Biology subjects

Fang, Z.

Publications and source records attributed to Fang, Z..

At least 19 recordsLinked to original sources

bioRxiv2026

Theta-Phase Targeted Encoding Gates Memory Sensitivity

Theta phase has been proposed to play an important role in memory. It may gate encoding and retrieval, such that certain phases are more favorable for memory processing than others, or become part of the memory representation, such that reinstating the encoding phase benefits retrieval. To test and distinguish these competing accounts, we used a closed-loop EEG system, allowing us to control the phase at which items were presented at encoding and retrieval. In an associative memory task, visual targets were presented at predefined phases of the ongoing mid-frontal theta rhythm, thereby pairing individual memory items with specific encoding phases. During retrieval, targets appeared either at the same phase as during encoding or at an alternative phase. We show that theta phase during encoding predicted subsequent memory sensitivity, while finding no evidence that retrieval phase nor the match between encoding and retrieval phases influenced performance. Therefore, our findings are consistent with a gating account, in which the rhythmic state at the moment of encoding influences the efficacy of memory formation.

neuroscience↗

bioRxiv2026

Development of CYP8B1-specific inhibitors for metabolic diseases

Changes in bile acids (BAs) balance lead to various metabolic diseases, yet current treatment options targeting these changes remain severely limited. CYP8B1 (cytochrome P450, family 8, subfamily B, polypeptide 1) is a liver-exclusive enzyme that controls the synthesis of 12-hydroxylated (12-OH) BAs. Inhibiting CYP8B1 is an attractive therapeutic strategy for attenuating 12-OH BA levels, remodeling the gut microbiota and thereby ameliorating metabolic diseases. CYP8B1-specific inhibitors are currently lacking due to structural similarities between the active site of CYP8B1 and other CYPs, and the lack of suitable high-throughput screening (HTS) assays. Herein, we developed a highly sensitive, and specific enzyme-coupled fluorescence assay to detect CYP8B1 activity, making it ideal for HTS. Employing this assay, structure-based drug design by protein X-ray crystallography, structure-activity relationship (SAR) analyses, and in vitro and in vivo validation, we developed novel CYP8B1-specific small molecule inhibitors. The lead candidate alters BA composition, favorably remodels the gut microbiota, and corrects metabolic disease in a metabolic dysfunction-associated steatotic liver disease (MASLD) mouse model. These findings reveal that pharmacologically inhibiting CYP8B1 is able to reshape the BA and gut microbiota profiles and effectively ameliorates metabolic diseases. Overall, CYP8B1-specific inhibitors represent a first-in-class promising, safe and effective therapeutic strategy against metabolic disorders.

pharmacology and toxicology↗

bioRxiv2026

An N-Terminally Acetylated Degron Specifically Recognized by the CTLHMKLN1-FAM72A E3 Ubiquitin Ligase for Regulatory Protein Degradation

N-terminal acetylation of cellular proteins has been implicated in their proteasomal degradation through the Ac/N-degron pathway. Despite representing a major branch of the N-degron system, only a limited number of degrons and their motifs have been identified and characterized in this pathway, and the mechanism underpinning the recognition of N-terminal acetylation has remained largely elusive. Here, by interrogating FAM72A-mediated degradation of UNG2, we pinpointed the N-terminally acetylated sequence of UNG2 as a bona fide Ac/N-degron specifically recognized by the CTLHMKLN1-FAM72A E3 ubiquitin ligase. Rather than relying solely on a single acetylated N-terminal residue, this degron features a consensus Nt-AcM1-[ILV]2-G3 motif that governs both E3 targeting and the intracellular stability of the neo-substrates uncovered through proteomic screenings. Cryo-EM structure of the UNG2-FAM72A-MKLN1 complex further elucidated the molecular details supporting the high specificity of degron recognition. Notably, this degron pathway is subject to multilayered regulation, highlighting the characteristic conditionality of degron system. Our findings thus define a distinct AcM-[ILV]-G/N-degron pathway and provide mechanistic insights into degron recognition dictated by both N-terminal acetylation and sequence motif context.

biochemistry↗

bioRxiv2026

Multi-parametric NIR-II fluorescence perfusion imaging towards quantitative stroke evaluation

