Biology subjects

Zhao, C.

Publications and source records attributed to Zhao, C..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

Perinatal exposures and upper respiratory tract microbiome composition are associated with age at first acute otitis media episode

Acute otitis media (AOM) is the most common bacterial infection of childhood and the leading indication for antibiotic prescriptions and healthcare consultations globally. Colonization of the upper respiratory tract (URT) microbiome by bacterial respiratory pathogens precedes AOM episodes; however, the factors that influence colonization susceptibility and subsequent AOM are not well understood. We hypothesized that perinatal exposures, including mode of delivery, intrapartum antibiotic exposure, and infant feeding influence the composition of the URT microbiome at birth, modifying risk of AOM in infancy. We characterized the URT microbiome in nasopharyngeal swabs collected from 163 infants at birth. Swabs were generally collected within two days of delivery (median [IQR] collection time: 25 [17, 45] hours) and microbiome composition was evaluated with 16S rRNA V4 sequencing. Exposures evaluated included birth mode, intrapartum antibiotic exposures, and feeding type at hospital discharge. AOM episodes were identified through electronic health records data. We built Cox proportional hazards models to determine if perinatal exposures and/or microbiome characteristics at birth were associated with the time to first AOM episode in the first two years of life. URT microbiome diversity and composition were associated with feeding type at hospital discharge, wherein exclusive formula feeding was associated with increased diversity and the presence of Staphylococcus and Haemophilus spp. Increased URT microbiome diversity was associated with younger age at first AOM episode. Our findings suggest that perinatal exposures may influence the composition of the birth URT microbiome, and that this early composition may be related to AOM susceptibility in infancy. ImportanceEar infections are the most common bacterial infection of childhood and the leading indication for healthcare consultation and antibiotic receipt. Previous studies have demonstrated that the microbes that inhabit the upper respiratory tract, known as the microbiome, influence risk of ear infection. This study sought to understand how exposures around the time of birth, including delivery type, maternal antibiotic exposures, and infant feeding, influence the development of the infant microbiome, and in turn, how the microbiome is related to ear infections. An analysis of nasal swabs collected from infants shortly after birth demonstrated that increased microbial diversity is associated with earlier age at first ear infection episode. Overall, this study demonstrates that exposures in early life influence respiratory microbiome development, which contributes to infection susceptibility.

microbiology↗

bioRxiv2026

Acute ketamine treatment produces long-term anxiolytic effects despite increasing oxidative stress in female Wistar Kyoto rats

Treatment-resistant depression and anxiety remain major challenges in psychiatry, particularly in female patients, who are disproportionately affected yet remain underrepresented in preclinical ketamine research. The present study investigated short- and long-term anxiolytic effects of acute subanesthetic ketamine administration in female Wistar-Kyoto (WKY) rats, a validated genetic model of treatment-resistant affective dysfunction. Subjects received a single intraperitoneal injection of saline vehicle or racemic ketamine (5, 10, or 15 mg/kg), followed by acoustic startle response (ASR) testing 24 hours and 7 days later. Oxidative stress was assessed using 8-oxo-2-deoxyguanosine (8-oxo-dG) immunofluorescence in the basolateral amygdala (BLA), prefrontal cortex (PFC), and hippocampus, alongside analysis of parvalbumin-positive (PV+) interneurons. Ketamine treatment produced dose- and time-dependent behavioral effects with 10 mg/kg eliciting the strongest delayed anxiolytic-like response at 7 days, while 15 mg/kg showed more immediate behavioral effects at 24 hours. While ketamine did not alter PV+ cell count, it significantly increased oxidative stress markers globally in the BLA and prelimbic region of the PFC and specifically in the PV+ interneurons in the BLA. The findings suggest that ketamines therapeutic effects in female WKY rats may involve region-specific modulation of stress circuitry and oxidative signaling rather than gross interneuron loss. Overall, the study provides evidence for sex-dependent and temporally dynamic effects of ketamine in a translational model of treatment-resistant anxiety and depression.

