Biology subjects

Hou, Y.

Publications and source records attributed to Hou, Y..

At least 37 records · Page 2Linked to original sources

bioRxiv2026

ContextTAD: Context-aware boundary learning for TAD calling from Hi-C contact maps

MotivationReliable topologically associating domain (TAD) calling from Hi-C contact maps remains difficult at high resolution and realistic sequencing depth. A central reason is that many callers learn boundary evidence largely from local signals, while domain compatibility is handled mainly during downstream decoding, so the learned boundary scores are not explicitly optimized for the TAD assembly step that ultimately determines the final calls. ResultsWe present ContextTAD, a deep-learning TAD caller that learns boundary evidence from broader local Hi-C windows that capture TAD-scale structural context. Instead of treating boundary prediction as an isolated per-bin classification problem, ContextTAD uses a context-aware representation to produce left- and right-boundary tracks that are explicitly optimized for downstream TAD assembly. Concretely, the model combines multiscale feature extraction from 2D Hi-C windows with a pair objective that rewards compatible boundary combinations and a count objective that regularizes window-level boundary evidence. Due to the limited availability of high-quality TAD annotations, supervised deep-learning methods for TAD calling remain rare. To address this bottleneck, we construct improved training annotations by integrating high-coverage Hi-C structure with complementary boundary-associated genomic signals, thereby providing more reliable supervision for model training. We benchmarked ContextTAD against a broad panel of alternative TAD callers across standard comparative evaluation, sequencing-depth robustness analysis, and cross-cell-type transfer settings, and found that it performed strongly against alternative tools across this wide range of settings, with the best overall recovery of biologically supported TADs. Availabilityhttps://github.com/ai4nucleome/ContextTAD Contactyanlinzhang@hkust-gz.edu.cn

genomics↗

bioRxiv2026

Guidance for high-quality functional gene embeddings from large language models

Large language models (LLMs) are increasingly used to generate gene embeddings, yet systematic benchmarks of prompting strategies and practical guidance for obtaining biologically meaningful representations remain limited. Here we present GEbench, an evaluation framework for assessing LLM-derived gene embeddings across different tasks, prompting strategies, and LLM architectures. GEbench revealed that embedding quality depends primarily on whether the input text contains explicit functional information, rather than on sparse gene identifiers or model size. Identifier-based embeddings showed weak biological organization, whereas embeddings derived from functional descriptions consistently achieved stronger functional separation and predictive performance. Notably, Self-Des, which extracts embeddings from model-generated gene function descriptions, enabled locally deployable LLMs to generate high-fidelity representations that approach the quality of expert-curated databases. Genome-scale analyses further supported these findings, indicating that explicit functional descriptions are an effective design principle for generating high-quality gene embeddings from LLMs.

bioinformatics↗

bioRxiv2026

PanVariants: Best Practice for Pangenome-based Variant Calling Pipeline and Framework

BackgroundAlthough pangenome references offer richer population diversity compared to linear references, current mainstream pangenome-based variant callers are limited to detecting only known variants stored in the graph. To address this limitation, we developed PanVariants, a novel pipeline designed to improve the detection of both known and novel variants accurately. We systematically evaluated its performance against the traditional linear alignment solution (BWA+GATK/Manta) and the existing pangenome-aware solution (DRAGEN/PanGenie) in three contexts: small variants (SNVs/indels) and structural variants (SVs) accuracy in Genome in a Bottle samples, clinical detection on positive samples, and application in cohort-based joint calling. ResultsBy integrating k-mer-based and mapping-based methods, PanVariants significantly reduced variant errors (FPs + FNs), achieving a 73% reduction compared to BWA+GATK and a 45% reduction compared to DRAGEN for SNVs. Retraining the DeepVariant model with high-quality DNBSEQ data further decreased errors by 15%. For SVs detection, PanVariants attained an F1-score of 89.39%, markedly outperforming DRAGEN (68.18%) and BWA+Manta (58.33%), approaching long-read sequencing performance (95.22%). In validation using clinical positive samples, PanVariants successfully detected all expected pathogenic variants while PanGenie failed. In the cohort joint-calling analysis, PanVariants detected more variants, made fewer Mendelian inheritance errors, and gave better per-sample accuracy than GATK. ConclusionsPanVariants establishes a robust framework and best-practice pipeline for pangenome-based variant detection, achieving both sensitive novel variant discovery and high accuracy for SNVs, indels and SVs. Our systematic evaluation of optional processing steps and input variables offers practical guidance for users. Validated across diagnostic and population-based applications, our findings strongly support the transition from linear to pangenome references in future genomics.

