Biology subjects

Xiang, Y.

Publications and source records attributed to Xiang, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Ultrasonic local anesthetic uncaging enables noninvasive, targeted, and transient knock-out of brain function

Noninvasive, spatially localized brain inhibition would enable precise neural circuit interrogation, mapping, and treatment. We previously presented SonoRopi, an acoustically activatable liposomal formulation of ropivacaine, and demonstrated its utility for targeted peripheral nerve anesthesia. Here, we extend SonoRopi to localized brain anesthesia following intravenous administration and transcranial focused ultrasound application. Ultrasonic uncaging of SonoRopi releases ropivacaine, which crosses the intact blood-brain barrier to anesthetize the sonicated region. SonoRopi uncaging in mouse primary visual cortex suppressed visually evoked cortical activity for one hour, while uncaging in rat somatosensory hindlimb cortex produced lateralized, reversible hindlimb sensory anesthesia. Off-target control sonication preserved function in both paradigms. No acute histologic injury or open-field behavioral abnormalities were detected following this intervention. SonoRopi thereby enables noninvasive, spatially targeted, reversible brain inhibition with physiologic and behavioral efficacy in two rodent species.

bioengineering↗

bioRxiv2026

CBX3 Deletion Overcomes Ferroptosis Resistance in Colorectal Cancer by Targeting IFNγ/SLC7A11/SLC3A2 Axis

System Xc- (SLC7A11/SLC3A2) maintains redox balance by supporting glutathione synthesis, thereby preventing lipid peroxidation and ferroptosis, and represents a promising therapeutic target in cancer. In this work, we revealed that an important epigenetic regulator, CBX3 deletion significantly increased intracellular ROS and lipid ROS level following ferroptosis inducing, particularly under IFN{gamma} stimulation, thereby sensitized CRC cells to ferroptosis in vitro using human CRC cells as well as in vivo using MC38 syngenetic tumors. RNAseq with mice syngeneic tumors revealed an 11-gene oxidative stress signature downregulated in Cbx3KO tumors after combined treatment; its low expression score correlated with improved overall survival in CRC patients. Among them, Slc7a11, Ptgs2, and Cth were identified as CBX3 direct target genes. Additionally, several amino acid transporters functionally coupled to Xc- antiporter, as Slc3a2, Slc7a5, Slc1a5 and Slc6a9, all significantly decreased in Cbx3KO tumor under RSL3+IFN{gamma} treatment. Mechanistically, CBX3 binds directly to the promoter of Slc7a11 or Slc3a2 to sustain their expression and redox balance to resist ferroptosis in CRC. Knockout of CBX3 in CRC cells significantly impaired RNA polymerase II recruitment to the promoter of SLC7A11 or SLC3A2 and reduced SLC7A11 and SLC3A2 expression, an effect that was further exacerbated under IFN{gamma} stimulation but restored by STAT1 deletion. Overall, CBX3 protects CRC from oxidative stress by maintaining the functional integrity of IFN{gamma}/STAT1/SLC7A11/SLC3A2 axis, positioning CBX3 as a potential therapeutic target for enhancing ferroptosis therapies in CRC.

cancer biology↗

bioRxiv2026

Glutamine and GLS1 are key metabolic checkpoints that Limit T cell Infiltration and Function in HNSCC

Head and neck squamous cell carcinoma (HNSCC) is characterized by poor survival and limited benefit from immune checkpoint blockade (ICB), underscoring the need to define targetable mechanisms of immune escape. In a prospective, patient-centered study, we investigated how tumor metabolic reprogramming impairs T cell function and drives ICB resistance. Targeted metabolomics identified two dominant and distinct tumor states: the Warburg phenotype and glutamine deprivation and revealed increased tumor-specific glutamine to glutamate turnover. Integrating spatial metabolic imaging with immunohistochemistry linked these states to immune-cold and immune-excluded tumor architectures. Metabolic states selectively reduced the expression of T cell chemokine receptors highly abundant and prognostic in HNSCC. Accordingly, T cell migration into tumor spheroids was reduced at lower glutamine concentrations. Interfering with glutamine metabolism by pharmacologic inhibition or genetic deletion of glutaminase-1 markedly increased tumor susceptibility to immune cell-mediated cytotoxicity in vitro and ex vivo and sensitized patient-derived tumor fragments (PDTFs) to ICB. This effect extended beyond glutamine restoration and involved disruption of glutathione-dependent ROS defense, impaired tumor proliferation, and increased MHC expression in tumor cells. Consistently, high expression of genes involved in glutamine and glutathione metabolism associated with reduced ICB response in patients. In contrast to HNSCC, other tumor entities including breast cancer show low glutaminase-1 expression. Accordingly, breast cancer PDTFs were not responsive to glutaminase-1 inhibition. Together, these data identify glutaminase-1 as a metabolic checkpoint driving immune escape in HNSCC, and support guided patient stratification for metabolism-targeted immunotherapy, similar to current precision approaches for mutation targeted drugs.

