Biology subjects

Qu, Y.

Publications and source records attributed to Qu, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Oncolytic Newcastle disease virus promotes tumor cell apoptosis through SUMO-1-dependent PRKN-mediated degradation of c-Myc

The oncogenic transcription factor c-Myc is aberrantly upregulated in many cancers and plays a central role in tumor cell proliferation and survival. However, direct pharmacological targeting of c-Myc remains challenging because of its intrinsically disordered structure. Newcastle disease virus (NDV) is an oncolytic virus with selective antitumor activity, but the molecular mechanisms by which NDV disrupts oncogenic signaling remain incompletely understood. Here, we show that NDV induces apoptosis in multiple tumor cell lines and suppresses tumor growth in an A549 xenograft model. NDV infection markedly reduced c-Myc protein levels in tumor cells in a time- and dose-dependent manner, and c-Myc knockdown further enhanced NDV-induced intrinsic apoptosis. Mechanistically, NDV promoted ubiquitin-proteasome-dependent degradation of c-Myc through ubiquitination at Lys142. Screening of candidate E3 ubiquitin ligases identified PRKN as a key mediator of NDV-induced c-Myc degradation. NDV infection induced SUMO-1-dependent nuclear translocation of PRKN, allowing PRKN to interact with nuclear c-Myc and promote its degradation. Disruption of this pathway by SUMO-1 or PRKN knockdown impaired PRKN nuclear accumulation, restored c-Myc stability, and attenuated NDV-induced apoptosis. Functionally, expression of the degradation-resistant c-Myc K142R mutant or restoration of c-Myc in c-Myc-knockdown cells protected tumor cells from NDV-induced apoptosis. Together, these findings identify a SUMO-1-PRKN-c-Myc signaling axis that mediates NDV-induced tumor cell apoptosis and reveal c-Myc destabilization as an important mechanism underlying the oncolytic activity of NDV.

cell biology↗

bioRxiv2026

Efferocytosis Remodels Osteoclast Metabolism and Enhances Antibacterial Defense

Osteoclasts are metabolically specialized for bone resorption, but how apoptotic-cell uptake shapes their immune function remains unclear. During Staphylococcus aureus bone infection, osteoclasts engulfed apoptotic neutrophils. In osteoclast cultures, apoptotic-neutrophil exposure reduced multinucleation and resorption, induced host-defense programs, and enhanced antibacterial activity, whereas macrophages showed no comparable antibacterial response. MERTK was required for efficient osteoclast efferocytosis. Ctsk-Cre-mediated Mertk deletion increased bacterial burden and bone loss, whereas transplantation of MERTK-competent bone marrow restored osteoclast efferocytosis and reduced infection-associated bone damage. Transcriptomic and extracellular flux analyses revealed enhanced glycolytic activity and altered lactate handling in efferocytic osteoclasts. Lactate supplementation reduced osteoclast formation without reproducing the antibacterial phenotype. Inhibiting lactate production or export attenuated antibacterial activity in efferocytic osteoclasts, whereas blocking lactate uptake enhanced it. These findings identify osteoclast efferocytosis as an immune adaptation that links apoptotic-neutrophil clearance to metabolic remodeling and antibacterial defense in bone.

cell biology↗

bioRxiv2026

Filling the Missing Puzzle: Integrating Interaction and Evolutionary Information to Enhance Protein Fitness Prediction in Inverse Folding

Protein inverse folding, which infers compatible amino acid sequences from a given backbone structure, is central to rational protein design. Current methods rely largely on sequence recovery rate as the evaluation metric, emphasizing structural compatibility but seldom addressing function. Yet functional activity depends on the proper network of non-covalent interactions-protein-ligand contacts being a prime example. We argue that function-oriented protein design is governed by three complementary constraints: structural, interaction, and evolutionary, none of which are handled in a unified manner by existing pretrained models. To bridge this gap, we introduce zPolaron, a graph neural network built on protein-ligand complexes for functional protein design. zPolaron represents residues and ligand atoms as a heterogeneous graph and jointly encodes structural, interaction, and evolutionary information by fusing physical-interaction-enhanced graph representations with ESM-2 embeddings. Across multiple fitness prediction benchmarks, zPolaron outperforms all baselines, with gains evident across structural, interaction, and evolutionary dimensions. Wet-lab experiments further confirm its utility in functional enzyme screening and design: the optimized CalA6-D205A-S160G mutant achieves a 17.84-fold improvement in activity, while the CalB-I121M mutant yields a 1.91-fold increase. Together, these results show that integrating interaction and evolutionary information fills the missing puzzle of inverse folding, enhancing protein fitness prediction beyond structural compatibility. zPolaron is available at https://github.com/zelixirSH/zPolaron.