Preclinical stroke research is severely hampered by the lack of high-throughput, quantitative perfusion imaging tools capable of bridging the gap between functional hemodynamics and structural injury. The emerging second near-infrared (NIR-II) fluorescence imaging offers superior spatial resolution and tissue penetration, but its utility remains restricted by a lack of standardized analysis protocols and inability to resolve in-depth infarct regions. Here, we introduce FIMPA, an integrated analytical framework for standardized, multi-parametric stroke evaluation based on NIR-II fluorescence perfusion imaging. FIMPA systematically processes the image sequence through motion correction and atlas registration to generate an anatomically aligned library of 125 vascular and hemodynamic parameters. Statistical screening identifies 67 stroke-correlated features, enabling objective, data-driven quantification of perfusion deficits. To resolve spatial infarct boundaries, we developed FIMPA-SI (FIMPA Stroke Index), a deep learning-based index utilizing a ViT-UNETR framework. By leveraging self-supervised pre-training with MRI data, FIMPA-SI suppresses dominant vascular signals to translate multi-parametric dynamics into anatomically precise stroke severity maps. Validated in tMCAO mice, FIMPA-SI achieves high spatial concordance with MRI and significantly outperforms conventional laser speckle contrast imaging and single-parameter metrics. FIMPA provides a robust solution for accelerating preclinical therapeutics and advancing optical neuroimaging.

bioengineering↗

bioRxiv2026

Optimized Kakadu Plum Extracts Inhibit Intracellular Oxidative Stress in Canine Small Intestinal Cell Model

Kakadu plum (Terminalia ferdinandiana), an Australian native fruit, is among the richest known dietary sources of vitamin C and hydrolysable tannins, yet its capacity to protect the intestinal epithelium against oxidative stress remains largely unexplored. This study optimised the extraction of bioactive compounds from freeze-dried Kakadu plum powder and evaluated their antioxidant activity using both chemical and cellular antioxidant in vitro assay. Phenolic compounds were extracted using three solvents (water, 80% ethanol, and 80% methanol) combined with shaking, ultrasound, or microwave assistance. Solvent, rather than processing technique, was the dominant determinant of antioxidant capacity: ethanol and methanol maximised total phenolic content, total flavonoid content, and DPPH radical-scavenging activity, whereas water extracts showed the highest ferric-reducing antioxidant power. Twenty-four phenolic compounds identified by HPLC-ESI-QTOF-MS/MS were mapped by network pharmacology to nine core oxidative-stress targets, and cross-species molecular docking predicted conserved binding of key phenolics to canine orthologs of PTGS2 and MMP2. In an H2O2-induced oxidative-stress in vitro cell model using canine small intestinal epithelial cells, both water (less than 25 ug/mL) and ethanol (less than 250 ug/mL) extracts significantly suppressed intracellular reactive oxygen species (ROS) in a dose-dependent manner, with the ethanol extract effective across a wider concentration range. This work demonstrated that Kakadu plum extract could be a promising natural, multi-target antioxidant ingredient for canine intestinal health, and provided a reference for future in vivo research.

biochemistry↗

bioRxiv2026

PFOS aggravates atherosclerosis via Bacteroides caecimuris expansion-driven bile acid remodeling and subsequent intestinal FXR-TLR3 signaling cascade

