Biology subjects

Meng, X.

Publications and source records attributed to Meng, X..

At least 19 recordsLinked to original sources

bioRxiv2026

Microbiota- and diet-specific T cells become Tregs by default

CD4+ T cells recognize antigens from microbiota, diet, and pathogens via T cell receptors (TCRs) and orchestrate immunity by differentiating into tolerogenic regulatory (Treg) or pro-inflammatory effector (Teff) lineages (e.g. TH1 or TH17) (1). Dysregulation of these responses underlies numerous gastrointestinal inflammatory and infectious diseases (2-6). The prevailing paradigm suggests that individual microbes and dietary antigens drive distinct cell fates (e.g., segmented filamentous bacteria [SFB] induce TH17 cells (7) whereas Helicobacter hepaticus (8) and diet (9) induce Tregs). However, the generality of this model is uncertain: several key organisms are atypical, and foundational studies often omitted a complex microbiome or a diverse polyclonal TCR repertoire. Here we develop a high-throughput pipeline to screen hundreds of TCRs from mice colonized from birth with a 116-strain human microbiota (hCom2v), demonstrating that TCRs recognizing microbiota or dietary antigens are overwhelmingly enriched in the induced Treg (iTreg) lineage. Endogenous CD4+ T cells specific for these antigens adopt a uniform iTreg phenotype in vivo, both in hCom2v-colonized and conventional mice. This baseline tolerance is robust to acute inflammation but breaks down following a 'two-hit' combination of inflammation and genetic susceptibility, allowing Teff to emerge against otherwise Treg-restricted antigens. These data support a revised paradigm in which antigen-specific Treg induction is the default response to foreign antigens in the healthy gut, and effector responses are an exception reflecting a perceived threat. Reframing gastrointestinal immunity as a tolerance-first system provides a framework for understanding inflammatory disease pathogenesis and suggests that therapeutic strategies should aim to restore a Treg-predominant baseline.

immunology↗

bioRxiv2026

Brain State Dynamics and Developmental Differences in Reading Comprehension

Reading comprehension is a complex cognitive task that involves dynamic interactions between the brain and external information. Previous studies on reading development primarily focused on localized or static brain activities. However, it remains an enigma how brain state dynamics evolve with development underlying reading comprehension. This study aims to address this issue by combining functional magnetic resonance imaging (fMRI) with Hidden Markov Model (HMM) to explore brain state dynamics. A total of 35 typically developing children and 31 adults were scanned while reading a story. Our results demonstrated a tripartite brain state organization, characterized respectively by high activities in the visual (State #1), language (State #2), and default mode network (DMN, State #3) regions. Children exhibited significantly longer dwell time in the DMN state (State #3) compared to adults, along with a higher probability of transitioning from the language state (State #2) to the DMN state (State #3). In addition, adults exhibited greater flexibility in state transitions during reading comprehension. Finally, the alignment between the dynamic states of children and the average states of adults was a significant positive predictor of their reading comprehension performance. This study provides a novel, intuitive perspective on how brain state dynamics evolve during the development of reading comprehension.

neuroscience↗

bioRxiv2026

Pharmacologic eIF2B Activation Rescues Neuropathy in CMT2 Subtypes by Normalizing the Integrated Stress Response

Among the many subtypes of Charcot-Marie-Tooth (CMT) disease, several result from mutations in genes encoding aminoacyl-tRNA synthetases, enzymes required for tRNA charging during cytoplasmic and mitochondrial translation. We report that activation of the integrated stress response (ISR) pathway is a shared molecular feature of tRNA synthetase-associated and other axonal CMT subtypes. RTX-117, a CNS-penetrant small molecule currently in Phase 1 clinical trials, targets eukaryotic initiation factor 2B (eIF2B), a key modulator of protein synthesis and the ISR pathway. Using cryo-EM studies, we have characterized the binding mode of RTX-117 to the eIF2B decamer. In GarsP278KY/+ mice, which develop early onset motor defects and axonal pathology that recapitulate CMT2D symptoms from tRNA synthetase mutations, RTX-117 treatment started after disease onset reduced chronic ISR activation and produced significant functional and electrophysiological improvement. We further identify ISR targets, including secreted proteins such as GDF15 and FGF21 that may serve as translational biomarkers for treatment response to RTX-117 in CMT disease. Broader surveillance of the ISR pathway across models of neurodegeneration reveals strong activation in several diseases and a correlation with disease progression, particularly in models of Alzheimers disease. These findings identify chronic ISR activation as a recurrent, though not universal, pathological mechanism of neurodegenerative disease models. Overall, our study identifies candidate biomarkers for CMT disease subtypes associated with defects in translational homeostasis and supports eIF2[a]-ATF4 axis modulation as a promising therapeutic strategy for this disease class. One Sentence SummaryRTX-117, a clinical stage eIF2B activator, blunts chronic ISR activation and improves nerve and motor function in a mouse model of Charcot-Marie-Tooth Disease Type 2D.