cancer biology↗

bioRxiv2026

Uncovering a Sex-Specific cAMP Landscape in Vascular Smooth Muscle

The second messenger, cyclic adenosine monophosphate (cAMP), plays a crucial role in regulating cellular function, including in vascular smooth muscle (VSM). While extensive research has focused on the compartmentalization of cAMP signaling in various cell types, the influence of biological sex on distinct cAMP pools and their functional implications in the VSM remains largely unexplored. To address this knowledge gap, we employed a multiscale experimental approach spanning targeted cAMP biosensors, wire myography, live-cell calcium imaging, proximity ligation assays, and in vivo ultrasound imaging. Our findings reveal a sex-specific distribution of cAMP signaling domains, with the cAMP pool selectively present in the SR of females but not males in the VSM. Ovariectomy abolished this sexual dimorphism, with SR cAMP pools disappearing in ovx females, mirroring the pattern observed in male cells. Males and ovx female VSM exhibited a significantly stronger RyR-PDE3/PDE4 association than sham females, suggesting that ovarian hormone-dependent sequestration of phosphodiesterases away from the SR licenses cAMP signaling in this compartment in female cells. Functionally, this sexually dimorphic cAMP compartmentalization correlates with smaller Ca2+ spark amplitude, reduced isoproterenol-induced relaxation, and higher pulse wave velocity in male and ovx compared to female VSM/vessels/mice. Together, these findings establish biological sex as a fundamental determinant of subcellular cAMP organization in VSM, with implications for understanding sex differences in vascular function and cardiovascular disease.

cell biology↗

bioRxiv2026

Constructing microbiome co-occurrence networks with confidence: A conditional, nonparametric, inference-based approach

Constructing microbial association networks is a common strategy for exploring relationships among taxa in microbiome studies. Although marginal correlation methods are easy to implement and allow formal inference, they can produce spurious edges driven by indirect associations through other taxa. Conditional graphical-modeling methods aim to recover direct associations, but many rely on Gaussian or linear assumptions and often provide limited uncertainty quantification. We propose a conditional, nonparametric approach based on the scaled expected conditional covariance (SEcov). SEcov measures population-level conditional association by residualizing each taxon with respect to the remaining taxa and scaling the resulting expected conditional covariance. The resulting estimator can incorporate flexible machine-learning methods for conditional-mean estimation and admits asymptotic normal inference, enabling p-values and confidence intervals for taxon-pair associations. We demonstrate through simulation studies that our proposed approach improves network recovery relative to other methods, and we illustrate the new method via construction of a co-occurrence network for the vaginal microbiome during pregnancy. IMPORTANCEHigh-throughput sequencing has made it possible to characterize microbial communities at large scale, and network analysis is widely used to summarize relationships among taxa. However, networks based on marginal correlations may include indirect associations, whereas many conditional graphical models rely on assumptions that may be difficult to justify for sparse, zero-inflated, compositional microbiome data. SEcov offers a practical alternative by estimating conditional associations nonparametrically and attaching inferential uncertainty to individual edges. This allows investigators to construct microbiome networks using statistically interpretable evidence for taxon-pair associations, rather than relying solely on arbitrary correlation cutoffs or regularization tuning parameters.

