Biology subjects

Zhu, K.

Publications and source records attributed to Zhu, K..

At least 19 recordsLinked to original sources

bioRxiv2026

In Utero MRI of Radial Glial Fibers Highlights Their Role in Modulating Fetal Cortical Folding

The process of cortical folding is fundamental to early human brain development. While the differential growth of the cortical layers is a known driver, the biomechanical contribution of key cellular microstructures remains unclear, and there is a lack of direct in vivo markers of cortical microstructures for time-resolved observations. Using a multimodal approach combining in utero and ex vivo fetal brain MRI, histology, and computational mechanical simulation, we investigated whether developmental reorganization of cortical radial microstructures, particularly the radial glial cell (RGC)-associated scaffold, is linked to cortical folding and may modulate its mechanical progression. We characterized this radial organization noninvasively using diffusion MRI-based radial fiber density (FDrad). ex vivo fetal brain MRI and histology supported that regional variations in FDrad were strongly associated with vimentin-labelled radial architecture. Our in utero MRI data revealed closely coordinated developmental changes in cortical FDrad and curvature, with their regional timings and spatial patterns significantly coupled across the cortex. Longitudinal in utero MRI data further supported the synchronized within-individual co-development of FDrad and curvature. Furthermore, biomechanical simulations showed that reducing radial reinforcement modulated differential-growth-driven buckling, producing earlier folding and a greater cortical curvature under otherwise matched model conditions. Collectively, this study introduces FDrad as a noninvasive in utero MRI measure sensitive to radial glial fibers. By integrating spatial, temporal, histological, and computational evidence, our findings suggested a model in which developmental disassembly of the radial scaffold may modulate the mechanical response of the fetal cortex to differential growth, thereby contributing to cortical folding.

neuroscience↗

bioRxiv2026

Integrative drug discovery for Parkinson disease with DIMER

Translating Parkinson disease (PD) genetics into therapeutic candidates requires approaches that account for interconnected disease mechanisms and their cellular context. Here we present DIMER (Direct and Indirect Module Evaluation for Repurposing and screening), a framework that connects network-based drug repurposing and target-directed virtual screening through shared disease-associated gene sets. Applying DIMER to PD, we integrated genetic risk and mechanism-associated gene sets with drug-target interactions, disease transcriptomes, and single nucleus expression profiles. Network analysis identified 423 repurposing candidates, with 71% of qualifying drug-gene set associations arising through targets outside the corresponding disease gene set. Transcriptional reversal analysis and cell type-resolved networks further distinguished candidates by their predicted effects on disease-associated expression and their cellular context, while target-set separation nominated potentially complementary drug combinations. For target-directed virtual screening, DIMER adapted a pretrained compound-protein interaction model to screening library chemistry and incorporated known-ligand similarity into compound ranking. Library adaptation improved compound-target pair classification performance, whereas ligand-informed ranking increased early enrichment of known active compounds. Consensus screening of 1.82 million compounds against seven PD-associated proteins yielded a shortlist of 22 compounds supported by at least five ranking runs. In a complementary experimental evaluation, four repurposing candidates (polydatin, fedratinib, sorafenib and venlafaxine) attenuated the loss of dopamine neuron marker signals in human midbrain organoids exposed to 6-hydroxydopamine. Together, these findings establish DIMER as a framework for translating disease-associated genes into pharmacological hypotheses and prioritizing candidates for experimental testing.