bioinformatics↗

bioRxiv2026

ProAR: Probabilistic Autoregressive Modeling for Molecular Dynamics

Understanding the structural dynamics of biomolecules is crucial for uncovering biological functions. As molecular dynamics (MD) simulation data becomes more available, deep generative models have been developed to synthesize realistic MD trajectories. However, existing methods produce fixed-length trajectories by jointly denoising high-dimensional spatiotemporal representations, which conflicts with MDs frame-by-frame integration process and fails to capture time-dependent conformational diversity. Inspired by MDs sequential nature, we introduce a new probabilistic autoregressive (ProAR) framework for trajectory generation. ProAR uses a dual-network system that models each frame as a multivariate Gaussian distribution and employs an anti-drifting sampling strategy to reduce cumulative errors. This approach captures conformational uncertainty and time-coupled structural changes while allowing flexible generation of trajectories of arbitrary length. Experiments on ATLAS, a large-scale protein MD dataset, demonstrate that for long trajectory generation, our model achieves a 7.5% reduction in reconstruction RMSE and an average 25.8% improvement in conformation change accuracy compared to previous state-of-the-art methods. For conformation sampling task, it performs comparably to specialized time-independent models, providing a flexible and dependable alternative to standard MD simulations.

molecular biology↗

bioRxiv2026

The enhanced multi-tissue atlas of regulatory effects in cattle

Cattle are integral to global food security, yet the molecular architecture of their complex traits remains poorly understood. Here, we present the Cattle Genotype-Tissue Expression (CattleGTEx) Phase 1 resource (https://cattlegtex.farmgtex.org/), a substantial expansion of the pilot study. By leveraging 12,422 RNA-seq profiles across 43 tissues and 82 breeds, we characterized 433,972 primary and 161,428 non-primary regulatory effects spanning seven molecular phenotypes. This high-resolution atlas resolves 75% of GWAS signals for 44 complex traits, significantly addressing the "missing regulation" in livestock. We propose a genetic regulatory model demonstrating how variants across multiple biological layers interact with specific biological contexts to shape phenotypic variation. Furthermore, CattleGTEx elucidates mechanisms underlying adaptive evolution between Bos taurus and Bos indicus, as well as artificial selection in dairy and beef breeds. Finally, by mapping evolutionary constraints on these regulatory effects, we demonstrate the translational value of this resource for prioritizing causal variants in human complex diseases. Together, Phase 1 of CattleGTEx provides a transformative framework for functional genomics, precision breeding, and comparative genetics.

genetics↗

bioRxiv2026

Interactome mapping in human excitatory neurons reveals novel risk genes and pathways in Alzheimer's disease

Alzheimers disease (AD) is an irreversible neurodegenerative disease defined by its molecular hallmarks - amyloid beta peptide plaques and neurofibrillary Tau tangles. Despite significant progress that has been made in uncovering a large number of genetic risk factors through extensive genomic sequencing and genetic studies, the molecular mechanisms driving AD-associated pathology and cognitive decline remain poorly understood. Therefore, alongside the identification of more risk genes, it is also paramount to study how these genes function and influence each other within the cellular pathways and overall molecular networks in AD-relevant brain cell types. However, current human protein-protein interactome datasets were all generated in either yeast or generic human cell lines. Consequently, many important neuronal interactions, especially neuron-specific ones, have yet been discovered. To address this critical gap, we developed a highly scalable, high-quality interactome mapping pipeline in human excitatory neurons derived from induced pluripotent stem cells (iPSC), and generated a comprehensive, neuron-specific interactome map, named ADNeuronNet, for key AD risk genes. ADNeuronNet consists of 1,767 high-confidence interactions among 1,189 proteins and is the only dataset enriched with neuron-specific genes when compared to known protein interactions, including previous large-scale interactome maps, for the same baits in the literature. Within ADNeuronNet, we identified 1,375 novel interactions, many of which are likely neuron specific. For example, we identified a neuron-specific interactor, RIN2, for major AD risk factor BIN1 and confirmed RIN2s function in recruiting BIN1 to RAB5 positive early endosomes, a process that has been well-associated with AD etiology. Additionally, we performed quantitative interaction perturbation analyses on AD risk genes with AD-associated mutations or isoforms and identified significant changes in 99 protein interactions among 11 different protein variants. Finally, we found that subunits from the anaphase-promoting complex/cyclosome (APC/C), another novel BIN1 interactors identified by ADNeuronNet, mediated modulation of Tau-aggregation in neurons via regulation of APOE expression, uncovering a previously unrecognized BIN1-APC/C-APOE regulatory axis in AD pathobiology. In summary, these findings illustrate how our neuron-specific ADNeuronNet can be leveraged to uncover new risk gene candidates and cellular pathways that help advance our understanding of molecular mechanisms underlying AD etiology.

