Biology subjects

Peng, J.

Publications and source records attributed to Peng, J..

At least 37 records · Page 2Linked to original sources

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

Reference-Based Library Construction Improves Performance in low-input diaPASEF Workflows

In low input mass spectrometry-based proteomics, Data Independent Acquisition (DIA), is quickly becoming the method of choice for label free quantification. Whether using empirical or in silico spectral libraries, performance is dependent on the library; however, the optimal library construction strategy for low input proteomics remains an open question. To address this, we examine and develop library construction approaches that are compatible with both spectrum-centric and peptide-centric analysis workflows. These approaches leverage a closely related, high-quality sample to improve library quality. First, we validated our approach in bulk sample amounts where we observed that the effects of gas-phase fractionation based library construction is dependent on the software framework, with improvements more pronounced in OpenSWATH compared to DIA-NN. In OpenSWATH, our peptide-centric library reconstruction workflow consistently outperforms a transfer learning strategy, an emerging alternative approach. In DIA-NN, trends are dependent on library source highlighting OpenSWATHs stronger dependence on the search space. In low-input applications, such as single-cell-equivalent injection amounts (100 pg) of HeLa cell digest on a timsTOF SCP, our library construction approach provided more pronounced improvements across both software tools compared to bulk samples. Using a peptide-centric reconstruction approach with the OpenSWATH analysis framework, we detected over 15,000 peptide precursors (2480 protein groups), a 90% improvement over the original library. Furthermore, using a spectrum-centric construction approach, peptide precursor identification rates improved over 6-fold ( [~]1000 to [~]6000). Our strategy provides a practical solution for generating high-quality libraries in low-input applications.

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

JUMPlion improves quantitative DIA proteomics through ion-level recovery of missing values

Incomplete quantification remains a persistent challenge in data-independent acquisition (DIA) mass spectrometry (MS), particularly in low-input and single-cell analyses. In identification-driven workflows, missing protein quantities often arise not from true absence of the corresponding peptides, but from failure to retain low-abundance signals from precursor or product ions for quantification. Here we present JUMPlion (local inference of ion-level missingness), a DIA quantification framework that re-examines MS raw files to recover missing values at the ion level before protein quantification. JUMPlion re-extracts precursor- and production signals directly from raw data, infers ion-level measurements within precursor-specific local quantitative neighborhoods, and combines complementary precursor- and production signals into downstream quantification. Using benchmark datasets acquired on multiple DIA platforms, JUMPlion increased protein-level completeness, improved fold-change accuracy, and enhanced detection of differentially abundant proteins while maintaining low differential-abundance false discovery rates. These gains were most evident in low-input and single-cell DIA datasets. Together, these results show that addressing missingness at the ion level before protein-level summarization can improve DIA quantification in diverse acquisition settings.

bioinformatics↗

bioRxiv2026

Targeting Gi/o-coupled GPCRs to inhibit nociceptors: insights from the serotonin receptor Htr1b and triptans

Pain perception is initiated upon activation of nociceptors of the dorsal root ganglia (DRG) and trigeminal ganglia. We identified G protein-coupled receptors (GPCRs) expressed in CGRP+ mouse and human DRG neurons and found that agonists of several identified Gi/o-coupled and orphan GPCRs attenuated neuronal excitability. Experiments focusing on the Gi/o-coupled serotonin receptor Htr1b, which is expressed in mouse and human CGRP+ DRG neurons, revealed that Htr1b/1d agonists, the triptans sumatriptan and zolmitriptan, attenuated CGRP+ neuron excitability in vitro and exhibited analgesia across several pain models, including neuropathic pain. Conditional genetic deletion experiments showed that triptan-induced analgesia is mediated by Htr1b expressed in A-fiber mechanonociceptors. Also, triptan-associated adverse effects are partially mediated by Htr1b-independent targets. Further testing identified the GPCR Gpr19 as an additional promising target for treating pain. These findings establish a preclinical screening platform for identifying novel analgesics and reveal nociceptor GPCRs that may be targeted to treat pain.

neuroscience↗

bioRxiv2026

GC-MS Profiling of Compounds produced by endophytic fungi ex-situ and from their host plants, Azadirachta indica and Melia azedarach collected in Kenya, Africa