cell biology↗

bioRxiv2026

Aggrecan hypomorphism accelerates the progression of post-traumatic osteoarthritis in mice

ObjectiveThe Agc1CreERT2 mouse line is a powerful tamoxifen-inducible genetic tool used for conditional gene manipulation specifically in cartilage. The aim of this study was to investigate the effects of aggrecan hypomorphism on the progression of post-traumatic osteoarthritis (PT-OA) in Agc1CreERT2 mice. MethodsProteoglycan content in cartilage samples from the knees of E18.5 embryos were quantified by sulfated glycosaminoglycan (sGAG) assay. Destabilization of the medial meniscus (DMM) surgery was performed to induce PT-OA in 12-week-old wild-type, heterozygous Agc1CreERT2/+ and homozygous Agc1CreERT2/CreERT2 mice. Progression of OA was assessed at 4-, 8-, and 12-weeks post-DMM by OARSI, synovitis and osteophyte maturation histopathology scores and micro-computed tomography ({micro}CT). Aggrecan deposition, cartilage matrix-degrading proteases, aggrecan and collagen II degradation neoepitopes were investigated by immunohistochemical staining, and serum C-terminal cross-linked telopeptide of type II collagen (CTX-II) levels by an enzyme-linked immunosorbent assay (ELISA). Chondrocyte apoptosis was analyzed with the terminal deoxynucleotidyl transferase (TdT) dUTP nick-end labeling (TUNEL) assay. The biomechanical properties of articular cartilage (AC) were investigated with indentation-type atomic force microscopy (IT-AFM). ResultsBefore DMM, homozygous Agc1CreERT2/CreERT2 mice had reduced sGAG and aggrecan levels and increased cartilage stiffness. After DMM, they exhibited increased cartilage degradation, synovitis, osteophyte formation and meniscus mineralization, chondrocyte apoptosis and cartilage stiffness compared with wild-type mice. Immunohistochemistry demonstrated increased expression of the aggrecanase ADAMTS-5, the metalloproteinase MMP-13, the aggrecan degradation neoepitope NITEGE and the collagen degradation neoepitope C1,2C in AC. ELISA also revealed elevated serum CTX-II levels. Heterozygous Agc1CreERT2/+ mice also exhibited accelerated PT-OA compared with wild-type mice, characterized by elevated CTX-II levels at 4-weeks, increased synovitis, osteophyte and soft tissue mineralization at 8-weeks, and more severe cartilage degeneration at 12-weeks post-DMM. ConclusionBoth homozygous and heterozygous Agc1CreERT2mice exhibit increased susceptibility to PT-OA, underscoring the importance of physiological aggrecan expression in maintaining joint homeostasis and regulating joint pathophysiology. These findings indicate that Agc1CreERT2/+mice are not phenotypically neutral in the DMM model and that this intrinsic susceptibility should be considered when interpreting studies employing inducible, cartilage-specific gene deletion.

molecular biology↗

bioRxiv2026

Integrated Patient-Derived Xenograft and Patient-Derived Cell Models Reveal Therapeutic Vulnerabilities Beyond Standard-of-Care Therapy in Endometrial Cancer

