Biology subjects

He, Y.

Publications and source records attributed to He, Y..

At least 181 records · Page 10Linked to original sources

bioRxiv2025

Selectivity of the time-dependent M. tuberculosis LeuRS inhibitor ganfeborole is driven by target vulnerability

Ganfeborole (GSK3036656) inhibits the Mycobacterium tuberculosis leucyl-tRNA-synthetase (mtLeuRS) and is in phase 2a clinical trials for the treatment of tuberculosis. Here we show that ganfeborole is a time-dependent inhibitor of mtLeuRS (IC50 1 nM) and generates a post-antibiotic effect of 77 h at 50xMIC (MIC 0.058 M) with M. tuberculosis H37Rv (Mtb), indicating that mtLeuRS is a highly vulnerable drug target and supporting the excellent in vivo efficacy of the drug. Ganfeborole is also a potent time-dependent inhibitor of Escherichia coli LeuRS (ecLeuRS, IC50 2 nM), however no antibacterial activity is observed towards E. coli up to 1 mM ganfeborole despite the observation that less potent ganfeborole analogs have antibacterial activity. To rationalize this observation, we propose that ganfeborole forms a complex with AMP that binds to the ecLeuRS editing site but does not impact aminoacylation. In support, addition of 12.5 M norvaline generates a ganfeborole MIC of 0.4 M since ecLeuRS is unable to hydrolyze norvaline-tRNALeu. Additionally, mutations that reduce the affinity and residence time of ganfeborole-AMP on ecLeuRS result in antibacterial activity. We propose that the activity of ganfeborole towards Mtb is because mtLeuRS is a highly vulnerable target so that only low levels of enzyme need to be inhibited by the ganfeborole-tRNALeu complex in contrast to ecLeuRS, which we previously demonstrated is a low vulnerability target.

biochemistry↗

bioRxiv2025

Distinguishing cortical indexes of arousal and awareness in sleeping patients with unresponsive wakefulness syndrome

Consciousness is assumed to be defined by two components: awareness and arousal. However, compared with awareness, the cortical index for arousal remains lacking in clues. Since patients with unresponsive wakefulness syndrome (UWS) are supposed to have no awareness but arousal, sleep-wake cycle in patients with UWS may reflect pure arousal fluctuations, and then could highlight the cortical indexes for arousal. This study recorded nighttime polysomnography in patients with UWS, patients with minimally conscious state and healthy controls. The EEG signal showed that spectral slope could index arousal, fluctuating among sleep stages in all three groups. Both spectral entropy and Lempel-Ziv complexity indexed awareness, showing a significant difference between conscious and unconscious states. These findings provide fundamental evidence for the two-component hypothesis of consciousness.

neuroscience↗

bioRxiv2025

DeepADR: Multi-modal Prediction of Adverse Drug Reaction Frequency by Integrating Early-Stage Drug Discovery Information via Kolmogorov-Arnold Networks

Adverse drug reactions (ADRs) are a major cause of clinical trial failure and post-market withdrawal, posing significant risks to public health and impeding drug development. While computational methods offer an alternative to costly preclinical testing, existing models often fail with novel compounds by requiring pre-existing information such as drug-ADR associations or by inadequately integrating diverse data sources. Here, we introduce DeepADR, a multi-modal deep learning framework for predicting both the occurrence and frequency of ADRs using early-stage, readily available data. DeepADR integrates chemical structures and biological target profiles with semantic representations of ADR terms derived from a large language model. These heterogeneous parameters are fused using a Kolmogorov-Arnold Network (KAN), which effectively models complex, non-linear cross-modal interactions to capture underlying toxicological mechanisms. Our model outperforms existing methods in predicting both ADR occurrence and frequency, demonstrating robust generalization to new chemical entities. By effectively integrating chemical, biological, and semantic datasets, DeepADR provides a powerful, scalable tool for the early-stage safety assessment and candidate prioritization. This framework not only facilitates the prioritization of safer drug candidates but also offers a methodology for predicting the toxicity of other hazardous materials, holding significant promise for advancing public health.

