Biology subjects

Chen, W.

Publications and source records attributed to Chen, W..

At least 73 records · Page 4Linked to original sources

bioRxiv2026

Coenzyme A is a redox sensing cofactor for malic enzyme 2 regulating oxidative stress and mitochondrial metabolism

Coenzyme A (CoA) is an essential cofactor required for numerous metabolic reactions, yet its ability to bind and regulate proteins remains poorly defined. Using a proteomic approach, we identified malic enzyme 2 (ME2) as a CoA-binding protein. ME2 uses NAD(P)+ to convert malate to pyruvate, generating NAD(P)H to support energy production and redox homeostasis. ME2 binds CoA at an allosteric site previously thought to bind NAD(P)+. Reduced CoA has minimal effect on ME2 activity, but the oxidized form, CoA disulfide, strongly activates ME2 by promoting ME2 tetramerization and a catalytically efficient closed conformation. Under oxidative stress, ME2 facilitates CoA disulfide formation, enhancing NADPH production and cellular defense against reactive oxygen species (ROS). Mice with ME2 mutation that cannot bind CoA show impaired muscle performance, elevated ROS, and mitochondrial dysfunction. These findings establish CoA as a redox-sensing cofactor, allowing cells to respond to ROS and promote mitochondrial metabolism, and expanding the function of this essential cofactor.

biochemistry↗

bioRxiv2026

Generation of High-Affinity Anti-GIPR Antagonist Antibodies with Sustained and Non-rebound Weight Loss in DIO Mice by AlfaBodY

The glucose-dependent insulinotropic polypeptide receptor (GIPR) is an attractive therapeutic target for metabolic disorders, with GIPR antagonism emerging as a promising strategy for obesity and type 2 diabetes. However, developing functional antibodies against GPCRs remains challenging due to their complex architecture and conformational dynamics. Here, we employed AlfaBodY, an iterative active learning platform integrating structural and sequence information, to in silico design human anti-GIPR antibodies. Through four rounds of optimization, we generated antibodies with high binding affinities. Lead candidates AB106-131 (KD 1.2 nM) and AB106-156 (KD 1.7 nM) exhibited 7 to 10-fold higher affinity than 2G10 (KD 12 nM) while maintaining comparable antagonistic activity in a cAMP reporter assay (IC50 4[~]5 nM). In diet-induced obese mice, AB106-156 alone induced weight loss comparable to that of semaglutide ([~] -15%), while preserving lean mass and achieving sustained weight control after treatment withdrawal. Co-administration with the GLP-1 receptor agonist semaglutide produced synergistic weight reduction (-25.4%) and markedly attenuated the fat-mass rebound observed with semaglutide alone. Our results demonstrate that AI-driven design can generate potent anti-GIPR antibodies with favourable in vivo efficacy, supporting further development of GIPR antagonist for obesity and related metabolic disorders. The AlfaBodY platform enables faster development of more efficacious biologic drugs.

pharmacology and toxicology↗

bioRxiv2026

Revisiting the Monascus genus (Eurotiales, Aspergillaceae): A Multilocus Phylogenetic Approach to Species Delimitation

Monascus spp. are economically important filamentous fungi that have been utilized in the production of beneficial metabolites such as Monascus pigments and monacolin K, as well as in the brewing of some Asian fermented foods. The delimitation of Monascus species has traditionally relied on phenotypic traits; however, this morphological classification approach is susceptible to subjective judgments and variations in cultural conditions and also may not necessarily be related to the actual genetic relationship. Consequently, synonymy and misidentification frequently occur in Monascus taxonomy, highlighting the urgent need for a convenient and reliable classification system for this genus. In this study, a phylogenetic analysis of 82 representative Monascus strains, encompassing all previously recognized species of the genus, was conducted based on the concordance of five gene genealogies (BenA, CaM, ITS, LSU, and RPB2) to clarify species delimitation and resolve phylogenetic relationships within Monascus. The results revealed that the genus Monascus is resolved into 11 species, which are clustered into two sections: Floridani (including M. argentinensis, M. flavipigmentosus, M. floridanus, M. lunisporas, M. mellicola, M. pallens, and M. recifensis) and Rubri (including M. pilosus, M. purpureus, M. ruber, and M. sanguineus). M. pilosus and M. sanguineus were reaffirmed as distinct species due to their well-supported and divergent phylogenetic lineages. Additionally, M. albidulus, M. anka, M. barkeri, and M. fumeus are synonymized with M. pilosus, while M. aurantiacus and M. rutilus are synonyms of M. purpureus. Finally, a comprehensive list of accepted Monascus species along with their corresponding barcode sequence data is provided.