bioinformatics↗

bioRxiv2026

Scarless SARS-CoV-2 Genome Engineering and Variant Analysis

In addition to causing cold and flu-like symptoms, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) can also cause chronic longer-term diseases. Antiviral drugs, especially used combinatorially, have the potential to reduce the severity of individual infections and prevent the development of chronic disease. One of the safest and most versatile reverse genetics systems for SARS-CoV-2 studies is a bacterial artificial chromosome (BAC)-based system harboring the WA1 strain full-length genome and attenuating deletions in the accessory open reading frame 3a and 7b proteins (ORF3a and ORF7b, respectively). Here, a scarless genome engineering technique called En Passant mutagenesis was used to change one amino acid in the viral main protease (Mpro P132) into the residue present in contemporary Omicron strains (H132), in order to more accurately study protease inhibitors and resistance mechanisms. This recombinant, attenuated viral system yields antiviral EC50 values for the active component of approved drugs including nirmatrelvir (Paxlovid) and ensitrelvir (Xocova) and, importantly, also enables a parallel assessment of drug efflux. For instance, the antiviral potency of nirmatrelvir improves 50-fold by inhibiting the P-Glycoprotein (P-Gp) transporter with ritonavir or tariquidar, whereas the potency of ensitrelvir is unaffected. This system also enables the safe isolation and characterization of viral variants with reduced sensitivity to drugs, as evidenced by Mpro M49L compromising the efficacy of ensitrelvir. Together, these systems combine to provide safe, reliable, and quantitative approaches for Mpro variant analysis and drug testing without the biosafety concerns of conducting these experiments using wildtype isolates.

microbiology↗

bioRxiv2026

PerturbTrace: Evaluating Feedback Use by AI Co-Scientist Agents in Perturbation Discovery

Recent advances in AI co-scientists have brought LLM agents into closed-loop experimental design. However, whether these agents use feedback from earlier rounds to revise subsequent experimental decisions remains unclear. We address this question with PerturbTrace, which evaluates each round-to-round transition through Feedback-to-State, State-to-Action, and Action-to-Outcome. These stages assess whether feedback is reflected in the agent's rationale and perturbation-selection strategy, whether the stated strategy guides the next perturbation batch, and whether that batch yields more hits than expected under random sampling. We evaluate four LLM agents on 17 screen-derived tasks and compare them with random selection, active learning, and LLM-guided Bayesian optimization baselines. Each agent outperforms the strongest non-agent method on at least 15 of the 17 tasks, yet controlled evaluations across six tasks show no consistent advantage from true feedback over random or no feedback. Among 576 transitions under true or random feedback, only 43 (7.5%) complete the full Feedback-State-Action-Outcome sequence, including 25 under random feedback. These findings show that high final recall does not necessarily indicate effective feedback use. They also highlight the need to evaluate closed-loop scientific agents by both their discovery performance and whether feedback changes their subsequent decisions.

bioinformatics↗

bioRxiv2026

Contextual Evaluation of MicroRNA Sequencing Data Harmonization: Performance in Sample Clustering

Reliable translation of microRNA sequencing data depends on effective harmonization to mitigate artifacts from variable experimental handling. Although many harmonization methods exist, prior evaluations have focused mainly on differential expression analysis, leaving the impact on subgroup discovery understudied. We present a framework for evaluating harmonization in the context of sample clustering, which integrates AI-augmented datasets, statistical evaluation pipelines, and accessible software tools, enabling systematic comparisons across diverse signal-to-artifact ratios and cluster composition settings. Using this framework, we show that harmonization can, often partially, restore clustering accuracy lost to artifacts, especially at moderate signal-to-artifact ratios, with the level of gains depending on the specific harmonization method, the paired clustering technique, and the cluster composition setting. We further confirm these findings by analyzing reconstructed cohorts from The Cancer Genome Atlas breast cancer microRNA sequencing data. Collectively, the results underscore the need for tailored harmonization to support reliable subgroup discovery and highlight the broader importance of context-specific workflows in translational genomics.