bioinformatics↗

bioRxiv2025

Approaching an Error-Free Diploid Human Genome

Complete human genomes now resolve regions long inaccessible to genetic analysis, yet repetitive and structurally complex sequences remain prone to assembly errors because of limitations in current assembly algorithms. We introduce a support-based framework founded on the principle that each sequencing read provides independent evidence for the underlying genome sequence. Implemented through Sufficient Alignment Support (SAS), the framework identifies regions lacking concordant read support, localizes residual errors, and guides correction. Applying SAS to T2T-YAO, a haplotype-resolved Han Chinese genome, produced a support-validated assembly in which nearly all sequences outside unresolved rDNA arrays and long homopolymer tracts are supported by independent sequencing evidence. This work establishes a scalable framework for genome validation and provides a support-validated East Asian diploid reference for investigating human genomic diversity.

genomics↗

bioRxiv2025

From Black Box to Biological Insight: AttentioFuse Unlocks Multi-Omics Dynamics in Lung Cancer

Lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), the major subtypes of non-small cell lung cancer (NSCLC), exhibit distinct molecular landscapes that demand precision in prognosis and therapy. While deep learning models achieve high predictive accuracy, their "black-box" nature limits clinical translation. To address this, we propose AttentioFuse, an interpretable deep learning framework employing a Reactome-guided mid-fusion strategy for multi-omics integration. AttentioFuse innovates through three pillars: (1) dual-phase learning to preserve omics-specific patterns via independent sub-networks, (2) hierarchical attention mechanisms (cross-omics, feature-level, and fusion-layer) to dynamically quantify layer contributions, and (3) integrated explainability combining DeepSHAP and global attention weights for gene-to-pathway interpretation. Evaluated on TCGA LUAD/LUSC cohorts, AttentioFuse matches state-of-the-art performance in TNM staging, while uncovering actionable biological insights. The framework validates pan-NSCLC mechanisms like AKT/mTOR metabolic control and histology-divergent Notch signaling roles, while revealing novel pathways--developmental reactivation (T-stage), microbiota-driven metastasis (M-stage), and ECM remodeling--providing testable hypotheses for progression and personalized therapy. Crucially, AttentioFuse bridges computational predictions to clinical practice by proposing molecularly-guided combination therapies. This paradigm shifts toward interpretable-aware AI advances oncology by transforming black-box predictions into biologically grounded decision-support tools.

bioinformatics↗

bioRxiv2025

Manipulating plant oxygen sensing through NCO substitution reveals trade-offs between growth and flooding tolerance

The oxygen-dependent degradation of Ethylene Response Factors VII (ERFVIIs) through the N-degron pathway is central to regulating the transcriptional responses to hypoxia in vascular plants. Plant Cysteine Oxidases (PCOs) control this step by catalysing the oxidation of an N-terminal Cys residue exposed by ERFVIIs. In the present study, we investigated the functional impact of replacing Arabidopsis PCOs with diverse N-terminal cysteine oxidases (NCOs) from across the three eukaryotic kingdoms, hypothesizing that structural and kinetic differences may influence gene regulation of ERFVII targets under hypoxia and thus impact stress tolerance. Combining structural analyses, in vitro biochemical characterisation and in planta complementation assays we observed that not all tested NCOs are functionally equivalent to the endogenous PCOs. In fact, despite the remarkable conservation of catalytic motifs, we identified key differences in enzyme architecture that appear to affect the enzymes capacity to regulate hypoxia responses in plants. Notably, NCO efficiency in oxidising ERFVII peptides inversely correlated with hypoxic gene expression under aerobic conditions and enhanced submergence survival, suggesting that partial ERFVII stabilization primes plants to cope with hypoxia. However, enhanced basal expression of hypoxia-responsive genes in turn correlated negatively with development and biomass accumulation, pointing to a trade-off between growth and stress resilience. Our findings demonstrate that tuning NCO activity can reshape the transcriptional and physiological hypoxia response, suggesting it is possible to enhance plant resilience under fluctuating oxygen conditions through enzyme engineering and precision breeding. Significance statementControl of low oxygen responses to improve crop flooding tolerance is a long-sought objective of molecular plant breeders. The oxygen-dependent oxidation of N-terminal cysteines in transcription factors is thought to be a key step in modulating the transcriptional response to hypoxia. In this study, we tested this hypothesis by substituting endogenous N-terminal cysteine dioxygenases with homologues from different species characterized by highly divergent sequences, structures, and kinetic properties. We show that indeed these variations effectively and predictably influence gene transcription in plants exposed to hypoxia, thereby affecting their tolerance to submergence. However, we also demonstrate that, unexpectedly, these substitutions impact plant growth and development under aerobic conditions, revealing a trade-off between flooding stress resilience and biomass accumulation or yield.