microbiology↗

bioRxiv2026

N-3 Polyunsaturated Fatty Acids Ameliorate Post-infarction Cardiac Dysfunction Through Modulation Of Adiponectin-Ceramide Metabolism

BackgroundIt has been shown that n-3 polyunsaturated fatty acids (n-3 PUFA) of marine origin exert significant beneficial effects on myocardial infarction (MI); however, the underlying mechanisms remained unclear. Ceramides play a vital role in the regulation of energy metabolism, mitochondrial function, and apoptosis. Through the integration of clincial studies and animal experiments, this study aimed to determine whether n-3 PUFA improved myocardial function by modulating ceramide metabolism. MethodsIn a case-control study, 100 patients with AMI and 100 healthy pariticipants were enrolled to measure serum ceramide concentrations. Meanwhile, mice were randomly allocated into 4 groups and administrated to a 3-week intervention with n-3 PUFA in triglyceride and phospholipid forms. A mouse model of MI was then established, followed by an additional 4 weeks of continuous intervention. Subsequent comprehensive assessments of cardiac function were performed in the mice. Finally, the mice were euthanized to conduct targeted ceramide lipidomic analysis and other relevant assays. ResultsThe levels of serum C16:0-, C18:0-, C20:0-, C24:1-ceramides and total ceramides in patients with acute myocardial infarction (AMI) were significantly higher compared with the healthy controls. In the murine model of myocardial infarction, pathological analysis via TTC staining demonstrated that interventions with fish oil (triglyceride form) and krill oil (phospholipid form) both significantly reduced myocardial infarct size. Concomitant echocardiographic assessment confirmed that both treatments markedly elevated left ventricular ejection fraction (LVEF), with the magnitude of improvement being significantly superior to that of the model control group. Concurrently, compared with the model group, the concentrations of ceramides in cardiac tissue and serum were significantly lower in the groups with fish oil and krill oil intervention. Western blot analysis further confirmed that n-3 PUFA intervention upregulated adiponectin expression, reduced ceramide accumulation in myocardial tissue, and inhibited mitochondria-mediated cardiomyocyte apoptosis, thereby improving cardiac function and prognosis following myocardial infarction. ConclusionsThis work demonstrates that n-3 PUFA exert cardioprotective effects following MI mediated by adiponectin-ceramide axis. However, there is no significant difference regarding therapeutic efficacy of n-3 PUFA in triglyceride or phospholipid forms.

