Biology subjects

Chen, G.

Publications and source records attributed to Chen, G..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Towards the Development of Isoenergetic Peptide Nucleic Acid Based Probes Targeting Double-Stranded RNAs Through Enhancing Sequence-Specific Stacking Interactions

Peptide nucleic acid (PNA), a synthetic nucleic acid analog, exhibits substantial potential in biotechnology and therapeutic applications due to its high binding affinity and nuclease/protease resistance. Chemically modified PNAs capable of forming stable triplex structures with double-stranded RNAs (dsRNAs) under near-physiological conditions further expand their utility by enabling sequence-specific precise targeting and probing of functional RNA structural motifs. However, the presence of inverted Watson-Crick pairs (C-G and U-A) may significantly weaken the triplex formation of the dsRNA-binding PNAs (dbPNAs). Our previous work demonstrated that dbPNA P3 (composed of L, T, and Q monomers for the recognition of G-C, A-U, and C-G base pairs, respectively) can stimulate ribosomal frameshifting by binding to rHP2, a model RNA hairpin structure in an mRNA, albeit with suboptimal efficiency, due to its significantly weakened Q*C-G triple formation. We hypothesize that incorporating s2U adjacent to Q may offer unique stacking and hydrogen bonding interactions facilitating the development of isoenergetic dbPNA-based probes binding toward dsRNAs with varied sequences. In this study, we investigate how incorporating s2U adjacent to Q residues in P3 influences its binding to rHP2. Bio-layer interferometry (BLI) and non-denaturing polyacrylamide gel electrophoresis (PAGE) analyses demonstrate that substituting T with s2U at the N-terminal position adjacent to Q (P3-2QT) enhances binding affinity by [~]10-fold compared to unmodified P3, whereas C-terminal substitution (P3-TQ2) yields only a 2-fold improvement. Consistent with these findings, a cell-free dual-luciferase reporter assay reveals that P3-2QT significantly increases ribosomal frameshifting efficiency compared to P3 and P3-TQ2. Molecular dynamics simulations further indicate that P3-2QT maintains enhanced PNA-PNA stacking stability, particularly between s2U3 and Q4, suggesting a structural basis for its superior activity. Intriguingly, analogous s2U substitution in the P5 oligomer with the Q replaced by L does not confer a comparable enhancement in binding to the target RNA (rHP1) and frameshifting stimulation, highlighting the context-dependent nature of this modification. To assess the broader applicability of the s2U-Q motif, we examined its effect in dbPNAs targeting the RNA panhandle structure of influenza virus A and precursor microRNA-21, respectively. Both PAGE and BLI data confirm that s2U incorporation improves binding affinity, reinforcing the generality of this strategy. These findings underscore the potential of sequence-dependent uracil thiolation in optimizing triplex-forming dsRNA-binding PNAs, warranting further exploration of modified nucleobase designs to enhance their binding and functional properties. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=159 SRC="FIGDIR/small/662161v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@15d86aborg.highwire.dtl.DTLVardef@1347369org.highwire.dtl.DTLVardef@852040org.highwire.dtl.DTLVardef@84b0a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThiolation of uracil upstream of Q base in PNA markedly enhances triplex binding affinity and enables isoenergetic and orthogonal targeting/activation. C_LIO_LIN-terminal s2U modification adjacent to Q residues increases PNA-dsRNA binding by [~]10-fold and significantly boosts ribosomal frameshifting efficiency. C_LIO_LIBiophysical assays and molecular dynamics simulations reveal that thiolation improves PNA stacking stability and slows dissociation, thereby enhancing functional activity toward structured RNA targets. C_LIO_LIThe general applicability of s2U modification is demonstrated with influenza A virus RNA panhandle structure and precursor micoRNA-21 targets, underscoring its broad potential for optimizing PNA-based therapeutics and biotechnological tools. C_LI

biochemistry↗

bioRxiv2025

Electric fields trigger ceramide-dependent budding of exosome vesicles into multivesicular endosomes and boost the generation of exosomes

