Biology subjects

Zhao, H.

Publications and source records attributed to Zhao, H..

At least 55 records · Page 3Linked to original sources

bioRxiv2026

Canonical NLR immune receptor architecture enforces EDS1-dependency onto divergent TIR domains

Toll/interleukin-1 receptor (TIR) enzymes are prominent immune components in diverse organisms across the tree of life. In flowering plants, TIRs are often integrated into nucleotide binding leucine-rich repeat (NLR) receptors whose oligomerization-dependent biochemical activities create second messengers perceived by ENHANCED DISEASE SUSCEPTIBILITY1 (EDS1)-family signaling complexes. TIR-NLRs and TIR proteins are present across the full spectrum of plant evolution, yet EDS1 signaling is a derived trait in seed plants. Here, we examined the functional dependency of diverse plant TIRs on the EDS1 pathway in the angiosperms Nicotiana benthamiana and Nicotiana tabacum. While the isolated TIR domains from non-seed plants generally required EDS1 for immune cell death activation, we also identified TIRs that functioned independent of EDS1. However, chimeric TIR-NLRs incorporating these diverse TIR domains onto the AtWRR4a receptor chassis showed a full reversion to EDS1-dependency. Extending this phenomenon further, we demonstrated that the AtWRR4a architecture enforces EDS1-dependence onto a bacterial TIR domain that is otherwise EDS1-independent. Collectively, our work demonstrates that NLR immune receptor architecture influences TIR-related immunity and provides further context to their ancient acquisition into plant immune systems.

plant biology↗

bioRxiv2026

Ribophorin-1 Governs Spike Abundance of Highly Pathogenic Coronaviruses by ER-associated degradation

The spike protein of highly pathogenic coronaviruses is indispensable for viral entry, pathogenesis, and immune evasion. However, specific host factors governing spike protein degradation and abundance on progeny virions remain largely uncharacterized. Here, we identify Ribophorin-1 (RPN1), a non-catalytic subunit of the oligosaccharyltransferase (OST) complex, as an ER-localized host restriction factor that selectively depletes spike to inhibit coronavirus infection. Conditional knockout of Rpn1 in mouse lung markedly exacerbates SARS-CoV-2 pathogenesis, increasing viral load, immune cell infiltration, and syncytium formation. In cells, RPN1 knockdown enhances susceptibility to diverse variants, whereas its overexpression attenuates infection. Nano-flow cytometry, cryo-EM and LSCM show that RPN1 reduces spike abundance on progeny virions, resulting in reduction in subsequent syncytium formation across SARS-CoV, SARS-CoV-2, and MERS-CoV. Mechanistically, RPN1 promotes VCP/p97-dependent retrotranslocation and AMFR-mediated ubiquitination of spike protein. Heterologous expression of RPN1s functional domain (residues 180-307) confers significant protection against SARS-CoV-2 both in vitro and in vivo. Collectively, our findings define an RPN1-initiated ERAD pathway that selectively targets coronavirus spike for proteasomal destruction, and provide proof-of-concept evidence that targeting this RPN1-dependent pathway represents a promising broad-spectrum antiviral strategy.

microbiology↗

bioRxiv2026

An N-terminal amphipathic helix governs activity and conformational dynamics of Nramp metal transporters

Nramps are a family of proton-coupled divalent metal ion transporters that play critical roles in maintaining homeostasis of essential metals. In humans, Nramp2 (DMT1) mediates iron uptake to support both cellular and systemic iron homeostasis, whereas Nramp1 exports metals from phagosomes, contributing to antimicrobial defense. A survey of experimentally solved Nramp structures reveals that an N-terminal helix (A) immediately preceding TM1 is folded only in the outward-facing or outward-facing occluded states, raising the question of its role in Nramp transport. Here, we combined mutagenesis, transport assays, and molecular dynamics simulations to investigate A. Our results show that deletion or substitution of conserved residues in A markedly reduces Fe2+ transport. Simulations indicate that A behaves as an amphipathic helix in the outward-facing state and preferentially stabilizes this conformation over the inward-facing state, thus acting as a key player in controlling conformational equilibrium. This study provides new insights into structural dynamics of Nramps and potentially sheds light on other LeuT-fold transporters.