Endometrial cancer (EC), the most common gynecologic malignancy in the USA, has seen limited improvement in patient outcomes over recent decades, underscoring the need for relevant preclinical models. To address EC heterogeneity, we established an integrated platform of patient-derived xenografts (PDXs) and matched patient-derived primary cancer cells (PDCs) for disease modeling and systematic drug sensitivity testing. Fresh tumor specimens (n=103) were collected from EC patients to generate PDXs in immunodeficient mice and corresponding PDCs. Fifty-three PDX models were successfully established (52% engraftment rate), with higher success observed in high-grade, recurrent, metastatic tumors (70%), compared with their low-grade counterparts (56%). Histopathologic and immunohistochemical analyses confirmed that PDX tumors faithfully preserved morphology, hormone receptor status, and intertumoral heterogeneity across multiple passages. Using 13 PDC models, we performed an unbiased screening of 179 FDA-approved oncology drugs, revealing marked intertumoral variability in drug response. Almost all PDC models exhibited limited sensitivity to NCCN-recommended therapies, highlighting the need for alternative treatment strategies. In contrast, multiple FDA-approved agents including epigenetic modulators, dual PI3-kinase/HDAC inhibitors, topoisomerase II inhibitors, and proteasome inhibitors demonstrated potent antitumor activity. Importantly, a low-dose combination of the DNA methyltransferase inhibitor 5-azacytidine and the histone deacetylase inhibitor romidepsin significantly suppressed tumor growth across six independent PDX models. Together, these findings establish a comprehensive PDX and PDC platform as a robust translational resource. By capturing the histopathologic and molecular diversity of EC and identifying clinically actionable therapeutic advantages, including an epigenetic combination regimen, this study offers a translational resource for preclinical drug evaluation.

cancer biology↗

bioRxiv2026

Learning to read a second language establishes a parallel L2 representation alongside the native one in the VWFA

Learning to read a second language requires the brain to incorporate a new writing system into an already established native-language reading network, yet how this process reshapes the Visual Word Form Area (VWFA) remains poorly understood. Using fMRI with a passive viewing paradigm, we systematically investigated how VWFA responses to Chinese (L1) and English (L2) words evolved across three groups of native Mandarin-speaking children at distinct stages of L2 literacy acquisition: L2 pre-readers, L2 beginning readers, and L2 advanced readers. We combined univariate activation analyses, representational similarity analysis, and supervised machine learning classification to investigate two theoretical accounts of VWFA reorganization: the Overlay Model, which predicts stable L1 responses as L2 responses emerge, and the Competing Model, which predicts competitive reallocation of neural resources from L1 to L2. We found that although the VWFA already exhibited robust selective responses to L1 words, L2-word selectivity was absent in L2 pre-readers but emerged robustly in beginning readers, with L1-word selectivity remaining stable throughout. Representational similarity analysis further revealed that robust L2 word representations within the VWFA emerged only after children began learning to read L2, while L1 word representations remained stable across all three groups. Finally, supervised machine learning analyses successfully discriminated among the three L2 literacy groups on the basis of L2 but not L1 activation patterns, indicating that VWFA responses to L2 alone were sufficient to capture childrens stages of L2 literacy acquisition, whereas responses to L1 were insensitive to L2 learning experience, providing no support for the Competing Models prediction of competitive neural reallocation away from L1. Together, these findings support the Overlay Model, demonstrating that the VWFA incorporates a new writing system within its existing cortical resources without compromising L1 print processing.

neuroscience↗

bioRxiv2026

OmicOS: A Comprehensive Omics Ecosystem Infrastructure and Agent System for the AI Era