plant biology↗

bioRxiv2025

Charting structural brain asymmetry across the human lifespan

Lateralization is a fundamental principle of structural brain organization. In vivo imaging of brain asymmetry is essential for deciphering lateralized brain functions and their disruption in neurodevelopmental and neurodegenerative disorders. Here, we present a normative framework for benchmarking brain asymmetry across the lifespan, developed from an aggregated sample of 128 primary neuroimaging studies, including 177,701 scans from 138,231 individuals, jointly spanning the age range from 20 post menstrual weeks to 102 years. This resource includes comprehensive, hemisphere-specific brain growth charts for multiple neuroimaging phenotypes: regional cortical grey matter volume, thickness, surface area, and subcortical volumes. Our findings reveal distinct spatial patterns of asymmetry, with early leftward asymmetry observed in association cortices and late rightward asymmetry in sensory regions. These trajectories support theories of the neuroplasticity of asymmetry and the role of both genetic and environmental factors in shaping brain lateralization. Additionally, we provide tools to generate asymmetry centile scores, which allow the quantification of individual deviations from typical asymmetry throughout the lifespan and can be applied to unseen data or clinical populations. We demonstrate the utility of these models by highlighting group-level differences in asymmetry in autism spectrum disorder, schizophrenia, and Alzheimers disease, and exploring genetic correlations with hemispheric specialization. To facilitate further research, we have made this normative framework freely available as an interactive open-access resource (upon publication), offering an essential tool to advance both basic and clinical neuroscience.

neuroscience↗

bioRxiv2025

Immune Biomarkers, Profiles, and Responses: A Vaccine Ontology Perspective

BackgroundVaccines have the ability to induce a range of immune responses under different conditions, for example stimulating the production of neutralizing antibodies to block pathogen entry or activating cytotoxic T-cells to eliminate infected cells. Many such immune responses have not been thoroughly examined and classified. The Vaccine Ontology (VO) is a community-based ontology in the domain of vaccinology. We here describe how VO is used to represent the variety of immune responses associated with vaccines, together with associated biomarkers and profiles. ResultsThe VO differentiates vaccination and vaccine immunization. The former is a process of administering a vaccine in vivo; the latter is the outcome of vaccine induction of immune response. This distinction is critical for understanding both the procedure of vaccination and the resulting immune effects. VO also models and represents various vaccine-induced responses at multiple biological levels, including population, organism, organ/tissue, cell, and gene/protein levels. Such an approach captures the complexity of vaccine-induced immunity, from population-wide trend (for example: herd immunity) to molecular mechanisms. VO defines immune biomarkers as material entities such as neutralizing antibodies that signify humoral immune response, and IFN-gamma that is indicative of cell-mediated responses. Such biomarkers provide measurable indicators of the immune systems functional state post vaccination, enabling robust evaluation of vaccine efficacy. VO classifies immune response profile and correlated profile (or correlate) of immune protection as process profiles, a class in the Basic Formal Ontology (BFO 2.0). Immune response profiles, such as Th1 (or Th2)- biased profile, can be induced by various vaccines and vaccine adjuvants. Different types of correlated profile of immune protection are also identified, such as mechanistic and non-mechanistic correlates of immune protection. Such distinctions help us to quickly identify biomarkers and associated prediction and measurement of different kinds of vaccine protection. ConclusionThe important immune-related terms for immune biomarkers, profiles, and responses are modeled ontologically in VO together with their interrelations. The results support enhanced classification and analysis of vaccine-induced immune responses and related biomarkers and immune profiles, leading to further understanding of the vaccine immune mechanisms and enhanced vaccine research and development.

