Metabolism is fundamental to organism physiology and pathology. From the intricate network of metabolic reactions, diverse chemical molecules, collectively termed as metabolites, are produced. In multicellular organisms, metabolite communication between different tissues is vital for maintaining homeostasis and adaptation. However, the molecular mechanisms mediating these metabolite communications remain poorly understood. Here, we focus on nucleosides and nucleotides, essential metabolites involved in multiple cellular processes, and report the pivotal role of the SLC29A family of transporters in mediating nucleoside coordination between the soma and the germline. Through genetic analysis, we discovered that two Caenorhabditis elegans homologs of SLC29A transporters, Equilibrative Nucleoside Transporter ENT-1 and ENT-2, act in the germline and the intestine, respectively, to regulate reproduction. Their knockdown synergistically results in sterility. Further single-cell transcriptomic and targeted metabolomic profiling revealed that the ENT double knockdown specifically affects genes in the purine biosynthesis pathway and reduces the ratio of guanosine to adenosine levels. Importantly, guanosine supplementation into the body cavity/pseudocoelom through microinjection rescued the sterility caused by the ENT double knockdown, whereas adenosine microinjection had no effect. Together, these studies support guanosine as a rate limiting factor in the control of reproduction, uncover the previously unknown nucleoside/nucleotide communication between the soma and the germline essential for reproductive success, and highlight the significance of SLC-mediated cell-nonautonomous metabolite coordination in regulating organism physiology.

Dopamine is a key chemical neuromodulator that plays vital roles in various brain functions. Traditionally, neuromodulators like dopamine are believed to be released in a diffuse manner and are not commonly associated with synaptic structures where pre- and postsynaptic processes are closely aligned. Our findings challenge this conventional view. Using single-bouton optical measurements of dopamine release, we discovered that dopamine is predominantly released from varicosities that are juxtaposed against the processes of their target neurons. Dopamine axons specifically target neurons expressing dopamine receptors, forming synapses to release dopamine. Interestingly, varicosities that were not directly apposed to dopamine receptor-expressing processes or associated with neurons lacking dopamine receptors did not release dopamine, regardless of their vesicle content. The ultrastructure of dopamine release sites share common features of classical synapses. We further show that the dopamine released at these contact sites induces a precise, dopamine-gated biochemical response in the target processes. Our results indicate that dopamine release sites share key characteristics of conventional synapses that enable relatively precise and efficient neuromodulation of their targets.

As animals adapt to new situations, neuromodulation is a potent way to alter behavior, yet mechanisms by which neuromodulatory nuclei compute during behavior are underexplored. The serotonergic raphe supports motor learning in larval zebrafish by visually detecting distance traveled during swims, encoding action effectiveness, and modulating motor vigor. We found that swimming opens a gate for visual input to cause spiking in serotonergic neurons, enabling encoding of action outcomes and filtering out learning-irrelevant visual signals. Using light-sheet microscopy, voltage sensors, and neurotransmitter/modulator sensors, we tracked millisecond-timescale neuronal input-output computations during behavior. Swim commands initially inhibited serotonergic neurons via GABA, closing the gate to spiking. Immediately after, the gate briefly opened: voltage increased consistent with post-inhibitory rebound, allowing swim-induced visual motion to evoke firing through glutamate, triggering serotonin secretion and modulating motor vigor. Ablating GABAergic neurons impaired raphe coding and motor learning. Thus, serotonergic neuromodulation arises from action-outcome coincidence detection within the raphe, suggesting the existence of similarly fast and precise circuit computations across neuromodulatory nuclei.

The cellular distribution of mitochondria in response to stress and local energy needs is governed by the relative activities of kinesin and dynein. The mechanism for switching between these two opposite polarity microtubule motors remains unknown. Here, we coupled a novel cellular synthetic cargo transport assay with AlphaFold2-guided mutagenesis to identify a regulatory helix in the mitochondrial adaptor protein (TRAK) that mediates switching between kinesin- and dynein-driven transport. Differences in the helix sequence explain why two near-identical TRAK isoforms transport mitochondria in predominantly opposite directions. Phosphorylation of the regulatory helix by stress-activated kinases causes the activation of dynein and dissociation of kinesin. Our results reveal a molecular mechanism for coordinating the directional transport of mitochondria in response to intracellular signals.

