bioRxiv Subject Collection: Neuroscience's Journal

Inhibition of Cxcr4 chemokine receptor signaling improves habituation learning and increases cAMP-PKA signaling in a zebrafish model of Neurofibromatosis type 1
Neurofibromatosis type 1 (NF1) is a neurogenetic disorder caused by loss of function mutations in the gene neurofibromin 1 (NF1). NF1 encodes neurofibromin, a multifunctional tumor suppressing protein that regulates Ras, cAMP, and dopamine signaling. NF1 predisposes patients to a wide range of symptoms, including peripheral nerve tumors, brain tumors, and cognitive dysfunction. Despite considerable work using animal models to investigate the role of neurofibromin in behavior, translating research into treatment for NF1-associated cognitive dysfunction has not yet been successful. Here, we identify Cxcr4 chemokine receptor signaling as a regulator of habituation learning and modulator of cAMP-PKA signaling in nf1 mutant larval zebrafish. Combining a small-molecule drug screen and RNAseq analysis, we show that cxcr4b expression is increased in nf1 mutants and that pharmacological inhibition of Cxcr4 with AMD3100 (Plerixafor) improves habituation learning in nf1 mutants. We further demonstrate that Plerixafor treatment activates cAMP-PKA pathway signaling but has limited effects on Ras-Raf-MEK-ERK pathway signaling in the nf1 mutant brain. CXCR4 was previously identified as a potential therapeutic target for neurofibromin-deficient tumorigenesis. Our results provide evidence that Cxcr4 signaling also regulates neurofibromin-dependent cognitive function.

Alk-Fam150b (augmentor α) expression in the paraventricular nucleus of the mouse hypothalamus at molecular resolution, and its sensitivity to acute stress
Augmentor (Fam150b) action on the ALK receptor (Alk) has gained significance as a hypothalamic signaling pathway with relevance to the control of food intake and energy homeostasis. In contrast, much less is known about the sensitivity of Fam150b-Alk expression and signaling upon noxious challenges. In this regard, acute stress is of particular interest because augmentor , released from afferents of the food intake circuit of the arcuate hypothalamus in the paraventricular hypothalamus (PVN), could link stress-induced changes in food consumption. Nevertheless, conflicting data exist on whether Fam150b mRNA is expressed in the PVN. Here, we combined single-cell RNA-seq and multiplexed in situ hybridization to demonstrate that both Fam150b and Alk are expressed in the PVN of adult mice, including corticotropin-releasing hormone (CRH)-containing neurons. As such, a dichotomy of CRH neurons is present through their mutually exclusive expression of either Fam150b or Scgn (secretagogin). Fam150b and Alk were not co-expressed. When inducing inflammation-associated stress, Fam150b but not Alk mRNA expression increased in a mifepristone-sensitive manner, implying regulation by peripheral glucocorticoid feedback. We suggest that augmentor -ALK signaling could underpin, at least partly, stress-induced changes in feeding and the control of body weight.

In vivo deep-brain microscopy at submicrometer resolution with refractive index-matched prism interfaces
The mammalian brain is a thick and densely layered structure comprising a huge number of neurons that work together to process information and regulate brain functions. Although various optical methods have been developed to investigate deep brain dynamics, they are limited by technical constraints, invasiveness, suboptimal spatial resolution, and/or a restricted field of view. To overcome these limitations, we developed an implantable, optically optimized microprism interface with a refractive index matched to that of brain tissue and water, enabling minimally-invasive, wide-field two-photon imaging method with enhanced brightness and sub-micron resolution in deep prefrontal areas.