Endophytic fungi residing within medicinal plants are emerging as prolific sources of structurally diverse bioactive secondary metabolites with applications in drug discovery. Azadirachta indica (Neem) and Melia azedarach (Melia), members of the Meliaceae family, are renowned for their rich phytochemical composition; however, the contribution of their endophytic fungi communities to this chemical diversity remains largely unexplored. Herein, endophytic fungi were isolated from leaves and bark of Neem and Melia collected in Kenya and cultured under distinct physical conditions, solid (plates) and liquid (broth) media to assess how culture environment influences compound production. Compounds were extracted and analyzed using gas chromatography-mass spectrometry (GCMS) to profile the chemical diversity associated with each endophytic fungi, physical culturing state and host plant. GCMS analysis revealed that while the host plant identity influences the presence of specific compounds, the dominant determinant of chemical diversity was intrinsic biosynthetic capacity of the endophytic fungi themselves. Several compounds were unique to endophytic fungi cultures, highlighting their role as independent sources of bioactive compounds. Culture conditions moderately influence metabolite profiles, demonstrating the importance of optimizing growth environments in experimental design and natural product bioprospecting. From the Neem samples, we found 53 compounds uniquely present in the broth samples (consisting of Neem powder and endophytic fungi), 22 found exclusively with the endophytic fungi from the Neem, and 31 compounds shared between the broth and the endophytic fungi samples. In Melia samples, 109 compounds were uniquely present in broth samples from Melia plant (consisting of Melia powder and endophytic fungi), 22 compounds were found exclusively with the endophytic fungi from the Melia, and 55 were shared between the broth and the endophytic fungi samples. Our comparative analysis assessed the Neem and Melia endophytic fungi exclusive samples and reported 12 shared compounds. 10 compounds were unique to Neem and 10 unique to Melia; however, their identities varied between the two categories. While GCMS enabled the identification of volatile and semi-volatile metabolites, future studies employing complementary metabolomic approaches, such as liquid chromatography-mass spectrometry (LCMS), ultra-high-performance liquid chromatography MS/MS (UHPLC MS/MS), or nuclear magnetic resonance (NMR) spectroscopy, would expand coverage to non-volatile, polar, and high molecular weight compounds, providing a more comprehensive understanding of endophyte-derived chemical diversity. These findings provide insights into the interplay between medicinal plants and their endophytes and establish a foundation for leveraging endophytic fungi from Neem and Melia as scalable sources of structurally complex natural products for pharmaceutical and biotechnological applications while minimizing ecological impact.

plant biology↗

bioRxiv2026

Molecular Dosimetry of DNA Adducts in Mice Exposed to Ethylene Oxide

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

pharmacology and toxicology↗

bioRxiv2026

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

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

genomics↗

bioRxiv2026

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

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

genomics↗

bioRxiv2026

Weather Characterization for Optimizing Genomic Prediction in Miscanthus sacchariflorus

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

genomics↗

bioRxiv2026

ENHANCING GENOMIC PREDICTION MODELS IN MISCANTHUS POPULATIONS BY INCORPORATING THE GENOTYPE-BY-ENVIRONMENT INTERACTION

Giant Miscanthus giganteus (Mxg) is one of the most promising perennial crops to generate biomass feedstock for bioenergy and biobased products. It is derived from the natural inter-species hybridization of Miscanthus sacchariflorus (Msa) and Miscanthus sinensis (Msi) species, thus population improvement within these species is crucial. Genomic selection (GS) is an attractive option to accelerate breeding of perennial grasses, such as Miscanthus, which requires up to three years of evaluation to produce reliable phenotypic data. Hence, genotypes are observed in multiple years and locations causing inconsistent response patterns from one year to the next, between location, and/or location-by-year combinations. These inconsistencies are known as the genotype-by-environment interaction effect (GxE). Although GS has been successfully implemented in multiple annual crops where straightforward cross-validation schemes exist to assess the levels of predictive ability that can be reached, for perennial crops new cross-validation schemes will help avoid data contamination. Here, we propose a series of cross-validation schemes to evaluate model performance for perennial crops. We perform a case study by analyzing one panel of each species (516 genotypes of Msa, 280 genotypes of Msi) scored for biomass yield at different locations around the world over several years. The results of the different cross-validation schemes provide insights about the usefulness of GS to accelerate the breeding process of Miscanthus species. In addition, leveraging the GxE effects of different types significantly increases predictive ability (up to 10% in Msa and 30% for Msi) compared to the conventional approaches based on main effects only.

genomics↗

bioRxiv2026

c-Fos enhances influenza virus replication by stabilizing the M2 protein and promoting autophagosome accumulation