neuroscience↗

bioRxiv2026

Ecological context shapes the interpretation of biodiversity intactness metrics

Developing biodiversity metrics that are both operational for decision-making and ecologically meaningful remains challenging. Mean Species Abundance (MSA) is widely used to quantify biodiversity intactness in applications including biodiversity scenario modeling, spatial assessment, policy and planning, and life cycle assessment (LCA). MSA compares species abundance under disturbed conditions with their abundance under a reference condition, but the pressure-impact relationships used to calculate it are generalized across studies and may not adequately represent variation among ecological contexts or taxonomic groups. This is particularly relevant when MSA is transferred across regions or used to support comparisons and environmental decision-making. We compiled biodiversity data from 7,064 forest fragments containing 21,761 species of arthropods, amphibians, birds, mammals, squamates, and plants across 11 biomes worldwide. We evaluated how MSA relates to broader ecological outcomes, including local and regional species richness and ecosystem productivity, and whether these relationships vary across ecological contexts (vegetation condition, landscape structure, and biome) and taxonomic groups. MSA was positively associated with local species richness and ecosystem productivity across all sites, but these associations weakened, disappeared, or reversed depending on ecological context and taxonomic group. For example, the MSA-productivity association held in only five of 11 biomes, and the MSA-regional richness association was negative in temperate conifer and boreal forests but positive in four other biomes. We propose a simple decision guide linking assessment goals, contextual evaluation, and interpretation to help identify when MSA can be used directly, when ecological context should guide its interpretation, and when complementary ecological information is needed. These findings may extend to other biodiversity metrics that compare impacted conditions with reference states and apply generalized impact relationships across ecological contexts and taxonomic groups.

ecology↗

bioRxiv2026

Venetoclax Resistance Creates Canonical Pyroptosis Vulnerability in AML

Venetoclax has transformed acute myeloid leukemia (AML) therapy, yet resistance is almost inevitable. Its diverse, often difficult-to-target drivers frequently converge on monocytic differentiation, a state that survives on MCL1 rather than BCL2. Here, we show that the very cells that escape BCL2 inhibition retain the canonical pyroptosis machinery of normal monocytes. Across 15 bulk and 4 single-cell cohorts, the canonical pyroptosis program was enriched in monocytic AML and monocytic leukemia stem cells, correlated with venetoclax resistance, and associated with inferior survival specifically after venetoclax-based therapy. In 24 cell lines and 142 patient specimens, both DPP8/9 inhibition by Val-boroPro and NLRP3 activation by lipopolysaccharide (LPS) plus nigericin preferentially killed monocytic AML cells through GSDMD-mediated pyroptosis, largely sparing non-monocytic AML and lymphoid leukemias. Venetoclax preferentially depleted primitive AML blasts but enriched a monocytic residuum vulnerable to canonical pyroptosis; combining venetoclax with Val-boroPro achieved deeper ex vivo clearance than either agent alone. In venetoclax-resistant xenografts, Val-boroPro suppressed leukemia progression, and adding venetoclax further reduced leukemia burden and prolonged survival beyond either monotherapy. Thus, venetoclax resistance itself exposes an alternative, druggable cell-death program that can be therapeutically exploited to overcome this resistance.

cancer biology↗

bioRxiv2026

Prior low-severity fires reduce the risk of persistent forest loss from subsequent fires

Intensifying fire regimes threaten forests globally, but the risk of persistent post-fire forest loss and its potential mitigation remains poorly quantified. We analyzed millions of wildfires worldwide from 2001 to 2024 and tracked recovery in satellite-observed forest structure and ecosystem function. Post-fire persistent forest loss, indicated by modeled non-recovery to pre-fire conditions over decadal timescales, affected 57.1% of burned forest area globally since 2001, with hotspots in Pacific temperate and southern boreal forests. We then identified 'crucial fires' as events exceeding a stringent modeled-risk probability threshold for persistent structural or functional non-recovery, with fire severity strongly predicting this loss. This severity dependence revealed a management pathway, as locations with prior low-severity fire experienced lower severity in subsequent wildfires and had lower modeled probability of becoming crucial. Under a model-based counterfactual scenario, applying the estimated severity attenuation was associated with a 7.6% reduction; the top 1% of road-accessible areas accounted for 35% of this reduction. These results provide a global framework for identifying where wildfire threatens forest resistance and where targeted low-severity fire management like prescribed fire might be used to combat global forest loss.

ecology↗

bioRxiv2026

Individual-tree phenology reveals climate-dependent responses to urban thermal heterogeneity across U.S. cities