BACKGROUNDPerfluorooctane sulfonate (PFOS) is a widely distributed persistent organic pollutant in the environment and has been associated with an increased risk of atherosclerosis. However, the underlying pathogenic mechanisms remain largely unclear. This study aimed to investigate the effects of PFOS on atherosclerosis and its associated gut-vascular axis. METHODSPseudo-germ-free mouse models and fecal microbiota transplantation (FMT) were used to determine the role of the gut microbiota in PFOS-induced atherosclerosis. Metagenomic sequencing was performed to characterize alterations in gut microbial composition following PFOS exposure, and targeted metabolomics was used to assess bile acid profiles in the ileum and plasma. Transcriptomic analysis of Bacteroides caecimuris (B.caecimuris) was conducted to explore the reasons for the increased abundance of B.caecimuris after PFOS exposure. In addition, intestinal transcriptomics and ChIP-qPCR were performed to validate transcriptional regulation within the FXR-TLR3 signaling axis. RESULTSAmong 127 participants with paired serum and fecal samples, including 82 patients undergoing coronary angiography with Gensini scores (GS score), fecal PFOS levels were significantly associated with lipid profiles and GS score, whereas serum PFOS showed no such association.Mechanistically, PFOS exposure promotes intestinal enrichment of B.caecimuris by upregulating its tolC gene, thereby enhancing efflux capacity.This microbial shift was accompanied by reduced levels of tauro-ursodeoxycholic acid (TUDCA) and aberrant activation of intestinal FXR signaling.Further analyses demonstrated that FXR activation upregulated TLR3 expression and promoted inflammatory responses and atherosclerosis progression via the TLR3-NF-{kappa}B signaling axis. Both intestinal epithelial-specific FXR deficiency (Fxr{Delta}IE) and TUDCA supplementation significantly suppressed pathway activation and alleviated disease phenotypes.Functional experiments identified TLR3 as a key downstream effector of FXR. Overexpression of TLR3 abolished the protective effects observed in Fxr{Delta}IE mice. Moreover, pharmacological inhibition of TLR3 using CU CPT-4a significantly improved established atherosclerotic lesions in vivo. CONCLUSIONSThis study identifies a gut microbiota-driven FXR-TLR3 signaling axis that mediates PFOS-induced atherosclerosis. These findings provide new mechanistic insights into environmentally induced cardiovascular disease and suggest potential targets for risk assessment and therapeutic intervention.

pathology↗

bioRxiv2026

EnzOracle: Mechanism-aware prediction of enzyme environmental adaptation via a classification-guided mixture-of-experts framework

Industrial biocatalysis increasingly requires enzymes capable of operating under extreme physicochemical conditions, yet most natural sequence data reflect adaptation to mild environments, leading conventional predictive models to suffer from regression-to-the-mean effects in extremophilic regimes. Here we present EnzOracle, a classification-guided mixture-of-experts framework that enables distribution-aware prediction of enzyme melting temperature (Tm), optimal catalytic temperature (Topt), and optimal pH (pHopt) directly from sequence. EnzOracle demonstrated robust predictive accuracy across diverse benchmarks, achieving RMSE of 5.245{degrees}C for Tm, 11.458{degrees}C for Topt, and 0.781 for pHopt. Beyond predictive accuracy, we introduce a trait-resolved molecular simulation strategy to evaluate whether EnzOracle-derived attribution patterns correspond to independent physical mechanisms. Across representative systems, attention hotspots mapped onto rigidity-conferring interaction networks for Tm, dynamically preorganized active-site ensembles for Topt, and pH-dependent electrostatic and hydration networks for pHopt. These orthogonal validations indicate that EnzOracle captures transferable biophysical principles of enzyme environmental adaptation rather than merely exploiting dataset-specific correlations, positioning sequence-based learning as a mechanism-aware framework for discovering stability and activity determinants across diverse catalytic landscapes.

bioinformatics↗

bioRxiv2026

Perturbation-guided mapping of colorectal cancer cell states to causal mechanisms

Colorectal cancer (CRC) cell atlases have refined descriptive maps of tumour ecosystems, yet cross-sample integration often obscures disease-relevant patient-specific variation and remains largely correlative, limiting insight into the mechanisms and state transitions that drive progression and treatment response. Here, we develop a continual learning framework to construct a comparative single-cell CRC atlas spanning over 300 patients and 1.5 million cells, preserving inter-patient variation while aligning healthy and malignant contexts. We resolve distinct non-canonical malignant cell states, including an endoderm-like state enriched in microsatellite-stable, KRAS-mutant CRC with features of oncofetal plasticity. Cell states are recapitulated in patient-derived organoids, establishing a tractable model of reprogramming. By linking the observational atlas to a large-scale perturbation atlas using relative representations, we map perturbations that drive cells toward defined phenotypic extremes. We connect cell states to therapeutic responses, showing that MAPK inhibition induces a shift away from a proliferative phenotype and converges towards a plastic, endoderm-like state. Together, this framework moves beyond static atlases to enable mechanistic modeling of cell-state regulation and causal inference toward cell-state-directed therapies.