pharmacology and toxicology↗

bioRxiv2026

A neuron-glia circuit anticipates hypoxia to regulate organismal oxygen use

Organisms must regulate metabolic resources such as oxygen (O2) and nutrients despite environmental variability and the energetic costs of their own actions1-3. Such regulation can occur reactively, through homeostatic corrections of recent imbalances, or predictively, through allostatic adjustments that anticipate future demand4,5. Predictive regulation is particularly important because metabolic resources often continue to be consumed for seconds to minutes after motor actions cease as tissues repay incurred costs, making it advantageous to prevent depletion before it occurs6. However, the cellular and circuit mechanisms for allostatic control remain largely unknown5,7,8. Using whole-brain neuronal and astroglial imaging and O2 measurements in behaving zebrafish, we identified a noradrenergic-astroglial circuit that detects, anticipates, and prevents internal O2 depletion. We found that swimming exacerbated internal hypoxia with a multi-second delay, but behavioral adaptations occurred before such self-generated hypoxia manifested, suggesting predictive control, confirmed using computational modeling. Noradrenergic neurons in the nucleus of the solitary tract directly detected brain hypoxia and received efference copies of swimming actions; these inputs summed at the level of membrane voltage to increase spiking and norepinephrine release when actions and resource scarcity co-occurred. Astroglia integrated noradrenergic input into prolonged Ca2+ elevation that tracked the O2 cost of recent actions and thereby predicted O2 debt relative to O2 availability, rising ~8 s before O2 fell. This astroglial prediction reorganized brain-wide activity to suppress locomotion and promote respiration, preempting O2 depletion. Silencing noradrenergic neurons or astroglial signaling abolished these hypoxia coping behaviors, whereas selective activation evoked them. This neuronal-astroglial mechanism constitutes a predictive control system that integrates physiological state with behavioral intent to avert metabolic crisis, revealing a cellular substrate for proactive energy management.

neuroscience↗

bioRxiv2026

IEKB: a comprehensive knowledge base for inner ear genetics integrating curated associations, cochlear interactions, Bayesian candidate prioritisation, explainable dark-gene support relations, and a scientific entity network

Inner-ear genetics has expanded rapidly, yet the supporting evidence remains dispersed across a vast literature and across resources that typically emphasise loci, variants, or expression data rather than integrated biological interpretation. Here we present the Inner Ear Knowledge Base (IEKB; https://earkb.org), an open database that unifies curated associations, cochlear interaction evidence, candidate prioritisation, explainable support relations, and network exploration for inner-ear research. IEKB was built with an automated agent-assisted curation workflow that combines schema-constrained literature extraction, continuous human monitoring, and final expert review by inner-ear genetics researchers. By systematically analysing 250,696 PubMed-indexed records retrieved across 16,563 screened genes, IEKB curates 6,051 gene-phenotype-disease associations from 2,494 genes across 43 phenotype categories and 4,102 cochlear gene-gene interactions with pathway, cell-type, and experimental context. IEKB further includes a Bayesian "dark matter" module that prioritises 243,071 candidate gene-phenotype associations for 13,229 genes across all 43 phenotypes (global AUC-ROC = 0.8603; global AUC-PR = 0.1674), together with a supervised dark-relation layer that ranks phenotype-specific known-gene support for each candidate and a multi-entity scientific network containing nearly 4,000 entities, 28,616 deterministic edges, and 83,712 literature-derived relational links. The web resource supports interactive search, multi-parameter filtering, gene-detail pages, bibliometric exploration, domain-specific enrichment against IEKB phenotype and disease gene sets, network visualisation, bulk download in CSV, JSON, SQLite, and XLSX formats, and natural-language evidence-grounded question answering through a companion conversational interface (IEKB QA). To our knowledge, IEKB is the first openly accessible inner-ear resource that integrates curated associations, cochlear interactions, probabilistic candidate prioritisation, auditable known-gene support relations for novel candidates, and a multi-entity scientific network within a single database. All data are released without registration under the CC BY 4.0 license.

bioinformatics↗

bioRxiv2026

Human brains implicitly and rapidly distinguish AI from human voices before decoding prosodic meaning