immunology↗

bioRxiv2025

VaxKG: Integrating The Vaccine Ontology And VIOLIN For Advanced Vaccine Queries And LLM-Powered Chat Systems

Vaccine research faces challenges in integrating diverse biomedical datasets. While the Vaccine Investigation and Online Information Network (VIOLIN) provides comprehensive vaccine data, implemented in traditional relational models limit complex analysis. Similarly, the Vaccine Ontology (VO) offers standardized semantic frameworks but lacks comprehensive empirical data. This study addresses these limitations by developing the Vaccine Knowledge Graph (VaxKG) that integrates VIOLINs dataset with VOs standardized terminology. Using Neo4j, we transformed 12 core VIOLIN tables into a graph structure enriched with VO concepts. The resulting knowledge graph comprises 28,123 VIOLIN data nodes and 101,282 VO resource nodes, connected by 412,865 relationships. Our comparative analysis of Brucella and Influenza vaccines demonstrates VaxKGs ability to enable complex semantic queries and reveal insights unavailable from either resource alone. We further demonstrate VaxKGs utility through VaxChat, a large language model application that leverages the VaxKG as Retrieval-Augmented Generation (RAG) for intuitive vaccine information access.

bioinformatics↗

bioRxiv2025

Mapping and Harmonization of CVX vaccine terms tothe Vaccine Ontology

BackgroundWith many vaccines developed and used, it is critical to standardize vaccine information. The OHDSI OMOP Common Data Model (CDM), widely used to support EHR data integration and analysis, leverages CVX, RxNorm, and RxNorm Extension codes to standardize vaccine-related records. However, these terminologies lack robust semantic relations, making the vaccine classification ineffective in OMOP CDM. To address this issue, our OHDSI Vaccine Vocabulary Working Group proposes to use the Vaccine Ontology (VO) to map these standards and build up its own semantic relations. As a first study of the work, we performed the mapping and alignment of the Vaccine Administered (CVX) codes with the VO using a combination of semi-automatic and manual mapping methods. ResultsA total of 273 CVX terms were first collected and classified. A high-level VO design pattern and an exact one-to-one mapping strategy were developed to guide the CVX-to-VO term mapping. To facilitate the manual mapping and harmonization process, we also developed and evaluated three semi-automated mapping approaches utilizing lexical and semantic information of vaccine concepts to map CVX to VO. These approaches suggested candidate VO mappings for CVX terms and also indicated CVX terms that were unmappable to VO and required new term additions to VO. The application of the best approach to the 2022-10-05 release of VO achieved an accuracy of 85.55% for its suggestions. The suggestions made by the semi-automated approaches were taken into account to further enhance the mappings, which led to our eventual mapping of all CVX terms to the latest version of VO. We innovatively proposed the inclusion of the passive vaccine branch in VO, which includes 24 immunoglobulins and antitoxins from CVX as passive vaccines. A specific CVX-VO OWL file was developed and added to the VO GitHub. Use case queries were developed to demonstrate its support for computer-assisted queries of vaccine groups based on CVX-VO hierarchies. ConclusionAll CVX terms were mapped to the VO using our combined semi-automatic and manual mapping methods. The mapped results enhanced semantic vaccine classification, providing a basis for further OMOP vaccine classification and EHR data analysis.