systems biology↗

bioRxiv2026

Med12 and Med13 prevent tumorigenic dedifferentiation of intermediate neural progenitors and premature loss of neural stem cells

Med12 and Med13 are components of the kinase module of the mediator complex. Mutations of Med12 and Med13 have been associated with neurodevelopmental disorders and various cancers. However, their functions in neural development are not well understood. Here we show that in the developing Drosophila brain, Med12 and Med13 are required to prevent tumorigenic dedifferentiation of intermediate neural progenitors (INPs) and maintain neural stem cell (NSC) self-renewal. We further demonstrate that Med12 and Med13 prevent INP dedifferentiation by coordinating with a subset of core mediator complex subunits to mediate the activation of genes required for INP fate commitment. In contrast, during the maintenance of NSC self-renewal, Med12 and Med13 antagonize the function of a different subset of core mediator complex subunits. Together, our findings reveal that Med12 and Med13 perform two distinct functions in neural progenitors by coordinating with one subset of core mediator complex subunits while antagonizing another. HighlightsO_LILoss of Med12 and Med13 causes dedifferentiation of intermediate neural progenitors C_LIO_LIMed12 and Med13 mediate the activation of target genes of PntP1 C_LIO_LILoss of Med12 and Med13 leads to premature loss of neural stem cells C_LIO_LIMed12 and Med13 act with one subset of core mediator subunits but oppose another C_LI eTOC blurbZhu and his colleagues show that Med12 and Med13 promote cell fate commitment of intermediate neural progenitor cells and self-renewal of neural stem cells. Med12 and Med13 perform these two distinct functions by coordinating with one subset of core mediator complex subunits while opposing another to regulate the expression of different target genes.

developmental biology↗

bioRxiv2026

Digital Twin Brain simulation and manipulation of a functional brain network underlying mental illness

Linking synaptic-level perturbations to distributed brain-network dynamics remains a central challenge for understanding and treating mental illness. Although recent whole-brain models can reproduce individual brain activity patterns, they largely function as descriptive simulators rather than mechanistic, intervention-capable systems. Here we present an intervention-capable digital twin of the human brain, integrating individual neuroanatomy and task-evoked dynamics within a neuronal-scale framework. Individualised digital twin brains recapitulate a participant-specific compact cortico-subcortical network phenotype that captures transdiagnostic psychopathology across population and clinical cohorts. In silico modulation of excitatory and inhibitory synaptic conductance produces bidirectional, heterogeneous network responses across individuals. Population-scale simulations stratify individuals and predict longitudinal symptom trajectories from DTB-derived response profiles. Independent pharmacological functional MRI data further validate the predicted baseline-dependent network responses in vivo. Together, these findings establish digital brain models as experimental platforms for mechanistic perturbation, behavioural prediction and stratification, providing a foundation for precision neuroscience and psychiatry.

neuroscience↗

bioRxiv2026

Fluorescence anisotropy structured illumination microscopy for quantitative super-resolved mapping of cell microenvironment and cytoskeletal dynamics

The crowded intracellular milieu shapes organelle architecture and dynamics, yet nanoscale heterogeneity in its physicochemical properties remains difficult to visualize with conventional fluorescence anisotropy (FA) imaging. Here, we develop fluorescence anisotropy structured illumination microscopy (FA-SIM), which employs orthogonal-polarization structured illumination with dual-angle detection to achieve [~]100-nm resolution and quantitative FA retrieval with 0.56% relative error, representing over 20-fold higher accuracy than conventional FA imaging. With low phototoxicity, FA-SIM enables dual-color, hour-long quantitative super-resolution imaging in cells. Using viscosity standards, defined nanoparticles and small-molecule drug binding assays, we validate FA-SIM as a quantitative reporter of rotational mobility and molecular interactions. In cells, FA-SIM resolves nanoscale crowding heterogeneity, correlates anisotropy landscapes with condensate dynamics, and uncovers a radial crowding gradient across the microtubule network and mitotic spindle. Long-term dual-color imaging further resolves coordinated actin-microtubule remodeling and associated microenvironmental changes. By enabling quantitative, super-resolved mapping of intracellular physical properties in living systems, FA-SIM provides a powerful platform for investigating the physical regulation of cellular organization and dynamics in health and disease.