genomics↗

bioRxiv2026

Joint Learning of Drug-Drug Combination and Drug-DrugInteraction via Coupled Tensor-Tensor Factorization with SideInformation

Targeted drug therapies offer a promising approach for treating complex diseases, with combinational drug therapies often employed to enhance therapeutic efficacy. However, unintended drug-drug interactions may undermine treatment outcomes or cause adverse side effects. In this work, we propose a novel joint learning framework for the simultaneous prediction of effective drug combinations and drug-drug interactions, based on coupled tensor-tensor factorization. Specifically, we model drug combination therapies and DDI by representing drug-drug-disease associations and drug-drug interaction profiles as coupled three-way tensors. To address the challenges of data incompleteness and sparsity, the proposed model integrates auxiliary drug similarity information, such as chemical structure similarities, drug-specific side effects, drug target profiles, and drug inhibition data on cancer cell lines, within a multi-view learning frame-work. For optimization, we adopt a modified Alternating Direction Method of Multipliers (ADMM) algorithm that ensures convergence while enforcing non-negativity constraints. In addition to standard tensor completion tasks, we further evaluate the proposed method under a more realistic new-drug prediction setting, where all interactions involving a previously unseen drug are withheld. This scenario closely aligns with real-world applications, in which reliable predictions for emerging or under-studied compounds are essential. We evaluate the proposed method on a comprehensive dataset compiled from multiple sources, including DrugBank, CDCDB, SIDER, and PubChem. Our experiments show that SI-ADMM maintains robust performance and achieves the best results comparing to other tensor factorization approaches, with or without auxiliary information, particularly in the new-drug prediction setting. The implementation of our method is publicly available at: https://github.com/Xiaoge-Zhang/SI-ADMM.

bioinformatics↗

bioRxiv2026

GRAVITY: Dynamic gene regulatory network-enhanced RNA velocity modeling for trajectory inference and biological discovery

RNA velocity techniques have emerged as efficient tools for unraveling the complex trajectories of cell development and differentiation. However, most of existing RNA velocity approaches are constrained by estimating transcriptional parameters for each gene in isolation and neglects the regulatory relationships among genes, which limits the ability to jointly resolve the dynamic rewiring of gene regulation and the underlying gene transcriptional kinetics across cell state transitions. To address these limitations, we present GRAVITY, a novel deep learning framework that explicitly integrates regulatory dynamics into transcriptional kinetics inference and utilizes a refined two-stage optimization strategy. Benchmarking across various simulated and real single-cell RNA sequencing datasets demonstrates that GRAVITY accurately infers both cellular and gene trajectories, along with their associated kinetic parameters. Most importantly, GRAVITY uncovers terminal cell states L5/L6 in embryonic brain development dataset. Furthermore, GRAVITY not only provides mechanistic insights by identifying the driver regulatory factors and modules governing cell fate, but also enables the systematic in silico simulation of cellular velocity changes induced by targeted regulatory perturbations.

bioinformatics↗

bioRxiv2026

Nab-paclitaxel fused with the de novo designed receptor binder exhibits enhanced tumor targeting and therapeutic efficacy

Chemotherapy has been widely used in cancer treatment, but most of the chemotherapeutic drugs rely mainly on passive accumulation due to lack of target specificity, which may lead to systemic toxicity and limited clinical utility. Recent advances in artificial intelligence-based protein design provide new opportunities for developing precision therapeutics. Here we modify the albumin-bound paclitaxel (Nab-PTX), one of the most widely used drugs in chemotherapies, by applying the fusion proteins of albumin with the de novo designed protein binders targeting human EGFR or HER2. The resulting particles, EGFRmb-Nab-PTX and HER2mb-Nab-PTX, retain the similar physicochemical properties of Nab-PTX while acquiring the receptor-specific binding capacities. The in vitro assays show that both binder-modified Nab-PTX particles have increased uptake and inhibitory effects significantly in the cancer cell lines with high receptor expression levels. Furthermore, the data from a xenograft model, including tumor growth, excised tumor analysis, organ histology, and fluorescence imaging, show that the binder-modified Nab-PTX enhances tumor accumulation, improves tumor suppression, and reduces off-target toxicity compared with the conventional Nab-PTX, suggesting that they may have promising clinical potential in cancer treatment. Overall, this strategy provides an adaptable modular platform for generating albumin-based chemotherapeutic drugs with target specificities, which can be readily customized with diverse target binders to enable precise cancer therapies.