bioinformatics↗

bioRxiv2026

Ayahuasca plant genomes reveal retrotransposon-derived centromeres and contrasting routes to DMT and β-carbolines

Ayahuasca, used religiously for centuries by Amazonian Indigenous peoples and increasingly studied for its therapeutic potential, owes its psychoactivity to two plants: Psychotria viridis supplies N,N-dimethyltryptamine (DMT), whereas Banisteriopsis caapi supplies beta-carbolines that render DMT orally active. Here we show that the two plants have distinct genome histories: P. viridis is hexaploid (2n = 6x = 66), consistent with autopolyploidy, and B. caapi is diploid (2n = 2x = 20). CENH3-bound regions of both species are nonetheless enriched in CRM-type Ty3/Gypsy retrotransposons, with additional LINE elements and CRM-derived satellites in B. caapi. In P. viridis, a single multifunctional methyltransferase catalyzes both N-methylations of tryptamine to DMT. In B. caapi, beta-carboline biosynthesis combines dedicated enzymes with an acetaldehyde-dependent Pictet-Spengler cyclization that can proceed non-enzymatically, followed by enantioselective oxidation to harmaline. These resources link the ethnobotanical history of ayahuasca to its molecular basis, providing genomes of both source plants, the genetic structure of traditionally recognized cultivated varieties, and the enzymes behind the alkaloids long combined in this decoction.

genomics↗

bioRxiv2026

Root-to-shoot ABA transport by NPF2.4 and NPF2.5 links stomatal regulation to photosynthetic performance

Fluctuating light strongly limits photosynthetic efficiency in plants, yet the mechanisms coordinating stomatal dynamics with rapid photosynthetic induction remain incompletely understood. Here, through a forward physiological screen in Arabidopsis, we identify the abscisic acid (ABA) transporters NPF2.4 and NPF2.5 as regulators of photosynthetic responses to fluctuating light. Loss-of-function npf2.4 and npf2.5 mutants exhibited accelerated stomatal opening and photosynthetic induction during light transitions, resulting in enhanced biomass accumulation and seed yield without compromising long-term drought tolerance. NPF2.4 and NPF2.5 mediated ABA uptake in yeast and were predominantly expressed in the root pericycle and cortex/epidermal tissues, respectively. Reciprocal grafting, xylem sap ABA measurements, and ABA treatment to the roots demonstrated that both transporters regulate root-to-shoot ABA transport. Our findings reveal that root-derived ABA modulates stomatal dynamics and photosynthetic efficiency under fluctuating light, establishing long-distance hormone transport as a key regulator of plant carbon gain in dynamic environments.

plant biology↗

bioRxiv2026

Parent-Child Cortical Similarity Indexes Multiple Dimensions of Sensitivity in the Association Between Family Environment and Adolescent Internalizing Symptoms