Rapid urbanization is altering the seasonal life cycles, or phenology, of urban trees. Diverse species composition and heterogeneous urban thermal environments can generate pronounced phenological variation within cities, yet such fine-scale seasonality remains poorly understood. This limits the ability to anticipate localized ecosystem services and disservices, including canopy cooling and pollen exposure. Here, we analyzed individual-tree phenology and their response to temperature across 76 cities in the contiguous United States by integrating municipal street-tree inventories, PlanetScope satellite time series, and 1-km near-surface urban air-temperature data. We quantified intra-city and cross-city phenological variation, estimated species-level associations between phenology and fine-scale urban temperature, and tested whether these associations vary with regional climatic context. Intra-city phenological variation rivaled cross-city variation for 33.3% of species in spring and 53.7% in fall. Temperature-phenology associations were widespread: warmer local urban environments were associated with earlier spring onset in 51.5% of cases and delayed fall senescence in 66.5%, with approximately two-thirds of these responses cascading to longer growing seasons. These associations varied with regional climate, generally showing stronger spring advancement and fall delay in cooler and wetter cities, but weaker or reversed responses in hotter or drier cities. Together, these results suggest that urban tree phenology is shaped by interactions among fine-scale urban temperature, species identity, and regional climate. Accounting for this heterogeneity can improve predictions of urban forest function and inform climate-resilient species selection and management. Plain Language SummaryStreet trees within the same city do not all leaf out in spring or lose their leaves in fall at the same time. This seasonal timing, called phenology, affects services such as shade and cooling, as well as disservices such as pollen exposure. We studied individual tree phenology and their temperature response in 76 U.S. cities using street-tree records, satellite observations, and local air-temperature data. We asked how much phenology varies within cities, how it responds to neighborhood-scale temperature differences, and whether these responses depend on regional climate. Variation within a city was often as large as variation among cities, especially in fall. Trees in warmer parts of cities generally leafed out earlier and lost their leaves later, often extending the growing season. However, these responses were stronger in cooler and wetter cities and weaker or sometimes reversed in hotter or drier cities. These findings show that urban tree seasonality depends on local temperature, species identity, and regional climate, which should be considered when selecting and managing trees for future cities. Key PointsO_LISubstantial within-city phenological variation is not captured by city-level averages. C_LIO_LIFine-scale temperature shaped phenology and growing-season length, but responses varied across species, cities, and phases. C_LIO_LITemperature sensitivity depended on regional climate, weakening or reversing in hotter or drier cities. C_LI

ecology↗

bioRxiv2026

TLR7 Signaling Regulates Embryonic Hematopoietic Stem Cell Development by Sensing microRNA-146a in Vertebrates

The specification of hematopoietic stem cells (HSCs) is tightly regulated by multiple transcription factors and signaling pathways. Inflammatory signaling is pivotal for embryonic HSC development, but the mechanisms that activate it in vivo remain poorly understood. Here, we show that Toll-like receptor 7 (TLR7) is essential for the emergence of embryonic HSC in both zebrafish and mouse embryos. TLR7 deficiency reduces HSC numbers but not primitive or definitive progenitors. Conversely, the TLR7 agonist R848 enhances embryonic HSC development. Mechanistically, TLR7 signaling acts through interferon regulatory factor 5 (IRF5) to induce the expression of inflammatory cytokines, which subsequently activate Notch signaling to promote HSC emergence through a non-cell-autonomous mechanism. Notably, we identify microRNA-146a (miR-146a) as a potential endogenous activator of TLR7, inducing inflammatory signaling and promoting HSC development. Pharmacological treatment with miR-146a significantly increases HSC numbers in zebrafish embryos. Together, our findings reveal a crucial role for miR-146a-TLR7-IRF5 signaling axis in HSC emergence, providing insights into the endogenous factors that drive tonic inflammatory signaling during normal hematopoiesis and suggesting the translational potential of TLR7 agonists and miR-146a for stem-cell-based therapeutics. Significance StatementThe embryonic origin of hematopoietic stem cells (HSCs) requires inflammatory signals, but the endogenous factor that triggers this process remains elusive. We identify microRNA-146a (miR-146a) as a natural activator of Toll-like Receptor 7 (TLR7) signaling, which is essential for HSC emergence. This miR-146a-TLR7 axis functions through IRF5 and inflammatory cytokines to activate the Notch signaling, specifically promoting embryonic HSC development. Our work addresses the critical question of endogenous ligands that mediate tonic inflammatory signaling in normal hematopoiesis, uncovers novel crosstalk between miRNAs and innate immunity in HSC specification, and identifies promising candidates for stem cell-based therapeutics.

