Neuronal potassium channel activity triggers initiation of mRNA translation through binding of translation regulators.

Neuronal activity stimulates mRNA translation crucial for learning and development. While FMRP (Fragile X Mental Retardation Protein) and CYFIP1 (Cytoplasmic FMR1 Interacting Protein 1) regulate translation, the mechanism linking translation to neuronal activity is not understood. We now find that translation is stimulated when FMRP and CYFIP1 translocate to the potassium channel Slack (KCNT1, Slo2.2). When Slack is activated, both factors are released from eIF4E (Eukaryotic Initiation Factor 4E), where they normally inhibit translation initiation. A constitutively active Slack mutation and pharmacological stimulation of the wild-type channel both increase binding of FMRP and CYFIP1 to the channel, enhancing the translation of a reporter for ß-actin mRNA in cell lines and the synthesis of ß-actin in neuronal dendrites. Slack activity-dependent translation is abolished when both FMRP and CYFIP1 expression are suppressed. The effects of Slack mutations on activity-dependent translation may explain the severe intellectual disability produced by these mutations in humans. HIGHLIGHTS: Activation of Slack channels triggers translocation of the FMRP/CYFIP1 complexSlack channel activation regulates translation initiation of a ß-actin reporter constructA Slack gain-of-function mutation increases translation of ß-actin reporter construct and endogenous cortical ß-actinFMRP and CYFIP1 are required for Slack activity-dependent translation. IN BRIEF: Malone et al . show that the activation of Slack channels triggers translocation of the FMRP/CYFIP1 complex from the translation initiation factor eIF4E to the channel. This translocation releases eIF4E and stimulates mRNA translation of a reporter for ß-actin and cortical ß-actin mRNA, elucidating the mechanism that connects neuronal activity with translational regulation.

A consistent map in the medial entorhinal cortex supports spatial memory.

The medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.

A consistent map in the medial entorhinal cortex supports spatial memory.

The medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.

The role of altered translation in intellectual disability and epilepsy.

A very high proportion of cases of intellectual disability are genetic in origin and are associated with the occurrence of epileptic seizures during childhood. These two disorders together effect more than 5% of the world's population. One feature linking the two diseases is that learning and memory require the synthesis of new synaptic components and ion channels, while maintenance of overall excitability also requires synthesis of similar proteins in response to altered neuronal stimulation. Many of these disorders result from mutations in proteins that regulate mRNA processing, translation initiation, translation elongation, mRNA stability or upstream translation modulators. One theme that emerges on reviewing this field is that mutations in proteins that regulate changes in translation following neuronal stimulation are more likely to result in epilepsy with intellectual disability than general translation regulators with no known role in activity-dependent changes. This is consistent with the notion that activity-dependent translation in neurons differs from that in other cells types in that the changes in local cellular composition, morphology and connectivity that occur generally in response to stimuli are directly coupled to local synaptic activity and persist for months or years after the original stimulus.

Normalizing translation through 4E-BP prevents mTOR-driven cortical mislamination and ameliorates aberrant neuron integration.

Hyperactive mammalian target of rapamycin complex 1 (mTORC1) is a shared molecular hallmark in several neurodevelopmental disorders characterized by abnormal brain cytoarchitecture. The mechanisms downstream of mTORC1 that are responsible for these defects remain unclear. We show that focally increasing mTORC1 activity during late corticogenesis leads to ectopic placement of upper-layer cortical neurons that does not require altered signaling in radial glia and is accompanied by changes in layer-specific molecular identity. Importantly, we found that decreasing cap-dependent translation by expressing a constitutively active mutant of the translational repressor eukaryotic initiation factor 4E-binding protein 1 (4E-BP1) prevents neuronal misplacement and soma enlargement, while partially rescuing dendritic hypertrophy induced by hyperactive mTORC1. Furthermore, overactivation of translation alone through knockdown of 4E-BP2 was sufficient to induce neuronal misplacement. These data show that many aspects of abnormal brain cytoarchitecture can be prevented by manipulating a single intracellular process downstream of mTORC1, cap-dependent translation.

Limbform: a functional ontology-based database of limb regeneration experiments.