Biology has accumulated a vast ecosystem of omics methods, but much of this ecosystem remains built for expert humans rather than scientific agents. Methods are scattered across Python packages, R/Bioconductor and CRAN workflows, command-line tools, incompatible data containers and implicit object states, making even routine analyses difficult for an AI system to choose, execute and verify reliably. Here we introduce OmicOS, a comprehensive omics ecosystem infrastructure and agent system that turns OmicVerse V2, an open-source omics community, into an executable foundation for agentic biology. OmicVerse V2 provides the community substrate: scalable AnnDataOOM-compatible rust backends, agent-friendly Python algorithms for single-cell, spatial, bulk and multi-omics analysis, interfaces to single-cell foundation models, and Python-native reconstructions of historically R-centred Bioconductor/CRAN-style workflows. OmicOS makes this substrate actionable by registering analytical functions as state-aware capability contracts, allowing agents to inspect live data objects, select valid methods, execute controlled workflows and record provenance. The result is not a fixed pipeline, but a programmable omics environment in which agents compose real analyses from verified community methods rather than inventing tools. Across external and purpose-built benchmarks, OmicOS ranked first among the evaluated systems, reaching 81.2% on BiomniBench. Adding OmicVerse to a minimal agent improved task completion by up to 34.2 percentage points with qwen-3.6-35b, and controlled ablations showed that the gains came from registry-grounded execution rather than from larger models, documentation retrieval or unrestricted tool exposure. The same infrastructure scaled to atlas-sized data, reproduced R-centred workflows in Python and converted external pathology software into agent-usable skills. In a discovery task starting from a whole-body spatial map and the term "Alzheimers disease", OmicOS composed a non-canonical workflow that integrated spatial expression, genetic association, eQTL and colocalization evidence to nominate a colon epithelial risk axis centred on PICALM, CD2AP and CR1. Together, OmicVerse and OmicOS define an open foundation for AI-era omics, showing how a community of biological methods can be transformed into a reliable, extensible and agent-operable system for discovery. HighlightO_LIOmicVerse 2.0 consolidates 694 methods spanning 11 omics domains into agent-callable high-level APIs. C_LIO_LIRebuildR automatically reconstructs and evolves R/Bioconductor methods as Python-native implementations under output-equivalence gates. C_LIO_LIOmicOS establishes a state-of-the-art omics agent harness, ranking first on general omics benchmarks across models and substantially improving the analytical capability of local open-source models. C_LIO_LICompositional use of ecosystem modules nominates a colon epithelial axis associated with Alzheimers disease risk. C_LIO_LIExternal algorithm packages supporting automatic iterative evolution can be integrated into the OmicOS ecosystem. C_LI

bioinformatics↗

bioRxiv2026

Virus-human protein-protein interactions predict viral phenotypes

Viral phenotypes such as host and tissue tropism are critical determinants of viral infection and transmission. Inferring viral phenotypes presents unique challenges compared to cellular organisms, as viruses rely entirely on host machinery for replication and survival. Current methods for predicting viral phenotypes mainly rely on viral genomic data, often overlooking host-related information. Here, we evaluated the utility of predicted virus-human protein-protein interactions (PPIs) in inferring diverse viral phenotypes using machine-learning algorithms. For predicting human infectivity, a PPI-based machine learning model outperformed both virus genomic and protein sequence-based models that used large language model embeddings. It also surpassed previous methods that incorporated both viral and host genomic data. The human proteins identified by the model were significantly enriched in functions related to viral infection and immune response. In predicting various phenotypes of human RNA viruses, PPI-based models performed better than virus sequence-based models in forecasting virulence, human transmissibility and transmission routes, while showing comparable performance to genomic sequence-based models in predicting tissue tropism. Finally, we demonstrated that a PPI-based model could distinguish high-risk HPV genotypes from low-risk ones. Proteins associated with high-risk HPV were involved in apoptosis and immune regulation, whereas those linked to low-risk HPV were enriched in telomere maintenance and DNA repair. Collectively, this study is the first to demonstrate the value of predicted virus-human PPIs in inferring viral phenotypes, thereby enhancing our understanding of the molecular mechanisms underlying these phenotypes. It also provides effective tools for risk assessment of emerging viruses, contributing to improved pandemic preparedness.