developmental biology↗

bioRxiv2026

Performance Attribution in pLM-Based Biological Relation Prediction

Protein language models (pLMs) have enabled strong performance in biological relation-prediction benchmarks, yet aggregate predictive metrics do not reveal which sources of information support that performance. We examined three pLM-based case studies--MetaESI, Deep-GNHV, and SAGEPhos--using frozen pLM-derived full-input baselines, component-restricted controls, train-derived endpoint or site priors, and selector-matched random restrictions. Generic tabular learners trained on frozen pLM-derived inputs achieved strong task-specific benchmark references, reaching AUROC/AUPRC values of 0.827/0.703 for the MetaESI-derived full-input rerun, 0.922/0.708 for DeepGNHV, and 0.896/0.893 for SAGEPhos. Across tasks, substantial predictive signal remained available from restricted endpoint, site/window, motif, or train-derived prior features. In the MetaESI-derived row-level evaluation, a self-label-excluding endpoint-prior control reached AUROC/AUPRC of 0.846/0.676 without sequence embeddings, numerically close to the frozen full-input reference. In a separate frozen-pooling experiment, GARD-selected positions did not outperform count-matched random token pooling. Endpoint-cold diagnostics further showed substantial performance degradation when one or both endpoint types were unseen during training. These findings do not diagnose leakage, imply memorization, exclude biological learning, or invalidate the evaluated models. Rather, they show that strong benchmark performance can reflect multiple overlapping information sources and that aggregate scores alone cannot establish pair-specific learning, the incremental value of task-specific architectures, selector-specific information content, or mechanistic biological interpretation. We therefore propose that such claims require matched attribution controls, representation-matched baselines, and evaluation protocols aligned with the intended generalization setting.

bioinformatics↗

bioRxiv2026

In vivo gene disruption and homology-directed repair in muscles and muscle stem cells using CRISPR/Cas9

Programmable endonucleases such as CRISPR/Cas9 provide powerful tools to edit mammalian genomes by engaging cellular mechanisms of DNA double-strand break (DSB) repair. CRISPR-catalysed homology-directed repair (CRISPR-HDR), though generally less efficient than other modes of DNA repair, holds particular promise to enable precise sequence replacement by targeted insertion of a homologous DNA template1,2. While recent studies have reported appreciable levels of HDR in cardiomyocytes in vivo3, skeletal muscle myofibres have historically been considered refractory to HDR-mediated genome editing4. Furthermore, how repair outcomes differ across tissues after systemic delivery of CRISPR/Cas9 editors, whether precise HDR editing can be achieved in regenerative tissue stem cells, and how developmental timing influences accessibility to CRISPR-induced repair remain unclear. Here, we use an adeno-associated virus (AAV)-delivered in vivo GFP-to-BFP colour-switching reporter system (AAV-GFP-to-BFP) to examine in vivo CRISPR-HDR with cellular- and tissue-level resolution. We find that postnatal cardiac muscle, skeletal muscle, and muscle stem cells undergo templated HDR at different rates across discrete developmental stages in mice. While HDR-edited muscle stem cells and myofibres were readily detectable after in vivo editing in juvenile mice, editing in neonatal mice yielded more efficient HDR in cardiac tissue. Based on these results, we adapted the CRISPR-HDR approach to rescue the therapeutically relevant Dmd mutation in mdx mice, demonstrating recoding to the wild-type protein sequence in both skeletal and cardiac muscles. These results provide a framework for advancing donor-templated DNA repair in living postnatal animals, and reveal unexpected cellular, developmental, and disease-related constraints on precise, therapeutic in vivo gene correction.

cell biology↗

bioRxiv2026

Mapping distribution of invasive plant species and uncertainty using citizen science, remote sensing, and deep learning