The influenza A virus (IAV) employs multiple strategies to hijack host cellular machinery for efficient replication. While autophagosome accumulation is known to promote IAV replication, and the viral matrix protein 2 (M2) ion channel protein plays essential roles in viral uncoating, assembly, and autophagy induction, the mechanisms regulating M2 stability remain incompletely understood. Furthermore, although c-Fos participates in the replication of various viruses, its function in IAV infection has not been characterized. Here, we identify c-Fos as a critical proviral host factor that enhances IAV replication through stabilizing M2 protein and promoting autophagosome accumulation. We demonstrate that IAV infection triggers M2-mediated cytosolic calcium elevation, which upregulates c-Fos expression. The induced c-Fos physically interacts with M2 and prevents its proteasome and lysosomal degradation, thereby increasing M2 accumulation. This stabilized M2 protein as a viral protein directly facilitates viral replication while simultaneously promoting autophagosome accumulation to further enhance the process. Our findings reveal a novel positive feedback loop in IAV infection, establishing c-Fos as a key regulator of IAV replication through M2 stabilization, and highlighting these interactions as potential therapeutic targets for antiviral intervention. IMPORTANCEInfluenza A virus (IAV) remains a significant global health threat, causing substantial morbidity and mortality. Understanding virus-host interactions is crucial for developing antiviral strategies. Here, we uncover a previously unrecognized positive feedback loop in which IAV exploits the host transcription factor c-Fos to enhance its own replication. We demonstrate that IAV infection upregulates c-Fos through M2-mediated calcium signaling, and the induced c-Fos in turn stabilizes the viral M2 protein. This stabilized M2 not only directly supports viral replication but also promotes autophagosome accumulation, further facilitating virus production. Our findings establish c-Fos as a critical proviral host factor and reveal a mechanism by which IAV hijacks host cellular machinery to create a favorable environment for efficient replication. The identification of this c-Fos-M2 axis not only advances our understanding of IAV-host interactions but also opens new avenues for therapeutic intervention targeting this vulnerability in the viral life cycle.

cell biology↗

bioRxiv2026

Single Plaque Proteomics Reveals the Composition and Dynamics of the Amyloid Microenvironment in Alzheimer's Disease

Alzheimers disease (AD) is characterized by amyloid plaques that form complex microenvironments in the brain. However, the molecular composition of these plaques and their temporal regulation are not well defined. Here, we developed a sensitive workflow for quantitative proteomic profiling of single plaques using refined laser capture microdissection and data-independent acquisition mass spectrometry (LCM-DIA-MS). From >200 plaques and control regions in AD mouse models (5xFAD and APP-KI) and human brains, we quantified >7,000 proteins, revealing stage-dependent, cell-type-related remodeling of the amyloid proteome (amyloidome). Temporal profiling uncovered early immune and lysosomal activation followed by engagement of RNA processing and synaptic pathways. Cross-model and cross-species analyses determined a conserved amyloidome including APOE, MDK, PTN, and HTRA1, validated by co-localization in imaging analysis. Network analysis highlighted modules in lipid transport, vesicle organization, and autophagy. These findings establish amyloid plaques as conserved, dynamic multicellular hubs that link amyloid accumulation to downstream cellular events.

neuroscience↗

bioRxiv2026

Cell type-resolved proteomics reveals intra- and intercellular signalingin Alzheimers disease

Alzheimers disease (AD) arises from pathological interactions among diverse brain cell types, but cell-specific proteomic changes remain underexplored. Here, we present deep proteomic profiling of sorted or proximity-labeled brain cells from AD mouse models (5xFAD and AppNL-G-F) at multiple ages, quantifying 13,411 proteins in microglia (three subtypes), astrocytes, oligodendrocyte precursor cells, and neurons. We identified 3,028 differentially abundant proteins across these cell types, the majority of which were not detected in bulk proteomic datasets, and constructed cell type-specific networks to define functional modules and hub proteins. Comparison with transcriptomic data revealed that [~]30% of proteomic changes are RNA-independent. Further analyses uncovered cross-cell type signaling proteins conserved in human AD brains, such as pleiotrophin (Ptn), which is transcriptionally enriched in astrocytes but accumulates in microglia. Importantly, recombinant PTN directly activates induced microglia-like (iMG) human cells. Thus, these findings provide a comprehensive cell type-resolved proteomic atlas of AD models, highlighting novel intra- and intercellular signaling events. HighlightsO_LIA high-resolution cell type-resolved proteomic atlas of Alzheimers disease mouse models C_LIO_LI[~]3,000 cell type-specific protein alterations identified beyond bulk tissue analyses C_LIO_LIProteomic profiling of microglial subtypes reveals subtype-specific changes in Alzheimers disease C_LIO_LIAstrocyte-microglia signaling is highlighted and validated through PTN-mediated interactions C_LI

neuroscience↗

bioRxiv2026

Principles of in situ protein sequencing: expansion microscopy-adapted Edman degradation and amino acid recognition