Exosomes hold immense therapeutic and diagnostic potential, yet their clinical translation remains constrained by low yields. Physiological electric fields (EFs), naturally occurring during wound healing and embryo development, have unexplored roles in exosome biogenesis. Here, we demonstrate that physiological-strength EFs (Direct Current, 50-200 mV/mm) dramatically enhance cellular exosome secretion, achieving a nearly 100-fold increase in a strength-dependent manner. Mechanistically, EFs augmented intraluminal vesicle formation within multivesicular bodies. We proposed a novel EF-driven membrane electroassembly and vesiculogenesis hypothesis and tested it using giant plasma membrane vesicles. We reconstructed EF-induced membrane remodeling and observed EFs driving inward budding of nanoscale exosome-like vesicles. Notably, alternating current EFs (50-400 Hz) exhibited significantly diminished efficacy compared to direct current EFs, highlighting membrane electropolarization-dependent modulation. Pharmacological inhibition of the lipid raft and ceramide almost abolishes EF-induced exosome secretion. Inhibition of the PI3K partially attenuated EF-triggered exosome release. Our findings not only unveil EFs as a potent physiological regulator of exosome secretion but also establish a novel, high-yield production strategy leveraging bioelectric cues. Significant statementWe reported that the bodys natural bioelectricity, the faint electric currents in healing wounds and embryo development, can trigger cells to release 100 times more "natural nanoscale vesicle couriers" called exosomes. This pure physical method is far more efficient than current available technologies, paving the way for breakthroughs in cancer early detection, stem cell therapies, and precision medicine. We found that gentle electric fields can "blow" the cell membrane to release exosomes like "soap bubbles". This explains how bioelectricity controls cell communication during development and wound healing. In short, harnessing the bodys hidden "electric language" unlocks a fast and natural way to mass-produce exosomes.

cell biology↗

bioRxiv2025

Highly accurate protein structure prediction-based virtual docking pipeline accelerating the identification of anti-schistosomal compounds

Schistosomiasis is a major neglected tropical disease that lacks an effective vaccine and faces increasing challenges from praziquantel resistance, underscoring the urgent need for novel therapeutics. Target-based drug discovery (TBDD) is a powerful strategy for drug development. In this study, we utilized AlphaFold to predict the structures of target proteins from Schistosoma mansoni and Schistosoma japonicum, followed by virtual molecular screening to identify potential inhibitors. Among 202 potential therapeutic targets, we identified 37 proteins with high-accuracy structural predictions suitable for molecular docking with 14,600 compounds. This screening yielded 268 candidate compounds, which were further evaluated in vitro for activity against both adult and juvenile S. mansoni and S. japonicum. Seven compounds exhibited strong anti-schistosomal activity, with HY-B2171A (Carubicin hydrochloride, CH) emerging as the most potent. CH was predicted to target the splicing factor U2AF65, and knockdown of its coding gene Smp_019690 resulted in a phenotype similar to CH treatment. RNA sequencing revealed that both CH treatment and Smp_019690 RNAi disrupted splicing events in the parasites. Further studies demonstrated that CH impairs parasite viability by inhibiting U2AF65 function in mRNA splicing regulation. By integrating RNAi-based target identification with structure-based virtual screening, alongside in vitro phenotypic and molecular analyses of compound-treated schistosomes, our study provides a comprehensive framework for anti-schistosomal drug discovery and identifies promising candidates for further preclinical development. Author SummarySchistosomiasis is a debilitating disease caused by parasitic worms and affects over 230 million people worldwide. For decades, control of the disease has relied on a single drug, praziquantel, which is less effective against juvenile parasites and increasingly threatened by the risk of drug resistance. To accelerate the discovery of new treatments, we integrated advanced computational tools with molecular validation. Using AlphaFold-predicted structures of 37 key schistosome proteins, we performed large-scale virtual screening to identify promising drug candidates. Over 200 compounds were prioritized for phenotypic screening on two major human-infective schistosome species, leading to the discovery of seven with potent anti-parasitic activity. Among them, Carubicin hydrochloride showed strong efficacy by targeting a critical splicing factor, U2AF65. Inhibiting this protein--either chemically or through gene silencing-- disrupted parasite movement, suppressed cell proliferation, and altered splicing events. Our work highlights the power of integrating AI-based protein modeling with phenotypic and mechanistic validation to accelerate drug discovery for neglected tropical diseases like schistosomiasis.