neuroscience↗

bioRxiv2026

Ginsenoside Ro ameliorates diabetic cardiomyopathy by maintaining ciliary homeostasis and enhancing antioxidation

ObjectiveTo investigate and clarify the role of Ginsenoside Ro (GRo) in diabetic cardiomyopathy (DiaCM) and to elucidate the molecular mechanism by which GRo ameliorates DiaCM. Methods[circled1] The construct of type 2 diabetic mouse model. The bought C57BL/6 male mice were housed in a specific pathogen-free (SPF) animal facility and randomly divided into control, STZ (model), STZ + GRo, and control+GRo groups. The STZ (model) and STZ + GRo groups were fed a high-fat and high-glucose diet combined with intraperitoneal injection of streptozotocin (STZ). The control and control + GRo groups were fed a normal diet, while the control + GRo and STZ + GRo groups were treated with GRo via oral gavage. Then, all groups were evaluated for cardiac function and structure by small animal echocardiography and histological staining including hematoxylin and eosin (HE) and Massons trichrome staining to screen and confirm diabetic cardiomyopathy in mice. Finally, immunofluorescence staining of cilia in mouse heart tissue was performed to determine whether GRo inhibits abnormal ciliary growth. [circled2] The construct of cell models. First, the CCK-8 (Cell Counting Kit-8) assay was used to separately evaluate the cytotoxicity of GRo and the combination of TGF-{beta}1 and PA in myocardial fibroblasts and cardiomyocytes. Subsequently, mouse myocardial fibroblast lines (MCFs) were treated with transforming growth factor-beta 1 (TGF-{beta}1), and H9c2 cardiomyocytes were treated with palmitic acid (PA). Both cell types then received the GRo treatment. [circled3] Molecular and cellular testing. Firstly, we measured serum levels of cardiac injury markers (CK-MB, MYO, and TNNI3), glutathione (GSH), and malondialdehyde (MDA). Secondly, we examined the expression of myocardial fibrosis-related genes (Col1a1, etc.), myocardial hypertrophy markers (Nppa, etc.), cilia-specific genes (Pkd1, etc.), and oxidative stress-related genes (Nrf2, etc.) in both animal and cell samples by Western blotting and RT-qPCR. Finally, we used immunofluorescence staining of myocardial fibroblasts to detect cilia length and phalloidin staining of cardiomyocytes to measure their cross-sectional area. [circled4] The correlation mechanism. Firstly, the cilia-specific inhibitory drug HIP-4 was used to disrupt cilia homeostasis by inhibiting cilia growth. Secondly, small activating RNA (saRNA) was used to upregulate the Pkd1 gene to verify whether GRo exerts its anti-fibrotic effects through the inhibition of PC1. Results[circled1] Animal level. A diabetic cardiomyopathy mouse model was successfully established by combining STZ injection with a high-fat and high-glucose diet, and treatment with GRo significantly ameliorated the associated symptoms. [circled2] Cellular level. We successfully established a myocardial fibrosis model by treating myocardial fibroblasts with TGF-{beta}1, and a myocardial hypertrophy model by treating cardiomyocytes with PA. Immunofluorescence staining demonstrated that GRo significantly decreased cilia length in the fibrosis model, while phalloidin staining showed that GRo significantly attenuated the increase in cardiomyocyte cross-sectional area. [circled3] Molecular level. Compared with the model group, GRo treatment significantly reduced serum levels of cardiac injury markers (CK-MB, MYO and TNNI3), glutathione (GSH) and malondialdehyde (MDA). Western blotting and RT-qPCR analyses of both animal and cell samples revealed that GRo markedly alleviated indicators of myocardial fibrosis and hypertrophy, while also suppressing cilia-specific genes and oxidative stress-related genes. Overall, GRo significantly ameliorated the markers associated with myocardial fibrosis and hypertrophy, and inhibited cilia-specific protein expression as well as oxidative stress parameters. [circled4] The correlation mechanism. The cilia-specific drug hedgehog pathway inhibitor 4 (HPI-4) was used to revealed that cilia homeostasis is closely linked to myocardial fibrosis and shortened cilia inhibit the fibrosis progression. Furthermore, upregulation of the Pkd1 gene by small activating RNA demonstrated that PC1 overexpression abrogates the therapeutic effect of GRo. Finally, GRo can alleviate DiaCM.