People encounter AI voices daily. Existing behavioral studies suggest listeners rely on prosodic cues such as intonation and expressiveness to detect audio deepfakes, reporting that AI voices sound prosodically less rich than human voices. To test whether prosodic processing drives deepfake discrimination in the neural time course of voice processing, we recorded electroencephalographic (EEG) data while participants listened to human and AI-generated speakers producing utterances in confident vs. doubtful prosody (tone of voice), with attention directed toward memorizing speaker names. We used voice cloning to control for speaker identity confounds between human and AI voices. Multivariate pattern analysis revealed that neural discrimination of human vs. AI voices emerged rapidly regardless of prosody (confident: 176 ms; doubtful: 134 ms), substantially preceding prosody discrimination (confident vs. doubtful within human voices: 2066 ms; within AI voices: 1366 ms). Acoustic analysis confirmed that prosodic distinctions became classifiable only at utterance offset (90% normalized duration), converging with neural evidence that prosody requires near-complete temporal integration. This temporal dissociation between rapid voice source discrimination and late-emerging prosody decoding suggests that prosody plays a smaller role in audio deepfake detection than listeners retrospectively report. Representational similarity analysis further revealed that spectral envelope features (mel-frequency cepstral coefficients; MFCC), rather than the visually salient high-frequency energy differences, drove neural human-AI discrimination, with MFCCs earliest independent contribution (228 ms) closely following the MVPA decoding onset (134-176 ms). Future studies may manipulate specific acoustic components to establish the causal sources of this rapid and sustained neural discrimination. Significance StatementPeople encounter AI voices daily, in phone calls, navigation apps, supermarket checkouts, and subway announcements. Using electroencephalography, we show that the human brain automatically and rapidly distinguishes everyday AI voices from human speech, even without conscious attention to voice source. Although people may attribute this ability to AI voices sounding monotone or prosodically unnatural, the brain relies on subtler acoustic signatures, enabling discrimination before prosodic information becomes available. Attempts to identify the specific acoustic features driving this neural detection were inconclusive, pointing to the need for future causal investigations. We encourage engineers and policymakers to ensure AI voices remain perceptually detectable, as increasingly humanlike AI voices could cognitively disadvantage the general public if they become indistinguishable from human speech.

neuroscience↗

bioRxiv2026

Cryo-EM elucidation of stoichiometric plasticity, asymmetric ligand recognition and allosteric coupling in human P2X2/3 heterotrimeric channels

P2X receptors are trimeric ATP-gated ion channels that assemble as homo- or heterotrimers, with heteromeric forms exhibiting intrinsic asymmetry that influences function. Here, we report four high-resolution cryo-EM structures of human P2X2/3 heterotrimers representing distinct functional states, including ATP-bound assemblies (P2X332 and P2X223), the apo form, and a ligand/ATP-bound closed conformation. The three ATP-binding sites show asymmetric recognition of MgATP{superscript 2}- and ATP-, and channel activation requires occupancy of only two MgATP{superscript 2}- molecules. Gefapixant binds a single allosteric site and selectively inhibits MgATP{superscript 2}-, but not ATP-, binding, indicating orthosteric-allosteric coupling within the heterotrimer. Structural features of the transmembrane domain define ion permeation, particularly for Ca{superscript 2}. Despite asymmetric ligand interactions, gating remains largely symmetric, with minor differences in desensitization. These findings provide a structural framework linking asymmetry to coordinated channel function and open avenues for subtype-selective therapeutic intervention.

biophysics↗

bioRxiv2026

Cervical Repetitive Magnetic Stimulation Enhances Respiratory Recovery by Modulating Neuronal Plasticity After Cervical Spinal Cord Injury