biochemistry↗

bioRxiv2026

Symmetry Analysis and Ancestral Sequence Reconstruction Reveal a Symmetrical Translocation Pathway and Activity Determinants of ZIP Metal Transporter

Membrane transporters frequently exhibit internal structural symmetry, reflecting evolutionary origins through gene duplication and fusion events, and this feature has been widely used to infer transport mechanisms. Yet its application to the ZIP (Zrt-/Irt-like protein) family of divalent metal transporters, which are crucial for trace metal homeostasis, has not been fully explored. Here, we apply combined symmetry analysis and ancestral sequence reconstruction to study the ZIP family. We show that internal symmetry is broadly present across prokaryotic ZIPs and is more prominent in reconstructed ancestral sequences, supporting an evolutionary pathway in which the modern 8-transmembrane (TM) ZIP fold arose from duplication, domain rearrangement, and fusion of an ancestral 4-TM protein. Leveraging this symmetry framework, we identify a continuous and symmetric metal translocation pathway composed of symmetric entrance, transport, and exit sites in the ZIP fold, and define gates that ensure alternating access. Application of this structural model to human ZIP4 enables identification of the gate-forming residues, and functional studies reveal that two residues (T529 and V533) in the external gate play a crucial role in controlling metal transport. In addition, comparison with ancestral sequences uncovers a set of LIV-1 subfamily-specific metal chelating residues (D504, E541, and D544) that break the symmetry in the ancestral sequences. Functional studies showed that these residues play distinct roles in transport. Together, our study demonstrates that combining internal symmetry analysis and ancestral sequence reconstruction for the ZIP family facilitates elucidation of metal translocation mechanism and identification of the subfamily-specific features that confer functional specialization.

biochemistry↗

bioRxiv2026

Functional screening of ZIP8 naturally occurring variants identifies pathogenic mutations and trafficking defects

The rapid expansion of human genomic data has revealed a large number of naturally occurring variants, creating a major challenge for functional annotation. The human metal transporter SLC39A8 (ZIP8) is a clinically important, promiscuous divalent metal transporter, yet most of its documented variants remain uncharacterized. Here, we developed a workflow to functionally evaluate ZIP8 variants by integrating laser ablation inductively coupled plasma time-of-flight mass spectrometry (LA-ICP-TOF-MS) with scaled-up cell-based transport assays. Using this method, we systematically analyzed 33 naturally occurring missense variants located in the extracellular domain (ECD) of ZIP8. The assay enables direct quantification of intracellular metal accumulation with substantially improved throughput ([~]150 samples per hour). Functional screening identified 14 potential pathogenic variants with significantly reduced transport activity. Comparison with computational predictions revealed a moderate correlation between activity and AlphaMissense pathogenicity scores (R2 = 0.423), while an error rate of [~]20% underscores the need for experimental validation. Flow cytometry analysis showed that most loss-of-function variants exhibit impaired trafficking of the protein to the cell surface possibly due to mutation-caused protein misfolding or instability. Structural mapping of activity-compromised variants, together with functional assessment of the ZIP8-ECD, highlights the importance of this domain in ZIP8 expression and intracellular trafficking. Together, this work establishes a scalable approach for functional screening of metal transporter variants and provides new insights into the structure-function relationships of ZIP8.

biochemistry↗

bioRxiv2026

Genomic Foundation Models Reveal Chromatin-Domain-Scale Transposable Element Impacts on Rice Genome Architecture