Molecular Biology↗

bioRxiv2026

Quantitative detection of gut microbial eukaryotes with EukDetect2 reveals global distribution of commensal protists and association with distinct microbial community structure

Microbial eukaryotes are prevalent members of host-associated and free-living microbial communities, but are routinely excluded from studies of these communities. Existing methods for eukaryote detection from whole metagenome sequencing are limited by contamination of eukaryotic reference genomes and incomplete taxonomic coverage. Our previously published tool EukDetect addressed these challenges using a curated database of universal BUSCO marker genes, but lacked validated quantitative abundance metrics and was built from a limited number of genomes. Here we present EukDetect2, incorporating a database containing 6,948 microbial eukaryotic genomes representing 6,594 unique species, 2,339 of which are newly added since EukDetect version 1, alongside quantitative metrics for estimating absolute and relative abundance of microbial eukaryotes. Using simulated data, we demonstrate accurate abundance estimation, no false positives from bacterial or host-derived reads, and equivalent or greater sensitivity and specificity than alternative taxonomic profiling tools across a range of microbial abundances and community compositions. Applying EukDetect2 across globally distributed human gut microbiome cohorts, we find that Blastocystis spp. and Dientamoeba fragilis are the most prevalent gut eukaryotes across cohorts, while host-associated fungi are consistently less prevalent than commensal protists. Blastocystis abundance is positively associated with a gut microbial community enriched for fiber-fermenting microbes and depleted for pro-inflammatory and industrialization-associated taxa. EukDetect2 provides sensitive, accurate, and quantitative metrics for investigating microbial eukaryotes from metagenomic samples.

microbiology↗

bioRxiv2026

Catalytic rewiring of RuvC-II catalytic site activates trans-cleavage in Fanzor2

Fanzors are eukaryotic RNA-guided endonucleases that mediate programmable cis-cleavage in eukaryotic cells, but their potential target-induced collateral (trans) cleavage activity remains largely unexplored. Here, we show that catalytic rewiring of the RuvC-II catalytic site activates robust trans-cleavage activity in Acanthamoeba polyphaga mimivirus Fanzor2 (ApmFz2). The representative variant ApmFz2-EP displayed DNA- and RNA-triggered trans-cleavage, attenuated cis-cleavage, minimal TAM dependence, and activation by as few as seven nucleotides of guide-target complementarity. Mechanistic analyses indicate that relieving steric constraints surrounding the conserved alternative glutamate within the RuvC-II activates trans-cleavage and reshapes target-recognition specificity. Coupling ApmFz2-EP with nucleic acid amplification enabled FINDER, a Fanzor-based diagnostic platform for sensitive pathogen detection and broad detection across genetically diverse target subtypes. In addition, the mismatch-sensitive ApmFz2-EA variant enabled specific single-nucleotide variant (SNV) genotyping. Together, this work expands the functional scope of Fanzors and identifies catalytic-center engineering as a strategy for developing compact, programmable nucleic acid diagnostics.

bioengineering↗

bioRxiv2026

scIsoAgent enables autonomous isoform-resolved characterization and sequence-informed interpretation of long-read single-cell transcriptomes