pathology↗

bioRxiv2025

Quantifying Protein-Protein Interaction with a Spatial Attention Kinetic Graph Neural Network

Accurate prioritisation of near-native protein-protein interaction (PPI) models remains a major bot-tleneck in structural biology. Here we present SAKE-PP, a physics-inspired, spatial-attention equivariant graph neural network that directly regresses interface RMSD (iRMSD) without any native references. Trained on docking decoys generated through our novel hierarchical sampling strategy applied to PDB-Bind dataset, SAKE-PP combines force-field-like attention with Laplacian-eigenvector orientation to couple local interaction forces with global topology. On the 2024PDB benchmark comprising 176 het-erodimers, SAKE-PP demonstrates effective optimization and selection of AF3 decoys, achieving improvements of 13.75% based on iRMSD statistics and 12.5% based on DockQ scores. It consistently outperforms the AF3 ranking score in multiple metrics, including overlap, hit rate, and correlation. In zero-shot evaluation of 139 antibody-antigen complexes, SAKE-PP improves the score-iRMSD correlation by 0.4. By unifying geometric deep learning with physics-based realism, SAKE-PP provides a robust, plug-and-play scoring function that streamlines reliable PPI evaluation and accelerates downstream structure-guided drug-design workflows.

bioinformatics↗

bioRxiv2025

DeepPGDB: A Novel Paradigm for AI-Guided Interactive Plant Genomic Database

DeepPGDB (https://www.deeppgdb.chat) is the first AI-driven plant genomics database designed to lower technical barriers in multi-omics research by enabling natural language-based data access and analysis. Integrating over 50 high-quality plant genomes, DeepPGDB combines fine-tuned large language models (LLMs) with prompt engineering to interpret user queries, generate standardized bioinformatics commands, and retrieve or visualize genomic data. Key functionalities include sequence retrieval, BLAST alignment, gene localization, expression profiling, and population genetics analysis, all presented via an intuitive conversational interface. A summarization module further enhances biological reasoning, inferring insights such as haplotype differentiation or protein properties. Benchmarking revealed that Deepseek-r1:14b optimized for short pre-prompts delivers high accuracy and speed. By bridging computational and biological expertise, DeepPGDB democratizes genomic research, fostering interdisciplinary collaboration and accelerating discoveries in agriculture and biotechnology.

bioinformatics↗

bioRxiv2025

Neural hierarchy for coding articulatory dynamics in speech imagery and production

Mental imagery is a hallmark of human cognition, yet the neural mechanisms underlying these internal states remain poorly understood. Speech imagery--the internal simulation of speech without overt articulation--has been proposed to partially share neural substrates with actual speech articulation. However, the precise feature encoding and spatiotemporal dynamics of this neural architecture remain controversial, constraining the understanding of mental states and the development of reliable speech imagery decoders. Here, we leveraged high-resolution electrocorticography recordings to investigate the shared and modality-specific cortical coding of articulatory kinematic trajectories (AKTs) during speech imagery and articulation. Applying a linear model, we identified robust neural dynamics in frontoparietal cortex that encoded AKTs across both modalities. Shared neural populations across the middle premotor cortex, subcentral gyrus, and postcentral-supramarginal junction exhibited consistent spatiotemporal stability during the integrative articulatory planning. In contrast, modality-specific populations for speech imagery and articulation were somatotopically interleaved along the primary sensorimotor cortex, revealing a hierarchical spatiotemporal organization distinct from shared encoding regions. We further developed a generalized neural network to decode multi-population neural dynamics. The model achieved high syllable prediction accuracy for speech imagery (79% median accuracy), closely matching the performance of speech articulation (81%). This model robustly extrapolated AKT decoding to untrained syllables within each modality while demonstrating cross-modal generalization across shared populations. These findings uncover a somato-cognitive hierarchy linking high-level supramodal planning with modality-specific neural manifestation, revolutionizing an imagery-based brain-computer interface that directly decodes thoughts for synthetic telepathy.