Alignment-based detection of transposable element (TE) insertion polymorphisms suffers from reference bias and multi-mapping errors in repetitive genomic regions, creating a fundamental validation bottleneck for population-scale structural variant catalogs. Here, we demonstrate that the OneGenome-Rice (OGR) genomic foundation model (GFM)--a 1.25 billion parameter Mixtral architecture trained on 422 rice genomes without TE annotations--provides an entirely orthogonal, alignment-free approach that resolves TE-mediated structural divergence at chromatin-domain resolution. At the CTB4a cold-tolerance locus on chromosome 4, OGR embeddings revealed that the aus subpopulation (NONA_BOKRA) carries 2.2-fold higher structural divergence from indica than japonica, consistent with its 728 subpopulation-exclusive cold-protective TE insertions. Sliding-window analysis across 4.4 megabases identified a 25.6-fold divergence enhancement at TE clusters relative to the conserved CTB4a gene body. Critically, the minimal effective resolution was established at approximately 20 kilobases--corresponding to the median size of topologically associating domains (TADs) in the rice genome--while individual TE sites at 500 base pairs were undetectable (P = 0.94). Non-neural baselines confirmed the signal derives from learned representations of genomic context rather than simple nucleotide statistics. These findings establish GFMs as orthogonal validation tools for population-scale TE genotyping and provide computational evidence that TE functional effects are organized at the chromatin-domain level, with direct implications for prioritizing functional TE variants in crop breeding.

plant biology↗

bioRxiv2026

Aldoximes serve as auxin precursors and repress phenylpropanoid metabolism in tomato

Aldoximes are amino acid-derived metabolites that serve as precursors of auxins and modulate phenylpropanoid production in Arabidopsis. However, the enzymes responsible for aldoxime production in Solanaceae remain unknown. Here, we report the identification of aldoxime-producing enzymes in tomato (Solanum lycopersicum) and examine how altered aldoxime production affects auxin production and phenylpropanoid metabolism. Through homology-based analysis, we identified five putative CYP79 homologs in tomato, among which SlCYP79DB32 and SlCYP79DB52 exhibited aldoxime-producing activity toward multiple amino acids, including phenylalanine and tryptophan. SlCYP79DB32 and SlCYP79DB52 converted phenylalanine into phenylacetaldoxime (PAOx), whereas only SlCYP79DB52 converted tryptophan into indole-3-acetaldoxime (IAOx). Stable isotope-labeled feeding experiments revealed that IAOx and PAOx can be converted to the auxins indole-3-acetic acid (IAA) and phenylacetic acid (PAA), respectively. Consistently, tomato plants engineered to overproduce IAOx and PAOx accumulated elevated levels of IAA and PAA. These plants also accumulated lower levels of phenylpropanoids. In Brassicaceae plants such as Arabidopsis and Camelina, aldoxime accumulation represses phenylpropanoid production by promoting degradation of phenylalanine ammonia-lyase (PAL). However, aldoxime accumulation did not reduce PAL activity in tomato, suggesting an alternative mechanism in this species. Transcriptome analysis revealed extensive transcriptional reprogramming in aldoxime-overaccumulating tomato plants, including upregulation of stress- and defense-related genes. Despite the observed reduction in phenylpropanoid levels, transcript levels of most phenylpropanoid biosynthetic genes were not decreased, suggesting possible post-transcriptional regulation of this repression. Together, our findings demonstrate that aldoximes can serve as intermediates in auxin biosynthesis in tomato and reveal that aldoxime-mediated repression of phenylpropanoid metabolism extends beyond Brassicaceae.

plant biology↗

bioRxiv2026

A sterol-binding pocket in iRhom1 underlies paralog-specific regulation of the sheddase ADAM17

ADAM17, the major sheddase in mammalian cells, releases membrane-tethered EGFR ligands and inflammatory cytokines, and is a central regulator of cell signalling. The rhomboid pseudoproteases, iRhom1 and iRhom2, function as essential cofactors of ADAM17, controlling its maturation and activation. In contrast to the well-characterized iRhom2, the mechanism and regulation of its ubiquitously expressed paralog iRhom1 remain undefined. Here, we present a 2.5 [A] cryo-EM structure of the full-length human iRhom1/ADAM17 complex, revealing a previously unrecognized sterol-binding pocket located between TMD2 and TMD5. Structure-guided mutagenesis and pharmacological perturbation of sterol binding demonstrate that sterol binding is required to stabilize the iRhom1/ADAM17 complex and sustain its shedding activity. Strikingly, this regulation is paralog-specific: iRhom2 precludes sterol binding and instead stabilizes ADAM17 through direct intramolecular interactions. Furthermore, two human iRhom1 variants associated with cardiac disease localize adjacent to the sterol-binding pocket and disrupt ADAM17 maturation and activity. Together, these findings uncover mechanistic divergence between iRhom paralogs and establish a sterol-binding pocket in iRhom1 as a critical determinant of ADAM17 stability, revealing a potential avenue for paralog-selective therapeutic targeting.