bioinformatics↗

bioRxiv2026

GalaxyVS: Exploring 100-Billion Compounds in Seconds

We present GalaxyVS, a hardware-software co-designed virtual screening framework built to explore the 100-billion commercially accessible chemical space in seconds, deployed at the National Supercomputing Center in Tianjin. Built upon the dense vector retrieval paradigm of DrugCLIP, GalaxyVS bypasses the structural dependencies and computational overhead of classical docking to enable rapid screening against experimentally determined as well as geometrically feasible pockets on AlphaFold-predicted structures. To scale this paradigm to the 100-billion level, the system must overcome the significant computational burden of offline representation encoding, critical memory and I/O bottlenecks during online retrieval, and the risks of diversity collapse and precision loss within final screening results. Utilizing the heterogeneous supercomputing infrastructure, GalaxyVS accelerates the offline encoding through deep operator adaptations and resolves online retrieval bottlenecks via disk-native vector indexing coupled with in-memory staging to ensure both broad accessibility and high throughput. Concurrently, a two-stage refinement protocol effectively mitigates diversity collapse and ensures high-fidelity affinity ranking. Consequently, GalaxyVS achieves a daily scoring throughput of 1.5 x 1016 target-ligand pairs, representing a six-orders-of-magnitude leap over previous supercomputing records. Driven by this throughput, we screened nearly 100,000 protein structures across six species against the 100-billion compound library in just 16 hours. The resulting comprehensive cross-species interaction landscape, GalaxyDB, will be openly released at https://galaxyvs.drugclip.com.

bioinformatics↗

bioRxiv2026

Ecology and engineering to modify the bile acid output of a defined microbial community

The bile acid pool, which is synthesized collaboratively by the host and its microbiome, impacts metabolism, immunity, and disease risk. Targeted microbiome interventions could in principle reshape the bile acid pool for therapeutic benefit, but practical strategies remain elusive. In the course of screening a complex defined community for metabolic phenotypes by dropping out individual strains, we observed that several of the single-strain dropout communities had markedly increased deoxycholic and lithocholic acid levels and a larger bile acid pool. In each of these communities, a second strain--Lactobacillus plantarum--had bloomed. The bile salt hydrolase activity of L. plantarum was necessary and sufficient to expand the size of the bile acid pool. An engineered community in which the bsh gene is overexpressed in multiple Lactobacillus strains confers on mice increased levels of secondary bile acid levels and a larger pool size. By overexpressing a different pair of bile acid metabolic genes in multiple strains of Lactobacillus--7- and 7{beta}-hydroxysteroid dehydrogenase--we changed the composition of the bile acid pool, enlarging it and redirecting it toward ursodeoxycholic acid. Together, these results demonstrate that fine details of the microbiomes strain composition can have a substantial effect on bile acid metabolism, and that rational manipulation of the microbiome can alter the size and composition of the bile acid pool.

microbiology↗

bioRxiv2026

A single-strain dropout screen reveals mechanistic links between microbial ecology and metabolism

The complexity of the gut microbiome has made it challenging to define the role of individual species in community-level function. Here, we constructed 56 single-strain dropout variants of a defined 118-member community and used each one to colonize a group of germ-free mice. In many cases, removing a single strain triggered a large reordering of a small group of species, which in turn altered the communitys metabolic output. En bloc removal of the eight-strain acetogen compartment markedly reduced acetate production and caused intestinal H2 accumulation and bloating; a specific subset of four acetogens was sufficient to relieve bloating and restore acetate production. Together, these data show that small disturbances in community composition can trigger a confined ecological reorganization with a large chemical phenotype, and they reveal novel strategies for engineering communities with altered metabolic output.

microbiology↗

bioRxiv2026

AlaRS-mediated lactylation shields PARP1 from ubiquitination to promote HNSCC progression

Abstract textMetabolic reprogramming in solid tumors causes massive lactate accumulation, yet how this drives oncogenesis remains incompletely understood. Here, we identify alanyl-tRNA synthetase (AlaRS) as a cellular lactyltransferase that promotes head and neck squamous cell carcinoma (HNSCC) progression. AlaRS directly catalyzes site-specific lactylation of the DNA repair protein PARP1 at lysines K249 and K667. Crucially, we uncover a competitive post-translational crosstalk wherein this lactylation directly antagonizes PARP1 ubiquitination. This "lactyl-shield" prevents proteasomal degradation, hyper-stabilizing the PARP1-Mortalin complex and sustaining tumor proliferation via p53/p21 dysregulation. To therapeutically exploit this mechanism, we identified chelerythrine (CHE) as a potent, selective inhibitor that directly binds the AlaRS catalytic center. CHE abrogates AlaRS lactyltransferase activity, destabilizes PARP1, and robustly suppresses HNSCC xenograft growth in vivo. These findings establish a novel metabolic-post-translational axis linking lactate accumulation to oncoprotein stabilization, providing a blueprint for targeting tRNA synthetase moonlighting functions in cancer.

