Biology subjects

Li, J.

Publications and source records attributed to Li, J..

At least 127 records · Page 7Linked to original sources

bioRxiv2026

Sequence-based modeling of plant epigenomes reveals cell-type-specific cis-regulatory grammar

How cis-regulatory sequences and their genetic variation govern chromatin accessibility and gene expression to establish plant cell identities remains incompletely understood, despite their fundamental roles in development, environmental responses, and phenotypic diversity. Here we present PEAgent, a framework for training, evaluating and interpreting deep-learning models that predict single-cell chromatin accessibility directly from DNA sequence, packaged in an interactive web portal and toolkit. Models were trained on single-cell chromatin-accessibility atlases of soybean, maize and rice, together spanning over 355,000 cells and 320 cell types and [~]150 million years of evolution. We unraveled a lexicon of 243 cell-type-resolved regulatory patterns, half of them composite, with TCP and bHLH showing the greatest influence and strongest conservation across species. Co-occurrence and in silico synergy analyses, explicitly modeling motif orientation and spacing, revealed two distinct cooperative modes acting at short and nucleosome-scale distances. We further showed that model predictions distinguish grass-conserved from rice-specific regulatory sequences far more accurately than sequence conservation scores alone, and validated the models predicted variant effects against cell-type-level chromatin accessible QTLs. PEAgent provides a foundational resource for decoding cell-type-specific plant cis-regulatory logic and interpreting noncoding variation in plants. HighlightsO_LIPEAgent predicts single-cell chromatin accessibility directly from DNA sequence in soybean, rice and maize. C_LIO_LINearly half of cell-type-resolved patterns are composite, with TCP and bHLH motifs most influential and conserved across species. C_LIO_LICo-occurrence and synergy analysis reveals distinct cooperative grammar at short and nucleosome-scale distances. C_LIO_LIPEAgent distinguishes grass-conserved from lineage-specific regulatory sequences better than sequence conservation scores alone. C_LI

genetics↗

bioRxiv2026

Engineering Oncolytic Measles Virus with MG53 Couples Pyroptotic Tumor Killing with Immune Microenvironment Remodeling to Enhance Checkpoint Immunotherapy in Non-small Cell Lung Cancer

Lung cancer is the leading cause of cancer-related mortality worldwide, with only approximately 30% of patients benefiting from current immune checkpoint immunotherapy. Oncolytic virotherapy offers a promising strategy to overcome this resistance; however, achieving both potent tumor cytotoxicity and robust antitumor immune activation remains a major challenge. Here, we engineered oncolytic measles virus expressing the tumor suppressor MG53 (TRIM72), designated rMeV-MG53, and evaluated its therapeutic potential against non-small cell lung cancer (NSCLC). rMeV-MG53 retained replication kinetics comparable to parental MeV while driving significantly greater cytotoxicity than an unarmed control in NSCLC cells. Mechanistically, MG53 arming amplified caspase-3-dependent apoptosis and potentiated Gasdermin E (GSDME) cleavage, engaging GSDME-mediated pyroptosis as a key tumor-killing mechanism; pharmacological inhibition confirmed caspase-dependent apoptosis as the initiating event leading to pyroptosis, while excluding necroptosis and ferroptosis as significant contributors to rMeV-MG53-induced cytotoxicity. MG53 overexpression induced an intrinsic pro-inflammatory transcriptional signature enriched for TNF, NF-{kappa}B, and IL-17 signaling, and rMeV-MG53 elicited significantly stronger type I interferon and pro-inflammatory cytokine induction than the unarmed control. In an immunocompetent, MeV-permissive syngeneic NSCLC model, intratumoral rMeV-MG53 achieved superior tumor growth inhibition, amplified caspase-3/GSDME pyroptosis, selectively upregulated Cxcl10, Ifng, and Il1b, and drove robust CD8 T-cell and Granzyme B effector infiltration. This immune remodeling was accompanied by compensatory PD-L1 upregulation, and combining rMeV-MG53 with anti-PD-L1 blockade achieved the strongest tumor suppression of all treatment groups. These findings establish MG53-armed oncolytic MeV as a strategy coupling GSDME-mediated pyroptosis with antitumor immune remodeling to sensitize immune-resistant NSCLC to checkpoint immunotherapy.

