected by the assumption of normalisation effects occurring at the level of the antennal lobe. Surprisingly, the learning capacity of this circuit, which is a simplification of the actual circuitry in the fly, seems to be greater than the capacity expressed by the fly in shock-odour association experiments (Young et al. 2010).

Evolution of the cognitive proteome: from static to dynamic network models.

Integrative analysis of the neuronal synapse proteome has uncovered an evolutionarily conserved signalling complex that underpins the cognitive capabilities of the brain. Highly dynamic, cell type specific and intricately regulated, the synaptic proteome presents many challenges to systems biology approaches, yet this is likely to be the best route to unlock a new generation of neuroscience research and CNS drug development that society so urgently demands. Most systems biology approaches today have focussed on exploiting protein-protein interaction data to their fullest extent within static interaction models. These have revealed structure-function relationships within the protein network, uncovered new candidate genes for genetic studies and drug research and development and finally provided a means to study the evolution of the system. The rapid maturation of medium and high-throughput biochemical technologies means that dissecting the synapse proteome's dynamic complexity is fast becoming a reality. Here we look at these new challenges and explore rule-based modelling as a basis for a new generation of synaptic models.

Evolutionary expansion and anatomical specialization of synapse proteome complexity.

Understanding the origins and evolution of synapses may provide insight into species diversity and the organization of the brain. Using comparative proteomics and genomics, we examined the evolution of the postsynaptic density (PSD) and membrane-associated guanylate kinase (MAGUK)-associated signaling complexes (MASCs) that underlie learning and memory. PSD and MASC orthologs found in yeast carry out basic cellular functions to regulate protein synthesis and structural plasticity. We observed marked changes in signaling complexity at the yeast-metazoan and invertebrate-vertebrate boundaries, with an expansion of key synaptic components, notably receptors, adhesion/cytoskeletal proteins and scaffold proteins. A proteomic comparison of Drosophila and mouse MASCs revealed species-specific adaptation with greater signaling complexity in mouse. Although synaptic components were conserved amongst diverse vertebrate species, mapping mRNA and protein expression in the mouse brain showed that vertebrate-specific components preferentially contributed to differences between brain regions. We propose that the evolution of synapse complexity around a core proto-synapse has contributed to invertebrate-vertebrate differences and to brain specialization.

Synaptome.db: a bioconductor package for synaptic proteomics data.

Summary: The neuronal synapse is underpinned by a large and diverse proteome but the molecular evidence is spread across many primary datasets. These data were recently curated into a single dataset describing a landscape of ∼8000 proteins found in studies of mammalian synapses. Here, we describe programmatic access to the dataset via the R/Bioconductor package Synaptome.db, which enables convenient and in-depth data analysis from within the Bioconductor environment. Synaptome.db allows users to obtain the respective gene information, e.g. subcellular localization, brain region, gene ontology, disease association and construct custom protein-protein interaction network models for gene sets and entire subcellular compartments. Availability and implementation: The package Synaptome.db is part of Bioconductor since release 3.14, https://bioconductor.org/packages/release/data/annotation/html/synaptome.db.html, it is open source and available under the Artistic license 2.0. The development version is maintained on GitHub (https://github.com/lptolik/synaptome.db). Full documentation including examples is provided in the form of vignettes on the package webpage. Supplementary information: Supplementary data are available at Bioinformatics Advances online.