Family environment is a robust predictor of adolescent internalizing symptoms, yet its strength varies across adolescents. We tested whether this variation is partly captured by how similar an adolescent's cortical structure is to their parent's and whether different forms of this similarity matter in different cortical systems. In 115 parent-child dyads from two cohorts, structural MRI quantified similarity across 400 cortical parcels in two forms: phenotypic similarity in regional morphometry and architectural similarity in interregional morphometric organization. Parent-child similarity was not directly associated with symptoms, whereas its interactions with family environment were jointly significant (chi-square(4) = 14.92, P = 0.005). Phenotypic similarity in the sensory-limbic system and architectural similarity in the association system each moderated the association between family environment and symptoms (both beta = 0.22). In both cases, the association was strongest among less similar dyads. The phenotypic interaction was distributed across sensory-limbic networks and was not reproduced by similarity to unrelated adults or by the child's cortical atypicality. The architectural interaction was concentrated in the dorsal attention and default mode networks and was distinguishable from unrelated-adult similarity but not from child atypicality. Together, these findings raise the possibility that markers of environmental sensitivity need not reside in the child alone. Parent-child cortical similarity may index a dyadic component of this sensitivity, one not adequately represented by a single similarity score but distributed across forms of similarity and cortical systems.

neuroscience↗

bioRxiv2026

A convergent behavioral-neural profile of parental dysregulation and the link between parent-child brain-state similarity and child negative affect

Parent-child neural similarity is often interpreted as a marker of attunement, but its developmental significance may not be uniformly positive across family environments. We tested whether the parent's own emotional and regulatory profile, measured both behaviorally and neurally, moderates the developmental significance of parent-child neural similarity. Applying a hidden Markov model (HMM-MAR; K = 6 states) to whole-brain fMRI timeseries from 32 parent-youth dyads who separately watched the same emotional film, we derived per-dyad measures of brain-state similarity. Parent-child dyads showed greater brain-state similarity than parent-shuffle null pairs (d = -0.55, p = .004), indicating dyad-specific alignment beyond responses shared across viewers of the same film. Parent self-reported internalizing showed only a weak direct relation to child negative affect (r = .31, p = .085). However, the association between neural similarity and child negative affect depended on parent internalizing (b = 5.62, p = .018; R^2): stronger parent-child similarity predicted higher child negative affect when parents reported higher, but not lower, internalizing. A parallel interaction emerged when parent internalizing was replaced by a neural index of the parent's own state dynamics (parent neural-state unpredictability, Htrans; b = 5.95, p = .004), indicating that the effect reflects the parent's broader regulatory profile rather than a single measurement modality. Both interactions were clearest in the primary model and only partially robust across model orders and parcellations. These findings suggest that dyadic neural similarity should be interpreted in context: resembling a parent with a dysregulated profile may confer risk rather than protection.

neuroscience↗

bioRxiv2026

RamiGlyph Captures Microglial Morphological Diversity and Predicts Functional States in Ischemia Reperfusion and Amyloid Pathology

Microglia are resident immune cells of the central nervous system, whose ramified processes rapidly remodel in response to injury. However, how to capture subtle morphological changes and whether functional state can be predicted from morphology remain open questions. Here, we present RamiGlyph, a contrastive learning framework integrating topological and structural features, trained on more than 20,000 reconstructed microglia. RamiGlyph not only distinguishes physiological and pathological states of microglia but also generalizes to neuronal cell type classification. Projection of microglia morphological embeddings revealed a continuum rather than discrete classes, from which a morphology score was derived to quantify dynamic process remodeling. To link morphology with function, Gromov Wasserstein optimal transport was used to align unpaired morphological and functional data across stages of ischemia reperfusion injury and amyloid pathology. These alignment results enable prediction of microglial functional states using RamiGlyph embeddings alone, with prediction reliability increasing upon cell aggregation. In summary, RamiGlyph provides a robust framework for resolving continuous microglial morphological variation and linking morphology to functional states across acute and chronic neuropathological contexts.