bioinformatics↗

bioRxiv2026

Metabolic signatures of cone-dominant and cone-degenerating retinas

Cone photoreceptors are essential for daylight vision, and their degeneration has more profound visual consequences than rod loss in retinal degenerative diseases. Metabolic dysfunction is closely associated with cone degeneration, however the relatively small population of cones in mice and humans have limited our understanding of cone-specific metabolism. Here, we leveraged cone-dominant and cone-degeneration mouse models including Nrl-/-, Cnga3-/-, and high-dose Triiodothyronine (T3) treatment to investigate cone-specific metabolism and their metabolic impacts on the retinal pigment epithelium (RPE). Across models, cone-dominant retinas consistently showed lower pyruvate abundance alongside increases in glutathione, purines, pentose phosphate pathway intermediates, and one-carbon metabolites. Increases in several key amino acids were also associated with higher cone abundance, such as proline, arginine, alanine, valine, leucine, and hypotaurine. Strikingly, aminoadipate, an intermediate in lysine catabolism, was the most robustly changed metabolite in the retina, showing highly consistent increases across models. Relative cone increases were also associated with metabolic changes in the RPE/choroid. Like the retina, RPE/choroids showed consistent increases in aminoadipate, proline, and hypotaurine, as well as xanthosine and betaine, alongside decreases in uracil. Moreover, proteomic analysis of Nrl-/- mice showed decreases in many key metabolite transporters in the RPE/choroid, including carriers for glucose, lactate, aspartate, glutamate, serine, lysine, taurine, and proline. Collectively, these findings further our understanding of cone-specific metabolism and highlight potential cone-specific metabolic vulnerabilities in retinal degeneration.

biochemistry↗

bioRxiv2026

Regional and tissue-specific metabolic differences in human neural retina and RPE/choroid

It is clear that the human retina and its underlying retinal pigment epithelium and choroid (RPE/choroid) form an interdependent metabolic ecosystem, but how metabolism differs between the cone-rich macula and rod-rich periphery remains unclear. Using targeted metabolomics, we quantified 133 metabolites in paired macular and peripheral neural retina and RPE/choroid explants from human donor eyes following short-term culture to restore metabolic activity. Distinct metabolic differences were identified between retinal regions and between tissues. Compared with the peripheral retina, the macula showed metabolic features consistent with greater glycolytic activity, increased NADH availability and higher levels of the neurotransmitter-associated metabolites N-acetyl-aspartate (NAA) and N-acetyl-aspartyl-glutamate (NAAG), consistent with increased energetic and neuronal activity. Compared with peripheral RPE/choroid, the macular RPE/choroid had higher levels of the flavin cofactor FAD together with NAD-related metabolites, including NAD, NADP and NAAD. Comparisons between the neural retina and RPE/choroid further showed that the neural retina was primarily associated with energy production and neurotransmission, whereas the RPE/choroid was associated with cofactor metabolism, nucleotide salvage and lipid metabolism. These findings are consistent with metabolic coupling between the neural retina and RPE/choroid. The macula has metabolic features consistent with high energetic demand, providing a potential metabolic basis for its selective vulnerability in macular disease.

neuroscience↗

bioRxiv2026

MEGA-ODE: Learning Biologically Structured and Navigable Continuous Perturbation Dynamics from Sparse Omics

Perturbation-omics experiments usually measure only a subset of molecular feature, intervention and time space, leaving many response trajectories, perturbation effects and disease- or differentiation-associated transitions unobserved. Here we present MEGA-ODE, a graph-constrained continuous-time framework for reconstructing sparse dynamic omics landscapes, predicting unmeasured molecular states and prioritizing virtual perturbations toward defined biological endpoints. MEGA-ODE integrates molecular-network priors, graph neural ordinary differential equations and context-adaptive mixture-of-experts routing. In L1000 transcriptomic perturbations and CPPA proteomic drug-response data, MEGA-ODE improved held-out-feature and unseen-perturbation prediction over baseline methods, and in SARS-CoV-2 infection time-series data it remained competitive for future-time-point forecasting. In a COVID-19 patient cohort, predicted intermediate profiles improved retrospective disease-stage stratification relative to observed profiles alone, while expert programs highlighted immune and inflammatory signals associated with severity. Across the MAPK drug-response and stem-cell differentiation case studies, graph- and expert-level attributions prioritized perturbation-associated MAPK edges, developmental regulators and TF-target relationships supported by independent promoter-proximal ChIP-seq overlap. In hESC-to-definitive-endoderm differentiation, MEGA-ODE prioritized candidate transcription-factor perturbations predicted to shift 12-36 h profiles toward 96 h definitive-endoderm marker signatures, framing trajectory navigation as a concrete hypothesis-generation task. Together, these results support biologically structured continuous-time modeling for prediction, interpretation and virtual-perturbation prioritization from sparse temporal omics data.