immunology↗

bioRxiv2026

SPgen: Proteome-wide Spatial Proteomics generation using multi-modality foundation models

Spatial proteomics (SP) measures the spatial distribution of proteins within tissues, providing important insights into tissue function, disease, and therapeutic response. However, current SP technologies profile only a small fraction of the proteome and are limited by cost and measurement noise. Recent AI approaches enable predicting spatial protein expression from transcriptomic or histopathological data, but are typically restricted to paired datasets covering only tens of proteins, limiting their ability to generalize beyond experimentally measured protein panels. Here we present SPgen, a multi-modal foundation-model framework for proteome-wide spatial protein prediction. SPgen integrates protein sequences, functional annotations, transcriptomic profiles, and spatial information to learn transferable representations that enable inference beyond experimentally profiled proteins. Across diverse spatial proteomics datasets, SPgen accurately reconstructs measured spatial patterns, reduces measurement noise, and enables proteome-wide spatial prediction.

bioinformatics↗

bioRxiv2026

DPCGS: a computational framework for linking GWAS to single-cell transcriptomics in complex traits and diseases

Complex traits and diseases arise from the interplay between genetic variation and cellular heterogeneity, making it essential to understand how genetic risk manifests at the cellular level. However, connecting genome-wide association studies (GWAS) to specific cell populations remains challenging due to cellular complexity and the prevalence of noncoding variants. Here, we present DPCGS, a computational framework that systematically integrates GWAS summary statistics with single-cell RNA-sequencing (scRNA-seq) data to identify trait-associated cell subpopulations, genes, and regulatory programs. Unlike existing approaches that primarily evaluate pathway enrichment or cell-type-level associations, DPCGS quantifies the enrichment of genetically prioritized genes within individual cells through a statistically calibrated gene-set scoring strategy, enabling high-resolution mapping of genetic risk to cellular states. Benchmarking across simulated and diverse human single-cell datasets demonstrates that DPCGS achieves superior accuracy, sensitivity, and robustness compared with existing methods, including scDRS and scPagwas. Applying DPCGS to Alzheimers disease and asthma reveals disease-associated cellular populations and uncovers potential molecular drivers, including CD74, FOS, and AP-1 family regulatory programs, providing insights into disease-specific immune and cellular mechanisms. By bridging genetic discoveries from GWAS with functional interpretation at single-cell resolution, DPCGS establishes a generalizable framework for dissecting the cellular architecture of complex traits and diseases. This approach enables systematic discovery of disease-relevant cell subpopulations, regulatory networks, and potential therapeutic targets, offering broad applications in human genetics, single-cell biology, and precision medicine.

bioinformatics↗

bioRxiv2026

Task-Based Value Generalization Correlates With Positive Overgeneralization and Bipolar Symptoms

Positive overgeneralization -- the tendency to generalize from specific successes to broad expectations of future reward -- has been linked to vulnerability to mania. Because positive overgeneralization has only been assessed using self-report measures, we have limited insight into the underlying cognitive process. Here, we introduce a behavioral paradigm that quantifies how reward generalizes to novel stimuli, and characterize individual generalization profiles by fitting psychometric functions to choice data. In an online transdiagnostic study (N = 163), broader task-based generalization was associated with both higher self-reported positive overgeneralization and subclinical bipolar traits. Response times provided converging evidence: a smaller increase in response time as stimuli became less similar to the rewarded stimulus was associated with higher positive overgeneralization. In computational simulations, we show that self-efficacy, operationalized as the relative weight placed on the best and worst available future actions, shapes generalization, with more weight on the best possible action broadening the spread of expected reward to similar states. Together, these findings show that positive overgeneralization can be measured behaviorally, and identify one computational factor that may determine how broadly reward generalizes.