cell biology↗

bioRxiv2026

GPNMB overexpression- a marker of resistance to CDK4/6 inhibitors

Resistance to cyclin-dependent kinase 4/6 inhibitors remains a major clinical challenge in treating estrogen receptor-positive breast cancer, with no reliable predictive biomarkers currently available for patient selection. To investigate resistance mechanisms, we generated drug-tolerant persisters (DTPs) to abemaciclib and palbociclib in a panel of estrogen receptor-positive breast cancer cell lines. Functional analyses revealed that DTPs showed resistance to CDK4/6 inhibition, maintained G1 arrest, and exhibited increased senescence phenotype. To identify clinically relevant markers of resistance, we compared transcriptomic profiles from DTPs with publicly available gene-expression data from the phase III PEARL trial. Glycoprotein non-metastatic B (GPNMB) emerged as one of the most strongly upregulated transcripts in DTPs, and also was amongst the genes associated with resistance in the PEARL dataset. We further verified that GPNMB overexpression (GPNMB-OE) in sensitive cells conferred resistance to CDK4/6 inhibition, and enhanced migratory capacity. Overexpression of GPNMB drove substantially faster tumor progression and eliminated the growth-inhibitory effect of abemaciclib, which remained highly effective in control tumors. Across all treatment arms, GPNMB-OE tumors failed to respond to CDK4/6 blockade, highlighting a strong resistance phenotype. These results identify GPNMB as a potent promoter of tumor progression and a key mediator of resistance to abemaciclib. Our findings position GPNMB as a potential biomarker and therapeutic target that may help identify patients unlikely to benefit from CDK4/6 inhibition.

cancer biology↗

bioRxiv2026

PopGenAgent: Tool-Aware, Reproducible, Report-Oriented Workflows for Population Genomics

Population genetics analyses typically require orchestrating multiple tools across heterogeneous tasks, from data curation to inference and visualization. In practice, a central bottleneck lies not only in reproducibility, but in maintaining consistency between intermediate diagnostics, analytical decisions, and downstream results as analyses evolve. Here we present PopGenAgent, an agentic workflow that formalizes population-genetic analyses as explicit, multi-step plans with declared inputs, outputs, and dependencies. Each step is instantiated from curated tool and visualization templates, while all intermediate artefacts are preserved within a unified, inspectable context. An interactive agent interface supports iterative plan construction and revision, and leverages accumulated outputs to assist interpretation and report drafting. In a case study of 26 populations from the 1000 Genomes Project, starting from a filtered PLINK dataset, PopGenAgent coordinated analyses including ROH, LD decay, PCA, ADMIXTURE, TreeMix, and f3. By structuring analyses as explicit, revisable plans with preserved intermediate states, PopGenAgent provides a transparent and extensible environment for conducting population-genetic analyses.

bioinformatics↗

bioRxiv2026

Sex-specific Genetic Regulatory Effects in Chickens

Sexual dimorphism is a defining vertebrate feature, yet its sex-specific molecular architecture remains poorly understood. Here we established a sex-balanced, uniformly reared chicken cohort to map this landscape, integrating individual whole-genome sequencing with 7,969 bulk and 779,380 single nucleus transcriptomes across 32 tissues from 280 birds. We identified 495,098 independent expression quantitative trait loci for 20,194 genes, including 10,937 loci modulated by cell-type composition. Notably, 340 genes were regulated by 449 loci in a sex-dependent manner, significantly enrichment in endocrine tissues like adipose and the adrenal gland. Furthermore, we fine-mapped 1,219 structural variants, demonstrating their unique roles to tissue- and sex-specific expression beyond SNPs. Ultimately, we showed the utility of these regulatory effects in elucidating the molecular basis of metabolism and complex traits in both chickens and humans. This comprehensive atlas of regulatory effects provides profound insights into the genomic and molecular basis of sexual dimorphism in vertebrates.