An understanding of human brain individuality requires the integration of data on brain organization across people and brain regions, molecular and systems scales, as well as healthy and clinical states. Here, we help advance this understanding by leveraging methods from computational genomics to integrate large-scale genomic, transcriptomic, neuroimaging, and electronic-health record data sets. We estimated genetically regulated gene expression (gr-expression) of 18,647 genes, across 10 cortical and subcortical regions of 45,549 people from the UK Biobank. First, we showed that patterns of estimated gr-expression reflect known genetic-ancestry relationships, regional identities, as well as inter-regional correlation structure of directly assayed gene expression. Second, we performed transcriptome-wide association studies (TWAS) to discover 1,065 associations between individual variation in gr-expression and gray-matter volumes across people and brain regions. We benchmarked these associations against results from genome-wide association studies (GWAS) of the same sample and found hundreds of novel associations relative to these GWAS. Third, we integrated our results with clinical associations of gr-expression from the Vanderbilt Biobank. This integration allowed us to link genes, via gr-expression, to neuroimaging and clinical phenotypes. Fourth, we identified associations of polygenic gr-expression with structural and functional MRI phenotypes in the Human Connectome Project (HCP), a small neuroimaging-genomic data set with high-quality functional imaging data. Finally, we showed that estimates of gr-expression and magnitudes of TWAS were generally replicable and that the p-values of TWAS were replicable in large samples. Collectively, our results provide a powerful new resource for integrating gr-expression with population genetics of brain organization and disease.

This report presents a comprehensive data release exploring the tissue microarchitecture of P7 aged mice using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) combined with machine learning-based segmentations of nuclei. The study includes high-resolution 3D volumes and nucleus segmentations for seven vital tissues—pancreas, liver, kidney, heart, thymus, hippocampus, and skin—from a single mouse. The detailed datasets are openly accessible on OpenOrganelle.org, providing a valuable resource for the scientific community to support further research and collaboration.

We can now measure the connectivity of every neuron in a neural circuit, but we cannot measure other biological details, including the dynamical characteristics of each neuron. The degree to which measurements of connectivity alone can inform the understanding of neural computation is an open question. Here we show that with experimental measurements of only the connectivity of a biological neural network, we can predict the neural activity underlying a specified neural computation. We constructed a model neural network with the experimentally determined connectivity for 64 cell types in the motion pathways of the fruit fly optic lobe but with unknown parameters for the single-neuron and single-synapse properties. We then optimized the values of these unknown parameters using techniques from deep learning, to allow the model network to detect visual motion. Our mechanistic model makes detailed, experimentally testable predictions for each neuron in the connectome. We found that model predictions agreed with experimental measurements of neural activity across 26 studies. Our work demonstrates a strategy for generating detailed hypotheses about the mechanisms of neural circuit function from connectivity measurements. We show that this strategy is more likely to be successful when neurons are sparsely connected-a universally observed feature of biological neural networks across species and brain regions.

Cortical neurons exhibit temporally irregular spiking patterns and heterogeneous firing rates. These features arise in model circuits operating in a 'fluctuation-driven regime', in which fluctuations in membrane potentials emerge from the network dynamics. However, it is still debated whether the cortex operates in such a regime. We evaluated the fluctuation-driven hypothesis by analyzing spiking and sub-threshold membrane potentials of neurons in the frontal cortex of mice performing a decision-making task. We showed that while standard fluctuation-driven models successfully account for spiking statistics, they fall short in capturing the heterogeneity in sub-threshold activity. This limitation is an inevitable outcome of bombarding single-compartment neurons with a large number of pre-synaptic inputs, thereby clamping the voltage of all neurons to more or less the same average voltage. To address this, we effectively incorporated dendritic morphology into the standard models. Inclusion of dendritic morphology in the neuronal models increased neuronal selectivity and reduced error trials, suggesting a functional role for dendrites during decision-making. Our work suggests that, during decision-making, cortical neurons in high-order cortical areas operate in a fluctuation-driven regime.

A major frontier in single cell biology is decoding how transcriptional states result in cellular-level architectural changes, ultimately driving function. A remarkable example of this cellular remodelling program is the differentiation of airway stem cells into the human respiratory multiciliated epithelium, a tissue barrier protecting against bacteria, viruses and particulate matter. Here, we present the first isotropic three-dimensional map of the airway epithelium at the nanometre scale unveiling the coordinated changes in cellular organisation, organelle topology and contacts, occurring during multiciliogenesis. This analysis led us to discover a cellular mechanism of communication whereby motile cilia relay mechanical information to mitochondria through striated cytoskeletal fibres, the rootlets, to promote effective ciliary motility and ATP generation. Altogether, this study integrates nanometre-scale structural, functional and dynamic insights to elucidate fundamental mechanisms responsible for airway defence.

Incentives tend to drive improvements in performance. But when incentives get too high, we can "choke under pressure" and underperform right when it matters most. What neural processes might lead to choking under pressure? We studied rhesus monkeys performing a challenging reaching task in which they underperformed when an unusually large "jackpot" reward was at stake, and we sought a neural mechanism that might result in that underperformance. We found that increases in reward drive neural activity during movement preparation into, and then past, a zone of optimal performance. We conclude that neural signals of reward and motor preparation interact in the motor cortex (MC) in a manner that can explain why we choke under pressure.