cell biology↗

bioRxiv2026

Physics-inspired computational methods for spatial transcriptomics reveal a dysplasia-restricted premalignant basin and a density-asymmetric autocrine niche in oral mucosal carcinogenesis

Spatial transcriptomics is often interpreted with tools that do not explicitly encode tissue-scale physical priors such as finite ligand diffusion, density-based state stability, or self-exciting spatial recruitment. We introduce three compact computational methods and apply them to a 326,554-cell spatial atlas of human oral mucosal carcinogenesis spanning normal mucosa, hyperplasia, oral lichen planus, and dysplasia. DC3 (Diffusion-Constrained Cell Communication) replaces distance-blind ligand-receptor co-expression with a ligand-specific exponential distance kernel. It down-weights short-range ECM-class interactions by three to four orders of magnitude relative to traditional scoring, remains rank-stable under diffusion-length perturbation and dys2 removal (Spearman rho >= 0.93), and better matches COMMOT than traditional co-expression on the dys2 benchmark. A within-section label-permutation null indicates that only 0.5-6.6% of the top MRB-autocrine DC3 signal is attributable to spatial clustering alone. A log-density landscape, reported in nats rather than thermodynamic units, places the Malignant_risk_basal (MRB) cluster in a high-density epithelial region with a reverse barrier of 2.43 nats from stress-proliferative basal cells on the full atlas, 1.93 nats after dys2 removal, and 1.0-1.9 nats under Silverman/Scott bandwidth rules. MRB local neighborhood entropy is stable at 0.67-0.68 bits across bandwidth and PCA-dimension choices. A spatial Hawkes model separates immune-cell background density from self-excitation; apparent dysplasia-level adaptive-immune attenuation is not significant by 10,000 label permutations (all FDR q = 1.00) and is retained only as a section-level observation. External checks in CELLxGENE Census and Puram 2017 reposition the MRB signature as a dysplasia-restricted aberrant-basal-differentiation phenotype rather than a marker preserved in fully malignant OSCC. The methods are presented as lightweight, interpretable tools, and the biological findings as hypotheses requiring independent validation. Author summarySpatial transcriptomics records both gene expression and tissue position, but common analysis pipelines often treat signaling and cell-state structure as if distance and tissue architecture were secondary details. We developed three simple methods that make these assumptions explicit: DC3 scores cell-cell communication with ligand-specific diffusion distances, a log-density landscape summarizes where epithelial states are densely populated in expression space, and a spatial Hawkes model separates baseline immune density from local self-excitation. Applied to an oral mucosal carcinogenesis atlas, these methods highlight a dysplasia-enriched Malignant_risk_basal (MRB) population with short-range autocrine signaling and mixed epithelial neighborhoods. We also stress-test each claim by removing the MRB-rich section, varying model parameters, running permutation tests, benchmarking against COMMOT, and checking public external datasets. These tests narrow the biological interpretation: MRB is best viewed here as a dysplasia-restricted aberrant-basal-differentiation phenotype, not as a proven step toward fully malignant oral squamous cell carcinoma, and the apparent dysplasia-level immune-cascade attenuation is not statistically supported. The study therefore offers both a reusable computational toolkit and a deliberately constrained biological hypothesis for future validation.

bioinformatics↗

bioRxiv2026

EA-PheWAS: Integrating Phenotype Embeddings with PheWAS for Enhanced Gene-Phenotype Discovery