genomics↗

bioRxiv2026

A specialized ARGONAUTE enables trans-species RNA interference in plant immunity

Trans-species RNA interference (tsRNAi), in which plants produce small RNAs (sRNAs) to silence target genes in pathogens, has emerged as a promising strategy for disease control. However, whether tsRNAi constitutes an endogenous, regulated immune response remains unclear. Here, we show that ARGONAUTE10 (AGO10) plays a critical role in pathogen-induced tsRNAi. Loss of AGO10 in Arabidopsis abolished pathogen gene silencing during infection, leading to hypersusceptibility to oomycete and fungal pathogens. Importantly, AGO10 rapidly responds to pathogen infection through increased protein accumulation and re-location into discrete cytoplasmic condensates, thus promoting the production of trans-species sRNAs at the pathogen infection sites. This immune responsiveness relies on the N terminal intrinsically disordered region (IDR) of AGO10, which is responsible for sensing and responding to immune activation. Specific features in the IDR partitions AGO10 into two deeply diverged subgroups, AGO10a and AGO10b, with the immune responsiveness and defense function evolutionarily conserved in AGO10a but not AGO10b. Together, these findings establish tsRNAi as a bona fide, evolutionarily conserved immune response and position AGO10 as a signal-responsive hub linking pathogen perception to tsRNAi-based defense.

plant biology↗

bioRxiv2026

Differential higher-order superassembly of HECT-type UBE3 ligases controlled by calcium signals

The yeast E4 ligase Hul5 transiently associates with the proteasome during heat shock and proteotoxic stress to maintain cytosolic protein quality control. Two of its human orthologs, UBE3B and UBE3C, have specialized to safeguard mitochondria and inherit Hul5 function, respectively. We determined cryo-electron microscopy structures of full-length human UBE3B and UBE3C in complex with calmodulin at 2.9-3.5 [A] in calcium-free and calcium-saturated conditions. Calmodulin acts as an inter-protomer molecular glue clamping UBE3B into ring-shaped anti-parallel homodimers or asymmetric trimers, and remodels UBE3C into a conformation competent for proteasome association. Calcium binding to calmodulin promotes disassembly of these higher-order complexes, toggling UBE3B/C from E4 to E3 activity. These findings reveal how calmodulin regulates higher-order architectures of E3/E4 ligases to rewire the ubiquitin-proteasome system via calcium signaling.

biochemistry↗

bioRxiv2025

Differential Alterations of Gut Microbiota in Girls with Isolated Obesity, Isolated Precocious Puberty, and Their Comorbidity

BackgroundChildhood obesity and precocious puberty (PP) are escalating public health concerns with a significant comorbidity. Emerging evidence implicates gut microbiota dysbiosis in both conditions, yet microbial associations with their co-occurrence remain unexplored. MethodsA total of 68 girls (18 normal, 25 PP-only, 18 obesity-only, and 7 obesity-PP comorbidity) were enrolled. 16S rRNA sequencing was applied to determine gut microbiota of girls. Microbial diversity, composition, and differential abundant taxa were analyzed. ResultsWhile overall microbial diversity and community structure were similar across groups, obesity-only subjects exhibited significantly increased microbial richness (Chao1 index, P<0.01 vs. all groups), characterized by a higher Firmicutes/Bacteroidota ratio (P<0.001 vs. normal/PP), increased Proteobacteria (P<0.001 vs. normal), and enrichment of genera including Blautia, Faecalibacterium, Roseburia, Anaerostipes, Haemophilus, and Lachnospiraceae_NK4A136_group (P<0.05 vs. other groups). PP-only subjects showed reduced Verrucomicrobiota (P<0.001 vs. normal/obesity) and elevated Escherichia_Shigella (P<0.05 vs. normal). Most strikingly, the obesity-PP comorbid group exhibited a unique gut microbiota profile characterized by a dramatic depletion of Firmicutes compared to the obesity-only group (P<0.001) and a remarkable nearly 100-fold increase in Verrucomicrobiota abundance (P<0.001 vs. other groups), suggesting a distinct microbial ecotype potentially contributing to the pathophysiology of this comorbidity. ConclusionThis study delineated unique gut microbiota alterations associated with isolated obesity, isolated PP, and their comorbidity in girls. The distinct dysbiotic pattern in comorbid obesity-PP suggested complex microbiota-host interactions, providing a new microbiological perspective for understanding the mechanism of comorbidity between obesity and precocious puberty.