neuroscience↗

bioRxiv2025

Conformational Dynamics of the Nuclear Pore Complex: Insights from Elastic Network Models and Coarse-Grained Simulations

The nuclear pore complex (NPC) serves as the primary channel regulating nucleocytoplasmic transport in eukaryotic cells. In recent years, atomic-resolution structural models of both the constricted and dilated states of the NPC central core have been resolved; however, the molecular mechanisms underlying the transition between these two states remain unclear. This study employed elastic network models (ENM) to identify the first three low-frequency collective motion modes of the NPC scaffold structure, revealing the intrinsic relationship between these modes and the two-state transition of the NPC scaffold. Molecular dynamics (MD) simulations were further utilized to achieve dynamic conformational transitions between the constricted and dilated states of the NPC scaffold, providing detailed insights into the dynamics of this process. Regarding the FG repeat sequences within the nuclear pore channel, simulations based on the inner ring heterotrimeric FG-Nups revealed significant differences in their distribution between the constricted and dilated states. Finally, by integrating multi-scale research findings, we propose a potential nucleocytoplasmic transport regulatory pathway that encompasses both NPC conformational changes and FG barrier dynamic behavior, providing novel support and validation for the dilation model.

biophysics↗

bioRxiv2025

Highly reproduceable tapered fiber optoacoustic emitters by in-situ photothermal curing of PDMS for single neuron stimulation

Precise neural modulation is an important tool in neuroscience research for intervene of specific neural pathways. Photoacoustic is an emerging technology enabling high precision non-genetic neural stimulation in vitro and in vivo. In this paper, we report a new method to prepare a Candle Soot - Tapered Fiber based Optoacoustic Emitter (CSTFOE) through an in-situ photothermal curing process. The emitter created was found with high spatial resolution and efficiency for neuromodulation. In the meanwhile, the in-situ photothermal curing procedure is highly reproducible and allows for fabricating the photoacoustic coating on the tiny tip of the tapered fiber with a diameter less than 20 m. With the high efficiency and reproducibility, we designed an integrated photoacoustic neuromodulation system incorporating the CSTFOE, a nanosecond pulsed laser for photoacoustic generation, and a function generator for synchronization, achieving effective neuromodulation in vitro.

neuroscience↗

bioRxiv2025

Bridging Scales: Spectral Theory Reveals How Local Connectivity Rules Sculpt Global Neural Dynamics in Spatially Extended Networks

The brains diverse spatiotemporal activity patterns are fundamental to cognition and consciousness, yet how these macroscopic dynamics emerge from microscopic neural circuitry remains a critical challenge. We address this by developing a spatially extended neural network model integrated with a spectral theory of its connectivity matrix. Our theory quantitatively demonstrates how local structural parameters, such as E/I neuron projection ranges, connection strengths, and density determine distinct features of the eigenvalue spectrum, specifically outlier eigenvalues and a bulk disk. These spectral signatures, in turn, precisely predict the networks emergent global dynamical regime, encompassing asynchronous states, synchronous states, oscillations, localized activity bumps, traveling waves, and chaos. Motivated by observations of shifting cortical dynamics in mice across arousal states, our framework not only explains this repertoire of behaviors but also offers a principled approach to inferring underlying effective connectivity changes from macroscopic brain activity. By mechanistically linking neural structure to dynamics, this work provides a powerful tool for understanding brain function and paves the way for identifying potential biomarkers for neurological disorders.

biophysics↗

bioRxiv2025

CCN1 forms a complex with GPC4 and heparin to fine-tune signaling activities for embryonic cortical neural stem cell maintenance

