The brain is tasked with choosing actions that maximize an animal's chances of survival and reproduction. These choices must be flexible and informed by the current state of the environment, the needs of the body, and the outcomes of past actions. This information is physiologically encoded and processed across different brain regions on a wide range of spatial scales, from molecules in single synapses to networks of brain areas. Uncovering these spatially distributed neural interactions underlying behavior requires investigations that span a similar range of spatial scales. Larval zebrafish, given their small size, transparency, and ease of genetic access, are a good model organism for such investigations, allowing the use of modern microscopy, molecular biology, and computational techniques. These approaches are yielding new insights into the mechanistic basis of behavioral states, which we review here and compare to related studies in mammalian species.

Brains encode behaviors using neurons amenable to systematic classification by gene expression. The contribution of molecular identity to neural coding is not understood because of the challenges involved with measuring neural dynamics and molecular information from the same cells. We developed CaRMA (calcium and RNA multiplexed activity) imaging based on recording in vivo single-neuron calcium dynamics followed by gene expression analysis. We simultaneously monitored activity in hundreds of neurons in mouse paraventricular hypothalamus (PVH). Combinations of cell-type marker genes had predictive power for neuronal responses across 11 behavioral states. The PVH uses combinatorial assemblies of molecularly defined neuron populations for grouped-ensemble coding of survival behaviors. The neuropeptide receptor neuropeptide Y receptor type 1 (Npy1r) amalgamated multiple cell types with similar responses. Our results show that molecularly defined neurons are important processing units for brain function.

Changes in gene regulation underlie much of phenotypic evolution. However, our understanding of the potential for regulatory evolution is biased, because most evidence comes from either natural variation or limited experimental perturbations. Using an automated robotics pipeline, we surveyed an unbiased mutation library for a developmental enhancer in Drosophila melanogaster. We found that almost all mutations altered gene expression and that parameters of gene expression-levels, location, and state-were convolved. The widespread pleiotropic effects of most mutations may constrain the evolvability of developmental enhancers. Consistent with these observations, comparisons of diverse Drosophila larvae revealed apparent biases in the phenotypes influenced by the enhancer. Developmental enhancers may encode a higher density of regulatory information than has been appreciated previously, imposing constraints on regulatory evolution.

Neural representations of head direction (HD) have been discovered in many species. Theoretical work has proposed that the dynamics associated with these representations are generated, maintained, and updated by recurrent network structures called ring attractors. We evaluated this theorized structure-function relationship by performing electron-microscopy-based circuit reconstruction and RNA profiling of identified cell types in the HD system of Drosophila melanogaster. We identified motifs that have been hypothesized to maintain the HD representation in darkness, update it when the animal turns, and tether it to visual cues. Functional studies provided support for the proposed roles of individual excitatory or inhibitory circuit elements in shaping activity. We also discovered recurrent connections between neuronal arbors with mixed pre- and postsynaptic specializations. Our results confirm that the Drosophila HD network contains the core components of a ring attractor while also revealing unpredicted structural features that might enhance the network's computational power.

An important question in early neural development is the origin of stochastic nuclear movement between apical and basal surfaces of neuroepithelia during interkinetic nuclear migration. Tracking of nuclear subpopulations has shown evidence of diffusion - mean squared displacements growing linearly in time - and suggested crowding from cell division at the apical surface drives basalward motion. Yet, this hypothesis has not yet been tested, and the forces involved not quantified. We employ long-term, rapid light-sheet and two-photon imaging of early zebrafish retinogenesis to track entire populations of nuclei within the tissue. The time-varying concentration profiles show clear evidence of crowding as nuclei reach close-packing and are quantitatively described by a nonlinear diffusion model. Considerations of nuclear motion constrained inside the enveloping cell membrane show that concentration-dependent stochastic forces inside cells, compatible in magnitude to those found in cytoskeletal transport, can explain the observed magnitude of the diffusion constant.

mTOR is a serine/threonine kinase and a master regulator of cell growth and proliferation. Raptor, a scaffolding protein that recruits substrates to mTOR complex 1 (mTORC1), is known to be phosphorylated during mitosis, but the significance of this phosphorylation remains largely unknown. Here we show that raptor expression and mTORC1 activity are dramatically reduced in cells arrested in mitosis. Expression of a non-phosphorylatable raptor mutant reactivates mTORC1 and significantly reduces cytotoxicity of the mitotic poison Taxol. This effect is mediated via degradation of PDCD4, a tumor suppressor protein that inhibits eIF4A activity and is negatively regulated by the mTORC1/S6K pathway. Moreover, pharmacological inhibition of eIF4A is able to enhance the effects of Taxol and restore sensitivity in Taxol-resistant cancer cells. These findings indicate that the mTORC1/S6K/PDCD4/eIF4A axis has a pivotal role in the death versus slippage decision during mitotic arrest and may be exploited clinically to treat tumors resistant to anti-mitotic agents.

Hippocampal activity represents many behaviorally important variables, including context, an animal's location within a given environmental context, time, and reward. Using longitudinal calcium imaging in mice, multiple large virtual environments, and differing reward contingencies, we derived a unified probabilistic model of CA1 representations centered on a single feature-the field propensity. Each cell's propensity governs how many place fields it has per unit space, predicts its reward-related activity, and is preserved across distinct environments and over months. Propensity is broadly distributed-with many low, and some very high, propensity cells-and thus strongly shapes hippocampal representations. This results in a range of spatial codes, from sparse to dense. Propensity varied ∼10-fold between adjacent cells in salt-and-pepper fashion, indicating substantial functional differences within a presumed cell type. Intracellular recordings linked propensity to cell excitability. The stability of each cell's propensity across conditions suggests this fundamental property has anatomical, transcriptional, and/or developmental origins.