bioinformatics↗

bioRxiv2025

Comparative analysis of naked mole-rat thermogenesis and its potential to maintain euthermia in response to cold

The naked mole-rat (NMR) is a subterranean rodent known for its unique thermal biology, exceptional longevity and resistance to cancer and hypoxia. However, its thermal biology remains controversial, with various reports describing NMRs as poikilotherms, heterotherms, mesotherms or partial homeotherms. Here, we investigated whether the thermogenic potential of NMR brown adipose tissue and its UCP1 differ from those in mice and whether the lack of thermal insulation causes extreme changes in NMR body temperature upon cold exposure. Through longitudinal molecular, thermal, metabolic, and behavioral measurements, we found that NMRs initiated non-shivering thermogenesis and elevated body temperature but could not sustain it due to excessive heat loss and limits to substrate availability. Our results suggest that NMRs represent a unique thermoregulatory category that doesnt fit neatly into traditional classifications. In vitro and in vivo experiments showed that the NMR UCP1 is functional and can be activated and inhibited as expected for most other mammals. We further demonstrated that artificial insulation can partially restore thermoregulatory capabilities in NMRs. This study employs an advanced methodology to characterize the thermal biology of NMRs and helps resolve a long-standing controversy in the field. Significance StatementThis study provides critical insights into the thermal biology of naked mole-rats (NMRs), resolving long-standing controversies regarding their thermoregulatory strategies. We highlight the unique adaptations and limitations of NMR physiology by demonstrating that NMRs possess functional non-shivering thermogenesis and UCP1 but fail to maintain homeothermy due to excessive heat loss. Our findings suggest that artificial insulation can partially restore their thermoregulatory capabilities, offering a new perspective on the evolutionary and ecological significance of fur loss in NMRs. This research advances our understanding of mammalian thermal biology and presents an updated model for NMRs, bridging gaps between previous conflicting reports.

physiology↗

bioRxiv2025

A Paracrine-to-Autocrine Shunt of GREM1 Fuels Colorectal Cancer Metastasis via ACVR1C

Tumor cells typically rely on paracrine stromal signals to guide malignant behaviors, but whether they acquire signaling autonomy to support metastasis remains unclear. We elucidate this in colorectal cancer (CRC) by uncovering a paracrine-to-autocrine shunt of Gremlin1 (GREM1), a canonical stromal-secreted antagonist of bone morphogenetic proteins (BMPs). We demonstrate that while GREM1 remains restricted to stromal cells in earlier-stage (I-III) CRC, its ectopic expression in tumor epithelium increases markedly in stage IV. Mechanistically, we identify ACVR1C as a novel, high-affinity epithelial receptor for GREM1. Their interaction activates SMAD2/3 signaling, which upregulates SNAI1 and GREM1, establishing a feedback loop that amplifies epithelial-mesenchymal transition (EMT). Disrupting this loop impairs CRC metastasis in vivo. Clinically, epithelial GREM1 or ACVR1C expression predicts metastasis and poor survival. These findings define a paradigm in which tumor cells hijack stromal GREM1 to establish a GREM1-ACVR1C autocrine loop that sustains EMT and metastasis, marking a shift toward signaling autonomy and revealing a targetable vulnerability in advanced CRC.