bioinformatics↗

bioRxiv2026

AutoScreen: AI Co-Scientist System for Target Discovery in Functional Genomics

Target discovery in functional genomics remains largely manual and time-consuming, lacking systematic tools for efficient and reproducible gene-level hypothesis generation. We introduce AutoScreen, an AI co-scientist system supporting target discovery through both Pre-screen Design, which constructs perturbation libraries de novo from free-text research descriptions, and Post-screen Analysis, which re-ranks experimental screen hits by integrating statistical scores with biological context. AutoScreen leverages the complementary strengths of multiple specialist agents that perform deep research into multi-modal evidence, information restructuring, parallel searches across >26 biomedical databases, evidence synthesis, and target review to provide transparent, explainable gene prioritization with pipeline provenance. Across 320 genome-scale CRISPR screens as expert-curated benchmarks, AutoScreen achieved a ~19% increase in validated-hit recovery among its top 100 predictions, relative to the strongest agent baseline, and required a 1.2-fold smaller library to recover the same number of hits at the top-500 reference point. AutoScreen reached mean average precision more than two orders-of-magnitude above random baseline. Further, we validated AutoScreen in cancer immune-evasion case studies focusing on natural killer (NK) and T-cell therapeutics. AutoScreen recovered NK-resistance genes that were initially lower-ranked in a leukemia screen, moving MUC1, PDPN, and LRRC15 from raw ranks of 118, 81, 1384 to 5, 44, 659, respectively. In follow-up tumor killing assay with primary human NK cells, individual perturbation validated all three hits successfully. Next, in prospective benchmarking across two cytotoxic T-cell-killing screens, AutoScreen recovered 77.1% of ground-truth hits called by the consensus of two gold-standard analysis pipelines (FDR<0.1), achieving 8% improvement over the strongest general-purpose LLM agent baseline. Finally, we re-analyzed >500 public datasets to construct the AutoScreen Resource Hub, a growing knowledge base of pre-computed, annotated reports for CRISPR screens, RNA-seq differential expression, and gene-level UK Biobank genome-wide association studies. This agentic AI approach enables real-time genomics benchmark construction that are continuously updated, expanded, for evaluating frontier AI co-scientists. Overall, AutoScreen enables AI-powered target discovery to be more auditable, scalable, extensible, and reproducible.

bioengineering↗

bioRxiv2026

Programmable Self-Assembly of RNA Nanostructures with >100 Unique Components

Sophisticated biomolecular functions often arise from large, precisely organized architectures, motivating efforts to construct increasingly complex structures through programmed nucleic acid self-assembly. Although RNA offers a richer repertoire of structural motifs and biological functions than DNA, RNA nanostructures constructed to date have remained substantially less complex and less scalable than their DNA counterparts, most notably DNA origami and DNA bricks, which rely on large libraries of synthetic single-stranded (ss) DNA strands. Directly adapting these strategies to RNA, however, faces two major barriers: (i) the high cost of producing large libraries of distinct synthetic ssRNA strands, and (ii) the limited stability of ssRNA under conditions commonly used for assembling multicomponent nucleic acid nanostructures. Here we present the double-stranded RNA (dsRNA) bricks approach, which addresses both barriers by adopting principles inspired by natural RNA systems: (i) many distinct RNA components ("bricks") are encoded within a single precursor transcript and released by enzymatic processing, (ii) each brick's predominantly double-stranded nature enhances stability. The resulting dsRNA bricks self-assemble through programmable branched kissing-loop interactions. Using this approach, we constructed complex two- and three-dimensional RNA nanostructures with more than 100 distinct components, representing, to our knowledge, the largest and most compositionally complex RNA nanoarchitectures reported to date. Overall, the dsRNA bricks approach establishes a scalable and robust framework for constructing complex RNA nanostructures with compositional and architectural sophistication approaching that of DNA-based systems, thereby opening new opportunities for programmable RNA materials and RNA-based devices.

synthetic biology↗

bioRxiv2026

Inhibition of integrin αvβ8-mediated TGFβ activation and active-TGFβ blockade promote anti-tumor immunity through distinct biological mechanisms