neuroscience↗

bioRxiv2026

Sustained epigenetic rejuvenation of serially engrafting human iPSC-derived HSCs

Hematopoietic stem cell (HSC) function declines with age, contributing to immunosenescence and inferior transplantation outcomes. Here, we generated iPSC-derived HSCs (iHSCs) from multiple adult donors and performed integrated epigenetic, transcriptional, telomeric, and functional analyses to see if they retain youthful identity across differentiation and serial transplantation. Longitudinal DNA methylation profiling revealed that, independent of donor age, epigenetic age was reset to near zero in iPSCs and remained under seven years across differentiation and transplantation. In contrast, hematopoietic identity was established through a two-phase process: directional remodeling during in vitro differentiation extinguished pluripotency programs and initiated hematopoietic regulatory networks, while long-term engraftment was associated with a second wave of promoter methylation differences that converged toward primary adult HSCs. Notably, methylation at age-associated sites and global entropy remained stable across both phases, and single-cell telomere analysis demonstrated restoration of telomere length in iHSCs compared to primary adult HSCs. Youthful epigenetic features were maintained through secondary transplantation. These findings demonstrate that long-term HSC identity can be achieved independently of epigenetic aging and establish a framework for evaluating rejuvenated stem cell-derived grafts in regenerative medicine.

cell biology↗

bioRxiv2026

Primitive GLMY Homology: An Algebraic Topology Approach for the Quantitative Characterization of Graph Pangenomes toward Population Genetic Analysis

A central task in population genetics is to identify genetic diversity in a population containing a number of individuals. In recent years, with the development of the third generation sequencing (TGS) technology, pan-genome research has become a hot topic. Although graphical representation has been a popular way to represent the pangenome, few works have attempted to describe it in a more mathematical way. In this paper, we used 79 high-quality assembly data of third-generation sequencing in yeast (includingSaccharomyces cerevisiae and Saccharomyces paradoxus) to construct the graph pangenome, and introduced the Primitive GLMY (Grigoryan-Lin-Muranov-Yau) Homology in algebraic topology to quantitatively represent the pan-genome. We further made an intriguing attempt to conduct a population genetic analysis of this resulting dataset from the topological features of the graph pangenome. We found that there was good agreement between the obtained results and the biological context. We believe this study has developed a method for population genetic analysis of the genetic diversity of genome structural variation.

genetics↗

bioRxiv2026

5β-Dihydrotestosterone reveals a mutant androgen receptor vulnerability in prostate cancer

Bipolar androgen therapy (BAT) exploits the paradoxical vulnerability of castration-resistant prostate cancer (CRPC) cells to rapid cycling between castrate and supraphysiologic androgen concentrations, but clinical BAT uses testosterone, which can also activate wild-type androgen receptor (AR) in androgen-responsive tissues, causing systemic side effects. 5{beta}-dihydrotestosterone (5{beta}-DHT) is a naturally occurring testosterone metabolite generally considered androgenically inactive because it binds wild-type AR weakly, yet its activity against clinically relevant AR mutants has not been systematically evaluated. Here, we tested whether 5{beta}-DHT and related 5{beta}-reduced testosterone metabolites activate AR signaling and growth programs in prostate cancer models that carry AR mutations. In C4-2 cells, 5{beta}-DHT and 3{beta}-etiocholanediol (3{beta}-ecdiol) increased canonical AR target genes, including KLK3 and TMPRSS2, with weaker activity than testosterone, whereas other 5{beta} metabolites showed limited activity. In androgen-responsive LNCaP and C4-2 models, 5{beta}-DHT and 3{beta}-ecdiol promoted cell growth under androgen-depleted conditions, and this effect was suppressed by enzalutamide, supporting AR dependence. RNA-seq confirmed that 5{beta}-DHT and 3{beta}-ecdiol induced androgen-response gene sets substantially overlapping with testosterone, albeit at lower transcriptional magnitude. Further, we found that 5{beta}-DHT, but not 3{beta}-ecdiol, suppresses cell proliferation of LNCaP, C4-2, and PC-3 cells stably expressing the clinically relevant AR gain-of-function mutants W742C and H875Y through activating AR-induced senescence-like features after high-dose exposure, consistent with the therapeutic logic of BAT. These findings identify 5{beta}-DHT as an overlooked mutant-AR agonist capable of BAT-like tumor suppression and propose it as a testosterone surrogate in BAT with potentially reduced systemic androgenic side effects. HighlightsO_LI5{beta}-DHT and 3{beta}-ecdiol promote AR-dependent prostate cancer cell growth C_LIO_LIBoth are weaker AR agonists than testosterone by RNA-seq and qPCR C_LIO_LISupraphysiologic 5{beta}-DHT suppresses growth via AR-mediated senescence C_LIO_LIGrowth suppression extends to AR mutants W742C and H875Y C_LIO_LI5{beta}-DHT may be a lower-androgenicity testosterone surrogate for BAT C_LI

cancer biology↗

bioRxiv2026

MolMAE: A Surface-Centric Multimodal Masked Autoencoder for Molecular Representation Learning

