Biology subjects

Qian, Y.

Publications and source records attributed to Qian, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

Efficient recovery of transgene-free edited tobacco using the transgene self-elimination system via de novo regeneration

Elimination of integrated editing constructs remains a major challenge for generating transgene-free progeny in plant genome editing based on Agrobacterium-mediated regeneration. Here, we adapted the Transgene Killer CRISPR (TKC) concept to tobacco and established the transgene self-elimination system, in which CRISPR/Cas9 is coupled with developmentally regulated BARNASE cassettes. Three pU3TR-derived vectors carrying DLL-, OsRP3- or PHAS-driven Barnase regenerated normally after leaf-disc transformation. The DLL construct pEB138 showed the highest T editing frequency, with 56 of 69 independent T plants (81.16%) carrying mutations at one or both NtDFR target loci. Under a stringent lineage-level screen in which up to 48 T progeny were tested per T line, 36 of 43 pEB138 lineages (83.72%) yielded uniformly marker-negative progeny for the three assayed T-DNA markers, compared with 0 of 7 lineages transformed with the parental pU3TR vector. The platform also supported recovery of dfr mutants with altered flower pigmentation and NtA622-edited lines with reduced nicotine accumulation. Moreover, introduction of DLL-iBarnase into the independent pDC45 editing backbone did not impair shoot regeneration, and representative NtBBL progeny showed markedly reduced nicotine content together with loss of the selectable cassette. These results demonstrate that BARNASE-assisted transgene elimination can be integrated with Agrobacterium-mediated de novo regeneration in a dicot, enabling one-step recovery of transgene-free edited tobacco progeny.

molecular biology↗

bioRxiv2026

CryoFAST: A Fab-Aided Cryo-EM Workflow for High-Resolution Structure Determination of Sub-50-kDa Protein Targets

Single-particle cryo-electron microscopy (cryo-EM) plays an increasingly important role in structure-based drug discovery (SBDD) by revealing target protein-ligand interactions at the molecular level, but its application has largely focused on large- and medium-sized protein complexes. Many therapeutically important targets are smaller than 50 kDa and remain highly challenging for cryo-EM analysis because of low signal-to-noise ratios and difficulties in particle alignment. Here, we describe cryoFAST, an integrated workflow that couples ligand-compatible Fab discovery and structure-guided computational maturation with biochemical triage, fiducial-complex assembly, and single-particle cryo-EM. Through phage-display screening and/or AI-guided design, we developed high-affinity Fab binders of approximately 50 kDa that stabilize the target without disrupting ligand-target interactions and then assembled Fab-target complexes for cryo-EM analysis. Using the therapeutically relevant extracellular domain (~20 kDa) of interleukin-4 receptor (IL-4R) as a proof of concept, we determined its cryo-EM structure at 2.74 Angstrom resolution and established a framework for elucidating ligand-binding modes. This workflow provides a practical, efficient, and potentially broadly applicable route to the structural characterization of small protein targets that are otherwise challenging to study. By extending the applicability of cryo-EM to high-value but technically challenging small targets, cryoFAST may open new opportunities for structure-based drug discovery.

biophysics↗

bioRxiv2026

The uplift of the Qinghai-Tibetan Plateau reshaped genome architecture and diversification of Asian torrent catfishes

Tectonic uplift and mountain building are major drivers of species diversification and adaptation, yet how they reshape genome architecture and promote evolutionary innovation remains poorly understood. Here, we generated multiple chromosome-level genomes for Asian torrent catfishes (Sisoridae) and performed phylogenomic and comparative genomic analyses. Our results support an Asian origin of Sisoridae and provide a refined phylogenetic topology for catfishes. Comparative analyses revealed extensive chromosomal rearrangements during sisorid evolution, indicating highly dynamic genome architectures. The timing of sisorid diversification overlaps with major phases of regional tectonic change, and analyses of phylogenetic discordance indicate heterogeneous contributions from incomplete lineage sorting and introgression. Elevational distribution was associated with variation in estimated molecular evolutionary rates, while exploratory scans identified lineage-specific candidates for selection and convergent evolution. Candidate coding and non-coding changes occur near genes with known roles in fin development, body patterning, and liver biology, but their effects on sisorid traits remain to be tested. Together, our results demonstrate how tectonic uplift reshaped genome architecture and diversification in Asian torrent catfishes and provide new insights into the genomic basis of adaptation in mountainous freshwater ecosystems.

