Anxiety is a prevalent negative emotional state induced by stress; however, the neural mechanism underlying anxiety is still largely unknown. We used acute and chronic stress to induce anxiety and test anxiety-like behavior; immunostaining, multichannel extracellular electrophysiological recording, and Ca2+ imaging to evaluate neuronal activity; and virus-based neuronal tracing to label circuits and manipulate circuitry activity. Here, we identified a hypothalamic region, the supramammillary nucleus (SuM), that plays an important role in anxiety-like behavior. We then characterized a small ensemble of stress-activated neurons (SANs) that are recruited by stress. These SANs respond specifically to stress, and their activation robustly increases anxiety-like behavior in male mice. We also found that ventral subiculum (vSub)-SuM projections, but not dorsal subiculum (dSub)-SuM projections, encode anxiety-like behavior and that inhibition of these vSub-SuM projections has an antianxiety effect. These results indicate that the reactivation of stress-activated supramammillary cells and relevant neural circuits is an important neural process underlying anxiety-like behavior.

Cells can adapt to various environments by changing their biomolecular profiles while maintaining physiological homeostasis. What organizational principles in cells enable the simultaneous realization of adaptability and homeostasis? To address this question, we measure Raman scattering light from Escherichia coli cells under diverse conditions, whose spectral patterns convey their comprehensive molecular composition. We reveal that dimension-reduced Raman spectra can predict condition-dependent proteome profiles. Quantitative analysis of the Raman-proteome correspondence characterizes a low-dimensional hierarchical stoichiometry-conserving proteome structure. The network centrality of each gene in the stoichiometry conservation relations correlates with its essentiality and evolutionary conservation, and these correlations are preserved from bacteria to human cells. Furthermore, stoichiometry-conserving core components obey growth law and ensure homeostasis across conditions, whereas peripheral stoichiometry-conserving components enable adaptation to specific conditions. Mathematical analysis reveals that the stoichiometrically constrained architecture is reflected in major changes in Raman spectral patterns. These results uncover coordination of global stoichiometric balance in cells and demonstrate that vibrational spectroscopy can decipher such biological constraints beyond statistical or machine-learning inference of cellular states.

Understanding bacterial behavior in confined environments is helpful for elucidating microbial ecology and developing strategies to manage bacterial infections. While extensive research has focused on bacterial motility on surfaces and in porous media, chemotaxis in confined spaces remains poorly understood. Here, we investigate the chemotaxis of Escherichia coli within microfluidic lanes under a linear concentration gradient of L-aspartate. We demonstrate that E. coli exhibits significantly enhanced chemotaxis in lanes with sidewalls compared to open surfaces. We attribute this phenomenon primarily to the intrinsic chiral clockwise circular motion of surface-swimming bacteria and the subsequent alignment effect upon collision with the sidewalls. By varying lane widths, we identify that an 8 μm width—approximating the radius of bacterial circular swimming on surfaces—maximizes chemotactic drift velocity. These results are supported by both experimental observations and stochastic simulations, establishing a clear proportional relationship between optimal lane width and the radius of bacterial circular swimming. Further geometric analysis provides an intuitive understanding of this phenomenon. Our results may offer insights into bacterial navigation in complex biological environments such as host tissues and biofilms, providing a preliminary step toward exploring microbial ecology in confined habitats and potential strategies for controlling bacterial infections.

Animal behavior is individually variable, and this variability is often consistent over time, a phenomenon called individuality or personality when multiple traits are involved. However, most studies test individuality in only one environment, even though behavior is known to be context-dependent. Analogous to the human 'person-situation debate,' we asked whether and to what extent behavioral individuality persists across changing environmental situations in Drosophila melanogaster. Using established and new behavioral assays, we examined three individual traits, namely exploration, attention, and anxiety, across varying environmental contexts, including temperature, visual cues, and arena shape, in both walking and flying flies. We found that individuality is strongly context-dependent, but even under substantial environmental changes, at least one behavioral trait retained individual-specific variation. Different environmental features did not affect individuality equally; instead, they formed a hierarchy in their influence on behavioral consistency. This hierarchy was supported by generalized linear modeling and hierarchical linear mixed-model analysis. Our results show that, as in humans, individuality in flies persists across different situations, although less strongly than across repeated tests in the same context. These findings establish Drosophila as a model for dissecting the developmental, neural, and genetic mechanisms underlying consistent individual differences in behavior across variable environments.

Emerging evidence suggests that Guanine nucleotide exchange factors (GEFs) and GTPase-activating proteins (GAPs) bind to the cytoskeleton or focal adhesions (FAs), controlling spatio-temporal Rho GTPase activity through feedback mechanisms. We explore such feedback in the Rho-specific GAP Deleted in Liver Cancer 1 (DLC1), which binds to FAs through mechanosensitive interactions. Using a FRET biosensor, we show that DLC1 loss of function leads to globally increased Rho activity and contractility in fibroblasts. Although Rho activity appears macroscopically steady, individual molecules undergo 'signaling flux'-a dynamic cycle of activation and deactivation. To measure this flux, we built a genetic circuit that enables both optogenetic activation of Rho and simultaneous readout of Rho activity. In cells at mechanical steady state, this reveals that DLC1 globally controls the rate of Rho deactivation, both at FAs and at the plasma membrane. Transient induction of local contractility, however, shows DLC1 associating with and dissociating from FAs during their reinforcement and relaxation, which might provide local positive feedback on Rho activity for robust FA disassembly. Together, our results indicate that DLC1 regulates Rho activity both globally at steady state and locally at FAs under tension, highlighting the complexity of spatio-temporal Rho GTPase signaling.