Molecular representation learning has become a central component of modern computational drug discovery. Existing molecular foundation models mainly rely on SMILES strings, two-dimensional molecular graphs, or three-dimensional atomic coordinates. However, many molecular properties are ultimately governed by the molecular surface, where intermolecular recognition, solvation, electrostatic complementarity, and ligand-protein interactions occur. In this work, we propose MolMAE, a surface-guided multimodal masked autoencoder for molecular representation learning. MolMAE takes molecular surface point clouds, three-dimensional molecular graphs, and SMILES-derived fragment and functional-group tokens as complementary input modalities, and learns a unified multimodal molecular embedding through functional-group-aligned masked autoencoding. During pretraining, chemically corresponding local regions are jointly masked across surface, graph, fragment, and functional-group views, forcing the model to reconstruct missing geometric, physicochemical, structural, and semantic information from the remaining context. While molecular surface reconstruction serves as the primary pretraining objective, graph-, fragment-, and functional-group-level reconstruction tasks provide complementary supervision that encourages the model to capture molecular topology, bonding patterns, stereochemistry, local chemical environments, and substructure organization. In addition to reconstructing surface geometry, MolMAE reconstructs surface-associated physicochemical fields, including electrostatic potential and Fukui-related descriptors, enabling the model to learn chemically meaningful surface representations. Pretrained on approximately 261K lead-like bioactive molecules, MolMAE achieves strong performance on the ESOL benchmark under scaffold splitting and competitive performance across multiple molecular property prediction tasks. These results suggest that molecular surface-guided pretraining can complement conventional graph-, sequence-, and atom-coordinate-based molecular representations, especially for property prediction tasks influenced by exposed surface geometry and surface-associated physicochemical patterns.

bioinformatics↗

bioRxiv2026

Forest degradation reshapes trophic functioning in vertebrate food webs across Amazonian forests

The Amazon is a mosaic of ecosystems, accounting for 13% of all known species globally, and responsible for providing a variety of ecosystem services, including a key role in global climate regulation. However, 18% of the Amazon forest cover has been lost completely and a further 38% degraded, threatening biodiversity and millions of local livelihoods. Currently, little is known about how this impacts the functioning of ecological communities. Here, we combine an energetic approach with biodiversity metrics to quantify ecosystem functioning in mammal and bird food webs across Amazonia, along a gradient of forest degradation linked to road proximity. We show that even under relatively low disturbance, ecosystem functions shift: carnivory increases closer to roads, driven by generalist species that persist under these conditions, whereas herbivory declines mainly due to reduced herbivore biomass and species richness. This suggests that processes associated with forest degradation can alter energy flow even where biodiversity metrics remain relatively unchanged, highlighting energetic approaches as sensitive indicators of ecosystem disruption.

ecology↗

bioRxiv2026

Chemotherapy-induced multicellularity drives drug-tolerant persistence state in tumor cells

Multicellularity is a well-documented microbial response to stress, however its role as an adaptive survival strategy in cancer remains unresolved. Here we reveal that drug stress, such as paclitaxel treatment, enable rapidly (within 24-48 hours) and efficiently ([~]20-40%) convert single mouse breast 4T1 cancer cells into clonal multicellular spheroids, ultimately generating multicellular masses. Notably, multicellularity is reversible: upon stress removal, most of them restore a unicellular lifestyle that quickly becomes dominant. This transient multicellular state shields cells from hostile niches, functions as a drug-tolerant persistence (DTP) reservoir, and fuels post-therapy relapse, revealing multicellularity as a facultative evolutionary pivot for fitness gain. Importantly, blocking primordial germ cell (PGC) specification suppresses the multicellularity transition. Our findings reveal that certain cancer cells enable adopt unicellular-multicellular life cycle through phenotypic plasticity, dynamically adapting to microenvironmental shifts to maximize fitness. This discovery reframes cancer evolution and the drug-tolerant persistence (DTP) state, highlighting multicellularity as an adaptive, stress-inducible survival strategy against therapy. HighlightO_LIDrug induced evolution from unicell to clonal multicellularity. C_LIO_LIProviding a novel DTP state. C_LIO_LIRevealing environment-dependent bistable plasticity: multicellular persistence (stress) versus unicellular proliferation (favorable). C_LIO_LIBlocking primordial germ cell (PGC) specification suppresses the multicellularity. C_LI

cancer biology↗

bioRxiv2026

Hepatitis C Virus Remodels Lipid Droplets to Promote Mitochondrial Fatty Acid Accumulation and Metabolic Activation