Alternative isoform usage can alter gene function independently of total gene expression, creating a need to resolve transcript isoforms at single-cell resolution. Long-read single-cell RNA sequencing meets this need by linking cellular identity to transcript isoforms and sequence-level features. Realizing its full biological value requires reproducible workflows that connect specialized long-read analysis with biological interpretation. Existing large language model (LLM)-based biomedical agents support general omics analysis, but are not designed for isoform-resolved long-read single-cell workflows. Here, we present scIsoAgent, an autonomous LLM-powered scientific agent for long-read single-cell RNA-seq analysis. scIsoAgent turns heterogeneous long-read single-cell inputs into traceable isoform-resolved workflows, using stage-aware planning and persistent computational context to support both execution and interpretation. Across complementary evaluations, this design improved the continuity from analysis planning to executable, interactive workflows compared with general-purpose LLM baselines. In real-data reanalysis, scIsoAgent recovered major findings from published long-read single-cell resources and extended a representative differential transcript usage event into a sequence-informed functional hypothesis. By linking full-length isoform sequences with model-inferred transcript properties, scIsoAgent connects observed isoform usage with potential sequence-level functional consequences. These results demonstrate that autonomous scientific agents can transform fragmented long-read single-cell analysis into coherent, reproducible workflows for isoform-resolved discovery and biological interpretation.

bioinformatics↗

bioRxiv2026

PhenoBIC: operator-free single-cell spatial phenotyping in multiplex imaging data using deep learning of cell staining patterns

Multiplex imaging is a valuable tool for spatially examining tissue microenvironments at the single-cell level to uncover biological and clinical insights. However, most multiplex image analysis workflows currently require manual intervention for cell phenotyping, which slows progress, demands human effort, and yields operator-dependent outputs. Here, we developed PhenoBIC, a pre-trained deep learning model for image classification of the multiplexed biomarker signals in a cell (Biomarker Imprint of a Cell) to classify cell phenotypes. We show that PhenoBIC (F1-score [~]0.88) outperforms manual gating (widely used) and other machine learning-based computational approaches for cell marker expression classification. We validated this across multiple biomarkers, tissue sampling strategies (whole biopsies and tissue microarrays), multiplex panels, imaging platforms, and tissue types. We have released our in-house training and validation datasets of [~]1.4 million manually curated cell expression ground truth labels. We have also open-sourced PhenoBIC and enabled its community-wide deployment via the QuPath interface.

bioinformatics↗

bioRxiv2026

An SE(3)-equivariant Crystal Structure Prediction Framework for Prospective Identification and Development of Bioactive Nucleoside Self-Assembling Materials

Flexible organic small molecules can assemble into supramolecular biomaterials whose properties are intrinsically governed by their crystal structures, yet experimental structure determination remains difficult to scale during molecular modification and materials optimization. Crystal structure prediction (CSP) provides a potential solution, but its prospective power is limited for flexible molecules owing to incomplete conformational sampling and the difficulty of identifying experimentally realized structures from large candidate sets. Here, an SE(3)-equivariant deep-learning workflow, SE3CSP, is developed for organic crystal structure prediction. By learning molecular conformations, unit-cell parameters and packing patterns from experimentally resolved crystal structures and integrating these predictions with MACE-based structure optimization, SE3CSP establishes an end-to-end pipeline from two-dimensional molecular representations to three-dimensional crystal structures. Using nucleosides as representative flexible self-assembling building blocks, SE3CSP achieves an overall prediction accuracy of [~]57%, substantially outperforming the benchmark MACE-based Genarris 3.0 workflow ([~]14%). Furthermore, a prospective prediction strategy is developed in which an SE3CSP-predicted density window ({+/-} 0.15 g/cm3) is applied prior to energy ranking and structural deduplication, enabling all experimentally realized structure to be consistently ranked within the top 1-2% of all generated candidates. Beyond structure prediction, SE3CSP-derived energy landscapes provide insight into potential single-crystal-to-single-crystal transformations and enable the identification of a nucleoside supramolecular material with dynamic breathing porosity, which is further developed as an adsorptive platform for inflammatory mediator removal with excellent anti-inflammatory performance and biocompatibility. These results establish SE3CSP as a practical framework for prospective CSP and highlight its utility in guiding the discovery and design of bioactive self-assembled materials.