Less is More: Lower Levels of Task-Induced Hippocampal Activation Predict Better Performance on a Separate Verbal Memory Evaluation
Memories that differ in content or duration differ in the extent to which they depend on the hippocampus, and also the part of the hippocampus, posterior (pHPC) or anterior (aHPC), that they implicate. Inter-individual differences in learning-related activation in different hippocampal subregions have been found to predict specific differences in memory abilities. The complexity of these relationships creates a setting that is ripe for theoretically informative investigation, but that can also lead to reports of spurious relationships that do not reflect underlying neurobiological associations. Across-study replication is therefore a critical first step toward understanding how differences in hippocampal activity drive individual differences in memory ability. In the domain of verbal memory, Wig et al. (2008) identified a negative relationship between task-induced activation in pHPC and out-of-scanner verbal memory test performance. Replicating this result in an independent sample of 86 participants, we identified the same negative correlation between pHPC activation during a Lithuanian word learning task and out-of-scanner California Verbal Learning Test (CVLT-II) performance. This replication represents a critical step toward understanding how pHPC supports verbal memory by answering the basic question of whether the relationship can be reliably observed.

Autism-associated SCN2A deficiency disrupts cortico-striatal circuitry in human brain assembloids
Profound autism spectrum disorder (ASD) is frequently attributable to single-gene mutations, with SCN2A (voltage-gated sodium channel NaV1.2) protein-truncating variants (PTVs) being one of the most penetrant. Although cortico-striatal circuitry is implicated as a key node in ASD, the impact of SCN2A deficiency on human neural circuits is unknown. Using the human cortico-striatal assembloid model, we show that the autism-causing PTV SCN2A-C959X impairs long-range cortical axonal projections, reduces striatal spine density, and attenuates excitatory cortical-striatal synaptic transmission. Surprisingly, these assembloids carrying the heterozygous SCN2A nonsense mutation exhibited pronounced network hyperexcitability, a human cell-specific phenotype not observed in Scn2a+/- mice, highlighting a human-specific circuit vulnerability. Collectively, our study unveils human circuit-specific dysfunctions of SCN2A deficiency and SCN2A-mediated ASD.

Cortical language areas are coupled via a soft hierarchy of model-based linguistic features
Natural language comprehension is a complex task that relies on coordinated activity across a network of cortical regions. In this study, we propose that regions of the language network are coupled to one another through subspaces of shared linguistic features. To test this idea, we developed a model-based connectivity framework to quantify stimulus-driven, feature-specific functional connectivity between language areas during natural language comprehension. Using fMRI data acquired while subjects listened to spoken narratives, we tested three types of features extracted from a unified neural network model for speech and language: low-level acoustic embeddings, mid-level speech embeddings, and high-level language embeddings. Our modeling framework enabled us to quantify the stimulus features that drive connectivity between regions: early auditory areas were coupled to intermediate language areas via lower-level acoustic and speech features; in contrast, higher-order language and default-mode regions were predominantly coupled through more abstract language features. We observed a clear progression of feature-specific connectivity from early auditory to lateral temporal areas, advancing from acoustic connectivity to speech- and finally to language-driven connectivity. These findings suggest that regions of the language network are coupled through feature-specific communication channels to facilitate efficient and context-sensitive language processing.

Aberrant cerebrovascular reactivity presents as an early biomarker of psychosis susceptibility in patients with 22q11.2DS
The brain's ability to regulate blood flow is fundamental to both its function and development. In the context of neurodevelopmental disorders such as schizophrenia, understanding the complex interactions between cerebrovascular health and brain function is crucial for unraveling the pathophysiology of psychosis. This study investigates the developmental trajectory of cerebrovascular reactivity (CVR) in 22q11 deletion syndrome (22q11.2DS) compared to healthy controls, and its association with psychosis susceptibility. Using a longitudinal data-set of resting-state fMRI, we mapped voxel-level CVR across development. We found significant and early CVR impairments in 22q11.2DS, and in particular in those who later developed positive psychotic symptoms (PPS+). These impairments were evident within the anterior cingulate cortex, frontal lobes, and globi pallidi (GB). We propose that the pattern of CVR reduction presenting early during childhood is possibly linked to blood brain barrier impairment. A decrease in CVR during childhood and within the frontal regions and GB was predictive of subsequent development of positive psychotic symptoms (PPS), which often occurs during adolescence in 22q11.2DS patients. These findings suggest that cerebrovascular health is critical for normal brain development, particularly in regions like the striatum, which are vulnerable to vascular damage due to their anatomical features. These results underline the potential of CVR as an early biomarker for psychosis vulnerability, emphasizing the need for targeted interventions to mitigate neurodevelopmental disruptions of cerebrovascular health in 22q11.2DS.