Cervical spinal cord injury (SCI) frequently leads to life-threatening respiratory insufficiency by disrupting descending phrenic pathways. There is growing interest in non-invasive neuromodulatory approaches to enhance plasticity of spared respiratory circuits. We investigated whether cervical repetitive magnetic stimulation (rMS) applied to the injured cervical spinal cord promotes ventilatory recovery in a preclinical mouse model. Adult mice received a unilateral C3 hemicontusion followed by either rMS or sham stimulation. We found that rMS-treated mice significantly improved recovery of tidal volume and minute ventilation at 21 days post injury(dpi) compared to sham controls under various breathing conditions (isoflurane anesthesia, poikilocapnic phase and hypercapnic challenge). Correspondingly, diaphragm EMG enhanced ipsilateral hemidiaphragm activity in ventral and medial regions, and even contralateral hemidiaphragm activity in its ventral part. This was associated with a marked attenuation of the inflammatory response at the cervical spinal cord level. Indeed, rMS lowered astroglial, fibrotic scarring, pro-inflammatory CD68-, Iba1- microglial/macrophage markers. Moreover, perineuronal net expression (WFA positive staining) is globally reduced in the ventral spinal horn, whereas at the lesion site it is markedly increased and tightly wrapped around motoneurons. Together, these findings demonstrate that rMS promotes functional respiratory recovery after cervical SCI through combined enhancement of diaphragmatic motor output and modulation of the inflammatory and extracellular environment. Together, these functional and cellular findings indicate that spinal rMS promotes a permissive, pro-regenerative environment supporting respiratory circuit plasticity. We conclude that rMS significantly enhances ventilatory recovery via reduced inflammatory response and improved intraspinal rewiring after high cervical SCI, suggesting it is a promising non-invasive strategy. The ability of rMS to engage spared respiratory networks and support neuroplasticity highlights its promise as a safe, non-invasive therapeutic strategy with translational potential for rehabilitation of breathing function after SCI. One Sentence SummaryNoninvasive cervical magnetic stimulation improves breathing after spinal cord injury by boosting diaphragm activity and reducing inflammation.

neuroscience↗

bioRxiv2026

DHHC7 palmitoylates KRAS4A and promotes mutant KRAS-driven pancreatic cancers

KRAS mutations underlie many human cancers. While inhibitors such as Sotorasib and Adagrasib targeting KRAS mutants have shown promise, additional strategies are required to address the broader spectrum of KRAS-driven cancers, particularly those displaying drug resistance. Thus, there is a need to better understand KRAS signaling and develop new therapeutic strategies. Here we show that KRAS4A is palmitoylated on Cys180 by a palmitoyl transferase, DHHC7 (gene name ZDHHC7). Palmitoylation promotes KRAS4A plasma membrane localization, and more importantly, nanoclustering. This in turn promotes the activation of ARAF and RAF1, but not BRAF. DHHC7 and KRAS4A Cys180 palmitoylation are important for the normal and anchorage independent growth of pancreatic cancer cell lines. Depletion of ZDHHC7 dramatically inhibits pancreatic tumor growth in mouse xenograft models. These studies provide new understandings about how palmitoylation regulates KRAS4A activity and suggest DHHC7 as a promising new target for KRAS mutant cancers.

cancer biology↗

bioRxiv2026

Integrated single-cell and bulk transcriptomic analysis leverages liver metastasis-related genes to develop a prognostic model for colorectal cancer patients

PurposeDifferentially expressed genes (DEGs) between colorectal cancer liver metastasis (CRLM) epithelium and primary colorectal cancer (CRC) epithelium (LMR DEGs) identified based on single-cell RNA sequencing (scRNA-seq) data may become new biomarkers for CRC prognosis. MethodsAn scRNA-seq dataset was used to describe the cellular landscape of primary CRC and CRLM and identify LMR DEGs. Prognostic LMR DEGs were identified in the bulk RNA-seq dataset. Based on the prognostic LMR DEGs, multiple machine learning algorithm combinations were compared in terms of their C-index, and the best model was selected for the construction of the LMR score. ResultsAmong the 2070 LMR DEGs, 426 prognostic LMR DEGs were ultimately obtained. The combination of the randomized survival forest (RSF) model and ridge regression had the highest C-index and was therefore used to construct a 15-gene scoring system (LMR score). In the external validation set, the 1- and 5-year AUCs of the LMR score were greater than those of the AJCC stage and other scoring systems constructed with a similar dataset. In addition, the LMR score was closely associated with factors that influence CRC outcomes, such as immune infiltration. ConclusionThe LMR score may be a reliable new biomarker for predicting the prognosis of patients with CRC.