Phenome-wide association studies (PheWAS) enable systematic exploration of relationships between genetic variants and clinical phenotypes derived from electronic health records (EHRs). Conventional regression-based PheWAS treats phenotypes separately and relies on binary phenotype representations, which limits statistical power for rare variants and rare phenotypes and reduces the ability to detect associations with phenotypes that are distributed across clinical codes. To address this limitation, we first developed EmbedPheScan, a phenotype embedding-based prioritization framework that summarizes the phenotypic profiles of rare loss-of-function variant carriers in a continuous embedding space. We then proposed EA-PheWAS by combining these embedding-derived signals with conventional regression-based PheWAS results using the aggregated Cauchy association test. Using the UK Biobank whole-exome sequencing and EHR data, we show that the proposed methods maintain appropriate false-positive control. We then performed genome-wide phenome scans across all genes and across biologically defined gene classes to evaluate EA-PheWAS relative to conventional PheWAS and EmbedPheScan, consistently finding that EA-PheWAS outperformed the other two methods. We illustrate the utility of EA-PheWAS focusing on four genes representing distinct scenarios, including strong-effect disease genes (PKD1, PKD2), genes with large numbers of rare LoF carriers (NF1), and genes with extremely sparse carrier counts (FBN1).

genetics↗

bioRxiv2026

Pan-cancer circular genomics identifies intratumoral Staphylococcus lugdunensis as a metabolic driver in bladder cancer

Intratumoral microbiota play key roles in cancer but are challenging to profile due to low biomass. We developed CIRCMIP, an enzymatic pipeline that eliminates linear DNA to enrich bacterial genomes, achieving 100-fold higher sensitivity than standard metagenomics. Applied to 312 pan-cancer specimens, CIRCMIP identified Staphylococcus lugdunensis as a signature bacterium enriched in early-stage bladder cancer (BLCA), where its presence predicts poor survival. Integrated modeling and lipidomics revealed that Staphylococcus lugdunensis colonization drives aberrant lipid metabolism with secretion of LPC14:0. Both Staphylococcus lugdunensis and LPC14:0 drive BLCA progression by promoting fatty acid uptake and {beta}-oxidation. Mechanistically, chemical proteomics revealed LPC14:0 as a direct PPAR{delta} ligand, binding via hydrogen bonds with Thr292/Thr289. This activation upregulates fatty acid transporters (CD36, FABP4) and metabolic enzymes (ACOX2), fueling malignant proliferation. Furthermore, CIRCMIP-derived biomarkers show robust diagnostic accuracy, establishing a new research paradigm and revealing the Staphylococcus lugdunensis-LPC14:0-PPAR{delta} axis as a therapeutic target in bladder cancer. HIGHLIGHTO_LICIRCMIP leverages DNA circularity to achieve 100-fold sensitivity in pan-cancer bacteriome profiling. C_LIO_LIStaphylococcus lugdunensis is a bacterial signature in early-stage bladder cancer that predicts poor patient survival. C_LIO_LIBacteria-derived LPC14:0 acts as a direct ligand for PPAR{delta} to drive host malignant metabolic reprogramming. C_LIO_LITargeting the Staphylococcus lugdunensis-LPC14:0-PPAR{delta} axis abrogates malignant progression in bladder cancer. C_LI

cancer biology↗

bioRxiv2026

The dynamic and heterogeneous structure of the non-canonical inflammasome

Inflammasomes are high molecular weight complexes that play an integral role in the innate immune system, triggering an inflammatory cascade to protect against cellular stresses such as pathogenic bacteria. Both canonical and non-canonical inflammasomes have been described in the literature and detailed structural studies of many components of the more complex and larger canonical versions have been reported. In contrast, corresponding structures of the non-canonical inflammasome have not emerged even though it consists of only two components: lipopolysaccharide (LPS) from gram-negative bacteria, and one of caspase-4 or caspase-5 in humans or caspase-11 in mice. Here we determine the stoichiometry of the non-canonical inflammasome using size-exclusion chromatography coupled with UV, refractive index, and light-scattering measurements, showing that the non-canonical inflammasome is heterogeneous, comprised of three major complexes with different numbers of LPS and caspase molecules. Solution Nuclear Magnetic Resonance (NMR) spectroscopy studies of the N-terminal Caspase Activation and Recruitment Domain (CARD) of caspase-11, that binds LPS, show that it is largely unstructured in the absence of lipid, with pervasive dynamics on the s-ms timescale. Formation of this complex increases the alpha-helical content of the CARD but the dynamics persist, multiple conformers are formed, and tertiary contacts are transient, consistent with formation of a molten globule. Our NMR results establish that the protease domain of caspase-11 is monomeric in isolation. As proteolysis is linked with dimerization, the protease domains are inactive in this state, but upon formation of the non-canonical inflammasome dimerization occurs, priming the complex for rapid processing of substrates. Significance StatementAnimals respond to injury, infection, or toxic materials via a process called inflammation. At the molecular level inflammation involves formation of large machines - inflammasomes - that are instrumental in triggering cascades that lead to an immune response. Although structural studies of canonical inflammasomes have emerged, much less is known about the structures of non-canonical inflammasomes. Using solution Nuclear Magnetic Resonance (NMR) spectroscopy in concert with other biophysical approaches we show that non-canonical inflammasomes are highly dynamic and structurally heterogeneous, and we characterize the different sized inflammasome particles that are formed in terms of their composition. We also show that once formed, non-canonical inflammasomes are primed to rapidly cleave substrate molecules, necessary for propagating the immune response.