Low-Rank Tensor Encoding Models Decompose Natural Speech Comprehension Processes
How does the brain process language over time? Research suggests that natural human language is processed hierarchically across brain regions over time. However, attempts to characterize this computation have thus far been limited to tightly controlled experimental settings that capture only a coarse picture of the brain dynamics underlying human natural language comprehension. The recent emergence of LLM encoding models promises a new avenue to discover and characterize rich semantic information in the brain, yet interpretable methods for linking information in LLMs to language processing over time are limited. In this work, we develop a low-rank tensor regression method to decompose LLM encoding models into interpretable components of semantics, time, and brain region activation, and apply the method to a Magnetoencephalography (MEG) dataset in which subjects listened to narrative stories. With only a few components, we show improved performance compared to a standard ridge regression encoding model, suggesting the low-rank models provide a good inductive bias for language encoding. In addition, our method discovers a diverse spectrum of interpretable response components that are sensitive to a rich set of low-level and semantic language features, showing that our method is able to separate distinct language processing features in neural signals. After controlling for low-level audio and sentence features, we demonstrate better capture of semantic features. Through use of low-rank tensor encoding models we are able to decompose neural responses to language features, showing improved encoding performance and interpretable processing components, suggesting our method as a useful tool for uncovering language processes in naturalistic settings.

Coordinated dynamics of excitatory and inhibitory synapse assembly
Neural circuits composed of multitudes of diverse synaptic connections self-organize during mammalian brain development. A balance between excitatory and inhibitory synaptic function is required for information processing by these neural circuits. Despite the importance of this balance, the interplay between excitatory and inhibitory synaptic assembly during circuit establishment remains unclear due to a lack of means to monitor both processes simultaneously. Here, we develop imaging approaches to visualize and track excitatory and inhibitory synapses concurrently. By applying these approaches, we find that despite continual dynamics, excitatory and inhibitory synaptic density remain at synchronized levels during synaptogenesis. These results support coordinated excitatory and inhibitory synapse assembly to maintain functional balance despite continual synaptic turnover.

Quantifying neuronal differentiation using temporal topological persistence
Neuronal anatomical differentiation relies on coordinated neurite dynamics to establish class-type morphology during development. Recent advances in high-throughput time-lapse imaging techniques have transformed our ability to track such growth dynamics, yielding comprehensive anatomical datasets of neuronal morphologies at unprecedented rates. However, analyzing these complex datasets using traditional morphometrics requires manual feature selection, which leads to costly and biased quantification. To overcome these limitations, we applied the Topological Morphology Descriptor (TMD) to extract topological representations of neurons from time-lapse imaging of two cell types within the well-established Drosophila larval sensory dendritic arborization (da) system. We first used TMD to accurately classify the developmental trajectory of Class I da sensory neurons across embryonic and larval stages. However, TMD alone failed to distinguish class-type-specific morphology in Class III neurons, particularly in detecting altered branching rate dynamics in genetically modified mutants. To address this, we extended TMD to a Temporal Topological Morphology Descriptor (TTMD) to incorporate temporal dynamics. TTMD successfully classified mutations in Drosophila Class III da neurons, capturing altered branching rate dynamics in mutant phenotypes. These findings highlight the power of TTMD as an unbiased, scalable framework for analyzing neuronal growth dynamics and linking structural development to genetic and functional variation.