cancer biology↗

bioRxiv2026

TattleTail: A Pyocin Prediction Tool

Tailocins are phage tail-like bacteriocins (PTLBs) thought to be remnants of prophages that have lost the ability to package viral genomes while retaining the ability to kill closely-related bacterial strains, thereby mediating bacterial competition. Tailocins produced by Pseudomonas aeruginosa are referred to as pyocins. Apart from their contribution to ecological fitness, they also have the potential to be harnessed as highly-specific antimicrobials to treat antibiotic resistant bacterial infections. Although pyocins lack the genetic components to package viral genomes, pyocin-encoding gene clusters share a high degree of genetic homology to phage tail genes, attributed to their shared ancestry. This poses a significant annotation-based challenge, as current prophage prediction tools, which rely on phage homology for prediction, can misclassify pyocins or tailocins as prophages. Pyocins unknowingly being misannotated as prophages is not only a bioinformatic issue, but can certainly confound experiments examining bacterial competition and prophage induction, if the experimental setup is based on this unintentional misannotation. In this study, we present "TattleTail", the first version of a bioinformatic tool designed to accurately identify tailocins in genome sequences, with a focus on identifying phage tail-derived pyocin-encoding gene clusters in P. aeruginosa in its first iteration. The tool leverages conserved pyocin gene cluster markers and accounts for the absence of canonical phage features, such as capsid, terminase and integrase genes, thereby distinguishing pyocins from intact and cryptic prophages. Validation in P. aeruginosa and non-P. aeruginosa genomes confirmed the presence of pyocin regions in all P. aeruginosa genomes, while none were detected in any non-P. aeruginosa genomes. Notably, TattleTail enabled the identification of representative pyocin-encoding gene clusters in clinical P. aeruginosa isolates. The identified pyocins in the clinical isolates were induced using mitomycin C, visualized via transmission electron microscopy, processed via tangential flow filtration and demonstrated bactericidal activity, thereby confirming TattleTail predictions. TattleTail aims to complement existing prophage prediction tools during genomic analyses involving phage-derived elements in bacterial genomes, allowing more accurate identification of these elements facilitated by robust discrimination between prophages and tailocins.

bioinformatics↗

bioRxiv2026

ERFMTDA: Predicting tsRNA-disease associations using an enhanced rotative factorization machine

MotivationtRNA-derived small RNAs (tsRNAs) have emerged as a novel class of regulatory molecules implicated in the pathogenesis of many human diseases, making them as promising biomarkers and therapeutic targets. However, existing computational methods for tsRNA-disease association prediction often overlook explicit biological attributes and complex feature interactions, limiting their predictive performance. ResultsWe propose ERFMTDA, an enhanced rotative factorization machine framework for predicting potential tsRNA-disease associations. ERFMTDA explicitly models complex interactions among heterogeneous biological features while integrating latent structural representations derived from the global association matrix. In addition, a biologically informed negative sampling strategy based on motif-level sequence similarity is introduced to improve the reliability of negative samples. Extensive experiments demonstrate that ERFMTDA consistently outperforms eleven state-of-the-art methods. Case studies on diabetic retinopathy and hepatocellular carcinoma further confirm its ability to prioritize biologically meaningful tsRNA-disease associations. Availability and implementationThe source codes and datasets of ERFMTDA are available at https://github.com/lanbiolab/ERFMTDA.