cancer biology↗

bioRxiv2026

Novel circadian clock activators display anti-obesity efficacy via suppression of adipocyte development and hypertrophy

The circadian clock exerts temporal coordination of metabolic processes to maintain homeostasis, and its disruption predisposes to the development of obesity and insulin resistance. Despite the established genetic basis of clock modulation in adipocyte development, whether it can be targeted for anti-obesity interventions remains to be explored. Here we report the novel actions of clock-activating molecules, chlorhexidine and a new derivative CM002, on inhibiting adipocyte development and hypertrophy that results in anti-obesity efficacy in vivo. Both chlorhexidine and CM002 were sufficient to activate clock in adipocytes with induction of core clock components and shortening of clock period length. Consistent with their clock-activating properties, these compounds suppressed the distinct lineage commitment and terminal differentiation stages of adipogenic precursor cells mediated via activation of the Wnt signaling pathway. Furthermore, CM002 attenuated lipid storage and adipocyte hypertrophy by suppressing the lipogenic and adipogenic program in a clock-dependent manner. Most importantly, CM002 administration in mice with diet-induced obesity was sufficient to induce clock activation in adipose depots, leading to robust suppression of adipogenic factors and lipogenic enzymes with marked effect on reducing fat mass and promoting insulin sensitivity. Collectively, our findings uncovered the anti-adipogenic properties of novel small molecule clock activators with demonstrated anti-obesity efficacy. These compounds provide novel chemical probes to dissect clock function in metabolic regulations with translational potential toward development of first-in-class clock-targeting drugs for anti-obesity therapy. HighlightsO_LIDiscovery of the anti-adipogenic properties of the clock activator chlorhexidine C_LIO_LIIdentification of a new clock-activating molecule CM002 C_LIO_LICM002 inhibits the lineage commitment and terminal differentiation of adipocytes C_LIO_LIClock activation by CM002 suppresses lipid storage in mature adipocytes C_LIO_LICM002 displays anti-obesity efficacy in diet-induced obesity model C_LI

physiology↗

bioRxiv2026

A cohesin-centric gene regulatory network resource with regulatory site annotations

Cohesin is a central regulator of transcription and chromatin organization, binding to the majority of cis-regulatory elements (CREs) and mediating enhancer-promoter communication through three-dimensional genome architecture. Although gene regulatory networks (GRNs) provide an interpretable framework for modeling transcriptional regulation, most existing GRN analyses focus on transcription factor (TF)-gene relationships and largely ignore regulatory sites. Consequently, cohesin-associated regulatory contexts are rarely incorporated into GRN reconstruction. Here, we present a cohesin-centric gene regulatory network database that explicitly integrates TF binding, regulatory sites, and gene targets into unified TF-site-gene regulatory paths. Building upon our previously developed multiomics resource CohesinDB, we mapped TF binding to cohesin-associated regulatory sites and linked these sites to their downstream target genes. The resulting Cohesin-GRN module in CohesinDB (http://cohesindb.wangjklab.com/) (http://120.24.147.32/)comprises 61,222,502 TF-cohesin site links and 2,228,634 cohesin site-gene links, collectively forming over 270 million TF-site-gene regulatory paths. By enabling a CRE-informed and site-aware representation of gene regulation, Cohesin-GRN bridges conventional TF-gene GRNs with regulatory site-centric mechanisms. Given the pervasive roles of cohesin in enhancer activity, transcriptional control, and human disease, Cohesin-GRN provides a valuable resource for exploring transcriptional dysregulation and gene regulatory networks.

bioinformatics↗

bioRxiv2025

Decoding Neoantigen-encoding Tumor-Specific Transcripts Unveils a Shared Target Reservoir for Immunotherapy in Hepatocellular Carcinoma