evolutionary biology↗

bioRxiv2026

Latent generative search unlocks de novo design of untapped biomolecular interactions at scale

De novo protein design has advanced rapidly, yet designing binders to polar, solvent-exposed epitopes and small, flexible ligands remains challenging. Such hydrated surfaces and flexible molecules, including carbohydrates, provide few of the hydrophobic contacts favoured by current methods and have largely resisted de novo binders. To address this challenge, here we introduce latent generative search for binder design, a novel framework that uses reward-guided search at inference time to steer the Proteina-Complexa generative model. The model codesigns sequence and structure - generating them together in a continuous latent space - and thereby removes the inverse-folding step on which current methods rely. In a screen of more than one million designs by multiplexed phage display, latent generative search produced more validated binders than every other method tested, its codesigned sequences surpassing post hoc redesign. It delivered high-affinity binders across therapeutic receptors, a viral attachment protein and intracellular signalling targets. Our approach also accessed previously untapped biology, generating the first de novo proteins that bind a free carbohydrate, including one that discriminates between blood-group antigens - a polar, flexible target class beyond the reach of current design methods.

bioengineering↗

bioRxiv2026

Ancient tandem duplication of CYCLOIDEA is associated with the evolution of capitulum development in Asteraceae

Asteraceae is one of the largest and most diverse families of flowering plants. Much of their evolutionary success is attributed to their unique inflorescence structure-the capitulum. Despite their profound adaptive and taxonomic significance, the evolutionary genetic mechanisms underlying Asteraceae capitulum diversification remains poorly understood. We integrated comparative genomic analyses of 35 Asteraceae genomes with developmental transcriptome profiling of ray and disc florets. Our results uncovered a deeply conserved CYC2e-CYC2c-CYC2d-CYC2g-CYC2b tandem gene cluster that is prevalent across the Asteraceae family. We demonstrated that this tandem cluster arose during the early evolutionary diversification of Asteraceae. Subsequent lineage-specific gene retention and loss events have reshaped the composition of this cluster across different Asteraceae lineages. Notably, key members of this cluster, particularly CYC2c, CYC2d, and CYC2g, are preferentially retained in lineages with ray florets, which is consistent with their roles in regulating the ray floret development. Collectively, our findings establish a mechanistic link between the evolutionary divergence, spatiotemporal expression differentiation of the CYC2 tandem cluster as a key genetic driver underlying the morphological diversification and evolutionary success of Asteraceae.

evolutionary biology↗

bioRxiv2026

Single-particle tracking reveals neuronal activity-dependent shuttling of ARC/ARG3.1 protein between cytoplasmic clusters and the nucleus

The activity-regulated cytoskeleton-associated protein (ARC/ARG3.1) is a key regulator of synaptic plasticity and has both synaptic and nuclear functions. ARC is known to undergo nuclear import and export, yet the dynamic transport behavior of individual ARC particles remains unknown. Using live-cell single-particle tracking, we directly visualize ARC nucleocytoplasmic transport and shuttling in primary hippocampal neurons. Synaptic activation by chemical long-term potentiation (cLTP) treatment increases shuttling behavior and reveals a previously underappreciated organization of ARC within the neuronal cell body cytoplasm, characterized by perinuclear ARC clusters. Disruption of the N-terminal ARC oligomerization motif markedly reduced both perinuclear cluster formation and nucleocytoplasmic shuttling. Together, these findings reveal an activity-dependent relationship between ARC self-assembly, perinuclear organization, and nucleocytoplasmic trafficking, providing a potential mechanism for coordinating the synaptic and nuclear functions of ARC during neuronal plasticity.

