Androgen signaling via the androgen receptor (AR) transcription factor is crucial to normal prostate homeostasis and prostate tumorigenesis. Current models of AR function are predominantly based on studies of prostate-specific antigen regulation in androgen-responsive cell lines. To expand on these in vitro paradigms, we used the mouse prostate to elucidate the mechanisms through which AR regulates another direct target, FKBP5, in vivo. FKBP5 encodes an immunophilin that has been previously implicated in glucocorticoid and progestin signaling pathways and that likely influences prostate physiology in the presence of androgens. In this work, we show that androgens directly regulate FKBP5 via an interaction between the AR and a distal enhancer located 65 kb downstream of the transcription start site in the fifth intron of the FKBP5 gene. We have found that AR selectively recruits cAMP response element-binding protein to this enhancer. These interactions, in turn, result in chromatin remodeling that affects the enhancer proper but not the FKBP5 locus as a whole. Furthermore, in contrast to prostate-specific antigen-regulatory mechanisms, we show that transactivation of the FKBP5 gene does not rely on a single looping complex to mediate communication between the distal enhancer and proximal promoter. Rather, the distal enhancer complex and basal transcription apparatus communicate indirectly with one another, implicating a regulatory mechanism that has not been previously appreciated for AR target genes.

Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in vitro and in vivo serotonin detection, respectively.

Cataloging the neuronal cell types that comprise circuitry of individual brain regions is a major goal of modern neuroscience and the BRAIN initiative. Single-cell RNA sequencing can now be used to measure the gene expression profiles of individual neurons and to categorize neurons based on their gene expression profiles. While the single-cell techniques are extremely powerful and hold great promise, they are currently still labor intensive, have a high cost per cell, and, most importantly, do not provide information on spatial distribution of cell types in specific regions of the brain. We propose a complementary approach that uses computational methods to infer the cell types and their gene expression profiles through analysis of brain-wide single-cell resolution in situ hybridization (ISH) imagery contained in the Allen Brain Atlas (ABA). We measure the spatial distribution of neurons labeled in the ISH image for each gene and model it as a spatial point process mixture, whose mixture weights are given by the cell types which express that gene. By fitting a point process mixture model jointly to the ISH images, we infer both the spatial point process distribution for each cell type and their gene expression profile. We validate our predictions of cell type-specific gene expression profiles using single cell RNA sequencing data, recently published for the mouse somatosensory cortex. Jointly with the gene expression profiles, cell features such as cell size, orientation, intensity and local density level are inferred per cell type.

Symbolic models play a key role in cognitive science, expressing computationally precise hypotheses about how the brain implements a cognitive process. Identifying an appropriate model typically requires a great deal of effort and ingenuity on the part of a human scientist. Here, we adapt FunSearch Romera-Paredes et al. (2024), a recently developed tool that uses Large Language Models (LLMs) in an evolutionary algorithm, to automatically discover symbolic cognitive models that accurately capture human and animal behavior. We consider datasets from three species performing a classic reward-learning task that has been the focus of substantial modeling effort, and find that the discovered programs outperform state-of-the-art cognitive models for each. The discovered programs can readily be interpreted as hypotheses about human and animal cognition, instantiating interpretable symbolic learning and decision-making algorithms. Broadly, these results demonstrate the viability of using LLM-powered program synthesis to propose novel scientific hypotheses regarding mechanisms of human and animal cognition.

A single nervous system can generate many distinct motor patterns. Identifying which neurons and circuits control which behaviors has been a laborious piecemeal process, usually for one observer-defined behavior at a time. We present a fundamentally different approach to neuron-behavior mapping. We optogenetically activated 1,054 identified neuron lines in Drosophila larva and tracked the behavioral responses from 37,780 animals. Applying multiscale unsupervised structure learning methods to the behavioral data identified 29 discrete statistically distinguishable and observer-unbiased behavioral phenotypes. Mapping the neural lines to the behavior(s) they evoke provides a behavioral reference atlas for neuron subsets covering a large fraction of larval neurons. This atlas is a starting point for connectivity- and activity-mapping studies to further investigate the mechanisms by which neurons mediate diverse behaviors.