molecular biology↗

bioRxiv2026

Histone modifications analysis reveals enhancers reprogramming during maternal-to-zygotic transition

Enhancers are important epigenetic regulatory components that coordinate spatiotemporal gene expression and play a crucial role in mammalian development, gene regulation, and illness. During early mammalian embryogenesis, histone modifications including H3K4me1, a canonical enhancer mark, and H3K27ac, which identifies active enhancers, are still poorly understood. This study profiles the genome-wide H3K4me1 and H3K27ac patterns in mouse oocytes and pre-implantation embryos using low-input CUT&RUN (cleavage under targets and release using nuclease) with input as low as 50 cells. Both markers are enriched in distal regions and exhibit distinct sequence preferences and reprogramming dynamics in pre-implantation embryos. H3K27ac, a mark of active enhancers, is reprogrammed at the 2-cell stage, whereas H3K4me1 is remodeled at the 4-cell stage and overlaps with H3K27ac, coinciding with accessible chromatin regions. Interestingly, H3K4me1 and H3K27ac are co-localized in promoter regions, where they are mutually exclusive to H3K4me3. Enhancers are dynamically remodeled during maternal-to-zygotic transition (MZT). there are three enhancer types: active enhancers (H3K4me1/H3K27ac), primed enhancers (H3K4me1), and poised enhancers (H3K4me1/H3K27me3). Active enhancers increase significantly after zygotic genome activation. Further, super-enhancers are present across the genome and are mainly enriched in promoters. The specific motifs enriched by super-enhancers may be associated with the variations of gene expressions at different stages.

developmental biology↗

bioRxiv2026

Yawning reveals energy-dynamics mismatch and neural inertia across state transitions

Yawning is a highly conserved behavior, yet its neural dynamics across arousal state transitions remain poorly understood. Classical reflex models fail to account for the precise timing of yawns during large-scale changes in brain network organization. Here, we investigated the neural signatures of yawning using simultaneous electroencephalography (EEG), electromyography (EMG), and kinematic recordings in a beagle model during wakefulness and propofol anesthesia. Propofol-associated yawning exhibited marked temporal asymmetry, with 89.2% of events occurring during emergence from anesthesia. To resolve the fast neural dynamics surrounding these events, we applied empirical mode decomposition coupled with the Hilbert-Huang transform (EMD-HHT) analyses. Across induction, recovery, and wakefulness, yawning was consistently associated with a transient decoupling between local and global network dynamics: a brief increase in local {gamma}-band power coincided with a drop in network flexibility, quantified by instantaneous frequency volatility; IFV). We termed this reproducible motif as "Energy-Dynamics Mismatch" (EDM). A machine learning classifier leveraging EDM features reliably decoded the brain state immediately preceding yawning. We further validated the presence of this signature in an independent human cohort during anesthesia induction. These findings indicate that yawning is not a passive reflex, but is tightly linked to moments of network instability during state transitions. From a computational perspective, EDG may reflect a transient "control effort" that preserves information integration in the presence of "neural inertia." This dynamic signature may provide a quantitative biomarker for monitoring arousal transitions brain-computer interface (BCI) interface state-alerting. Significance StatementYawning is a ubiquitous and evolutionarily conserved behavior, yet its neural basis remains elusive. Using high-resolution neural recordings in a cross-species model, we show that yawning is not a passive reflex, but reliably coincides with a distinct, transient brain-state signature. Specifically, yawning occurs when local high-frequency cortical activity briefly increases while global network flexibility decreases - a pattern we term an "Energy-Dynamics Mismatch" (EDM). We propose that EDM reflects a compensatory neural process engaged during moments of network instability, consistent with overcoming neural inertia to preserve information integration. By identifying this conserved neural marker, this work advances the biological understanding of yawning and suggests a potential biomarker for detecting critical brain states in future BCI.