neuroscience↗

bioRxiv2026

An AI System for Autonomous Algorithm Evolution in Drug Development

Artificial intelligence (AI) is increasingly permeating the drug development pipeline. Numerous algorithms for accelerating this multi-stage and multi-task process have been constructed, which depends heavily on expert design and labor-intensive task-specific optimization. Given that AI-driven acceleration of drug development is recognized as a cumulative, often synergistic, effect across multiple stages, the autonomous evolution of existing algorithms across the entire pipeline is demanded to achieve a holistic advancement. Here, we present DrugEvolve, a multi-role large language model system for systematic and autonomous algorithm evolution in drug development. DrugEvolve realizes a closed-loop evolution process by incorporating Researcher, Engineer, and Analyst domains, and enables an iterative design, implementation, evaluation, and refinement of algorithm by leveraging scientific knowledge and accumulated evolutionary experience. Across eleven representative tasks spanning target identification, drug discovery, preclinical study, and clinical trial, DrugEvolve autonomously evolved the corresponding task-specific algorithms and achieved substantial performance enhancement on 120 benchmark test sets. Moreover, it showed robust generalizabilities across heterogeneous data modalities (ranging from biological sequence and graph to molecular topology and textual language), and realized gains in both predictive and generative tasks. Collectively, this AI system can serve not only as an algorithmic infrastructure for drug development, but also as a transferable paradigm for broader scientific domains.

pharmacology and toxicology↗

bioRxiv2026

Cumulative Transfer Function for Assessment of MRI-Induced RF Heating Risk in Pediatric Patients Implanted with Bifurcated Leads

PurposeRF-induced heating remains a major barrier to MRI access for patients with epicardial cardiac implantable electronic devices (CIEDs). Although ISO/TS 10974 Tier-3 transfer function (TF) methods are established for unbranched leads, no analogous framework exists for bifurcated leads, in which branch asymmetry and inter-branch coupling may substantially alter heating. We developed and validated a cumulative transfer function (cTF) framework to address this gap. MethodsFollowing ISO/TS 10974 Tier-3 formalism, we measured, calibrated, and validated cTFs for a commercial 35 cm bipolar epicardial lead at 1.5 T. The framework explicitly accounts for branch-specific response and cross-branch coupling. Validation was performed with 24 canonical lead configurations in a homogeneous phantom and, without recalibration, in a heterogeneous anthropomorphic pediatric phantom with clinically derived trajectories. A single-branch TF approximation served as a comparator. The validated cTF was applied to predict RF heating across adult and pediatric human models at multiple imaging landmarks. ResultsCompared with the single-branch TF approximation, the cTF reduced prediction error by nearly 70% in the primary validation dataset. In secondary validation, the cTF maintained low error across clinically relevant trajectories and imaging landmarks. In human models, the framework revealed marked anatomy- and landmark-dependent variation in predicted heating for the tested 35 cm lead, with low predicted heating in pediatric models and substantially higher heating in selected adult chest and upper abdominal imaging scenarios. ConclusionThe cTF provides a validated framework for RF-heating assessment of bifurcated leads and substantially improves prediction accuracy over single-branch TF approximations that neglect branch coupling.

bioengineering↗

bioRxiv2026

Foundation Model RNAGAN Enhances Biomedical Insight of Nasopharyngeal Carcinoma Metastasis

RNAGAN (version 2.0, https://github.com/ZhaozhengHou-HKU/RNAGAN-2.0.git) is a published foundation model that analyzes single-cell and bulk-level RNA sequencing samples and enables multiple applications that enhance medical insights. Here we applied this model to Nasopharyngeal Carcinoma (NPC) as in-context few-short format (i.e., the model was never trained with any NPC data). We conducted all four supported functions, which include sample stratification, vectorization, pseudo data generation, and marker identification. The results were then used for identifying metastatic NPC and to investigate mechanisms associated with NPC metastasis. Examination with stratification showed that the accuracy of RNAGAN results for evaluating the metastasis risk in NPC patients are comparable to or outcompeted recently published risk estimation linear prediction model. Vectorization results present consistency across multiple cohorts and RNAGAN model versions. In the task of identifying markers and mechanisms related to NPC metastasis, incorporating pseudo data substantially enhanced the representativeness of single-cohort-based differential expression (DE) analysis. Moreover, RNAGAN identified metastasis-related marker genes based on single cohort, were concordant with the ground truth obtained across multiple cohorts (p=1.05e-9). Regarding biomedical mechanisms, RNAGAN enabled second-order feature extraction, unveiling a remarkable domination of the protective function of adaptive immune responses (as indicated by IL21R levels) over the hazardous function of chronic, non-resolving innate inflammation (as indicated by S100A8 levels) against NPC metastasis after first-line treatment. This association demonstrates a high degree of consistency with the external cohort. This study demonstrates the utility of the foundation model RNAGAN in uncovering therapeutic insights for novel cancer types without extra training. We reveal a critical spatial mechanism preventing distant metastasis via humoral anti-tumor immunity in NPC. High S100A8 expression by innate antigen-presenting cells (APCs) triggers an inflammatory cascade promoting epithelial-mesenchymal transition (EMT) and metastasis. However, when germinal center IL21R+ B cells simultaneously colocalize with these innate signals, they override this suppressive tissue stress. Spatial analysis shows that a high S100A8/IL21R intersection within tumor regions strictly distinguishes treatment responders, whereas non-responders display spatial mismatch or S100A8+ hyper-infiltration. This coordinated innate-adaptive cross-talk sustains functional tertiary lymphoid structures (TLS) that mature IgG-secreting plasma cells, which opsonize and eliminate emerging EMT tumor cells before systemic escape. Consequently, while S100A8 alone is an unreliable prognosticator, its spatial colocalization with IL21R is a robust protective indicator overlooked by conventional bulk analysis methods.