Certain marine invertebrates harbor chemosynthetic bacterial symbionts, giving them the remarkable ability to consume inorganic chemicals such as hydrogen sulfide (H2S) rather than organic matter as food. These chemosynthetic animals are found near geochemical (e.g., hydrothermal vents) or biological (e.g., decaying wood or large animal carcasses) sources of H2S on the seafloor. Although many such symbioses have been discovered, little is known about how or where they originated. Here, we demonstrate a new chemosynthetic symbiosis in the giant teredinid bivalve (shipworm) Kuphus polythalamia and show that this symbiosis arose in a wood-eating ancestor via the displacement of ancestral cellulolytic symbionts by sulfur-oxidizing invaders. Here, wood served as an evolutionary stepping stone for a dramatic transition from heterotrophy to chemoautotrophy.The “wooden-steps” hypothesis [Distel DL, et al. (2000) Nature 403:725–726] proposed that large chemosynthetic mussels found at deep-sea hydrothermal vents descend from much smaller species associated with sunken wood and other organic deposits, and that the endosymbionts of these progenitors made use of hydrogen sulfide from biogenic sources (e.g., decaying wood) rather than from vent fluids. Here, we show that wood has served not only as a stepping stone between habitats but also as a bridge between heterotrophic and chemoautotrophic symbiosis for the giant mud-boring bivalve Kuphus polythalamia. This rare and enigmatic species, which achieves the greatest length of any extant bivalve, is the only described member of the wood-boring bivalve family Teredinidae (shipworms) that burrows in marine sediments rather than wood. We show that K. polythalamia harbors sulfur-oxidizing chemoautotrophic (thioautotrophic) bacteria instead of the cellulolytic symbionts that allow other shipworm species to consume wood as food. The characteristics of its symbionts, its phylogenetic position within Teredinidae, the reduction of its digestive system by comparison with other family members, and the loss of morphological features associated with wood digestion indicate that K. polythalamia is a chemoautotrophic bivalve descended from wood-feeding (xylotrophic) ancestors. This is an example in which a chemoautotrophic endosymbiosis arose by displacement of an ancestral heterotrophic symbiosis and a report of pure culture of a thioautotrophic endosymbiont.

Effective classification of neuronal cell types requires both molecular and morphological descriptors to be collected in situ at single cell resolution. However, current spatial transcriptomics techniques are not compatible with imaging workflows that successfully reconstruct the morphology of complete axonal projections. Here, we introduce a new methodology that combines tissue clearing, submicron whole-brain two photon imaging, and Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to assign molecular identities to fully reconstructed neurons in the mouse brain, which we call morphoFISH. We used morphoFISH to molecularly identify a previously unknown population of cingulate neurons projecting ipsilaterally to the dorsal striatum and contralaterally to higher-order thalamus. By pairing whole-brain morphometry, improved techniques for nucleic acid preservation and spatial gene expression, morphoFISH offers a quantitative solution for discovery of multimodal cell types and complements existing techniques for characterization of increasingly fine-grained cellular heterogeneity in brain circuits.Competing Interest StatementThe authors have declared no competing interest.

Short-term memories link events separated in time, such as past sensation and future actions. Short-term memories are correlated with slow neural dynamics, including selective persistent activity, which can be maintained over seconds. In a delayed response task that requires short-term memory, neurons in the mouse anterior lateral motor cortex (ALM) show persistent activity that instructs future actions. To determine the principles that underlie this persistent activity, here we combined intracellular and extracellular electrophysiology with optogenetic perturbations and network modelling. We show that during the delay epoch, the activity of ALM neurons moved towards discrete end points that correspond to specific movement directions. These end points were robust to transient shifts in ALM activity caused by optogenetic perturbations. Perturbations occasionally switched the population dynamics to the other end point, followed by incorrect actions. Our results show that discrete attractor dynamics underlie short-term memory related to motor planning.

Each training step for a variational autoencoder (VAE) requires us to sample from the approximate posterior, so we usually choose simple (e.g. factorised) approximate posteriors in which sampling is an efficient computation that fully exploits GPU parallelism. However, such simple approximate posteriors are often insufficient, as they eliminate statistical dependencies in the posterior. While it is possible to use normalizing flow approximate posteriors for continuous latents, some problems have discrete latents and strong statistical dependencies. The most natural approach to model these dependencies is an autoregressive distribution, but sampling from such distributions is inherently sequential and thus slow. We develop a fast, parallel sampling procedure for autoregressive distributions based on fixed-point iterations which enables efficient and accurate variational inference in discrete state-space latent variable dynamical systems. To optimize the variational bound, we considered two ways to evaluate probabilities: inserting the relaxed samples directly into the pmf for the discrete distribution, or converting to continuous logistic latent variables and interpreting the K-step fixed-point iterations as a normalizing flow. We found that converting to continuous latent variables gave considerable additional scope for mismatch between the true and approximate posteriors, which resulted in biased inferences, we thus used the former approach. Using our fast sampling procedure, we were able to realize the benefits of correlated posteriors, including accurate uncertainty estimates for one cell, and accurate connectivity estimates for multiple cells, in an order of magnitude less time.

Animals smelling in the real world use a small number of receptors to sense a vast number of natural molecular mixtures, and proceed to learn arbitrary associations between odors and valences. Here, we propose how the architecture of olfactory circuits leverages disorder, diffuse sensing and redundancy in representation to meet these immense complementary challenges. First, the diffuse and disordered binding of receptors to many molecules compresses a vast but sparsely-structured odor space into a small receptor space, yielding an odor code that preserves similarity in a precise sense. Introducing any order/structure in the sensing degrades similarity preservation. Next, lateral interactions further reduce the correlation present in the low-dimensional receptor code. Finally, expansive disordered projections from the periphery to the central brain reconfigure the densely packed information into a high-dimensional representation, which contains multiple redundant subsets from which downstream neurons can learn flexible associations and valences. Moreover, introducing any order in the expansive projections degrades the ability to recall the learned associations in the presence of noise. We test our theory empirically using data from . Our theory suggests that the neural processing of sparse but high-dimensional olfactory information differs from the other senses in its fundamental use of disorder.