Radial glial cells (RGCs) are the neural stem cells (NSCs) in the developing brain, capable of self-renewing and generating neurons and glia in order. The developmental behaviors of NSCs are determined by extrinsic factors from the NSC niche, together with intrinsic factors. Here, we show that Cellular Communication Network 1 (CCN1), a secreted matricellular protein, is an autocrine factor derived from RGCs in the embryonic telencephalon. Loss of Ccn1 in RGCs leads to accelerated lineage progression of RGCs, and causes premature cell cycle exit and neuronal differentiation, resulting in a reduction in NSC pool size, and in turn, at the expense of the perinatal gliogenesis. Mechanistically, CCN1 interacts with an RGC-expressed membrane protein GPC4, one of the heparan sulfate proteoglycans (HSPGs), and is required for GPC4 to maintain NSCs through the Sonic Hedgehog (Shh) signaling pathway, depending on the binding of heparin to CCN1. Our work provides a potential mechanism for the dynamic and sophisticated interconnections of niche components that meet the needs of context-dependent maintenance, differentiation, and fate determination of NSCs.

developmental biology↗

bioRxiv2025

Neural organoids protect engineered heart tissues from glucolipotoxicity by transferring versican in a co-culture system

Metabolic disorders could cause dysregulated glucose and lipid at the systemic level, but how inter-tissue/organ communications contribute to glucolipotoxicity is difficult to dissect in animal models. To solve this problem, myocardium and nerve tissues were modeled by 3D engineered heart tissues (EHTs) and neural organoids (NOs), which were co-cultured in a generalized medium with normal or elevated glucose/fatty acid contents. Morphology, gene expression, cell death and functional assessments detected no apparent alterations of EHTs and NOs in co-culture under normal conditions. By contrast, NOs significantly ameliorated glucolipotoxicity in EHTs. Transcriptomic and protein secretion assays identified the extracellular matrix protein versican as a key molecule that was transferred from NOs into EHTs in the high-glucose/fatty acid condition. Recombinant versican protein treatment was sufficient to reduce glucolipotoxicity in EHTs. Adeno-associated virus-delivered versican overexpression was sufficient to ameliorate cardiac dysfunction in a murine model of diabetic cardiomyopathy. These data provide the proof-of-concept evidence that inter-tissue/organ communications exist in the co-culture of engineered tissues and organoids, which could be systemically studied to explore potential pathological mechanisms and therapeutic strategies for multi-organ diseases in vitro.

cell biology↗

bioRxiv2025

PepSeek: Universal Functional Peptide Discovery with Cooperation Between Specialized Deep Learning Models and Large Language Model

Recent computational foundation models have revitalized the scientific discovery pipeline. However, developing foundational models for functional peptide discovery is costly due to the scarcity of wet-lab validated data. Meanwhile, conventional deep learning models are hard to generalize to unseen tasks or data distribution. Here, we introduce PepSeek, a universal approach for peptide discovery that synergistically integrates the most advanced large language model (LLM) with specialized small models. PepSeek harnesses the robust reasoning and generalization capabilities of LLM while leveraging the high predictive accuracy of specialized models trained for tasks such as antimicrobial activity regression and functional peptide generation. We have devised multiple collaborative strategies and task-specific modules demonstrating leading performance in peptide identification and generation. Notably, PepSeek achieves remarkable zero-shot prediction accuracy for peptides with diverse functionalities. We used PepSeek to identify a group of broad-spectrum antimicrobial peptide that exhibits low toxicity and high activity against drug-resistant bacteria, with the best surpassing all peptides currently undergoing clinical trials. Our framework establishes a new pipeline for scientific discovery with the help of LLM and specialized models.

bioinformatics↗

bioRxiv2025

A light-induced microprotein triggers regulated intramembrane proteolysis to promote photo sensing in a pathogenic bacterium.