cancer biology↗

bioRxiv2026

Paralogous lncRNAs CYTOR and MORRBID share a conserved trans acting function in MEK ERK signaling

Background: Long non-coding RNAs (lncRNAs) exhibit rapid evolutionary turnover, often driven by genomic duplication. How paralogous lncRNAs maintain, partition, or diverge in function across distinct genomic contexts remains poorly understood. The evolutionarily conserved lncRNA MORRBID and its primate-specific paralog CYTOR provide a natural framework to interrogate the functional consequences of lncRNA duplication. Results: Although CYTOR and MORRBID have acquired distinct transcript variants influenced by their divergent genomic environments, we demonstrate that they maintain a robust, shared core function encoded by near-identical dominant two-exon transcripts. Using SNP-based paralog-specific quantification, we found that CYTOR contributes more strongly to the shared transcript pool, while both transcripts localize predominantly to the cytoplasm, consistent with a shared trans-acting function. Simultaneous repression of CYTOR and MORRBID consistently impairs cell adhesion and migration across multiple cancer models. Mechanistically, the shared CYTOR/MORRBID transcript pool associates with MEK2 and sustains MEK-ERK signaling. This signaling axis promotes FOSL1 expression and AP-1-linked transcriptional output, including expression of the downstream effector EPHA4, whose role was supported by rescue experiments. Patient tumor transcriptomes and healthy single-cell datasets further supported the associated mesenchymal, adhesion, and epithelial-mesenchymal transition program. Conclusions: Our findings establish that paralogous lncRNAs can retain a conserved mechanistic core despite context-dependent transcriptional divergence. The CYTOR/MORRBID transcript pool defines a shared lncRNA signaling module that supports MAPK-ERK signaling and adhesion-migration programs across cancer and mesenchymal-like cellular contexts. This defines a unified mechanistic framework for the shared core function of these widely studied paralogous lncRNAs.

genetics↗

bioRxiv2026

Dysfunction of the colliculus-pulvinar pathway in children with developmental dyslexia

While evidence suggests magnocellular deficits in the geniculostriate pathway in adults with dyslexia, neural deficits in the subcortical pathways during childhood remain unclear. Here, we used high-resolution fMRI to investigate subcortical abnormalities in Chinese children with developmental dyslexia. Fast achromatic motion stimuli and slowly drifting chromatic gratings were used to assess magnocellular (M) and parvocellular (P) functions, respectively. Relative to controls, children with dyslexia showed a selective reduction in responses to the M stimulus in the ventromedial pulvinar (vmPul) and the superficial layers of the superior colliculus (SCs), along with significantly reduced SCs-vmPul connectivity. Importantly, while vmPul responses to the M stimulus were positively associated with reading skills in healthy controls, this correlation was absent in children with dyslexia. Unlike previous findings in adults, the lateral geniculate nucleus (LGN) exhibited a non-selective reduction in responses to both stimuli, no volume reduction, and no correlation with reading ability. These findings demonstrate a selective deficit to achromatic motion processing in the colliculus-pulvinar pathway in children with dyslexia, which contributes to their reading difficulties. This early subcortical disruption differs from, and precedes, the neural deficits previously reported in the adult LGN, offering new insight into the developmental trajectory of dyslexia.