Background and AimsPrimary liver cancer, predominantly hepatocellular carcinoma (HCC), has limited therapeutic options. While mutation-derived neoantigen vaccine holds promise, its success is hindered by low antigen availability. This study explores transcriptome-derived neoantigens (neoantigen-encoding tumor-specific transcripts, neoTSTs) in HCC, characterizing their features, generation mechanisms, and therapeutic potential. Approach and ResultsWe analyzed RNA-seq data from 1,013 liver cancer patients and constructed a multi-layered reference dataset. Using a customized pipeline, we identified an average of 60 neoTSTs per patient, significantly surpassing mutation-derived neoantigens (neoMuts). NeoTSTs exhibited higher population frequencies, with 73.1% providing multiple epitopes, and were validated through mass spectrometry and HLA transgenic mouse models. Mechanistically, neoTSTs were generated via retained introns, transposable element activation, HNF4A-regulated alternative promoters, and de novo transmembrane domain (TMD) generation. Single-cell analysis revealed neoTSTs cover >75% of tumor cells and identified antigen-presenting cancer-associated fibroblasts (apCAFs) that enriched in immunotherapy responders and amplified CD4 T-cell responses via MHC-II presentation. In murine HCC models, neoTST vaccination outperformed neoMuts, inducing dual MHC-I/II activation and significant tumor growth inhibition. ConclusionsNeoTSTs represent a superior neoantigen source in HCC, compensating for the limitations of mutation-derived targets. Their abundance, sharedness, and dual MHC pathway activation highlight their potential for personalized immunotherapy, particularly in low-TMB tumors. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/692877v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@1bedfedorg.highwire.dtl.DTLVardef@5f6c95org.highwire.dtl.DTLVardef@d1deb5org.highwire.dtl.DTLVardef@743e85_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

tACS induces state-dependent shifts in inhibitory phase preference consistent with phase precession

Phase-dependent modulation of motor inhibition suggests that oscillatory timing plays a key role in inhibitory control. Here we show that rhythmic brain stimulation can selectively alter this timing in a manner consistent with phase precession. Participants performed an anticipatory stop-signal task while receiving 20 Hz transcranial alternating current stimulation (tACS) over the pre-supplementary motor area, with stop signals presented at defined phases of the stimulation waveform. An additional control experiment with no tACS was conducted, in which the stop signal phase was derived post-hoc from the ongoing EEG. In both tACS and control sessions, stopping efficacy varied sinusoidally with beta phase, confirming that inhibitory control is intrinsically phase-dependent. During tACS, however, the phase associated with the strongest inhibition advanced progressively across the session by about 180{degrees}, and this effect was specific to failed-stop trials. No comparable shift occurred without stimulation. These findings indicate that rhythmic drive can bias the temporal phase at which inhibition is most effective when the brain is in an error-prone state, revealing a state-dependent, precession-like reorganization of inhibitory timing. We propose that tACS interacts with error-related plasticity to promote gradual phase advancement, placing the system within spike-timing-dependent learning windows, whereas intrinsic fluctuations alone appear insufficient to facilitate cumulative precession. More broadly, these results suggest that phase precession may represent a general coding principle extending beyond hippocampal circuits, enabling the motor system to adaptively adjust inhibitory control through oscillatory timing mechanisms. HighlightsO_LIBeta tACS over preSMA induces shifts in phase preference during motor inhibition C_LIO_LIPrecession-like phase shifts emerge selectively during failed but not successful stops C_LIO_LIPhase precession was absent in a control experiment without tACS C_LIO_LItACS might provide a method to probe phase precession mechanisms in cognition C_LI

neuroscience↗

bioRxiv2025

Closed-loop anti-phase tACS over preSMA reduced oscillatory beta synchronization and modulated motor inhibition performance