biophysics↗

bioRxiv2026

DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery

Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generation, optimizing candidate prioritization, and enabling more scalable and cost-effective drug discovery pipelines. However there is currently a lack of objective assessments of LLM performance to ascertain their advantages and limitations over traditional drug discovery platforms. To tackle this emergent problem, we have developed DrugPlayGround, a framework to evaluate and benchmark LLM performance for generating meaningful text-based descriptions of physiochemical drug characteristics, drug synergism, drug-protein interactions, and the physiological response to perturbations introduced by drug molecules. Moreover, DrugPlayGround is designed to work with domain experts to provide detailed explanations for justifying the predictions of LLMs, thereby testing LLMs for chemical and biological reasoning capabilities to push their greater use at the frontier of drug discovery at all of its stages.

bioinformatics↗

bioRxiv2026

Network Toxicology, Molecular Docking, and Molecular Dynamics Simulations Reveal the Mechanism of Tetrabromobisphenol A in Bullous Pemphigoid.

Bullous pemphigoid (BP) is an autoimmune blistering disease with a growing incidence, and environmental factors are receiving increasing attention. Tetrabromobisphenol A (TBBPA), a widely used brominated flame retardant, is a significant environmental pollutant. However, the molecular mechanisms by which TBBPA contributes to BP pathogenesis remain unclear. This study integrated network toxicology, molecular docking, and molecular dynamics (MD) simulations to systematically investigate the molecular mechanisms of TBBPA-induced BP. Using network toxicology, we identified 797 potential targets of TBBPA and 446 BP-related targets. A Venn diagram analysis revealed 48 common targets. Protein-protein interaction (PPI) network and topological analyses further identified five core hub targets: TNF, CXCL8, MMP9, ICAM1, and ITGB1. Gene enrichment analysis indicated that these targets were significantly enriched in immune-inflammatory pathways, such as leukocyte migration, inflammatory responses, and the IL-17 signaling pathway, as well as in various pathogen infection and cancer-related pathways. Molecular docking revealed that TBBPA stably binds to all five core targets with binding energies [≤] -5 kcal/mol, driven primarily by hydrophobic interactions and {pi}-{pi} stacking. Subsequent MD simulations confirmed that TBBPA complexes with TNF, CXCL8, and MMP9 remained stable throughout the 100 ns simulation. The overall protein structures remained compact, and the ligands were effectively encapsulated within the binding pockets, forming stable networks of hydrogen bonds and hydrophobic interactions. In conclusion, this study, for the first time, proposes a systematic molecular framework using integrated computational biology. Our findings suggest that the environmental pollutant TBBPA may act as a potential risk factor in BP pathogenesis by targeting core proteins (TNF, CXCL8, and MMP9). These interactions potentially disrupt critical signaling pathways related to immune inflammation, cell migration, and tissue remodeling. This study offers a novel mechanistic hypothesis regarding environmental chemical exposure in autoimmune blistering diseases, although further experimental validation is required. HighlightsO_LINetwork toxicology identified 48 common targets linking Tetrabromobisphenol A(TBBPA) exposure to Bullous Pemphigoid (BP). C_LIO_LIFive core targets (TNF, CXCL8, MMP9, ICAM1, ITGB1) were screened as potential mediators. C_LIO_LITBBPA stably binds to TNF, CXCL8, and MMP9 with binding energies [≤] -5 kcal/mol. C_LIO_LIMolecular dynamics simulations confirm stable binding and structural integrity of complexes. C_LIO_LIThis study provides a mechanistic framework for TBBPA as an environmental risk factor in BP. C_LI