neuroscience↗

bioRxiv2026

Ultrasound-led stratification of carpal tunnel syndrome reveals structure-function mismatch

The severity stratification of carpal tunnel syndrome (CTS) relies on ultrasound morphological markers and electromyography. However, it remains unclear how structural imaging can reliably infer functional impairment. Clarifying the structure-function relationship is critical for efficient diagnostic pathways. A retrospective cohort of 55 patients with symptoms related to CTS was analyzed at the Shanghai Sixth Peoples Hospital. All patients were subjected to ultrasound and EMG. 72.7% cases were diagnosed with CTS with a female predominance and equal left-right involvement. Random-forest classifiers were trained using surrogate splits, and performance was evaluated using predictions outside the bag. A full-feature model (34 candidate variables) was compared against a simplified model (8 core variables) capturing the core morphological and electrophysiological features. A residual-based framework was then used to characterize the structure-function mismatch within severity grades (1a-3c). The simplified model improved discriminative performance compared to the full-feature model (AUC 0.789 to 0.824). The simplified model achieved an overall accuracy of 77.3%. Analysis of predicted probability distributions and 10-bin calibration curves indicated stable and clinically interpretable risk estimation in most probability ranges. Permutation-based importance analysis confirmed that both ultrasound and electrophysiological features contributed substantively to prediction. Residual-based grading further revealed structure- function heterogeneity within each main severity grade. CTS severity can be stratified using a limited set of complementary morphological and electrophysiological features. Structure-function mismatch supports an imaging-led initial screening, with electrophysiology reserved for selected patients.

bioengineering↗

bioRxiv2026

Experimental Validation of Finite Element Models for Directional DBS: The Critical Role of Boundary Conditions on VTA Accuracy

ObjectiveComputational models and visualization toolboxes for Deep Brain Stimulation (DBS) increasingly rely on pre-computed electric field libraries to estimate the Volume of Tissue Activated (VTA). However, the boundary conditions (BCs) and source models used to generate these fields vary widely across studies, and there is currently no experimental consensus regarding which parameters most accurately reflect the physical device output. The objective of this study was to experimentally validate the electric potential distribution of directional DBS leads in order to determine the optimal Finite Element Method (FEM) configuration. ApproachThe voltage distribution surrounding a Boston Scientific Vercise Gevia directional lead was mapped in a saline phantom using a custom high-precision robotic scanning system. Experimental measurements were compared against six FEM configurations that varied in source formulation (Dirichlet vs. Neumann boundary conditions) and ground definitions. For each configuration, the resulting VTA volume was computed to assess the clinical impact of modeling assumptions. ResultsThe FEM configuration implementing a Dirichlet (voltage) boundary condition on the active contact with a grounded implantable pulse generator (IPG) surface demonstrated the highest accuracy, achieving a Symmetric Mean Absolute Percent Error (SMAPE) of less than 9% across all contact levels. In contrast, conventional current-controlled simulations employing Neumann boundary conditions with disparate ground definitions substantially overestimated electric field spread. Suboptimal boundary condition selection resulted in an approximate 67% overestimation of VTA volume (137 mm3 vs. 82 mm3) relative to the experimentally validated model. SignificanceAlthough clinical DBS systems operate as current sources, standard Neumann (current density) boundary conditions do not adequately represent the equipotential behavior of the electrode-tissue interface, resulting in nearly a two-fold error in predicted VTA volume. To improve the validity of predictive clinical models, we recommend the use of Dirichlet boundary conditions derived from the device operating impedance (V = Itarget x Zmeasured) rather than conventional current density specifications.