Hepatitis C virus (HCV) depends on host lipid metabolism and lipid droplets (LDs) for genome replication, assembly, and particle production, yet how LD structure and lipid utilization change over the course of infection remains incompletely understood. Here, we investigated the temporal remodeling of LD-associated metabolic pathways during HCV JFH-1 infection of human hepatoma Huh7 cells. HCV infection transiently increased LD fluorescence intensity at 24 hours post-infection (hpi), followed by normalization or relative loss of LD signal at later time points. Concomitantly, LDs became progressively clustered and enlargement during late infection, despite reduced protein levels of the canonical LD fusion proteins CIDEA, CIDEB, and CIDEC, suggesting that HCV-induced LD enlargement occurs through CIDE-independent mechanisms. Transcriptomic, RT-qPCR, and immunoblot analyses revealed time-dependent regulation of genes and proteins involved in LD structure, triglyceride synthesis, lipolysis, lipid uptake, and mitochondrial fatty acid utilization. Subcellular fractionation demonstrated preferential accumulation of fatty acids in mitochondrial fractions at 24-72 hpi. This redistribution was accompanied by increased oxygen consumption rate, elevated extracellular acidification, and progressive reactive oxygen species accumulation, indicating infection-associated metabolic activation and oxidative stress. Pharmacological inhibition of DGAT1-dependent LD biogenesis, LIPA-dependent lysosomal lipid hydrolysis, LIPE/HSL-dependent lipolysis, or CPT1-dependent mitochondrial fatty acid transport markedly reduced mitochondrial fatty acid accumulation and suppressed HCV-induced respiratory activity. Inhibition of LIPA or LIPE/HSL reduced both HCV RNA and core protein levels, whereas inhibition of CPT1 or DGAT1 had more pronounced effects on core protein than on viral RNA. Together, these findings support a model in which HCV dynamically remodels LDs, mobilizes LD-associated fatty acids, and redirects them toward mitochondria to support infection-associated metabolism and downstream stages of the viral life cycle. Lipid hydrolysis and mitochondrial fatty acid trafficking therefore represent potential host-directed targets for limiting HCV infection. SIGNIFIGANCEHepatitis C virus depends on host lipid metabolism for replication, assembly, and production of infectious particles, but how it uses lipid droplets over time remains incompletely understood. This study shows that hepatitis C virus dynamically remodels lipid droplets, causing an early increase in lipid storage followed by droplet enlargement and mobilization of fatty acids during later infection. The released fatty acids preferentially accumulate in mitochondria, where they are associated with increased cellular respiration and oxidative stress. Blocking lipid droplet formation, lipid breakdown, or fatty acid transport to mitochondria reduced this metabolic response and decreased viral RNA or core protein accumulation. Inhibition of lysosomal acid lipase and hormone-sensitive lipase suppressed both viral RNA and protein levels. These findings identify lipid droplet breakdown and mitochondrial fatty acid trafficking as important host processes used by hepatitis C virus and as potential targets for host-directed antiviral intervention.

microbiology↗

bioRxiv2026

GAMETE maps the genetic architecture of chromatin accessibility in rice pollen at single-nucleus resolution

BackgroundDissecting the genetic basis of cell-type-specific gene regulation during gametogenesis remains a major challenge. Population-level studies mask cellular heterogeneity, while the extreme data sparsity of single-cell profiling has precluded robust genetic mapping. ResultsWe present GAMETE (Genetic Architecture Mapping of Epigenetic Traits at single-nucleus resolution), an integrated framework combining MPS-ATAC-seq, a high-throughput single-nucleus ATAC-seq method, with robust computational approaches for genotype inference and genetic mapping from sparse single-nucleus data. Applying GAMETE to hybrid rice pollen, we simultaneously profiled chromatin accessibility and inferred the genotypes of 4,887 individual haploid nuclei, revealing distinct chromatin landscapes across pollen cell types. This uncovered an unexpected elevation of chromatin accessibility at gypsy retrotransposons in microspores preceding their silencing in sperm cells, exposing a surveillance gap that may contribute to genome expansion. The synchronized profiling enabled us to construct a single-cell recombination map, map segregation distortion loci and trace their developmental origins, and identify 16,113 chromatin accessibility QTLs (caQTLs), 48.6% of which exhibited pronounced cell-type specificity. Analysis of introgression lines supported a cell-type-specific trans-acting caQTL regulating the pollen-essential gene DTM1. ConclusionsThis work establishes GAMETE as a broadly applicable framework for dissecting the genetic architecture of chromatin accessibility during gametogenesis without the need for subsequent generations.