neuroscience↗

bioRxiv2026

Uncovering Carbohydrate Metabolism and Endogenous Hormone Regulation during Flush Phenology in Citrus Trees using Proteomics and Metabolomics

Rapid shoot growth (flushing) phenology is a fundamental developmental process in perennial woody plants such as citrus. In a separate study, we identified physiological shifts from photosynthesis to mobilization of nitrogen and carbohydrate to support new shoot growth. However, the underlying molecular and biochemical signals remain largely unknown. Here, we integrated proteomic and metabolomic analyses to investigate carbohydrate and hormone dynamics across three flush stages in Citrus sinensis: quiescent period (stage 1), new shoot initiation (stage 2), and full expansion (stage 3). Sucrose, maltose, and trehalose accumulated in apical leaves during early shoot initiation and declined during subsequent shoot expansion, indicating depletion of carbohydrate reserves and enhanced resource remobilization. These changes were accompanied by coordinated regulation of starch-metabolizing enzymes, including ADP-glucose pyrophosphorylase, -amylase, and isoamylase, supporting a transition from carbon storage to carbon export during active shoot growth. Indole-3-acetic acid increased continuously across stages, while trans-zeatin and gibberellin A{square} showed opposite trends in apical versus basal leaves before jointly increasing at stage 3. Hormone analysis revealed dynamic and coordinated signaling changes during flush development. Abscisic acid declined from stage 1 to 2, whereas jasmonoyl-isoleucine and salicylic acid increased from stage 2 to 3. Some hormone-responsive proteins, including Gretchen Hagen 3 and Gibberellin-insensitive dwarfing 1, exhibited expression patterns consistent with hormonal fluctuations. Together, these results support a stage-specific regulatory framework in which carbohydrate metabolism and hormone signaling are tightly coordinated to regulate rapid source-sink transitions during citrus flush development. HighlightWe reveal how carbohydrate metabolism and hormone signaling are spatiotemporally coordinated during citrus shoot growth phenology, and we develop an integrated metabolic-hormonal model that connects carbon allocation to developmental transitions.

plant biology↗

bioRxiv2026

Human neuromodulatory assembloids to study serotonin signaling and disease

Neuromodulators influence critical functions of the developing human brain and regulate behavioral states. Dysfunction of neuromodulatory systems is often involved in neuropsychiatric disease and many drugs for these conditions act on these signaling pathways. Recent advances in stem cell biology have made it possible to derive a wide range of cells across the developing human nervous system in regionalized organoids and to functionally integrate them into assembloids, however they currently do not systematically incorporate neuromodulation. Here, we generated human midbrain-hindbrain organoids (hMHO) from human induced pluripotent stem (hiPS) cells and fused them with human cortical organoids (hCO) to form neuromodulatory assembloids (hNMA). We focus on serotonin (5-hydroxytryptamine, 5-HT) as one key neuromodulator and found characteristic gene expression patterns and electrophysiological properties of serotonergic neurons (5-HT neurons) in the hMHO. In hNMA, 5-HT neurons projected into hCO, released 5-HT and modulated cortical network activity. To explore the applicability of this system in human disease, we studied 22q11.2 deletion syndrome (22q11.2DS), a common microdeletion associated with high risk for neuropsychiatric disease and defects in 5-HT signaling. We found aberrant 5-HT dynamics in hNMA from patient hiPS cell lines that were rescued by administration of a selective serotonin reuptake inhibitor (SSRI). Taken together, hNMA can be used to study human 5-HT dynamics and uncover disease phenotypes which could facilitate therapeutic development.

neuroscience↗

bioRxiv2026

HSC71 acetylation confers protection against Spiroplasma eriocheiris infection by inhibiting apoptosis and promoting ROS production in arthropods