cancer biology↗

bioRxiv2025

Human cortex organizes dynamic co-fluctuations along sensation-association axis

The human brain dynamically organizes its activity through coordinated fluctuations, whose spatiotemporal interactions form the foundation of functional networks. While large-scale co-fluctuations are well-studied, the principles governing their amplitude-dependent transitions--particularly across high, intermediate, and low-amplitude regimes--remain unknown. We introduce a co-fluctuation score to quantify how instantaneous functional interactions reorganize with global amplitude dynamics. Using resting-state fMRI data, we identified amplitude-dependent co-fluctuation transitions between functional systems hierarchically aligned with the sensorimotor-association (SA) axis: sensorimotor networks dominated high-amplitude co-fluctuations, associative systems prevailed during intermediate amplitudes, and limbic system preferentially engaged in low-amplitude states. This hierarchy underwent developmental refinement from childhood to adulthood and adaptively reconfigured under external stimuli. Replicated across four independent samples including 7T fMRI, these findings establish the SA axis as infrastructure for amplitude-dependent transitions of co-fluctuation states. Our framework bridges transient coordination and stable functional architecture, demonstrating how brain networks balance external processing (high-amplitude states) with internal cognition/emotion (mid-to-low amplitude states) through amplitude-stratified interactions.

neuroscience↗

bioRxiv2025

Medial preoptic area FoxO1 controls metabolic adaptation in a sexually dimorphic manner

The medial preoptic area (MPOA) of the hypothalamus is essential for metabolic adaptation to environmental challenges, though the molecular mechanisms underlying this process remain poorly understood. Here, we investigate the role of Forkhead transcription factor O1 (FoxO1), a key mediator of stress adaptation, in MPOA-dependent metabolic responses to temperature and nutritional changes. Our findings reveal sex-specific responses to both nutritional and temperature challenges. In female mice, but not males, a high-fat diet (HFD) challenge decreased FoxO1 expression in the MPOA. Specific deletion of FoxO1 in MPOA neurons (FoxO1-KOMPOA) had no effect on body weight under normal chow-fed conditions but protected females from HFD-induced obesity (DIO). These protected females exhibited increased lean mass, decreased fat mass, enhanced thermogenesis, increased energy expenditure, and reduced food intake under HFD conditions. They also showed enhanced cold-induced heat production at 6{degrees}C, though this effect vanished at thermoneutrality (30{degrees}C). The protection against DIO was abolished by ovariectomy (OVX) and was not restored by 17{beta}-estradiol supplementation, suggesting an estrogen-independent mechanism. Conversely, constitutive activation of FoxO1 in MPOA neurons (FoxO1-CAMPOA) increased DIO susceptibility in both sexes. Together, these findings demonstrate that FoxO1MPOA plays a crucial role in coordinating metabolic adaptation to nutritional and temperature challenges specifically in female mice.

physiology↗

bioRxiv2025

In Vivo Tensor-Valued Diffusion MRI Reveals Isotropic-Anisotropic Kurtosis Mismatch in a Rat MCAO Stroke Model

BackgroundTensor-valued diffusion MRI (dMRI) enables the separation of total kurtosis into isotropic and anisotropic components, offering improved specificity over conventional Diffusion Kurtosis Imaging (DKI). In this study, we introduce a novel framework for detecting anisotropic-isotropic kurtosis mismatch and evaluate its utility in characterizing stroke lesions. MethodsWe performed tensor-valued dMRI in a rat model of middle cerebral artery occlusion (MCAO). Metrics including anisotropic kurtosis (MKA) and isotropic kurtosis (MKI) were quantified to assess microstructural tissue heterogeneity. Histological validation was conducted using co-registered tissue sections. ResultsWe observed significant mismatches between mean diffusivity (MD), mean kurtosis (MK), as well as specifically in MKA and MKI in ischemic regions. Notably, MKA and MKI exhibited statistically significant alterations compared to contralateral regions. Moreover, these metrics strongly correlated with pathological findings. The mismatch highlighted microstructurally distinct subregions within the lesion. ConclusionsThis is the first study to propose an anisotropic-isotropic kurtosis mismatch framework in vivo. Tensor-valued dMRI may offer a more specific imaging biomarker for stroke, with potential for improving lesion subtyping and guiding therapeutic decisions.

biophysics↗

bioRxiv2025

Transcriptional Elongation-Associated RNA Processing Errors in Induced Cellular Growth Arrest