bioinformatics↗

bioRxiv2026

Dynamic shifts in brain criticality support cognitive processing

Systems operating near their critical point, or close to a transition between order and disorder, have computational advantages. In the case of neural networks, proximity to criticality is proposed to support optimal brain function. However, different cognitive processes rely on disparate computational demands. Using large-scale electrophysiological recordings in behaving rodents, we examined how critical dynamics in the hippocampus are regulated during learning and sleep-dependent memory consolidation. We found that operating near criticality enables learning by facilitating hippocampal coordination with input regions and maximizing flexibility of neural representations. In contrast, the hippocampal network shifts toward a more ordered, subcritical regime during sleep memory replay, and recovers its proximity to criticality through cholecystokinin interneurons-mediated inhibition. Overall, our findings provide a biophysical substrate for understanding how critical dynamics in neuronal networks can support a variety of brain functions. Importantly, our results suggest that optimal learning systems, whether biological or artificial, may require a dynamic regulation between flexible and rigid states, and can offer biophysical constraints to guide the design of Large Language Models (LLM) tuned to criticality.

neuroscience↗

bioRxiv2026

Automated high-throughput fabrication of patient-specific vessel-on-chips enables a generative AI digital twin--Cascade Learner of Thrombosis (CLoT) for personalized thrombosis prediction

We developed an integrated platform combining high-throughput automated biofabrication, systematic patient-derived tissue experiments, and specialized artificial intelligence to enable patient-specific computational "digital twins" for thrombosis prediction. Our automated manufacturing platform fabricates 80 fully assembled, patient-specific vessel-on-chips within 10 hours from clinical imaging--a [~]100-fold improvement over manual methods--achieving sub-micron precision through novel two-stage pneumatic motion control and integrated optical feedback. Using these chips, we systematically captured thrombosis across 491 high-fidelity videos spanning 6 patient-derived vascular geometries, 5 distinct anatomical injury sites, and 14 anticoagulant/antiplatelet interventions, establishing a "physical twin" experimental corpus. We trained CLoT (Cascade Learner of Thrombosis), a conditional video diffusion model efficiently adapted via lightweight Low-Rank Adaptation (LoRA) to generate realistic thrombosis videos conditioned on patient-specific geometry, injury location, and drug treatment. Rigorous benchmarking against state-of-the-art commercial models (Sora, Wan, Kling, Seedance, Hailuo, Hunyuan) reveals CLoT achieves 7.38-fold superior temporal biological consistency and 5.3-fold higher spatial morphological fidelity. Prospective validation on unseen patients demonstrates >90% temporal accuracy. This integrated paradigm--combining automated fabrication with domain-specialized generative AI--establishes proof-of-concept for personalized medicine enabled by digital twins trained on human-derived vascular anatomy, enabling pre-treatment antithrombotic evaluation while providing a replicable template for translating tissue engineering into clinical practice.

bioengineering↗

bioRxiv2026

Individualized and stereotypical seizure semiology in a porcine model of post-traumatic epilepsy.

Large-animal models of traumatic brain injury may yield translatable data on epileptogenesis, given their similarities in anatomy, brain size, and immune systems to humans. Adult male and female swine received bilateral cortical impact (N=16) or sham surgery (N=6) and were screened for convulsions via video-EEG for up to one year. Post-traumatic epilepsy (PTE) was defined as 2 seizures after 1-week post-injury. Nine out of sixteen pigs (56%) receiving bilateral cortical impact developed PTE, with an average 4.6 months ({+/-}3.4,SD) latent period. Seizures (N = 199) began focally, sometimes with motor onset including automatisms, before becoming generalized, with tonic-clonic or tonic convulsions. We defined a library differentiating peri-ictal behaviors (N = 31) from rhythmic/odd behaviors typical in healthy pigs (N = 12). Seizures had an average of 7.3 behaviors per seizure (max 26) lasting an average of 1.8 minutes (max 7.9). For seizures comprised of multiple convulsive episodes, the first convulsion had a greater number of peri-ictal behaviors than subsequent convulsions (P < 0.001). The array of peri-ictal behaviors displayed was pig-specific, with many behaviors consistently observed across seizures. The seizure frequency detected was 0.38/day. This large-brain model of PTE exhibits a variable period of epileptogenesis, a substantial rate of PTE, and an expansive repertoire of ictal behaviors. This first description of semiology in this species will serve as a guide for other porcine epilepsy models. Biofidelic models of PTE are expected to increase our understanding of the pathophysiology, enabling the identification and testing of therapeutics that translate into human patients. HighlightsO_LIThe average time from bilateral cortical impact to post-traumatic epilepsy in swine is 6 months, and is highly variable, ranging from 2 to 47 weeks post-TBI. C_LIO_LISwine with post-traumatic epilepsy display an array of specific behaviors around convulsions, distinct from pigs without post-traumatic epilepsy. C_LIO_LIThough the duration of convulsion was typically a few seconds, the entire seizure, with the associated peri-ictal behaviors, lasts up to 7.9 minutes. C_LIO_LIThe complexity of behaviors around convulsions tended to increase from early traumatic seizures to post-traumatic seizures. C_LIO_LIPeri-ictal behaviors observed around convulsions in an individual were often displayed prior to the first convulsion. C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=115 SRC="FIGDIR/small/708000v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@9523aborg.highwire.dtl.DTLVardef@158cfa6org.highwire.dtl.DTLVardef@1a8b499org.highwire.dtl.DTLVardef@e8c86f_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2026