bioengineering↗

bioRxiv2026

A paradoxical relationship between mitochondrial calcium regulation and retinal ganglion cell degeneration after axon damage

Retinal ganglion cells (RGCs) degenerate in optic neuropathies like glaucoma and traumatic optic nerve injury leading to irreversible vision loss. Higher levels of homeostatic Ca2+ and canonical Ca2+ regulated signaling promote RGC survival in animal models of glaucoma and optic nerve injury. Mitochondrial dysfunction is also a hallmark of degenerating neurons, including RGCs. Here, we investigate the intersection of mitochondrial function, Ca2+ homeostasis, and cellular resilience by performing an optic nerve crush model of RGC degeneration while monitoring and manipulating mitochondrial Ca2+ levels (mito-Ca2+). We find that mito-Ca2+ is predicative of RGC survival in that surviving RGCs are enriched for higher homeostatic mito-Ca2+ levels. Mitochondrial dysfunction was observed where mito-Ca2+ was reduced in RGCs after injury, regardless of survival. We then examined the importance of higher mito-Ca2+ in surviving RGCs by altering mito-Ca2+ levels and Ca2+ transit using pharmacological and AAV-mediated approaches. Paradoxically, treatment to decrease mito-Ca2+ increased survival to ONC. We then manipulated mito-Ca2+ permeability by altering the expression levels of the mitochondrial calcium uniporter (MCU) pore forming subunit that allows Ca2+ to enter mitochondria from the cytoplasm. Overexpressing MCU reduced RGC survival to injury, while shRNA knockdown of MCU increased RGC survival. These results reveal a complex relationship between mito-Ca2+ and RGC degeneration and suggest that well-surviving RGCs may be under chronic mitochondrial stress due to higher homeostatic mito-Ca2+ levels.

neuroscience↗

bioRxiv2026

CHOP promotes the transition to chronic integrated stress response signaling with suppression of hepatocyte identity

The transcription factor CHOP promotes cell death during ER stress, but it is strongly induced even by moderate stresses that do not result in appreciable cell death. Its role during less severe stresses--especially in intact tissues in vivo--is poorly understood. Here, we both deleted and restored CHOP specifically in hepatocytes and challenged animals with ER stress in vivo. We found that CHOP influenced stress-dependent hepatocyte gene expression through two previously unappreciated mechanisms. It directly suppressed the expression of transcriptional master regulators of hepatocyte identity and metabolism. And more broadly, it exacerbated ER stress through the promotion of protein synthesis, which led to persistent activation of the integrated stress response (ISR) despite dephosphorylation of eIF2. This shift to second-phase ISR signaling was phenocopied by deletion of the protective UPR sensor ATF6, suggesting that it reflects a transition from an acute stress response to a chronic one. Our findings show that CHOP augments the capacity of the ISR and UPR to continue to mount a protective response even after eIF2 phosphorylation has been suppressed. In vivo, where ISR signaling intersects with hepatocyte gene regulatory networks, this transition favors lipid dysregulation, highlighting a pathway through which CHOP impacts tissue function independent of cell death.

cell biology↗

bioRxiv2026

A unified population-genetic framework for inferring double reduction and higher-order two-locus linkage disequilibrium in autopolyploids

Polyploid genomes exhibit complex allelic interactions driven by polysomic inheritance that cannot be adequately captured by conventional pairwise linkage disequilibrium (LD) models, leaving higher-order dependency structures largely unresolved. Here, we develop a unified population-genetic framework that jointly infers double reduction and higher-order allelic associations in autopolyploids. By modeling dosage-dependent gametic structure, our approach reformulates LD as a hierarchical system, enabling decomposition of allelic dependencies beyond pairwise interactions. Through simulations, we show that conventional estimators systematically confound higher-order allelic structure with pairwise LD, leading to biased inference and loss of identifiability under dosage uncertainty. In contrast, our joint likelihood framework achieves asymptotically unbiased estimation and reveals that effective double reduction emerges as a composite parameter linking meiotic configuration to population-level allelic reshaping. Applying this model to Arabidopsis arenosa and cultivated potato, we uncover fundamental contrasts in genomic architecture. In natural populations, higher-order LD self-organizes into structured, long-range dependency networks, whereas predominantly clonal populations exhibit collapse of higher-order dependency structure. We further identify a spatial antagonism between double reduction and higher-order LD, demonstrating that elevated double reduction compresses the physical scale of allelic interactions. Our results demonstrate that autopolyploid genomes are organized as higher-order dependency systems rather than collections of pairwise associations, establishing higher-order allelic structure as a fundamental extension of classical LD theory.