bioengineering↗

bioRxiv2026

MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization

Computational drug discovery, particularly the complex workflows of drug molecule screening and optimization, requires orchestrating dozens of specialized tools in multi-step workflows, yet current AI agents struggle to maintain robust performance and consistently underperform in these high-complexity scenarios. Here we present MolClaw, an autonomous agent that leads drug molecule evaluation, screening, and optimization. It unifies over 30 specialized domain resources through a three-tier hierarchical skill architecture (70 skills in total) that facilitates agent long-term interaction at runtime: tool-level skills standardize atomic operations, workflow-level skills compose them into validated pipelines with quality check and reflection, and a discipline-level skill supplies scientific principles governing planning and verification across all scenarios in the field. Additionally, we introduce MolBench, a benchmark comprising molecular screening, optimization, and end-to-end discovery challenges spanning 8 to 50+ sequential tool calls. MolClaw achieves state-of-the-art performance across all metrics, and ablation studies confirm that gains concentrate on tasks that demand structured workflows while vanishing on those solvable with ad hoc scripting, establishing workflow orchestration competence as the primary capability bottleneck for AI-driven drug discovery.

bioinformatics↗

bioRxiv2026

Spatio-temporal mapping of immune cell dynamics during human sequential lymph node metastasis

Regional lymph node (LN) metastasis critically influences distant metastatic progression, anti-tumour immunity, and patient prognosis. While tumour-induced immune modulation in tumour-draining LNs (TDLNs) has been extensively studied using murine models, the systematic reconstruction of the immune system from primary tumours through TDLNs and subsequent lymph nodes in human cancer progression remains understudied. Here, we utilised integrated multi-omics approaches, including imaging mass cytometry, single-cell RNA sequencing, Visium and Xenium spatial transcriptomics, and multi-colour immunofluorescence to systematically characterise immune cell dynamics across 147 paired primary tumours, sentinel TDLNs (S-TDLNs), and secondary axillary LNs (ALNs) obtained from 50 treatment-naive triple-negative breast cancer patients with different progression statuses. Our comprehensive profiling revealed critical immune alterations, such as decreased type-2 conventional dendritic cells (cDC2), naive T cells, and B cells, along with an increase in immunosuppressive macrophages. Developing a novel single-cell transformer model, we identified substantial alterations in various immune cell populations, notably MARCO+ macrophages, which strongly correlated with breast cancer patient survival outcomes. Spatial analysis combined with our newly integrated cell-cell interaction platform revealed diminished immune cell communication and impaired priming interactions among dendritic cells, B cells, and T cells within metastatic lymph nodes and primary tumour sites. In an independent neoadjuvant immunotherapy cohort of 36 TNBC patients with 52 lymph node samples, we found preservation of CD1c cDC2 in lymph nodes predicted pathological complete response and longer event-free survival, highlighting cDC2 as a potential biomarker and therapeutic target. Collectively, this systematic mapping of immune landscape alterations during human sequential LN metastasis provides essential insights for understanding cancer metastasis mechanisms and paves the way for innovative immunotherapeutic strategies.

immunology↗

bioRxiv2026

Circulating MicroRNAs as Potential Diagnostic Biomarkers for Heart Failure: A Systematic Review and Meta-Analysis

BackgroundCurrent heart failure (HF) biomarkers (e.g., BNP/NT-proBNP) have limitations in specificity and performance in HF with preserved ejection fraction (HFpEF). Circulating microRNAs (miRNAs) are promising novel biomarkers. This study aimed to comprehensively evaluate the diagnostic stability of circulating miRNAs for HF, identify novel candidates, and prioritize them for clinical translation. MethodsWe conducted a systematic review and meta-analysis. PubMed, Embase, and Cochrane Central were searched from inception to March 2025. Studies comparing miRNA expression in HF versus control groups using blood or tissue samples were included. Data were extracted, and study quality was assessed using the Newcastle-Ottawa Scale (human) and SYRCLEs tool (animal). A random-effects model pooled log odds ratios (logORs) for each miRNA. Subgroup analyses were based on species, ethnicity, and sample type. Evidence quality was graded using the GRADE framework. ResultsEighty-six studies (61 human, 25 animal) with 3,023 samples were included. Meta-analysis identified 71 consistently dysregulated circulating miRNAs (58 up, 13 down) in HF. Key upregulated miRNAs included miR-21 (logOR=8.15, 95% CI: 7.55-8.74), miR-423-5p, and miR-210. Key downregulated miRNAs included miR-144 and miR-126. Subgroup analyses revealed differences by species, ethnicity (Asian vs. non-Asian), and sample type (serum vs. plasma). GRADE assessment classified five miRNAs (miR-1, miR-21, miR-221, miR-423-5p, miR-148a) as high-quality evidence. ConclusionsThis meta-analysis identifies a panel of circulating miRNAs with stable expression in HF, with miR-21 and miR-423-5p being the most robust. Evidence grading provides a clear priority list (e.g., miR-21, miR-423-5p) for clinical validation. Subgroup heterogeneity highlights the need for standardized protocols and precision diagnostics in future biomarker development.