Transforming Growth Factor {beta} (TGF{beta}) is a potent immunosuppressor and a primary driver of resistance to cancer immunotherapy. While preclinical models have long suggested that TGF{beta} inhibition could synergize with immune checkpoint inhibitors, these effects have proven difficult to replicate in clinical settings. The highly regulated TGF{beta} pathway can be inhibited through various mechanisms, including neutralizing activated ligands or inhibiting upstream activators, such as integrins. Recent structural data demonstrated that integrin v{beta}8 can enable TGF{beta}1/3 signaling without releasing the active cytokines from their Latency-Associated Peptides, suggesting that ligand-blocking antibodies may have limited access to their epitopes. Here, we show that integrin v{beta}8 blockade, while achieving anti-tumor responses similar to those of anti-TGF{beta} antibodies, does so through a distinct mechanism of action. Anti-v{beta}8 is 3 orders of magnitude more potent at inhibiting v{beta}8-mediated TGF{beta} activity than a commonly used antibody against the mature form of the cytokine. Whereas TGF{beta} ligand inhibition has little effect on TGF{beta} signaling in tumor-draining lymph nodes (tdLN) and requires IFN{gamma} for its anti-tumor effects, v{beta}8 blockade strongly inhibits TGF{beta} signaling in tdLN and, in combination with PD-L1 blockade, drives tumor control through an IFN{gamma} -independent mechanism that strictly requires T cell egress from tdLN. Combined v{beta}8 and anti-PD-L1 blockade enhances antigen presentation in dendritic cells (DCs) and, unlike TGF{beta} ligand blockade, improves the efficiency of DC-induced T cell activation in response to cross-presented antigen. These findings suggest that v{beta}8 blockade can disable an immunologically critical source of TGF{beta} signaling that is not addressed by antibodies targeting TGF{beta} ligands, suggesting a promising new approach to TGF{beta} pathway modulation. One Sentence SummaryUnlike ligand-neutralizing antibodies, v{beta}8 blockade suppresses TGF{beta} in tdLN and boosts DC-T cell activation, a differentiated immunotherapy strategy.

immunology↗

bioRxiv2026

BiomniBench: Process-level Evaluation of LLM Agents for Real-world Biomedical Research

LLM agents now perform real biomedical research, but evaluating them rigorously is hard. Outcome-only benchmarks fail in two ways. First, a correct final answer can come from memorization, reward hacking, or wrong reasoning that produces the right number by chance. Second, valid alternative analyses are marked wrong simply because they differ from the reference. We introduce BiomniBench, a process-level evaluation framework that scores the full agent trajectory against expert-designed, task-specific rubrics. Our first release, BiomniBench-DA, contains 100 data-analysis tasks across 17 task types, 5 disease areas, and a general-biology category, each based on a paper from journals such as Nature, Cell, and Science and co-developed with an original author or a domain expert. Benchmarking frontier and open-weight models across four agent harnesses reveals three findings. Frontier and open-weight bases cluster within a few points of each other, with substantial headroom for all models. The agent harness shifts scores by more than the gap between successive model generations. Agents reliably ground claims in real sources yet consistently fall short on method selection, biological interpretation, and scientific reasoning. BiomniBench is the first process-level benchmark for LLM agents in biomedical research, providing the dimension-level diagnostics that outcome scoring cannot. Datasethuggingface.co/datasets/phylobio/BiomniBench-DA

bioinformatics↗

bioRxiv2026

ProteinFlux: accurate, rapid and scalable generative prediction of protein dynamics driven by post-translational modifications

The function of proteins, the building blocks of life, in health and disease depends not only on their 3D-conformational states but most importantly on the dynamic transition between states controlled by a wide array of post-translational modifications (PTMs). Recent major advances have been made in our ability to predict static 3D structures; however, understanding and predicting the impact of PTMs on protein conformational dynamics remains a major question and challenge in the field. Molecular dynamics (MD) simulation remains the major computational approach for studying protein dynamics. However, the high computational cost, lack of integration of PTMs as conditioning inputs and inefficient generation of continuous protein dynamics largely precludes PTM-regulated conformational dynamics and the study of slow conformational processes. To address this critical bottleneck, we developed ProteinFlux, a flow-matching generative framework that links PTM-conditioned conformational dynamics to evolutionary constraints encoded by PTM sites. Evolutionary information plays a critical role in capturing conformational dynamics beyond sequence identity, and PTM sites inherently encode evolutionary constraints critical to protein functional regulation. We therefore built FluxSite, a dual-modal PTM site predictor that integrates sequence evolutionary information and 3D structural features to generate a continuous conditional signal encoding conservation and functional importance for each predicted site. FluxSite achieves robust generalization across 18 PTM types and 30 disease-associated proteomes. ProteinFlux generates phosphorylation-conditioned, all-atom conformational trajectories across diverse protein fold classes, faithfully reproducing both thermodynamic properties such as free energy landscapes and kinetic features such as conformational transition pathways. It outperforms state-of-the-art predictors while achieving inference speeds several orders of magnitude faster than traditional MD. In addition, we introduce DynaMo-phos, a benchmark dataset of phosphorylated protein MD simulations. Together, ProteinFlux, FluxSite and DynaMo-phos provide a scalable, high-throughput platform for elucidating PTM-driven conformational mechanisms, with potential applications across allosteric drug design, functional annotation of disease-associated modifications and mechanism-guided therapeutic development.