Invasive plants pose a major environmental problem, threatening biodiversity, altering ecosystem functions, and causing economic loss. Climate change is altering environmental conditions, potentially facilitating the spread of invasive plant species, posing challenges for ecosystem management and biodiversity conservation. Accurate predictions of invasive species distributions are therefore essential for effective monitoring and early intervention. Species distribution models (SDMs) have become an important tool for predicting species habitats, but many studies rely on traditional machine learning approaches, focus on single-species predictions and overlook uncertainty associated with future climate scenarios. This study aims to evaluate the performance of a deep learning-based SDM framework, Deepbiosphere, for predicting both native and invasive plant species distributions on a regional scale, the US state of Michigan, and to assess how climate scenario uncertainty influences spatial predictions of invasive species risk particularly on two focal invasive species. Results show that Deepbiosphere outcompeted other baseline models by on average of 10.98% with a mean AUC-ROC of 0.79 across 1553 vascular plant species. For two invasive species Rhamnus cathartica and Ailanthus altissima, Deepbiosphere respectively improved modeling accuracy by an average of 56.41% and 74.99%, suggesting its enhanced predictive capability for invasive species. Current predictions indicated that R. cathartica is already broadly suitable across much of Michigan, whereas A. altissima is currently more restricted to southern regions. Under future climate scenarios, both species were projected to expand northward, with a particularly strong expansion signal for A. altissima. Prediction uncertainty was spatially heterogeneous, where general circulation models (GCMs) were the dominant source of uncertainty across most of the state. By integrating citizen science, remote sensing, and deep learning, we produced high-resolution risk-uncertainty maps for key invasive species and highlighted the importance of explicitly mapping uncertainty to support more informed invasive species management under climate change.

ecology↗

bioRxiv2026

Gateway: patient olfactory neurons for large-scale discovery in neurodegenerative disease

An estimated 42% of Americans over age 55 will develop dementia, but the molecular understanding of dementia and neurodegenerative disease is constrained because the living human brain cannot be routinely sampled during disease progression. Olfactory sensory neurons provide a clinically accessible neuronal tissue source with developmental, transcriptional, and disease-relevant links to the central nervous system. Here we describe Gateway, a platform that combines device guided olfactory epithelium biopsy, onsite fixation, and 10x Genomics FLEX RNA profiling to generate single-cell transcriptomic data from living patient neurons. We present a 4-million-cell atlas representing 202 human donors, including healthy controls and individuals with neurodegenerative diseases, and release it as an open resource through CELLxGENE. We define the cellular composition of the human olfactory epithelium and show that Gateway captures neuronal functional and compartmental programs and detects more brain-enriched genes than other clinically accessible transcriptomic sample types. In exploratory analyses of Alzheimers Disease and Parkinsons Disease, we identify dys-regulation of pathways and GWAS-implicated genes related to key neurodegenerative mechanisms such as neuroinflammation, endolysosomal biology, proteostasis, and synaptic maintenance. Together, this atlas and clinical workflow establish living patient olfactory neurons as a scalable complementary modality for neuroscience research, target discovery, and biomarker development in neurodegenerative disease.

neuroscience↗

bioRxiv2026

Primary productivity declines when species composition and climate are mismatched

Climate change drives shifts in species composition, but turnover in many communities lags behind the current pace of change. Anticipating the impact of the resulting community-climate disequilibria on ecosystem functioning is critical. Present-day communities may already be out of equilibrium with climate, providing an opportunity to estimate the effects of disequilibrium before they become more widespread. We analyzed plant community composition and function data from [~]60,000 rangeland monitoring sites across the western US to measure how community-climate disequilibrium contributes to spatial and temporal variation in net primary productivity (NPP) - a key ecosystem function. We found that communities were already substantially out of equilibrium with climate and accounting for this disequilibrium helped explain patterns of NPP. Communities farthest from equilibrium were less productive than those that were closely matched with climate. Our findings suggest that future increases in community-climate disequilibrium may further impair ecosystem functioning.

ecology↗

bioRxiv2026

Estimating Daily Taxon-specific Tree Pollen at a 1-km Resolution in Atlanta, GA from 2020 to 2024

While tree pollen is a major trigger of allergic respiratory conditions and different taxa exhibit varying allergenic potentials, the lack of high-resolution, taxon-specific exposure metrics have limited our ability to identify which local pollen taxa are primarily responsible for respiratory illness. Traditional pollen monitoring networks, which have an intermittent sampling schedule, are not ideal for examining the delayed effects of pollen exposure due to the missing days. In this study, we developed a modeling framework integrating atmospheric dispersion effects, taxa-specific phenology, and machine learning to predict daily counts of 13 tree taxa in the five-county Metro Atlanta area, Georgia at a 1-km resolution from 2020 to 2024. Machine learning model performance was validated with daily pollen counts collected by a multi-site monitoring network equipped with automated pollen sensors. Findings showed that Betula and Quercus pollens exhibited higher predictive performance than other taxa, with R2 values ranging from 0.69 to 0.92 and from 0.71 to 0.89, respectively. Our 1-kilometer prediction data provides gapless exposure metrics to understand the spatial and temporal variability in pollen exposure, can facilitate investigation of urban pollen hotspots and support epidemiologic studies of pollen-related respiratory outcomes.