pharmacology and toxicology↗

bioRxiv2026

FishMamba-1: A Linear-Complexity Foundation Model for Deciphering Polyploid Cyprinid Genomes

The Cypriniformes order, comprising essential aquaculture species like carps and minnows, presents unique genomic challenges due to complex whole-genome duplication (WGD) events and extensive repetitive elements. Conventional annotation tools and Transformer-based foundation models often struggle to capture long-range dependencies in these expanded genomes due to quadratic computational complexity. Here, we introduce FishMamba-1, the first genomic foundation model tailored for the aquatic clade, built upon the selective state-space model (SSM) architecture. By leveraging Mamba-2s linear scaling efficiency, FishMamba-1 processes context windows of 32,768 base pairs (32k)--significantly surpassing the 4-6k limit of standard DNA Transformers--enabling the modeling of distal regulatory patterns on a single GPU. We curated Cypri-24, a comprehensive dataset comprising 28.8 Gb of high-quality genome assemblies from 24 representative species, to pre-train FishMamba-1 on 15 billion tokens. Subsequent fine-tuning for genome segmentation (FishSegmenter) demonstrates the models capability to annotate gene structures at single-nucleotide resolution with remarkable precision. Evaluation on a held-out test set reveals that FishMamba-1 achieves a precision of 64.6% in exon identification, effectively distinguishing coding regions from the vast non-coding background without relying on RNA-seq evidence. Furthermore, interpretability analysis confirms that the model captures biological syntax such as splice acceptor motifs. FishMamba-1 provides a scalable, open-source framework for decoding the complex genomes of non-model organisms, providing a scalable computational resource to support downstream applications in molecular breeding and ecological monitoring. The complete source code, pre-trained model weights, and datasets are freely available at https://github.com/lu1000001/FishMamba. Additionally, the FishMamba Hub, a web-based inference platform, is accessible at https://huggingface.co/spaces/lu1000001/FishMamba-Hub to facilitate real-time genomic segmentation for the aquatic research community. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/710409v1_ufig1.gif" ALT="Figure 1"> View larger version (71K): org.highwire.dtl.DTLVardef@f5ee02org.highwire.dtl.DTLVardef@1dcfd31org.highwire.dtl.DTLVardef@1729ef8org.highwire.dtl.DTLVardef@22e4f1_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

Nab-paclitaxel fused with the de novo designed receptor binder exhibits enhanced tumor targeting and therapeutic efficacy

Chemotherapy has been widely used in cancer treatment, but most of the chemotherapeutic drugs rely mainly on passive accumulation due to lack of target specificity, which may lead to systemic toxicity and limited clinical utility. Recent advances in artificial intelligence-based protein design provide new opportunities for developing precision therapeutics. Here we modify the albumin-bound paclitaxel (Nab-PTX), one of the most widely used drugs in chemotherapies, by applying the fusion proteins of albumin with the de novo designed protein binders targeting human EGFR or HER2. The resulting particles, EGFRmb-Nab-PTX and HER2mb-Nab-PTX, retain the similar physicochemical properties of Nab-PTX while acquiring the receptor-specific binding capacities. The in vitro assays show that both binder-modified Nab-PTX particles have increased uptake and inhibitory effects significantly in the cancer cell lines with high receptor expression levels. Furthermore, the data from a xenograft model, including tumor growth, excised tumor analysis, organ histology, and fluorescence imaging, show that the binder-modified Nab-PTX enhances tumor accumulation, improves tumor suppression, and reduces off-target toxicity compared with the conventional Nab-PTX, suggesting that they may have promising clinical potential in cancer treatment. Overall, this strategy provides an adaptable modular platform for generating albumin-based chemotherapeutic drugs with target specificities, which can be readily customized with diverse target binders to enable precise cancer therapies.

cancer biology↗