bioinformatics↗

bioRxiv2026

LNGCN: A Distance-Aware Dynamics Network for Protein-Protein Interaction Prediction

High-throughput PPI screening requires not only interaction discrimination but also stable, calibrated scores for candidate prioritization and efficient experimental allocation. Conventional graph neural networks may over-smooth deep residue representations, reducing score separation among high-confidence candidates. We therefore developed LNGCN, a distance-aware continuous-time graph framework for experimental PPI prioritization. LNGCN integrates residue-level structural graphs with liquid neural dynamics, uses radial distance to drive continuous graph evolution, and applies hierarchical calibration to convert raw outputs into prioritization scores. It performed robustly on balanced human, 1:10 imbalanced, and cross-species yeast bench-marks. On the imbalanced dataset, the top 5% of candidates achieved 85.1% precision. LNGCN also preserved residue-level representation diversity better than conventional GCN variants. In FGF23-FGFR1c--Klotho, SHP2 signaling, Tdk1 oligomer-dependent binding, and a TPR study, it enriched known partners near the ranking top, and experimentally supported novel interactions. Overall, LNGCN enables sustained enrichment of experimentally relevant PPI candidates.

bioinformatics↗

bioRxiv2026

EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics

Enzyme kinetic parameters underpin mechanistic biology but remain sparse in physiological context. We present EnzCast, a multi-modal framework jointly predicting Km, kcat, kcat/Km, and Ki from protein sequence, 3D structure, substrate chemistry, and experimental conditions, paired with IsoKin, an isoform-resolved Bayesian framework converting EnzCast priors into patient-specific in vivo kinetics. Trained on KinBench, the largest curated kinetics database, task-adaptive EnzCast achieved R2 = 0.413, 0.455, 0.227, and 0.105 for Km, Ki, kcat, and kcat/Km, surpassing all baselines on catalytic tasks. Systematic condition scans recovered compartment-specific pH direction inversion and pathway-level temperature responses. In a 20-patient colorectal cancer single-cell cohort, IsoKin reduced posterior uncertainty by 73.3% and 77.3%, revealing cell-type-specific rewiring. Orthogonal validation--scFEA flux, DepMap essentiality (permutation P = 0.0008) and TCGA survival-- provided mixed but directionally consistent support. Together, EnzCast and IsoKin bridge in vitro prediction, condition-aware biochemical interrogation and patient-resolved in vivo inference.

bioinformatics↗

bioRxiv2026

Dissecting the coordinated progression of cell states in spatial transcriptomics with CoPro

Spatial transcriptomics enables the study of how cells coordinate their molecular states within tissue, providing insight into both normal function and disease processes. A key challenge is to identify gene expression programs that vary continuously across space and are coordinated between cell types. We present CoPro, a computational framework for detecting the spatially coordinated progression of cellular states. CoPro can operate in both supervised and unsupervised modes to identify gene programs that co-vary within or between cell types, and to disentangle multiple overlapping spatial patterns. CoPro can be applied to single-cell-level spatial transcriptomics datasets, including MERFISH, SeqFISH+, Xenium, and histology-imputed transcriptomic data. We demonstrate the utility of CoPro with data collected from colon, brain, liver, and kidney tissues. In the colon, CoPro separates epithelial differentiation along the crypt axis from spatially localized inflammatory signals. In the aging liver, it identifies multiple aging-associated cellular programs superimposed on anatomical zonation. In the brain, the flexible kernel design enables the decoupling of the gene expression gradient along the dorsal-ventral and medial-lateral axes. In the kidney, CoPro identifies tubule-vasculature coordination that is essential in nephron function. These results demonstrate CoPros utility for analyzing spatial coordination of gene expression in complex tissues and disentangling overlapping biological processes, such as anatomical organization and disease-associated variation.