SUMMARY: The ability of certain organisms to completely regenerate lost limbs is a fascinating process, far from solved. Despite the extraordinary published efforts during the past centuries of scientists performing amputations, transplantations and molecular experiments, no mechanistic model exists yet that can completely explain patterning during the limb regeneration process. The lack of a centralized repository to enable the efficient mining of this huge dataset is hindering the discovery of comprehensive models of limb regeneration. Here, we introduce Limbform (Limb formalization), a centralized database of published limb regeneration experiments. In contrast to natural language or text-based ontologies, Limbform is based on a functional ontology using mathematical graphs to represent unambiguously limb phenotypes and manipulation procedures. The centralized database currently contains >800 published limb regeneration experiments comprising many model organisms, including salamanders, frogs, insects, crustaceans and arachnids. The database represents an extraordinary resource for mining the existing knowledge of functional data in this field; furthermore, its mathematical nature based on a functional ontology will pave the way for artificial intelligence tools applied to the discovery of the sought-after comprehensive limb regeneration models.

A bioinformatics expert system linking functional data to anatomical outcomes in limb regeneration.

Amphibians and molting arthropods have the remarkable capacity to regenerate amputated limbs, as described by an extensive literature of experimental cuts, amputations, grafts, and molecular techniques. Despite a rich history of experimental efforts, no comprehensive mechanistic model exists that can account for the pattern regulation observed in these experiments. While bioinformatics algorithms have revolutionized the study of signaling pathways, no such tools have heretofore been available to assist scientists in formulating testable models of large-scale morphogenesis that match published data in the limb regeneration field. Major barriers preventing an algorithmic approach are the lack of formal descriptions for experimental regenerative information and a repository to centralize storage and mining of functional data on limb regeneration. Establishing a new bioinformatics of shape would significantly accelerate the discovery of key insights into the mechanisms that implement complex regeneration. Here, we describe a novel mathematical ontology for limb regeneration to unambiguously encode phenotype, manipulation, and experiment data. Based on this formalism, we present the first centralized formal database of published limb regeneration experiments together with a user-friendly expert system tool to facilitate its access and mining. These resources are freely available for the community and will assist both human biologists and artificial intelligence systems to discover testable, mechanistic models of limb regeneration.

Planform: an application and database of graph-encoded planarian regenerative experiments.

SUMMARY: Understanding the mechanisms governing the regeneration capabilities of many organisms is a fundamental interest in biology and medicine. An ever-increasing number of manipulation and molecular experiments are attempting to discover a comprehensive model for regeneration, with the planarian flatworm being one of the most important model species. Despite much effort, no comprehensive, constructive, mechanistic models exist yet, and it is now clear that computational tools are needed to mine this huge dataset. However, until now, there is no database of regenerative experiments, and the current genotype-phenotype ontologies and databases are based on textual descriptions, which are not understandable by computers. To overcome these difficulties, we present here Planform (Planarian formalization), a manually curated database and software tool for planarian regenerative experiments, based on a mathematical graph formalism. The database contains more than a thousand experiments from the main publications in the planarian literature. The software tool provides the user with a graphical interface to easily interact with and mine the database. The presented system is a valuable resource for the regeneration community and, more importantly, will pave the way for the application of novel artificial intelligence tools to extract knowledge from this dataset. AVAILABILITY: The database and software tool are freely available at http://planform.daniel-lobo.com.

Towards a bioinformatics of patterning: a computational approach to understanding regulative morphogenesis.

The mechanisms underlying the regenerative abilities of certain model species are of central importance to the basic understanding of pattern formation. Complex organisms such as planaria and salamanders exhibit an exceptional capacity to regenerate complete body regions and organs from amputated pieces. However, despite the outstanding bottom-up efforts of molecular biologists and bioinformatics focused at the level of gene sequence, no comprehensive mechanistic model exists that can account for more than one or two aspects of regeneration. The development of computational approaches that help scientists identify constructive models of pattern regulation is held back by the lack of both flexible morphological representations and a repository for the experimental procedures and their results (altered pattern formation). No formal representation or computational tools exist to efficiently store, search, or mine the available knowledge from regenerative experiments, inhibiting fundamental insights from this huge dataset. To overcome these problems, we present here a new class of ontology to encode formally and unambiguously a very wide range of possible morphologies, manipulations, and experiments. This formalism will pave the way for top-down approaches for the discovery of comprehensive models of regeneration. We chose the planarian regeneration dataset to illustrate a proof-of-principle of this novel bioinformatics of shape; we developed a software tool to facilitate the formalization and mining of the planarian experimental knowledge, and cured a database containing all of the experiments from the principal publications on planarian regeneration. These resources are freely available for the regeneration community and will readily assist researchers in identifying specific functional data in planarian experiments. More importantly, these applications illustrate the presented framework for formalizing knowledge about functional perturbations of morphogenesis, which is widely applicable to numerous model systems beyond regenerating planaria, and can be extended to many aspects of functional developmental, regenerative, and evolutionary biology.