genetics↗

bioRxiv2026

BioClaw: Human-Bot Research Collaboration Ecosystems in Group Chats

Day-to-day research discussions in group chats often generate hypotheses, analysis requests, and interpretation decisions, yet executing those analyses still requires researchers to leave the conversation and rely on fragmented local tools, databases, visualization software, and literature search engines. In this work, we present BioClaw, a human-bot research collaboration ecosystem that converts natural-language requests in group conversations into tool-grounded analyses executed within isolated Docker containers. Deployed across 8 messaging platforms, BioClaw turns each group chat into a persistent execution workspace. Its design combines multi-channel orchestration, per-group state and workspace management, and isolated containerized execution for reliable shared use over long-lived conversations. To support practical research workflows, BioClaw combines containerized execution with preinstalled 31 biomedical tools and 95+ skills. The application of BioClaw spans various biomedical domains (e.g., genomics, clinics, structural biology) and data modalities (e.g., sequencing data, EHR data, protein structure data). These results establish the viability of embedding executable, tool-rich agent workflows within shared digital workspaces, positioning group chats as a transformative paradigm for collaborative scientific discovery and innovation.

bioinformatics↗

bioRxiv2026

A Haplotype-resolved Telomere-to-Telomere Pig Genome

Haplotype-resolved telomere-to-telomere (T2T) pig genomes have not been reported, but may be essential for xenotransplantation and interspecies chimerism. Here we describe fully phased paternal and maternal T2T assemblies of an Erhualian pig (Ssc_EHL), enabling chromosome-scale reconstruction at base resolution. We uncover 2,498 (paternal haplotype) and 2,577 (maternal haplotype) protein-coding loci previously unresolved in Sscrofa11.1, while maintaining high concordance for anchored gene models between haplotypes. Applying this haplotype-resolved T2T annotation to publicly available pig-to-human kidney xenograft transcriptomes further allows the resolution of xenograft cell states and reveals newly resolved, cell-type-enriched genes. Cross-species synteny and orthology analyses against human identified a subset of high-confidence pig-specific genes with limited human counterparts, thus, refining the genomic basis of interspecies divergence. By integrating these orthology features with organ transcriptomes, we further defined an Interspecies Chimerism Compatibility score to prioritize organ-enriched, highly conserved loci with minimal redundancy imbalance, providing haplotype consistent targets for engineering organ-deficient pigs. Together, this haplotype-resolved T2T pig genome provides a foundational resource for mechanistic studies and rational genome engineering in xenotransplantation and interspecies chimerism.

genomics↗

bioRxiv2026

Decoding Immunomodulatory Hydrogels for Arthritis: Comparative Insights from Predictive Machine Learning and Large Language Models

Hydrogels are increasingly recognized as promising therapeutics for arthritic joints, extending their traditional role as mechanical lubricants to modulators of joint immunity. However, the rational design of these materials remains challenging, with progress largely driven by empirical experimentation. To address this, we curated a comprehensive database of 220 hydrogel formulations from 317 published studies and applied an interpretable machine learning (ML) framework to uncover the relationships between hydrogel design parameters and the arthritis severity score. Using a Random Forest algorithm, our model achieved an external validation accuracy of 0.67 in predicting effective hydrogel therapies for arthritis. Analysis revealed a clear hierarchy of design principles: the choice of functional agent, base polymer, and elastic modulus were the most influential predictors of therapeutic efficacy, with composite agents, protein-based polymers, and softer hydrogels most strongly associated with positive therapeutic outcomes. Mechanistic investigations further demonstrated that successful hydrogels promote an anti-inflammatory M2 macrophage phenotype. Benchmarking against classical statistical methods and a large language model framework showed that our ML approach provided more robust, nuanced insights into complex feature interactions. This data-driven framework offers a generalizable blueprint for the rational design of next-generation immunomodulatory hydrogels, paving the way for more effective arthritis therapies.