NRF2 pathway activation and SPP1⁺TREM2⁺ macrophages drive chemoradiotherapy resistance in esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is among the most aggressive cancers, with low rates of durable response to chemoradiotherapy and limited therapeutic options for relapsed disease. To uncover mechanisms of treatment resistance and relapse, we performed comprehensive multi-omics profiling of >100 pre-treatment and post-relapse ESCC tumors from a prospective clinical trial (NCT04694391), integrating whole-exome/genome sequencing, bulk RNA-sequencing, single-cell RNA sequencing, and spatial transcriptomics. We identify somatic alterations in NFE2L2/KEAP1 in nearly 40% of relapsed patients, which are associated with upregulation of NRF2 signaling targets in resistant tumors and cell line models. At single-cell and spatial resolution, relapsed tumors are enriched for NRF2-activated epithelial cells that physically co-localize with immunosuppressive SPP1TREM2 macrophages. This co-localization suggests a synergistic interaction between NRF2-driven tumor programs and macrophage-mediated immune suppression that promotes relapse after chemoradiotherapy. Our findings nominate NFE2L2/KEAP1 mutations as predictive biomarkers for patient stratification and highlight therapeutic targeting of NRF2 signaling and SPP1TREM2 macrophages as rational strategies to overcome resistance in ESCC.

cancer biology↗

bioRxiv2026

Z-RNA-ZBP1 axis drives Tau-mediated neurodegeneration

Although the causes of Tau aggregates vary, once Tau aggregates are formed, their neurotoxicity significantly contributes to neuronal death and cognitive decline in tauopathies, with Alzheimers disease (AD) being the most well-known example. Despite its central pathogenic role, however, effective therapeutic strategies targeting neurotoxicity of Tau remain poor. Here we demonstrate the pathogenic role of neuronal cell death in Tau-related neurodegeneration. Tau-expressing neurons undergo cell death through Z-DNA-binding protein 1 (ZBP1) activation triggered by endogenous Z-RNAs. These Z-RNAs are derived from reactivated transposable elements (TEs) that are typically silenced within heterochromatin. Tau aggregates show a strong affinity for H3K9me3-modified chromatin, effectively sequestering these epigenetic marks from Heterochromatin Protein 1 (HP1), thereby disrupting the condensation of constitutive heterochromatin. Clinically, an inverse correlation between ZBP1 expression levels in excitatory neurons and cognitive performance in AD patients was observed. Importantly, Zbp1 haploinsufficiency significantly ameliorated cognitive deficits in aged Tau-transgenic mice (24-month-old), highlighting the therapeutic potential of ZBP1 inhibition to strive against neurodegeneration in tauopathies.

neuroscience↗