bioinformatics↗

bioRxiv2026

Ultrasonic potentiation of ketamine neuromodulation

The psychiatric utility of ketamine is limited by its dissociative and systemic side effects. Recently, to enable precision ketamine pharmacotherapy, we introduced SonoKet, ketamine-loaded acoustically activatable liposomes that enable focused ultrasound (FUS)-targeted ketamine delivery to millimeter-sized brain regions. In initial studies, we observed that SonoKet uncaging targeted ketamine to the ultrasound-treated brain region, while inducing greater electrophysiologic and behavioral functional effects than dose-matched free ketamine. To further define these uncaging-potentiated neuromodulatory effects, we used solid-phase microextraction (SPME) coupled to LC-MS/MS to investigate the effect of ultrasound and SonoKet uncaging on key neurotransmitters in real-time. SPME probes were used to sample ketamine, its metabolites, and glutamate, GABA, serotonin (5-HT), and dopamine in the medial prefrontal cortex (mPFC), nucleus accumbens (NAc), and retrosplenial cortex (RsC) of awake rats. Sampling occurred before and after intravenous administration of either SonoKet, free ketamine, or saline, with FUS targeted to either a frontolimbic or caudal brain region. FUS alone did not yield significant changes in neurotransmitter concentration, nor did it affect the pharmacodistribution of free ketamine. In contrast, FUS generally increased the neurotransmitter response to free ketamine, suggesting an ultrasonic potentiation of ketamine neuromodulation. SonoKet (0.75 mg/kg) uncaging with FUS elicited further elevations in glutamate, GABA, and 5-HT within the FUS-targeted region, along with an increase in dopamine in the NAc when the frontolimbic region was sonicated. These increases were similar to or higher than those induced by 10 mg/kg free ketamine alone or 0.75 mg/kg free ketamine combined with FUS, especially with frontolimbic SonoKet uncaging. Altogether, FUS potentiates ketamine-induced neuromodulation, with spatially specific and synergistically greater effects when ketamine is spatially localized via ultrasonic uncaging. This strategy could augment ketamine pharmacotherapy for psychiatric diseases, while limiting its dissociative and abuse liabilities. HighlightsO_LIFocused ultrasound potentiated ketamine-driven glutamate, serotonin, and dopamine release C_LIO_LILocalized ketamine delivery with SonoKet uncaging drove synergistically greater region-specific neurochemical responses C_LIO_LIUncaging boosts ketamine effects at a fraction of the ketamine dose C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=152 SRC="FIGDIR/small/741494v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@15c75c8org.highwire.dtl.DTLVardef@138d06eorg.highwire.dtl.DTLVardef@fc0c56org.highwire.dtl.DTLVardef@18e233b_HPS_FORMAT_FIGEXP M_FIG C_FIG

pharmacology and toxicology↗

bioRxiv2026

Epigenetic Reactivation of Lineage Differentiation to Target Leukemia

Chromatin regulation critically influences gene expression and cancer progression, yet the functions of chromatin adaptors remain incompletely defined. Using focused CRISPR screening, we identified TRIM28, a multi-domain chromatin adaptor, as a dependency in acute leukemia, where its depletion impaired leukemia cell proliferation in vitro and in vivo, while activating neutrophil differentiation programs. Integrative transcriptomic and chromatin profiling revealed that TRIM28 acts as a co-repressor of neutrophil-associated loci independently of H3K9 methylation, and that TRIM28 loss drives terminal differentiation of leukemia cells into functionally mature neutrophil-like cells with reduced leukemic potential. We developed a selective small-molecule TRIM28 inhibitor that binds the TRIM28 PHD-bromodomain, phenocopies TRIM28 loss across biochemical and cellular assays, exhibits low micromolar anti-leukemia activity, induces neutrophil differentiation, and synergizes with Menin inhibition. Together, these findings, spanning target discovery, mechanism of action, and chemical probe development, establish TRIM28 as a regulator of myeloid cell fate and a promising pro-differentiation therapeutic target in acute leukemia.