bioengineering↗

bioRxiv2026

Drosophila core circadian clock neurons peptidergically regulate activity of insulin-producing cells

Central pacemaker neurons use a combination of external stimuli and neuropeptide signaling to synchronize molecular oscillations leading to circadian behaviors. The clock network structure and signaling between these pacemaker neuron groups have been well described, but how these pacemakers communicate with specific brain output regions remains poorly understood. Here, we identified how "core" clock neurons in Drosophila, the ventrolateral neurons (LNvs), signal to the proto-hypothalamic region, the pars intercerebralis (PI). We provide evidence to show that LNvs functionally modulate insulin-producing cells (IPCs) of the PI in a time-of-day-dependent manner. This functional connectivity relies on neuropeptidergic signaling of two classical clock neuropeptides: pigment dispersing factor (PDF) and short Neuropeptide F (sNPF). Connectomic analysis does not identify any direct synaptic inputs from clock neurons to IPCs. Small ventrolateral clock neurons, which secrete both PDF and sNPF are 15-20 mm away from IPCs, suggesting that volume transmission across these distances may be possible in the fly dorsal protocerebrum. Peptide application with functional imaging of IPCs provides insight into how these two neuropeptides may act synergistically via their receptors to signal to IPCs. Our findings indicate that LNvs can signal to IPCs by volume transmission and also form indirect multisynaptic circuits with IPCs. Thus, neuropeptides associated with intra-clock signaling also serve as circadian cues to non-clock neurons, which may model more broadly how clock peptides communicate to clock output regions. Author SummaryCircadian clocks in the brains of animals from flies to humans allow animals to anticipate daily environmental changes and temporally coordinate internal processes. The central clock in the brain serves as a master pacemaker that sets the pace of circadian clocks in all other tissues. Fruit flies have been an essential model system for discovering the genetic and cellular underpinnings of circadian rhythms, with work in flies being awarded the 2017 Nobel Prize in Physiology or Medicine. Brain clocks in flies and mammals use classical synaptic communication and neuromodulatory peptides to signal between the neurons that make up the brain clock. Our study asks whether the neuropeptides that signal between clock neurons also signal from clock to non-clock neurons. We found that the signature fly clock neuropeptide pigment dispersing factor signals outside the brain clock to insulin producing cells of the brain. There are no synaptic connections from clock neurons to insulin producing cells, instead signaling occurs by diffusion of peptide signals across tens of microns, termed volume transmission. Our findings point to the possibility that neuropeptide volume transmission may be a general feature not only of intra-clock signaling but also of brain clock output signaling to non-clock neurons.

neuroscience↗

bioRxiv2026

DNA-triggered AIM2 condensation orchestrates immune activation and regulation

The innate immune sensor AIM2 detects cytosolic DNA and initiates inflammatory responses, yet its activation mechanism remains incompletely understood. Here, we show that AIM2 undergoes liquid-liquid phase separation upon DNA binding, forming dynamic condensates both in vitro and in cells. These condensates serve as platforms for inflammasome and PANoptosome assembly, promoting immune activation across multiple pathways. Direct structural determination from condensates reveals the assembly of active-form ASC filaments. Mechanistically, liquid-phase condensation is governed by multivalent interactions involving different AIM2 domains, including previously uncharacterized regions and species-specific elements. In vitro and in vivo assays show that mutants specifically disrupting condensation impair immune complex assembly, cell death initiation, antimicrobial defense, and intestinal homeostasis. Moreover, AIM2-DNA condensates function as regulatory hubs targeted by host- and pathogen-derived factors to balance immune homeostasis or facilitate immune evasion. These findings establish liquid-phase condensation as a fundamental mechanism of AIM2 activation and a potential therapeutic target.