In vivo neural activity of electrosensory pyramidal cells: Biophysical characterization and phenomenological modeling
Burst firing is an important property of neuronal activity, thought to enhance sensory encoding. While previous studies show significant differences in burst firing between in vivo and in vitro conditions, how burst firing contributes to neural coding in vivo and how it is modulated by underlying biophysical mechanisms when neurons are under active synaptic bombardments remains poorly understood. Here, we combined intracellular recordings and computational modeling to investigate how cellular and synaptic mechanisms can explain the in vivo firing activity of electrosensory lateral line lobe (ELL) pyramidal cells in Apteronotus leptorhynchus. We developed a biophysically detailed compartmental model incorporating voltage-gated currents, NMDA receptor-mediated calcium influx, calcium-activated SK channels, calcium handling, and stochastic synaptic inputs to reproduce in vivo firing activities of ELL pyramidal cells. Specifically, using bifurcation analysis, we identified dynamical transitions between quiescent, tonic, and bursting regimes, governed by interactions among SK conductance, NMDA receptor activation, and applied current. Model parameters were optimized against in vivo data, accurately reproducing action potential waveforms and temporal dynamics, including characteristic bimodal interspike interval distributions reflecting intra- and inter-burst intervals. We further developed a modified Hindmarsh-Rose model incorporating dual adaptation variables and stochastic noise. This simplified phenomenological model successfully captured burst firings comparable to those observed in the biophysical model and recorded data, while replicating diverse firing patterns observed across the population. Finally, parameter sensitivity analysis revealed slow adaptation dynamics and noise intensity as key determinants of spiking variability within cells. Overall, our modeling results demonstrate that in vivo bursting arises from synergistic interactions between intrinsic conductances (e.g., NMDA-SK coupling), calcium mobilization, and synaptic stochasticity, offering a potential reconciliation for discrepancies with in vitro firing activity. The models provide mechanistic insights into how background synaptic activity modulates burst firing and validate simplified frameworks for studying population-level dynamics.

Evolution's boldest trick: Neurotransmission modulated whole-brain computation captures full task repertoire
The perhaps most important unsolved problem in neuroscience is how the brain survives in a complex world by performing a rich repertoire of computation on a minimal energy budget. The brain is much better at adapting to the multiplicity of stimuli and outcomes than current generations of computers, artificial neural deep learning and reservoir model architectures. Yet, at first glance the brain appears to use a fixed anatomical architecture to perform the necessary huge variety of computations. But evolution's boldest trick is that in fact the brain's effective connectivity is constantly being updated through neuromodulation to allow the rich repertoire of computation. Inspired by this, we created a whole-brain model using empirical neurotransmitter maps modulating the underlying local regional dynamics. This NEMO (neurotransmission modulated) whole-brain model is able to flexibly compute the full task repertoire and associated functional connectivity of the neuroimaging data from 971 healthy participants. For each individual we defined a measure of 'brain computability' as the fitting of the NEMO whole-brain model to all tasks performed by the individual. Importantly, brain computability correlates with both behavioural performance on individual tasks and with a general behavioural measure of intelligence. Overall, our proposed unifying NEMO framework offers a natural way to sculpt different brain dynamics in a fixed brain architecture to compute the rich repertoire of tasks required for surviving and thriving.