cancer biology↗

bioRxiv2026

PertDiffBench: Benchmarking Diffusion Models for Single-Cell Perturbation Response Prediction

Diffusion models are increasingly used to predict transcriptional responses to perturbations, but whether they improve on simpler generative and representation-based baselines remains unclear. Existing evaluations often do not separate the effects of model architecture, input representation, biological context and metric choice, making it difficult to determine where diffusion-based methods are useful. Here we introduce PertDiffBench, a standardized benchmark for diffusion-based transcriptomic perturbation prediction across single-cell and bulk RNA-seq datasets. PertDiffBench evaluates diffusion-based models across three complementary evaluation settings: standard prediction in known single-cell contexts and bulk perturbation conditions, generalization to unseen cell types, species, drugs and intermediate time points, and stress tests of feature dimensionality, input representation, noise type and gene ordering. Across these settings, diffusion models did not show a consistent advantage. scGen remained a strong baseline in common prediction tasks, whereas scDiffusion was the most competitive diffusion-based method in several generalization settings. Temporal imputation showed a different pattern, with a simple DDPM operating directly in expression space outperforming more specialized models. Stress tests showed that performance was model dependent and sensitive to feature dimensionality, encoder choice, noise type and gene ordering. Pretrained encoders did not consistently improve performance, with the classical scVI representation slightly exceeding STATE in seen-condition and unseen-cell-type settings. These results indicate that diffusion-model performance in perturbation response prediction depends strongly on task design and representation choice. PertDiffBench provides a practical framework for evaluating these models under biologically varied and stress-tested conditions.

bioinformatics↗

bioRxiv2026

Cross-Modal Benchmarking of Acoustic Prosody and Ventral Striatal BOLD for Depression-Related Anhedonia Classification: A Pre-Registered Study with the ClinicalWhisper Pipeline

Can computational analysis of a brief voice recording classify depression-related anhedonia as effectively as task-based fMRI? We address this question through a pre-registered cross-modal benchmarking study (osf.io/bsvrj) that evaluates two independent classification pipelines against depression-related anhedonia operationalized via self-report. Anhedonia-- the diminished capacity to experience pleasure or motivation to pursue rewards--is a transdiagnostic marker of reward-system dysfunction that predicts treatment resistance in major depressive disorder, yet current assessment requires either expensive functional neuroimaging or subjective self-report scales, neither of which scales to routine screening. We benchmarked two independent classification pipelines: Stream A extracted 88 acoustic prosody features from DAIC-WOZ clinical interviews (n = 142) using the ClinicalWhisper pipeline (Whisper Large-v3, pyannote diarization, OpenSMILE eGeMAPS v02); Stream B extracted nucleus accumbens BOLD activation during a reward task from the UCLA ds000030 dataset (n = 272). Three classifiers (logistic regression, random forest, gradient-boosted trees) were evaluated under stratified 5-fold cross-validation with fixed, pre-registered hyperparameters. Stream A achieved a best AUC-ROC of 0.63 (random forest; permutation p = .049), confirming H1 that acoustic prosody classifies PHQ-8-defined anhedonia above chance at = .05 (uncorrected), though this result does not survive Bonferroni correction for three classifiers (/3 = .017). Bootstrap analysis of {Delta}AUC confirmed non-inferiority relative to Stream B (H2: 95% CI lower bound > -0.10). However, Stream B itself did not achieve above-chance classification (p = .057), so this non-inferiority finding reflects comparable, modest performance across both modalities rather than equivalence to a validated neural biomarker. Pitch variability features (F0) ranked among the top 5 predictors by SHAP value in the gradient-boosted trees model (H3: partially supported). An exploratory combined model (eGeMAPS + recurrence quantification analysis) reached AUC = 0.65 (GBT; p = .032), though this lift was not statistically significant by DeLong test (z = -0.44, p = .66). These results provide initial evidence that vocal prosody, acquired through a standardized, open-source pipeline, carries depression-related information comparable to fMRI-derived ventral striatal activation for binary classification. However, neither streams operationalization isolates anhedonia from general depressive symptomatology, and the cross-modal design compares different constructs across different cohorts. We refer to the classification target throughout as the "anhedonia composite" to acknowledge that PHQ-8 Items 1+2 conflate anhedonia with dysphoria. We discuss these constraints and their implications for the causal framework motivating this work.