immunology↗

bioRxiv2026

AI-Guided CRISPR Screen Accelerates Discovery of New Drug Targets

Psoriasis affects over 125 million people worldwide, yet the mechanistic understanding of keratinocyte-driven inflammation remains incomplete, limiting therapeutic innovation beyond costly systemic biologics that are prone to side effects. Here, we performed the first genome-wide CRISPR knockout screen in primary human adult epidermal keratinocytes to systematically identify regulators of IL-17 receptor A (IL17RA), a central node in psoriatic inflammation. To prioritize therapeutically tractable targets from over 19,000 screened genes, we integrated a large language model - VirtualCRISPR - trained on functional genomics data, identifying arachidonate 5-lipoxygenase (ALOX5) and oxytocin receptor (OXTR) as high-confidence novel hits with minimal prior association with psoriasis. Multi-omics validation revealed that ALOX5 and OXTR regulate IL17RA expression through distinct signaling pathways - ALOX5 through lipid mediators that stabilize the receptor at the cell surface, and OXTR through calcium signaling that reprograms cellular metabolism. Topical delivery of their inhibitors Zileuton (ALOX5) and Cligosiban (OXTR) exhibited therapeutic efficacy comparable to systemic anti-IL17RA antibody in the imiquimod-induced psoriasis model, suppressing pathogenic Th17/Tc17 responses, polarizing macrophages toward anti-inflammatory phenotypes, and normalizing epidermal hyperproliferation. Proteomic profiling in human 3D organotypic skin and murine models confirmed on-target pharmacology and revealed convergent suppression of neutrophil-keratinocyte inflammatory circuits. The use of VirtualCRISPR significantly shortened the timescale from screen to the identification of druggable hits with robust validation, and this work establishes a blueprint for integrating AI-driven target prioritization with functional genomics to accelerate therapeutic discovery.

genomics↗

bioRxiv2026

Lateral hypothalamic input engages a disinhibitory microcircuit in the dorsal raphe to promote behavior activation

Behavioral activation involves coordination between hypothalamic and brainstem systems that regulate movement and internal state, but the circuit logic underlying this interaction remains poorly defined. The dorsal raphe nucleus (DRN), a major serotonergic hub, integrates diverse inputs to influence behavioral inhibition and activation, yet how hypothalamic signals shape its activity is unclear. Here, we identify a disinhibitory pathway through which the lateral hypothalamus (LHA) promotes behavioral activation via DRN. Using intersectional viral tracing, electrophysiology, and single-nucleus RNA sequencing, we show that LHA inputs preferentially target GABAergic DRN neurons that locally inhibit 5-HT neurons. Silencing these DRN neurons innervated by LHA increased locomotor and repetitive behaviors, decreased local inhibition, and enhanced cFos activity in serotonergic neurons, consistent with circuit-level disinhibition. Molecular profiling revealed distinct transmitter identities and transcriptional signatures of LHA-targeted versus LHA-projecting DRN populations. Together, these findings delineate a hypothalamic-raphe circuit that transforms hypothalamic drive into serotonergic activation, revealing a mechanism by which the lateral hypothalamus promotes behavioral activation through local inhibitory control. HIGHLIGHTSO_LILHA inputs preferentially target non-serotonergic, transcriptionally distinct DRN neurons C_LIO_LILHA-innervated DRN neurons form extensive local inhibition C_LIO_LISilencing LHA-innervated DRN neurons reduced local inhibitory tone and activate serotonin neurons C_LIO_LIDisrupting this circuit drives behavioral activation with repetitive motor pattern, not anxiety C_LI

neuroscience↗