Visual processing of manipulable objects in the ventral stream is modulated by inputs from parietal action systems
Functional object use requires the integration of visuomotor representations processed in the dorsal visual pathway with representations of surface texture and material composition of objects, processed in the ventral visual pathway. Do regions in the ventral visual pathway project outputs to dorsal visual pathway action systems, or are the outputs of the dorsal visual pathway communicated to the ventral visual pathway to modulate processing? And what are the white matter pathways that mediate structural connectivity in support of functional object use? Here we show that the left inferior parietal cortex, a region within the dorsal visual pathway, exerts a direct effect on neural responses in ventral occipital-temporal cortex during visual processing of manipulable objects. We studied a series of consecutively enrolled participants in the pre-operative phase of their neurosurgical care (N = 109) with lesions principally distributed throughout the left hemisphere. Participants completed an object processing category localizer functional MRI experiment in which they viewed images of manipulable objects, animals, faces, and places. We then used Voxel-based Lesion-Activity Mapping (VLAM), a technique in which functional responses in a region-of-interest are used to predict variance in voxel-wise lesion incidence throughout the brain. In the VLAM analyses performed here, we found that lesions to the left anterior intraparietal sulcus and left supramarginal gyrus, two inferior parietal regions known to support object-directed grasping and manipulation, respectively, cause reduced neural responses for manipulable objects (compared to faces, places and animals) in the fusiform gyrus. Parietal lesions do not affect neural responses during visual processing of places in the same region of the fusiform gyrus, even though places elicit stronger responses in the fusiform gyrus than manipulable objects. Seventy-five of 109 participants took part in a common diffusion MRI protocol, permitting a connectome-wide analyses relating white matter fiber integrity to the strength of functional responses for manipulable objects in the left fusiform gyrus. This analysis demonstrated that the descending portion of the left arcuate fasciculus mediates parietal-to-temporal lobe connectivity for manipulable objects, supporting the integration of action representations with conceptual and perceptual attributes of objects. By combining voxel-based and connectome-wide lesion-symptom mapping methods with functional MRI, we have demonstrated that structural connectivity to dorsal visual pathway areas supporting skilled manual action shape category-specific neural responses for manipulable objects within the ventral visual pathway.

Single-cell spatial map of cis-regulatory elements for disease-related genes in the macaque cortex
Single-cell spatial transcriptomes have demonstrated molecular and cellular diversity in the brain, but gene regulatory mechanisms underlying transcriptomic profiles and disease pathogenesis remain largely unknown in primates. Here we performed single-nucleus Assay for Transposase-Accessible Chromatin followed by sequencing (snATAC-seq) for ~1.6 million macaque cortical cells, and identified distinct chromatin accessibility profiles of cis-regulatory elements (CREs) for various cell types. By integrative analysis with large-scale spatial transcriptome data, we found that these CREs showed laminar and regional preferences, with their regional accessibility exhibiting striking dependence on the region's hierarchical level. Cross-species comparison of snATAC-seq data revealed human/macaque-enriched layer-4 glutamatergic neurons and LAMP5/LHX6-expressing GABAergic neurons as well as human/macaque-specific CREs for genes related to neurodevelopment and psychiatric diseases. Importantly, risk single-nucleotide polymorphisms for many brain disorders strongly associated with human/macaque-specific CREs in glutamatergic neuronal types and those for Alzhemer's disease strongly associated with CREs exclusively in microglia. Our results provided the basis for understanding the spatial gene regulatory mechanisms underlying cellular diversity and disease pathogenesis in the primate cortex.

Single-cell transcriptomics reveals probiotic reversal of neonatal morphine-induced gene disruptions underlying adolescent pain hypersensitivity.
Neonatal morphine is commonly administered in the Neonatal Intensive Care Unit (NICU) to manage pain. However, its long-term effects on neurodevelopment of pain pathways, remain a significant concern. The midbrain is a core region that plays a central role in pain processing and opioid-mediated analgesia. Here, we performed single-cell RNA sequencing to study gene expression in 107,427 midbrain single cells from adolescent mice neonatally exposed to either saline, morphine, or morphine with the probiotic Bifidobacterium infantis (B. infantis). We found broad alterations in transcriptomics within neurons, astrocytes, oligodendrocytes, and microglial cells. Analysis of differentially regulated genes revealed down regulation of HOX genes and upregulation of pathways related to neurotransmitter signaling and pain in adolescence that were neonatally treated with morphine. Interestingly, neonatal probiotic supplementation mitigated these morphine-induced alterations on the transcriptome. This study presents the first single-cell RNA sequencing dataset of the adolescent midbrain following neonatal morphine exposure and probiotic intervention. These findings offer new insights into the neurodevelopmental impact of early opioid exposure and highlight the therapeutic potential of microbiome-targeted interventions.