plant biology↗

bioRxiv2026

Targeting cancer-associated cell surface RNAs with oligonucleotide-drug conjugates enables broad antitumor activity

Recent studies have revealed the presence of RNAs on mammalian cell surfaces, yet the linkage of cell surface RNAs (csRNAs) to cellular states and their potential as extracellular accessible drug targets remains unexplored. Here we develop Cell-surface and Intracellular RNAs Co-mapping (CIRCmap), a highly multiplexed in situ profiling approach that simultaneously detects thousands of cell-surface and intracellular RNAs at single-cell resolution. Using CIRCmap, we uncover that csRNA distributions are correlated with cellular states and identified cancer-associated csRNAs. Integrative analysis of cell-surface and intracellular RNAs within the same cells implies that the csRNAs undergo endocytosis and endolysosomal trafficking, which is further supported by perturbation experiments and co-localization visualization. We design oligonucleotide-drug conjugates (ODCs) that target cancer-associated csRNAs for endocytic delivery of cytotoxic payloads selectively to cancer cells. ODCs exhibit broad antitumor activity in cell lines and an in vivo mouse tumor model, opening a promising new avenue for targeted cancer therapy.

cell biology↗

bioRxiv2026

White-matter-microstructure-informed whole-brain models reveal localized excitation-inhibition imbalance in schizophrenia

Providing early diagnosis and personalized treatment for psychiatric disorders like schizophrenia remains challenging, due to important interpersonal differences and still elusive neuronal mechanisms. Whole-brain network models show promising results with clinical relevance for individualized treatment recommendations in neurological disorders. However, their applicability to psychiatry is still limited as models fail to account for inter-individual differences in the correlation structure of brain dynamics. What physiological mechanisms should models incorporate to better account for individual profiles of brain dynamics in schizophrenia patients and healthy controls? Our study compares various metrics of white matter structure and microstructure to inform connection weights between regions. To do so, we inferred regional parameters of whole-brain mean-field models with The Virtual Brain simulator to account for empirical functional connectivity from resting-state functional magnetic resonance imaging of schizophrenia patients and healthy controls. We found that using global fractional anisotropy or apparent diffusion coefficient of white matter fibers to inform the weights in neural mass models can drastically improve model performance. The data-model correlations of simulated and empirical data were significantly improved (from 0.2 to 0.7) over using the number or density of fibers as in many state-of-the-art methods. This approach allows us to uncover personalized maps of excitation-inhibition imbalance, hypothesized to underlie symptoms in schizophrenia. These maps prove meaningful in that they can predict diagnosis better than model-independent neuroimaging benchmarks. Our findings highlight the importance of white matter microstructure in whole-brain modeling. The novel white-matter-informed models reveal mechanisms that can cause altered brain dynamics in schizophrenia and could inform treatment in personalized psychiatry.

neuroscience↗

bioRxiv2026

BioReason-Pro: Advancing Protein Function Prediction with Multimodal Biological Reasoning

Protein function annotation is fundamental to understanding biological mechanisms, designing therapeutics, and advancing biomedical research. Current computational methods either rely on shallow sequence similarity or treat function prediction as isolated classification tasks, failing to capture the integrative reasoning across sequence, structure, domains, and interactions that expert biologists perform to infer function. We introduce BioReason-Pro, the first multimodal reasoning large language model (LLM) for protein function prediction that integrates protein embeddings with biological context to generate structured reasoning traces. A key input into BioReason-Pro is the set of GO term predictions made by GO-GPT, our autoregressive transformer that captures hierarchical and cross-aspect dependencies of GO terms. BioReason-Pro is trained via supervised fine-tuning on synthetic reasoning traces generated by GPT-5 for over 130K proteins and further optimized through reinforcement learning. It achieves 73.6% Fmax on GO term prediction and an LLM judge score of 8/10 on functional summaries, substantially outperforming previous methods. Evaluations with human protein experts show that BioReason-Pro annotations are preferred over ground truth UniProt annotations in 79% of cases. Remarkably, BioReason-Pro predicted a novel interaction partner for the renal cancer biomarker RCDG1, which we confirmed in the lab by co-immunoprecipitation. In other binding-partner predictions, its per-residue attention localized to the exact contact residues resolved in cryo-EM structures. Together, GO-GPT and BioReason-Pro establish a framework for protein function prediction that combines precise ontology modeling with interpretable biological reasoning.