genomics↗

bioRxiv2026

Graph-based modeling of multiparametric MRI deciphers molecular states of high-grade glioma invasion with prognostic implications

AbstractThe infiltrative, non-enhancing margin of IDH wildtype high grade glioma (IDHwt HGG) harbors distinct molecular programs that drive invasion and therapeutic resistance, yet remains largely unevaluable by conventional tissue sampling approaches and by conventional imaging. Here we show that this invasive architecture is encoded within multiparametric MRI (mpMRI) feature relationships and can be decoded using a graph-based framework trained on multiregional image-localized biopsies. Across 134 spatially matched biopsy-imaging pairs from 35 patients with primary IDHwt HGG (29 glioblastomas (GBM) and 6 non-glioblastoma HGGs), unsupervised graph community detection identifies two imaging-defined clusters that localize to invasive tumor regions without molecular supervision. Transcriptomic profiling associates these clusters with neuronal (NEU) and glycolytic-plurimetabolic (GPM) molecular programs. Building on this framework, a graph convolutional network (GCN) accurately predicts NEU and GPM transcriptional states in independent training and validation cohorts and significantly outperforms conventional convolutional neural networks. Applied to whole-tumor mpMRI volumes, the trained GCN generates spatially resolved probability maps that quantify the distribution and relative burden of NEU and GPM programs across both MRI contrast-enhancing and non-enhancing invasive regions. These imaging-derived molecular maps stratify patients by overall survival. Increased GPM burden is associated with poorer survival, consistent with the aggressive behavior associated with mesenchymal-like transcriptional programs in IDHwt HGG. In contrast, increased NEU burden is associated with improved survival, identifying a previously unrecognized imaging-derived prognostic biomarker that was not detected by biopsy-based molecular classification alone. Together, these findings establish a graph-based imaging framework for spatially resolved molecular classification of invasive IDHwt HGG and demonstrate that whole-tumor molecular state architecture carries prognostic information beyond conventional tissue sampling.

cancer biology↗

bioRxiv2026

Residual Multi-Modal Learning for Pan-Breast-Cancer Drug Response Prediction

Predicting drug sensitivity across diverse cancer cell lines remains a fundamental challenge in precision oncology, particularly for data-scarce cell lines where per-cell-line models overfit and lookup-table approaches cannot generalise to unseen biological contexts. We present DL4DR, a Two-Tower Residual Late Fusion deep learning model that addresses this challenge through content-based, identity-free genomic conditioning. The Cell Line Tower encodes each cell line as a 3 x 139 x 139 genomic image - encoding gene expression, mutation severity, and copy-number variation as RGB channels - using a convolutional encoder that maps directly from biological content, never from a cell line ID. The Compound Tower combines three complementary molecular representations: D-MPNN graph message passing, ORNN octave convolutional image features, and an ECFP hard-memorization head that preserves activity-cliff resolution. Predictions are composed as a residual sum: f = fhard +{lambda} (zC) {middle dot} fresidual, where the learned gate{lambda} modulates how much interaction signal supplements the memorization baseline. Evaluated across 51 breast cancer cell lines (136,342 records), Residual Fusion outperforms the ECFP-Only baseline in 48/51 cell lines (94.1%), with {Delta}R2 > 0.02 in 26/51 (51.0%). On the leave-cell-line-out split - the decisive test of genomic generalisation - the mean {Delta}R2 = +0.016 across all 51 lines demonstrates that the genomic encoder learns transferable biological signal beyond cell line identity. External validation on 601 cell lines across 27 cancer tissue types (CellTiter-Glo dataset; 0 cell line overlap with training) achieves median R2 = 0.627, within the range of the internal random-split performance (R2 = 0.61-0.69), confirming pan-cancer generalisation. GradCAM interpretability on the Cell Line Tower recovers TP53 among the top-five cross-cell-line genomic activators (5/51 cell lines) alongside several uncharacterised candidate genes (e.g. FSIP2, 6/51) - without any prior pathway annotation - providing partial biological validation of the learned representation, while also indicating that a substantial share of the encoders top-ranked signal corresponds to genes with no current annotation as breast cancer drivers. Code and data are available at https://github.com/bayjuan5/DL4DR.