OPM quantum sensors enhance non-invasive neuroimaging performance
Much of our understanding of how neural circuit activity relates to human behaviour as well as mental health, stems from non-invasive assessments of the central nervous system. Further progress in key questions, however, requires a higher differentiation of circuit-level activity, which remains constrained by the sensitivity and resolution of existing instruments. Here we show that neural quantum sensing with optically pumped magnetometers (OPMs) addresses this issue by significantly outperforming EEG and conventional MEG in both signal-to-noise ratio (SNR) and signal coherence across trials. SNR for OPMs was increased by up to ~200% compared to EEG and by up to ~40% compared to conventional MEG. Likewise, the neuronal signal coherence across trials of OPMs was increased by up to ~40% compared to EEG and by up to 20% compared to conventional MEG. Our data emphasize the important role of OPMs for non-invasive neuroimaging, paving the way for significant advances in basic research as well as translational applications.

Bright and Photostable Voltage Sensors Derived from mBaoJin
Genetically encoded voltage indicators (GEVIs) are powerful tools for monitoring neuronal activity, but their application, particularly for long-term recordings in vivo, is often limited by photobleaching under the required high illumination intensities. This constraint restricts the total duration of continuous or trial-based experiments, crucial for studying processes like synaptic plasticity or circuit dynamics during behavior. Here, we introduce ElectraON and ElectraOFF, a pair of green fluorescent eFRET-based GEVIs engineered by incorporating a photostability-enhanced derivative of the bright monomeric fluorescent protein mBaoJin with Ace opsin variants. Critically, Electras demonstrate over 6-fold improved photostability compared to state-of-the-art eFRET GEVIs, pAce, and Ace-mNeon2, under one-photon illumination, while characterized by bright green fluorescence, millisecond kinetics, and good membrane localization. This enhanced stability translates to a 3- to >10-fold extension in functional recording duration, maintaining reliable spike detection in both cultured neurons in vitro and sparsely labeled neurons in the awake mouse cortex in vivo. We demonstrated sustained in vivo recordings exceeding 30 minutes, with instances surpassing one hour. Furthermore, Electras show functionality under scanless two-photon excitation in cultured cells. These highly photostable indicators significantly extend the temporal window for voltage imaging, broadening the scope of accessible biological questions.

Detection of astrocyte epigenetic memory in in vitro systems, experimental autoimmune encephalomyelitis and multiple sclerosis samples
We recently described astrocyte pro-inflammatory epigenetic memory based on multiple complementary in vivo and in vitro studies, and the analysis of multiple sclerosis samples. Based on bioinformatic analyses, ODea and Liddelow argued that the astrocyte epigenetic memory we described is the result of contamination with immune cells, particularly myeloid cells. We rebut ODea and Liddelow arguments as follows: (1) We show substantial purity of astrocytes analyzed in in vivo and in vitro systems; (2) We recapitulate astrocyte memory responses using five independent pure astrocyte in vitro systems, and show its dependency on the histone acetyl transferase p300; and (3) Using the Liddelow lab bioinformatic pipeline to implement purity and cell-quality criteria, we detect astrocyte epigenetic memory in five independent scRNA-seq experimental autoimmune encephalomyelitis (EAE) and multiple sclerosis (MS) astrocyte datasets. These additional analyses and studies provide further support for the existence of astrocyte pro-inflammatory epigenetic memory.

Normalization of Prefrontal Network Dynamics Prevents Cognitive Impairments After Developmental Insult
The neurodevelopmental period is highly sensitive; insults during this period impair neural network connectivity, causing lasting cognitive deficits associated with many neuropsychiatric disorders. Medial prefrontal cortex (mPFC) networks subserve flexible behavior, but the mechanisms underlying their disruption after developmental insults remain unclear. We used an early-life seizure (ELS) model to investigate how mPFC networks become impaired and tested whether adrenocorticotropic hormone (ACTH), a clinically relevant neuroprotective peptide, could restore network function. Using in-vivo single-unit recordings during baseline and fear extinction learning, we found ELS-induced dysfunction was characterized by reduced neuronal firing, rigid spike-timing, and weakened functional connectivity, all predicting impaired extinction learning. ACTH treatment prevented these deficits, preserving dynamic spike-timing, flexible connectivity, and network organization. Advanced graph neural network modeling identified neuronal features predictive of cognitive outcomes, revealing potential biomarkers broadly relevant to other developmental disorders. These findings highlight fundamental mechanisms of mPFC network dysfunction and emphasize the translational potential of targeting network dynamics to restore cognition in neurodevelopmental disorders.