bioinformatics↗

bioRxiv2026

A cinnamyl alcohol dehydrogenase scaffold organizes monoterpenoid indole alkaloid biosynthesis

Biosynthesis of ~3,000 monoterpenoid indole alkaloids (MIAs), including the anticancer drug vinblastine, involves the highly unstable intermediate strictosidine aglycone. Its formation by strictosidine {beta}-glucosidase (SGD) and subsequent conversion by geissoschizine synthase (GS) occur in spatially separated compartments, representing a major biosynthesis bottleneck. Here we discover VinBLAST, a cinnamyl alcohol dehydrogenase-like protein repurposed as a scaffold for efficient processing of this labile intermediate. VinBLAST physically mediates SGD and GS interaction in the nucleus and allosterically enhances GS catalytic efficiency. VinBLAST homologues from diverse plant families enhance biosynthesis of several representative MIAs, with the production of catharanthine increased to ~160 mg L-1 in yeast, nearly 1,000-fold higher than previous studies. Our discovery provides a missing link in organizing MIA biosynthesis and enables scalable bioproduction of geissoschizine-derived therapeutics.

synthetic biology↗

bioRxiv2026

SpacerScope: Binary-vectorized, genome-wide off-target profiling for RNA-guided nucleases without prior candidate-site bias

The precision of CRISPR/Cas systems is fundamental to their application in plant and animal biotechnology. However, comprehensive off-target assessment remains a bottleneck, particularly in large, complex genomes where existing tools often suffer from prohibitive computational costs, poor search-space convergence, and limited sensitivity toward non-canonical alignments involving insertions and deletions (indels). To address these limitations, we developed SpacerScope, an off-target analysis framework that enables unbiased, genome-wide discovery by leveraging binary vectorization and high-speed bitwise operations. Benchmarking against CIRCLE-seq data demonstrated that SpacerScope recovered 100% of validated off-target sites (6,142/6,142) within our defined parameter space, achieving zero false negatives while identifying additional high-risk sites with complex edit distances. Unlike conventional tools such as Cas-OFFinder, CHOPCHOP, and CRISPOR, SpacerScope maintains high sensitivity for indel-inclusive off-targets that are otherwise overlooked. Our results establish SpacerScope as a high-speed, high-sensitivity solution for ensuring genome-editing specificity across diverse and complex genomic landscapes. The full source code, documentation, and multi-platform executables are available at https://github.com/charlesqu666/SpacerScope. Graphical TOCSpacerScope is a genome-wide off-target profiling tool for RNA-guided nucleases that combines binary vectorization, bitwise prefiltering, compact 2-bit substitution search, and right-end-anchored indel validation. In the evaluated datasets, it recovered all 6,142 parameter-defined true off-target sites from CIRCLE-seq and showed improved detection of indel-containing candidate sites relative to the previous tools tested here. SpacerScope provides interpretable event-level annotations and empirical risk scores for downstream guide assessment. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/715005v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@bad2ddorg.highwire.dtl.DTLVardef@169b0aborg.highwire.dtl.DTLVardef@1cde3b2org.highwire.dtl.DTLVardef@1fbd11_HPS_FORMAT_FIGEXP M_FIG C_FIG Practitioner Points> SpacerScope enables genome-wide off-target profiling with explicit support for substitutions, insertions, and deletions in a single workflow. > The method combines binary-channel prefiltering with right-end-anchored validation to reduce the search space while preserving sensitivity within the evaluated parameter ranges. > SpacerScope returns event-level match annotations and empirical risk scores, supporting more interpretable downstream guide selection and benchmarking.

bioinformatics↗