neuroscience↗

bioRxiv2026

Ca2+-activated CKL3 phosphorylates nucleoporin 58 to reprogram nuclear transport and execute effector-triggered immunity

Recognition of pathogen effectors by nucleotide-binding leucine-rich repeat receptors (NLRs) activates effector-triggered immunity (ETI) in plants through resistosome formation, which generates a sustained cytosolic Ca2+ influx and ultimately leads to programmed cell death (PCD) at infection sites and resistance. However, the mechanisms linking Ca2+ influx to ETI execution remains a major knowledge gap. Using TurboID proximity labeling with the central transport channel nucleoporin 58 (Nup58) as a probe, we show that, instead of deregulation, ETI induction restricts general nuclear trafficking while selectively enhancing nuclear import of defense-related proteins. This switch is driven by elevated cytosolic Ca2+, which binds to the conserved Asp-149 in CASEIN KINASE 1-LIKE 3 (CKL3), promoting its interaction with and phosphorylation of Nup58 at Ser-149, thereby altering nuclear pore selectivity. This study identifies CKL3 and Nup58 as key regulators of ETI by establishing a mechanistic link from resistosome-mediated Ca2+ influx to nuclear transport reprogramming and immune execution.

plant biology↗

bioRxiv2026

Heat stress induces persistent placental dysfubction and fetal growth restriction via the ERS-MAPK-apoptosis axis

Maternal heat stress (HS) is an emerging risk factor for adverse pregnancy outcomes, yet how gestational heat exposure causes persistent placental dysfunction remains unclear. In the present study, we established a murine HS model (38.5{degrees}C, 2.5 h/day, E0-E12.5) followed by thermal recovery to E17.5. HS reduced fetal weight during early gestation and caused persistent fetal growth restriction after recovery, despite partial placental weight restoration. Histological analyses revealed early reductions in the junctional and labyrinth zones, followed by sustained labyrinthine deficiency and compensatory junctional zone expansion. Consistently, HS impaired placental vascularization, with reduced vessel length and area, decreased CD31 and -SMA abundance, and altered angiogenesis-related gene expression. HS also triggered oxidative stress, weakened antioxidant capacity, disrupted anti-inflammatory signaling, reduced tight junction protein expression, and compromised barrier integrity. Mechanistically, HS induced excessive endoplasmic reticulum stress, accompanied by increased CHOP, phosphorylated ERK, and cleaved caspase-3. In conclusion, our data unveil a heat-induced placental insufficiency program that restricts fetal growth through vascular, redox, barrier, and ERS-MAPK-apoptotic remodeling. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=107 SRC="FIGDIR/small/730306v1_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@c73748org.highwire.dtl.DTLVardef@fe7b3eorg.highwire.dtl.DTLVardef@14d5e6dorg.highwire.dtl.DTLVardef@72572f_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2026

SciCore-Omics: a tri-modal foundation model unifying histology, spatial transcriptomics and language for spatial biology

Histomorphology and spatial transcriptomics capture complementary aspects of tissue biology, but their relationships remain difficult to extract, align, and interpret at scale. Existing foundation models typically connect histology, omics, or language only pairwise, which limits their capacity to jointly infer molecular states, decode spatial tissue organization, and generate biologically grounded explanations. Here, we show SciCore-Omics, the first tri-modal foundation model linking histology images, spatial transcriptomics, and biological language. We constructed a spatially paired image-gene-text dataset comprising 151,182 spots across multiple tissues and performed a three-stage progressive training of SciCore-Omics on this dataset. Across gene expression prediction and spatial domain recognition, SciCore-Omics achieved 23.6-80.9% relative gains in task-specific metrics over the strongest external baselines. It further showed robust zero-shot generalization in histopathology classification, outperforming GPT-5 by 6.16 percentage points in mean accuracy across four benchmarks. Expert evaluation in 10 breast cancer cases confirmed its H&E-only case-level molecular reasoning capability. Together, our method demonstrates that a tri-modal framework can effectively bridge histomorphology and molecular state, providing a more general and interpretable foundation model for computational pathology and omics analysis.

bioinformatics↗