bioinformatics↗

bioRxiv2026

Clinically derived micro- and nanoplastics uptake drives spatiotemporally confined metabolic stress revealed by bond-selective imaging

Although microplastics and nanoplastics (MP/NP) are pervasive environmental contaminants, our understanding of cellular toxicity remains incomplete, as adverse effects are often attributed to long-term intracellular accumulation, while the spatiotemporal onset of cellular damage remains poorly defined. Here, we employ chemical-bond-selective stimulated Raman scattering (SRS) microscopy and cell models that decouple continuous exposure from intracellular retention to directly visualize clinically derived MP/NP-cell interactions. Cellular stress occurs primarily during MP/NP exposure, accompanied by alterations in lipid droplet (LD) composition. In contrast, following extracellular removal, intracellularly retained MP/NP become largely inert, with recovery of lipid metabolism and cellular functions. Lipidomics identifies arachidonic acid (AA) as a key dysregulated metabolite, and SRS imaging further reveals transient, spatially confined AA enrichment in MP/NP-proximal LDs during uptake. Importantly, phospholipid coating of MP/NP attenuates LD alterations and cytotoxicity while preserving particle internalization, establishing uptake-driven metabolic stress, rather than long-term intracellular retention, as primary source of MP/NP-induced damage.

biophysics↗

bioRxiv2026

Comparative assessment of gene drive release patterns and spread with a hex-based model for large-scale simulations

Spatial population genetic and ecological modeling is often necessary to predict outcomes accurately. One example is gene drive, a rapid process involving spread of gene drive alleles through a population, usually to suppress pests or reduce transmission of vector-borne disease. Several existing models have been used to assess gene drive and other spatial processes. However, each of these has limitations, such as high computational cost and limited scalability, difficulty in incorporating environmental factors and complex lifecycles, or potentially simplified spatial structure. To overcome these challenges, we propose a hexagon-based computational framework that is designed to mimic continuous space for rapid genetic wave advances. This allows us to accurately simulate a larger spatial domain with lower computational investment. We implemented this model and compared the wave speeds of different gene drives with those obtained from other models. The results showed good agreement when hexagon width and dispersal were properly calibrated. We then determined optimal circular and linear (along roads) release patterns for a variety of gene drives and Wolbachia bacteria. To demonstrate the application of our framework to a hypothetical scenario, we constructed a model Culex quinquefasciatus mosquitoes on Hainan Island. We then evaluated the outcome of different gene drive release strategies, showing the transgenic insect release level necessary to achieve high gene drive coverage and how this could be further optimized based on mosquito and human distribution. Overall, our hex-based population genetic framework provides a flexible platform for realistic and large-scale models for gene drive and related applications.

ecology↗

bioRxiv2026

FORGE reveals an information spectrum encoded in RNA tertiary-structure geometry

Coarse RNA coordinate representations are widely used, yet the biological information they encode remains unquantified. We introduce FORGE, which converts a seven-atom RNA geometry representation into 935 interpretable descriptors and reports which residue-level annotations this geometry supports. On 4,135 post-2025 RNA chains, FORGE recovered 64.6% of native nucleotides; a six-atom control lacking the glycosidic nitrogen retained 58.5%, locating most of this signal in phosphate-sugar geometry. Confidence was sharply graded: abstaining from the least-confident half of positions raised accuracy to 94.4%, yet many chains remained only partially identifiable. The same descriptors predicted base-pair state far better than a DMS-like proxy or protein-proximal context. Native-decoy, OpenKnot and solved-pseudoknot analyses showed that nucleotide identifiability, foldability and experimental design score are separable: AlphaFold3 reproduced the experimental fold for one of four AI-designed constructs and none of the sequences FORGE read from their geometry. FORGE provides a reproducible audit layer for RNA structural interpretation.

bioinformatics↗