Light, a ubiquitous environmental stimulus, shapes behavior and physiology across all domains of life. While photoreceptors are widespread in bacterial genomes, their functional roles and signal transduction mechanisms in non-photosynthetic bacteria remain understudied. Light represses biofilms and virulence factors through a bacteriophytochrome photoreceptor BphP and response regulator AlgB in the human pathogen Pseudomonas aeruginosa. Here, we used transposon mutagenesis screening to identify a conserved hypothetical microprotein, DimA, as the master activator of the photo-sensing cascade. Transcriptomics, luciferase reporter assays and physiological assays revealed that deletion of dimA abolishes light-dependent suppression of virulence factors and biofilms. Mechanistically, we demonstrated that DimA activates the site-I protease AlgW, triggering regulated intramembrane proteolysis of the anti-sigma factor MucA, liberating sigma factor AlgU ({sigma}22), which promotes algB expression. We discovered a positive feedback loop where light-activated AlgB upregulates dimA expression, thereby amplifying the photosensory response. This work establishes DimA as a crucial activator of photo sensing and expands our understanding of bacterial adaptation to changing light environments.

microbiology↗

bioRxiv2025

Large-scale single-neuron recording using the uFINE array in human cortex

Monitoring neural population activity at single-cell resolution is essential for driving fundamental research and clinical innovations. However, translating advanced recording techniques from animal models to humans remains a significant challenge. Flexible neural electrodes have recently emerged as powerful tools for large-scale single-unit recordings due to their superior biocompatibility and high recording density. Here, we demonstrate reliable, high-density single-unit recordings during intraoperative procedures in human patients using ultra-Flexible Implantable Neural Electrode (uFINE) arrays. The uFINE array exhibited sufficient mechanical robustness to maintain structural integrity throughout surgical operations. We successfully recorded 616 single units from 10 patients, with up to 135 single units simultaneously recorded. The flexibility of uFINE array minimized signal disturbances from brain pulsations, enabling stable and continuous single-unit detection. Stimulus and response tuning were observed at the level of individual neurons in awake patients. This uFINE-based recording approach offers unique opportunities to investigate human-specific cognitive functions and develop next-generation brain-machine interfaces.

bioengineering↗

bioRxiv2025

Comprehensive Neural Representations of Naturalistic Stimuli through Multimodal Deep Learning

A central challenge in cognitive neuroscience is understanding how the brain represents and predicts complex, multimodal experiences in naturalistic settings. Traditional neural encoding models, often based on unimodal or static features, fall short in capturing the rich, dynamic structure of real-world cognition. Here, we address this challenge by introducing a video-text alignment encoding framework that predicts whole-brain neural responses by integrating visual and linguistic features across time. Using a state-of-the-art deep learning model (VALOR; Vision-Audio-Language Omni-peRception), we achieve more accurate and generalizable encoding than unimodal (AlexNet, WordNet) and static multimodal (CLIP) baselines. Beyond improving prediction, our model automatically maps cortical semantic spaces, aligning with human-annotated dimensions without requiring manual labeling. We further uncover a hierarchical predictive coding gradient, where different brain regions anticipate future events over distinct timescales--an organization that correlates with individual cognitive abilities. These findings provide new evidence that temporal multimodal integration is a core mechanism of real-world brain function. Our results demonstrate that deep learning models aligned with naturalistic stimuli can reveal ecologically valid neural mechanisms, offering a powerful, scalable approach for investigating perception, semantics, and prediction in the human brain. This framework advances naturalistic neuroimaging by bridging computational modeling and real-world cognition.

neuroscience↗

bioRxiv2025

Uncovering Shared and Tissue-Specific Molecular Adaptations to Intermittent Fasting in Liver, Brain, and Muscle