Gamma synchrony is ubiquitous in visual cortex, but whether it contributes to perceptual grouping remains contentious based on observations that gamma frequency is not consistent across stimulus features and that gamma synchrony depends on distances between image elements. These stimulus dependencies have been argued to challenge the idea that the visual system groups image elements by synchronizing the neural assemblies that encode them. Here, we argue instead that these dependencies may shape synchrony in perceptually meaningful ways. Indeed, according to the theory of weakly coupled oscillators (TWCO), synchrony-based grouping mechanisms require stimulus dependence. Synchronization among coupled oscillators depends on frequency dissimilarity and coupling strength, which in early visual cortex relate to local feature dissimilarity and physical distance, respectively. We manipulated these factors in a texture segregation experiment wherein human observers identified the orientation of a figure defined by reduced contrast heterogeneity compared to the background. Human performance followed TWCO predictions both qualitatively and quantitatively, as formalized in a computational model. Moreover, we found that when enriched with a Hebbian learning rule, our model also predicted human learning effects: Increases in model gamma synchrony due to perceptual learning predicted improvements in texture segregation across sessions. Taken together, our data suggest that the stimulus-dependence of gamma synchrony captures local image statistics and is linked to the stimulus-dependence of texture segregation, and that the effect of visual experience on gamma synchrony provides a viable perceptual learning mechanism for training-induced improvements in texture segregation. Our results suggest that gamma synchrony with its inherent stimulus dependencies can provide a plausible mechanistic basis for perceptual grouping and visual scene segmentation.

Meiosis creates haploid gametes through two sequential M phases. While many studies have focused on meiosis I, the molecular events which drive and define meiosis II are largely unknown. Here, we report a novel cell synchronisation strategy which allows for collection of budding yeast Saccharomyces cerevisiae cells arrested at metaphase I or metaphase II, enabling better characterisation of meiosis II events. The method relies on chemically-inducible dimerisation of ectopic copies of spindle assembly checkpoint (SAC) proteins Mps1 and Spc105. Using this synthetic SAC (SynSAC) approach, we found that the SAC response is weaker in metaphase I compared to metaphase II and that the PP1 binding site within Spc105 contributes to restraining the MI SAC response. Furthermore, we demonstrate the utility of the SynSAC approach by analysing the composition and phosphorylation of kinetochores from metaphase I and metaphase II. This revealed an increase in the abundance of outer kinetochore proteins in meiotic metaphase I and reduced phosphorylation on metaphase II kinetochore proteins. Overall, we present the SynSAC method as a valuable tool for analysis of both meiotic metaphases.

Sleep-wakefulness regulation dynamically evolves along development in a wide range of organisms. While the mechanism regulating sleep in adults is relatively well understood, little is known about its counterpart in early developmental stages. Here, we report a neuropeptidergic circuitry that modulates sleep in developing Drosophila larvae. Through an unbiased screen, we identified the neuropeptide Hugin and its receptor PK2-R1 as critical regulators of larval sleep. Our genetic and behavioral data suggest that HugPC neurons secrete Hugin peptides to activate insulin-producing cells (IPCs), which express a Hugin receptor PK2-R1. IPCs, in turn, release insulin-like peptides (Dilps) to regulate sleep. We further show that the Hugin/PK2-R1 axis is dispensable for adult sleep. Our findings thus reveal the neuromodulatory circuit that regulates developmental sleep in larvae and highlight differential impacts of the same modulatory axis on early-life sleep and adult sleep.

Human neurons derived from stem cells show increased structural complexity and stronger synaptic connections after exposure to psilocin, the active metabolite of the psychedelic psilocybin.

Here, we review the history, advancements, and broad utility of the NTR/prodrug system, and suggest future strategies for developing versatile ablation models. As a chemogenetic tool, the nitroreductase (NTR)/prodrug system enables precise spatiotemporal control over cell ablation. The technology leverages bacterial nitroreductase enzymes (e.g., nfsB) to convert inert prodrugs into cytotoxic agents, thereby allowing researchers to induce targeted cell death. Following its landmark application in zebrafish with metronidazole (MTZ) in 2007, the system’s utility has expanded to other essential model organisms, including Drosophila, Nematostella, Xenopus, medaka, and rodents, facilitating detailed studies of tissue damage and regeneration. This review highlights how the NTR system has been deployed to model a spectrum of human diseases, including Parkinson’s disease, retinal degeneration, demyelinating disorders, and kidney disease. These models provide valuable platforms to study pathogenesis in vivo. Furthermore, the precise and controllable nature of NTR ablation makes it an ideal tool for high-throughput chemical and genetic screens aimed at discovering pro-regenerative and protective compounds. The development of NTR2.0, an enzyme variant with over 100-fold greater activity, along with more potent prodrugs such as ronidazole (RNZ), has dramatically broadened experimental possibilities. These improvements permit chronic ablation and long-term disease modeling at well-tolerated drug concentrations. Here we present some key considerations including transgenic design for optimal cell-type specificity, calibrating expression levels for desired ablation kinetics, and suitable controls to allow interpretation. These best practices will allow the researcher to develop a precise, reproducible, and versatile platform for either modeling human disease or dissecting regenerative mechanisms.