pharmacology and toxicology↗

bioRxiv2026

Molecular Dosimetry of DNA Adducts in Mice Exposed to Ethylene Oxide

Ethylene oxide (EtO) is a highly reactive industrial chemical and classified as a known human carcinogen with a putative mutagenic mode of action (MOA). Its genotoxic potential is primarily mediated through alkylation of DNA, resulting in the formation of the mutagenic adduct O6-(2-hydroxyethyl)-2-deoxyguanosine (O6-HE-dG). The N7-(2-hydroxyethyl)guanine (N7-HE-G) adduct is formed in greater abundance and is generally considered to be non-mutagenic. However, dose-response relationships of these DNA adducts, particularly at low inhalation exposure levels (i. e., below 3 ppm), remain unknown. These data are necessary to inform the biological plausibility of different statistical dose-response models that have been applied to human or animal data used for cancer risk assessment. In the present study, male and female B6C3F1 mice were exposed to EtO (0, 0.05, 0.1, 0.5, 1, 50, 100, and 200 ppm) 6 hours/day for 28 consecutive days. Immediately following the last exposure, DNA was extracted from lung, liver, bone marrow, and mammary gland, and further utilized to measure DNA adduct levels using highly sensitive mass spectrometry platforms. N7-HE-G was detected in all tissues and exposure groups, showing linear dose-response relationships in the low-dose range ([≤]1 ppm) and increased sharply and exposure-disproportionately in the high-dose range ([≥]50 ppm). Despite a very low limit of detection, O6-HE-dG, in contrast, was not detected at exposures <50 ppm in any tissue consistent with at most a shallow linear exposure response. At higher exposures ([≥]50 ppm), O6-HE-dG exhibited a dose-response pattern of N7-HE-G. Notably the mammary gland, despite being anatomically distant from the site of inhalation, exhibited the second-highest levels of both adducts at higher doses. This study provides the first reliable quantitative dose-response evidence of DNA adducts in tumor target and non-target (liver) tissues across a wide range of EtO exposures. The two DNA adducts differ markedly in their abundance, repairability and mutagenic potential and together provide a molecular MOA dose-response framework to inform both quantitative cancer risk assessment and genotoxic hazard characterization.

pharmacology and toxicology↗

bioRxiv2026

A Dynamic Oligomerization Network Coordinates Hemagglutinin-Mediated Membrane Fusion on Influenza Virions

Influenza A virus (IAV) entry is mediated by the trimeric glycoprotein hemagglutinin (HA), which undergoes low-pH-triggered conformational rearrangements to drive membrane fusion. Although biochemical and biophysical studies have long suggested that HA trimers function cooperatively, the structural basis for this cooperativity on intact virions has remained unclear. Here, using cryo-electron tomography (cryo-ET) and subtomogram averaging (STA), we determine the in situ structure of prefusion HA on native virions at 3.58 [A] resolution, enabling atomic modeling of membrane-anchored HA. We show that neighbouring HA trimers engage in lateral interactions mediated by HA1 subunits, assembling into flexible dimeric, pentameric, and hexameric organizations that form locally ordered lattices on the viral surface. A 6.0 [A] reconstruction of the HA-dimer reveals the molecular interfaces underlying these assemblies. Disruption of the dominant HA1-HA1 interaction markedly impairs viral entry and slows membrane fusion kinetics. Together, these findings define a virion-level mechanism for coordinated HA activation and establish a structural framework for understanding cooperative membrane fusion by class-I viral fusion proteins.

biophysics↗

bioRxiv2026

Optimizing resource allocation in Miscanthus breeding with sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

genomics↗

bioRxiv2026

Multi-trait Multi-environment Genomic Prediction Strategies for Miscanthus sacchariflorus Populations

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

genomics↗