Intermittent fasting (IF) has emerged as a powerful dietary intervention with profound metabolic benefits, yet the tissue-specific molecular mechanisms underlying these effects remain poorly understood. In this study, we employed comprehensive proteomics and transcriptomics analysis to investigate the systemic and organ-specific adaptations to IF in male C57BL/6 mice. Following a 16-hour daily fasting regimen (IF16) over four months, IF reduced blood glucose, HbA1c, and cholesterol levels while increasing ketone bodies, indicative of enhanced metabolic flexibility. Proteomic profiling of the liver, skeletal muscle, and cerebral cortex revealed tissue-specific responses, with the liver exhibiting the most pronounced changes, including upregulation of pathways involved in fatty acid oxidation, ketogenesis, and glycan degradation, and downregulation of steroid hormone and cholesterol metabolism. In muscle, IF enhanced pyruvate metabolism, fatty acid biosynthesis, and AMPK signaling, while suppressing oxidative phosphorylation and thermogenesis. The cerebral cortex displayed unique adaptations, with upregulation of autophagy, PPAR signaling, and metabolic pathways, and downregulation of TGF-beta and p53 signaling, suggesting a shift toward energy conservation and stress resilience. Notably, Serpin A1c emerged as the only protein commonly upregulated across all three tissues, highlighting its potential role in systemic adaptation to IF. Integrative transcriptomic and proteomic analyses revealed partial concordance between mRNA and protein expression, underscoring the complexity of post-transcriptional regulation. Shared biological signaling processes were identified across tissues, suggesting unifying mechanisms linking metabolic changes to cellular communication. Our findings reveal both conserved and tissue-specific responses by which IF may optimize energy utilization, enhance metabolic flexibility, and promote cellular resilience.

molecular biology↗

bioRxiv2025

Domain-general neural effects of associative learning and expectations on pain and hedonic taste perception

Predictive cues significantly influence perception through associative learning. However, it is unknown whether circuits are conserved across domains. We investigated how associative learning influences perceived intensity and valence of pain and hedonic taste and whether the mechanisms that support expectancy-based modulation vary as a function of aversiveness and modality. Sixty participants were randomly assigned to receive either painful heat, unpleasant liquid saline, or pleasant liquid sucrose during fMRI scanning. Following conditioning, cues that were initially associated with low or high intensity outcomes were intermittently followed by stimuli calibrated to elicit medium intensity ratings. Learned cues modulated expectations and subjective outcomes similarly across domains. Consistent with this, the orbitofrontal cortex exhibited domain-general anticipatory activation. The left anterior insula mediated domain-general cue effects on subjective intensity, whereas the thalamus mediated cue effects on subjective valence. Notably, these two regions were nearly identical to those previously implicated in mediating cue effects on pain (nearly identical to those previously implicated in cue effects on mediating pain (Atlas et al., 2010). Pain specificity was evident when we measured variations in stimulus intensity, whether we used univariate or multivariate approaches, but there was minimal evidence of specificity by modality or aversiveness when we examined cue effects on medium trials. These findings suggest that shared neural circuits mediate the effects of learned expectations on perception, linking pain with other areas of affective processing and perception across domains.

neuroscience↗

bioRxiv2025

Dissecting organoid-bacteria interaction highlights decreased contractile force as a key factor for heart infection

Bacterial endocarditis is a fatal cardiovascular disease exacerbated by weakened heart contraction, yet the direct impact of cardiac contractility on bacterial adhesion remains elusive. Here, we present a novel quantitative physics model integrating finite element analysis and live-cell imaging to uncover their strong correlation. Using this model, we quantified the real-time force magnitude generated by organoid-type cardiac microtissue derived from healthy donors and dilated cardiomyopathy patients - mimicking normal and suppressed heart contractility, respectively - to the approaching bacteria in a real fluidic system. The data revealed that weakened cardiac contractility facilitated bacterial invasion of the myocardium. Verifying this finding in a mouse transverse aortic constriction model demonstrated that increasing heart contraction efficiently mitigated bacterial invasion, with a 25% increase in heart contractility reducing endocarditis risk by 80%. Our findings demonstrate that patient-derived cardiac organoids provide a physiologically relevant platform for studying bacterial infections in vitro, offering high clinical fidelity. This platform establishes a valuable tool for drug screening and the development of novel therapeutic strategies.

bioengineering↗