Neural oscillations dynamically coordinate communication across distributed brain networks, and their pathological synchronization is a common feature of neurological and psychiatric disease. In Parkinsons disease, excessive beta-band synchronization within cortico-basal ganglia-thalamo-cortical loops is closely linked to motor impairment, highlighting the need for neuromodulation strategies that can selectively and adaptively reshape this brain synchrony online. Transcranial alternating current stimulation (tACS) offers a non-invasive approach to modify endogenous oscillatory activity, yet it is typically delivered in an open-loop manner, limiting control over the timing, direction, and functional network consequences of oscillatory modulation. Here, we developed an online adaptive EEG-guided closed-loop system that continuously tracked the instantaneous phase of ongoing cortical oscillations and delivered stimulation either aligned (in-phase) or 180 degrees opposed (anti-phase) to the endogenous beta rhythm. Thirty-eight right-handed healthy participants performed a stop-signal task while receiving adaptive closed-loop 4 x 1 HD-tACS over pre-supplementary motor cortex (preSMA) targeting beta-band activity (15-30 Hz) implicated in motor inhibition, under in-phase, anti-phase, or sham stimulation. Anti-phase tACS produced a robust suppression of beta synchrony relative to both in-phase and sham stimulation, consistent with destructive interference of the endogenous rhythm. Behaviorally, anti-phase tACS reduced inhibition performance by impairing the stopping process, whereas in-phase stimulation reduced peak force rate during go trials and decreased variability of the go response, consistent with increased inhibitory tone and stabilization of ongoing motor states. These results provide converging behavioral and neurophysiological evidence for destructive interference of anti-phase tACS and establish a mechanistic framework for phase-specific closed-loop neuromodulation, paving the way to implement a non-invasive therapeutic strategy aimed at desynchronizing pathological beta in neurological conditions such as Parkinsons disease.

neuroscience↗

bioRxiv2025

Engineering Linear Cap-independent mRNA Vaccines with Intrinsic Adjuvanticity for Potent Cancer Immunotherapy

mRNA cancer vaccines have shown promising efficacy in early-phase clinical trials, but existing platforms struggle to boost antitumor efficacy without added cost or complexity. Here, we present a streamlined linear cap-independent RNA (LciRNA) cancer vaccine platform that achieves stable expression in vivo without 5 capping or ribonucleotide modification, and innately stimulates immune responses to enhance antitumor immunity. By fusing a UPA protective sequence, composed of a viral exoribonuclease-resistant RNA (xrRNA) and a poly(A) binding protein (PABP) motif, with an optimized Enterovirus A internal ribosome entry site, LciRNA resists 5 exonuclease decay and drives superior in vivo expression. Mechanistically, UPA sequence not only impedes exonuclease-mediated decay but also recruits RNA-binding proteins to stabilize LciRNA. Moreover, LciRNA robustly activates dendritic cell pattern-recognition receptor pathways, promotes dendritic cell maturation, and upregulates proinflammatory signals. In murine melanoma and HPV-associated tumor models, it elicits strong systemic and intra-tumoral T cell responses and superior tumor control, demonstrating how immune stimulation-translation synergy underpins its efficacy. This work establishes a next-generation cost-effective mRNA cancer vaccine platform with simplified production and enhanced efficacy, highlighting immuno-translation coupling as a paradigm for future mRNA cancer vaccines.

bioengineering↗

bioRxiv2025

scLinguist: A pre-trained hyena-based foundation model for cross-modality translation in single-cell multi-omics

Single-cell multi-omics provides complementary insights into cellular states, heterogeneity, and regulatory programmes. However, paired assays remain costly, low-throughput, and technically challenging, whereas large-scale single-modality data such as scRNA-seq are abundant but do not capture protein-level biology. Here we present scLinguist, a foundation model for cross-modality translation introduces a three-stage framework: self-supervised pretraining on large-scale unimodal datasets to learn modality-specific representations, post-pretraining on limited paired data to capture cross-modality relationships, and inference to predict missing modalities (e.g., protein from RNA) in fine-tuning or zero-shot settings. Systematic benchmarking shows that scLinguist consistently outperforms state-of-the-art methods in predicting protein abundance from RNA across diverse biological contexts. It achieves high predictive performance while preserving cellular heterogeneity and further enables mechanistic and generalizable inference under simulated gene perturbations. Furthermore, scLinguist exhibits strong transferability across health states and datasets. By leveraging abundant unimodal data and minimizing dependence on paired assays, scLinguist establishes a scalable and versatile framework for cross-modality translation in single-cell analysis.

cell biology↗