Multivariate pattern analysis reveals resting-state EEG biomarkers in fibromyalgia
Fibromyalgia (FM) involves widespread musculoskeletal pain and hypersensitivity, often accompanied by neurological, cognitive, and affective disturbances. Resting-state electroencephalography (EEG) studies have revealed abnormal brain activity in affected individuals, with anxiety and symptom duration potentially exacerbating these alterations. This study applied multivariate pattern analysis to differentiate resting-state EEG signals between FM patients and healthy controls across frequency bands associated with pain processing, while incorporating state and trait anxiety scores. It also examined differences between patients with short- and long-duration symptoms and identified the most relevant scalp regions contributing to the models. Fifty-one female participants (25 FM patients, 26 controls; aged 35-65) were included. Patients were classified into short-term (12) and long-term (13) groups. EEG was recorded and power spectral density values were extracted and normalized to be used to train machine learning classifiers. Anxiety scores were included in the patient-control analysis. Haufe transformed weights were computed to determine key scalp contributions. The models successfully distinguished patients from controls and between FM subgroups, with accuracies exceeding 0.70 across all frequency bands. Including anxiety scores substantially improved classification, with accuracies reaching 0.99. These findings highlight significant differences in resting-state EEG activity between patients and controls, and between FM subgroups, underscoring the relevance of emotional and neural factors. Frequency-specific alterations in pain-related scalp regions support the view of disrupted pain processing in FM. Resting-state EEG, combined with multivariate pattern analysis, may support the development of biomarkers to enhance diagnosis and guide treatment strategies in clinical settings.

A covalent recognition strategy enables conspecific mate identification
The olfactory system can detect an uncountable number of volatile molecules while retaining the ability to discriminate amongst very similar ones. We identified a unique mechanism employed by insect odorant receptors to discriminate amongst pheromones, chemical communication signals that orchestrate courtship and mating behavior. By coupling cryogenic electron microscopy (cryo-EM) and functional mutagenesis, we find that males of the silkmoth Bombyx mori distinguish between two quasi-identical compounds -bombykol, an alcohol, and bombykal, an aldehyde- by establishing a reversible covalent bond between the pheromone receptor and bombykal. Bombykol, instead, binds to the same receptor through hydrogen bonds, with significantly lower potency. The unique ability of aldehydes to establish a reversible covalent bond allows moths to unequivocally distinguish between compounds that differ only in the presence of a single hydrogen atom. Further, as many important odorants are aldehydes, this work illuminates a new binding mode available to the olfactory system to achieve high selectivity for these compounds.

Heat-off responses of epidermal cells sensitize Drosophila larvae to noxious inputs
Perception of external thermal stimuli is critical to animal survival, and although an animal's skin is the largest contact surface for thermal inputs, contributions of skin cells to noxious temperature sensing have not been extensively explored. Here, we show that exposure to heat transiently sensitizes Drosophila larvae to subsequent noxious stimuli. This sensitization is induced by prior stimulation of epidermal cells but not nociceptors, suggesting that epidermal cells modulate nociceptor function in response to heat exposure. Indeed, we found that Drosophila epidermal cells are intrinsically thermosensitive, exhibiting robust heat-off responses following warming to noxious temperatures as well as responses to cooling below comfortable temperatures. Further, we found that epidermal heat-off calcium responses involve influx of extracellular calcium and require the store-operated calcium channel Orai and its activator Stim. Finally, epidermal heat-off responses and heat-evoked nociceptive sensitization exhibit similar temperature dependencies, and we found that Stim and Orai are required in epidermal cells for heat-evoked nociceptive sensitization. Hence, epidermal thermosensory responses provide a form of adaptive sensitization to facilitate noxious heat avoidance.