microbiology↗

bioRxiv2025

NyxBind: enhancing DNN representations via contrastive learning for TFBS prediction

While pretrained genomic language models effectively capture general DNA sequence patterns through masked language modeling, they often struggle to discriminate subtle yet biologically critical differences among transcription factor binding site (TFBS) motifs. Recent studies suggest that contrastive learning can enhance the discriminative power of embeddings by explicitly modeling inter-instance similarities and differences. Building on this insight, we introduce NyxBind, the first TFBS prediction model that applies contrastive learning across multiple TFBS types to enhance regulatory sequence representations. NyxBind better captures discriminative sequence features, enabling more accurate and biologically meaningful TFBS prediction. Extensive evaluations show that NyxBind consistently outperforms alternative models across multiple TFBS classification benchmarks, demonstrating strong robustness and generalizability. Moreover, NyxBind supports both full-parameter and parameter-efficient fine-tuning while maintaining high performance, and supports accurate motif visualization, aligning closely with experimentally validated transcription factor binding profiles. The code are available at https://github.com/ai4nucleome/NyxBind.

bioinformatics↗

bioRxiv2025

Within-subject rhythmicity and stability of hunger, satiety, and physiological markers: insights from a five-day laboratory study in time-isolation conditions

Hunger and satiety are interoceptions involved in the control of food intake. How they change across the day and from one day to the next, as well as their relationships with physiological parameters, remain poorly understood. In a five-day laboratory study, twenty healthy male participants (24.2 {+/-} 3.3 years) were given three meals per day (breakfast, lunch, dinner), at times based on each individuals internal time. Subjects had an 8-h sleep opportunity at night recorded by polysomnography, during which time they were exposed to 4 different light intensities (0, 3, 8, or 20 lux). Hunger and satiety were assessed before and after each meal. Heart rate was measured continuously and glucose levels were sampled every 15 minutes. We found that: 1) Hunger and satiety varied dynamically with the lowest levels at breakfast and the highest at lunch and dinner (p < 0.01); 2) the intensity of hunger and satiety remained stable across the five experimental days, and were subject-dependent; 3) Heart rate was higher after than before meals (p < 0.0001), and higher around breakfast than dinner; 4) Glycemia before and after meals was respectively stable throughout the day and over the study period; 5) Interoception was correlated with fluctuations in heart rate, glucose levels and sleep duration. Overall, our results reveal that hunger and satiety perception are individual characteristics. To better understand the role of interoception in food intake control, both hunger and satiety should be measured, but separately in their respective states.

physiology↗

bioRxiv2025

Human coronary artery tri-culture organ-chip recapitulates anti-inflammatory effect of pulsatile wall strain

Inflammation is a precursor to vascular diseases, including atherosclerosis, and is modulated by the local biomechanical environment. There is an urgent need for improved in vitro models, to advance understanding, and to test new therapeutic approaches.. This study describes the development and characterization of a human coronary artery organ-chip model of vascular inflammation, with physiological biomechanical stimulation. Human coronary artery endothelial cells and smooth muscle cells were cultured on appropriate extracellular matrices in the two adjoining channels of the Chip-S1(R) from Emulate Inc. Both endothelial and smooth muscle cells demonstrated characteristic phenotypic identity, shown by expression of CD31 and -SMA respectively. Application of physiological pulsatile tensile strain induced alignment of both cell types, perpendicular to strain direction, as seen in vivo. Addition of TNF- to the vascular channel drove an inflammatory response in both cell types, shown by upregulation of ICAM-1 and P65, and attachment and invasion of circulating THP-1 monocytes. Strain field analysis revealed pressure-dependent spatial variation with 12% strain in the center of the chip, and 5% towards the ends. Pulsatile tensile strain reduced the inflammatory response to TNF- with a greater localized inflammatory response in areas of lower strain, further replicating in vivo behavior. In conclusion, we present a fully characterized, tri-culture model of the human coronary artery which recapitulates the physiological effects of pulsatile vessel dilation on morphology and localized inflammatory susceptibility. Our model was developed upon a commercially-available, organ-chip platform, allowing for rapid adoption for therapeutic testing, and fundamental discovery science.

bioengineering↗