molecular biology↗

bioRxiv2026

Acid stress modulates metabolo-inflammatory pathways in oral epithelial cells

Acidification of the oral environment has been implicated in the initiation and progression of oral pathologies including oral cancer, but how acidic environments modulate normal oral epithelial cell (OEC) responses to microbial ligands is not understood. This study examined the impact of acidic stress on OEC morphological, molecular, and functional responses to toll-like-receptor ligand engagement in vitro. OEC cultures were exposed to either normal (pH:=:8.0) or acidified growth media (pH:=:3.0) for 24 hours prior to machine-learning-guided morphological analysis and exposure to either toll-like receptor (TLR)5 (flagellin) or TLR2/TLR1 (Pam3CSK4) agonists. Multiplex gene expression technology was used to quantify the transcriptional responses of metabolic-and immune-related genes at 6 hours post-TLR agonist exposure. OEC-mediated production of transforming growth factor-beta (TGF-{beta}) was assessed by enzyme-linked immunosorbent assay at 2-, 6-, and 24-hours post-agonist exposure. Results showed that acid exposure induced significant changes to OEC morphology resembling epithelial-mesenchymal transition, the differential expression of n=197 metabolic-and n=43 immune-related genes and significantly increased OEC TGF-{beta}1 production. The results demonstrate that acid stress skews normal OECs towards pro-inflammatory and pro-oncogenic phenotypes when faced with concomitant microbial ligand challenge and provide key molecular clues to OEC survival strategies with potential implications for elucidating the early molecular events in the development of epithelial dysplasia. Article HighlightsO_LIAcute acid exposure reduces survival of OECs C_LIO_LIA subpopulation of OECs is resistant to acid-mediated cell loss and undergo morphometric changes consistent with epithelial-mesenchymal transition C_LIO_LIConcurrent acid stress and TLR stimulation modulates transcription of immune and metabolic genes in OECs C_LIO_LIAcid stress increases TGF-{beta}1 protein production of OECs following TLR agonist stimulation C_LI

cell biology↗

bioRxiv2026

Fast and accurate resolution of ecDNA sequence using Cycle-Extractor

Extrachromosomal DNA (ecDNA) plays a key role in cancer pathology. EcDNAs mediate high oncogene amplification and expression and worse patient outcomes. Accurately determining the structure of these circular molecules is essential for understanding their function, yet reconstructing ecDNA cycles from sequencing data remains challenging. We introduce Cycle-Extractor (CE) for reconstruction. CE accepts a breakpoint graph derived from either short or long read sequencing data as input and extracts a cycle with the maximum length-weighted-copy-number. CE utilizes a mixed-integer linear program (MILP) and a separate traversal procedure, enabling fast optimization and compatibility with free solvers. We evaluated CE against CoRAL (long-read-based quadratic optimization), Decoil (long-reads), and AmpliconArchitect (AA for short reads) on both simulated data and real cancer cell lines. On simulated ecDNA, CE achieves performance comparable to CoRAL across three accuracy metrics and consistently outperforms AA and Decoil. On cancer cell lines, CE produces longer and heavier cycles than AA, and achieves performance similar to CoRAL. Moreover, CE is, on average, 40x faster than CoRAL. These results demonstrate that CE accurately reconstructs ecDNA from both short- and long-read sequencing data, while long-read inputs allow CE to recover more complete and higher-confidence ecDNA structures. CE improved the prediction of many ecDNA structures. On a PC3 ecDNA containing MYC, CE uses ONT data to reconstruct a substantially larger and higher-copy sequence (4.2 Mbp) compared to the short-read-derived reconstruction (690 Kbp). CRISPR-CATCH experiments confirm the presence of a large ecDNA molecule, validating the long-read-based CE reconstruction.

bioinformatics↗