Heat shock proteins (HSP) are increasingly recognized as pivotal regulators of host innate immunity. However, the role of HSP70 post-translational modifications in modulating the immunologic functions during pathogenic infection remains poorly understood. Here, we demonstrated that heat shock cognate 71 kDa protein (HSC71), a member of the HSP70 family, was deacetylated following infection with intracellular pathogen Spiroplasma eriocheiris. HSC71 deficiency exacerbated hemocyte apoptosis and increased host susceptibility to S. eriocheiris infection. Conversely, ectopic expression of hyperacetylation-mimetic HSC71(K579Q) in Drosophila S2 cells conferred robust protection against S. eriocheiris by suppressing apoptosis and augmenting reactive oxygen species (ROS) production. Mechanistically, carnitine O-acetyltransferase (Crat) was identified as the specific acetyltransferase for HSC71. Crat acetylated HSC71 at lysine 579 (K579), which in turn impeded its interaction with the E3 ubiquitin ligase CHIP, thereby stabilizing HSC71 by attenuating ubiquitin-mediated degradation. Crat deficiency induced apoptosis and reduced ROS levels in hemocytes, thereby exacerbating S. eriocheiris intracellular proliferation. Furthermore, acetylation of HSC71 at K579 weakened its binding to superoxide dismutase (SOD), resulting in impaired SOD activity and consequent accumulation of microbicidal ROS. Pharmacological inhibition of the deacetylase SIRT1 with EX-527 enhanced HSC71 acetylation and ROS production, conferring resistance to S. eriocheiris infection in crabs. Notably, EX-527 similarly enhanced the acetylation of Drosophila HSC71 homolog, HSPA8, which in turn impaired its interaction with SOD. This led to elevated ROS levels and restricted intracellular proliferation of S. eriocheiris in S2 cells, demonstrating evolutionary conservation of this mechanism among arthropods. Therefore, this study established the modulation of HSC71 acetylation as a promising strategy to enhance arthropod immunity. Author summaryMembers of the HSP70 family are indispensable components of host innate immunity, and their functions are finely tuned by post-translational modifications. This study investigated the mechanisms that underlie acetylation modification of HSC71 mediated resistance to S. eriocheiris infection in crab. Our results revealed that the acetyltransferase Crat and the antioxidant enzyme SOD directly interacted with HSC71. Mechanistically, Crat acetylated HSC71 at K579, which prevented its ubiquitination by promoting the disassociation of E3 ubiquitin ligase CHIP, thereby improving HSC71 stability. In HSC71-deficient or Crat-deficient crabs, hemocyte apoptosis was markedly enhanced, leading to higher host mortality upon S. eriocheiris challenge. Meanwhile, K579 acetylation on HSC71 weakened the interaction between HSC71 and SOD, resulting in the accumulation of intracellular ROS and thereby restricting S. eriocheiris propagation. Administration of the SIRT1 inhibitor EX-527 enhanced HSC71 acetylation, elevated ROS production and reduced host susceptibility to S. eriocheiris infection. These findings revealed a novel role for Crat-mediated acetylation of HSC71 in orchestrating host defense through two synergistic mechanisms: suppressing apoptosis and promoting ROS accumulation. Thus, targeting HSC71 acetylation may represent a promising avenue to combat S. eriocheiris infection.

immunology↗

bioRxiv2026

Improved prediction of virus-human protein-protein interactions by incorporating network topology and viral molecular mimicry

The protein-protein interactions (PPIs) between viruses and human play crucial roles in viral infections. Although numerous computational approaches have been proposed for predicting virus-human PPIs, their performances remain suboptimal and may be overestimated due to the lack of benchmark dataset. To address these limitations, we first constructed a carefully curated benchmark dataset, ensuring non-overlapped PPIs and minimum sequences similarity of both human and viral proteins in the training and test sets. Based on this dataset, we developed vhPPIpred, a machine learning-based prediction method that not only incorporated sequence embedding and evolutionary information but also leveraged network topology and viral molecular mimicry of human PPIs. Comparative experiments demonstrated that vhPPIpred outperformed five state-of-the-art methods on both our benchmark dataset and three independent datasets. vhPPIpred also achieved high computational efficiency, requiring relatively low runtime and memory. Finally, vhPPIpred was demonstrated to have great potential in identifying human virus receptors, and in inferring virus phenotypes as the virus-human PPIs predicted by vhPPIpred can be used to effectively infer virus virulence. In summary, this study provides a valuable benchmark dataset and an effective tool for virus-human PPI prediction, with potential applications in antiviral drug discovery, host-pathogen interaction research and early warnings of emerging viruses.

bioinformatics↗