Summary ParagraphTranscription elongation factors control post-initiation steps of gene expression by RNA polymerase II (RNAPII). We have established distinct mechanistic roles for the essential elongation factors PAF1, NELF, SPT5, SPT6, and the Super Elongaiton Complex (SEC) via acute depletion of each individually in auxin-inducible degron lines. Here, we leverage these degron lines to explore the regulatory intersection of transcription elongation control and pre-mRNA processing. Integrating long- and short-read RNA-seq data to quantify transcript isoform usage at single-molecule resolution, we identify elongation factor-specific RNA processing regulons including a cellular senescence-enriched regulon shared by NELF and SPT6. We then show that long-term depletion of NELF or SPT6 results in reversible growth arrest following early upregulation of a small group of genes, which include the senescence-associated genes CDKN1A (p21) and CCN2. We perform genetic suppressor screens that implicate the elongation factor Elongin A (ELOA) in NELF or SPT6 depletion-induced growth arrest. ELOA loss suppresses NELF depletion-induced pre-mRNA processing defects and the 3 extension of RNAPII occupancy past transcription end sites (TES) at genes induced by NELF depletion. ELOA also occupies TES-proximal regions under normal conditions, and acute ELOA depletion results in a loss of RNAPII processivity at the 3 end of genes, opposing the effects of NELF or SPT6 depletion. Finally, we demonstrate that genetic loss of ELOA confers a growth advantage to aging human primary dermal fibroblasts. These findings establish the existence of novel ELOA-dependent mechanisms regulating transcription maturation, and links these mechanisms to the complex phenomena of cellular senescence and aging.

molecular biology↗

bioRxiv2025

Predicting the Evolutionary and Functional Landscapes of Viruses with a Unified Nucleotide-Protein Language Model: LucaVirus

Predicting viral evolution and function remains a central challenge in biology, hindered by high sequence divergence and limited knowledge compared to cellular organisms. Here, we introduce LucaVirus, a multi-modal foundation model for viruses, trained on 25.4 billion nucleotide and amino acid tokens covering nearly all known viruses. LucaVirus learns biologically meaningful representations capturing relationships between sequences, protein/gene homology, and evolutionary divergence. Using these embeddings, we developed downstream models that address key virology tasks: identifying hidden viruses in genomic "dark matter", annotating enzymatic activities of uncharacterized proteins, predicting viral evolvability, and identifying antibody candidates for emerging viruses. LucaVirus achieves state-of-the-art results in three tasks and matches leading models in the fourth with one-third the parameters. Together, these findings demonstrate the power of a unified foundation model to comprehensively decode the viral world and establish LucaVirus as an efficient and versatile platform for AI-driven virology, from virus discovery to functional and therapeutic predictions.

bioinformatics↗

bioRxiv2025

MimicNeoAI: An integrated pipeline for identifying microbial mimicry antigens and tumor neoantigen partners

Tumor-associated microbial antigens represent promising immunotherapy targets, yet systematic identification methods remain underdeveloped. We developed MimicNeoAI, a computational pipeline integrating BiLSTM networks to identify microbial epitopes, mutation-derived neoepitopes, and their microbial mimics from sequencing data. Training on validated epitope datasets yielded 0.90 AUC with 91% accuracy on experimental validation sets. Application to colorectal cancer revealed that microbial epitopes, despite originating from a nine-fold smaller peptide pool, generated twice the immunogenic candidates (153 vs 75) compared to mutation-derived neoepitopes. These microbial epitopes exhibited exclusive tumor-specificity with no overlap in normal tissues. Single-cell TCR sequencing confirmed clonal expansion against 75% of predicted highly immunogenic epitopes, with molecular dynamics simulations demonstrating positive correlation between predicted immunogenicity and HLA-epitope-TCR binding stability. Collectively, our pipeline systematically unveils abundant, tumor-specific, and highly immunogenic microbial epitopes, providing a computational framework for developing broadly applicable cancer immunotherapies that leverage the tumor microbiome as an untapped source of therapeutic targets.

bioinformatics↗