The ability to map protein identity, with resolution sufficient to infer interactions, would support analysis of how proteins work together, or malfunction, in biological processes and diseases. Although several emerging technologies aim towards single-molecule protein sequencing, they require proteins to be removed from the nanoscale spatial context of cells and tissues. Expansion microscopy (ExM) has facilitated a diversity of chemical analyses by isotropically separating molecules throughout a specimen after permeation via a charged hydrogel, followed by gel swelling. Here, we adapt key protein sequencing steps - Edman degradation and amino acid recognition - to the ExM gel context. Using testbed peptides in ExM gels, we show that N-terminal amino acids can be recognized over multiple cycles of in-gel Edman degradation. This principle-oriented study demonstrates sequencing chemistry on defined synthetic constructs, rather than endogenous proteins in biological samples. These results establish principles of in situ protein sequencing and provide a framework for future in situ protein sequencing developments, including the development of higher specificity and affinity amino acid binders.

biochemistry↗

bioRxiv2026

FluNexus: a versatile web platform for antigenic prediction and visualization of influenza A viruses

Influenza A viruses continuously undergo antigenic evolution to escape host immunity induced by previous infections or vaccinations, consequently causing seasonal epidemics and occasional pandemics. Antigenic prediction and visualization of influenza A viruses are crucial for precise vaccine strain selection and robust pandemic preparedness. However, a user-friendly online platform for these capabilities remains notably absent, despite widespread demand. Here, we present FluNexus (https://flunexus.com), the first-of-its-kind, one-stop-shop web platform designed to facilitate the prediction and visualization of the antigenic change in emerging variants. FluNexus features a data preprocessing module for hemagglutinin subunit 1 (HA1) and hemagglutination inhibition (HI) data across three major public health threat subtypes (H1, H3 and H5). Meanwhile, FluNexus provides an interactive interface for online antigenic prediction and offers practical guidance for researchers. Most notably, FluNexus offers the visualization of influenza A virus antigenic evolution, providing intuitive insights into its antigenic dynamics. Specially, FluNexus proposes a novel manifold-based method for positioning antigens and antisera, generating accurate antigenic cartographies even with sparse HI data. By alleviating the programming burden on biologists, FluNexus supports more informed decision-making in vaccine strain selection and strengthens surveillance and pandemic preparedness. HighlightsO_LIFluNexus features a data preprocessing module for HA1 and HI data spanning the H1, H3, and H5 subtypes. C_LIO_LIFluNexus facilitates online antigenic prediction utilizing ten state-of-the-art antigenic prediction tools, and offers practical guidance based on a comparative evaluation of their performance. C_LIO_LIFluNexus provides a visualization module for mapping antigenic evolution of influenza A viruses, incorporating a novel manifold-based method for antigenic cartography. C_LI

bioinformatics↗

bioRxiv2026

Mechanism study on lactic acid promoting intervertebral disc degeneration by regulating senescence and DNA damage of cartilage endplate stem cells

Intervertebral disc degeneration, a leading cause of low back pain with incompletely elucidated molecular mechanisms, was studied via integrated in vivo/vitro approaches. This study first reveals that lactic acid accelerates intervertebral disc degeneration by inducing cartilage endplate stem cells senescence and DNA damage, thereby activating the P16/P21/P53-centered senescence pathway. In a rat tail vertebra puncture-induced intervertebral disc degeneration model, degenerated discs exhibited increased lactic acid levels, narrowed intervertebral spaces, and disrupted nucleus pulposus structure (P<0.05). In vitro, 0/2/6/10 mM lactic acid dose-dependently suppressed cartilage endplate stem cells viability (10 mM group: 15.7% of the control), elevated intracellular reactive oxygen species (ROS, 2.8-fold relative to the control), induced G0 cell cycle arrest (10 mM group: 85.63%), reduced EdU-positive cells (8.62%), and increased {beta}-galactosidase-positive cells (10 mM group: 33.06%) and {gamma}-H2AX foci (all P<0.01).Molecularly, lactic acid significantly upregulated P16 (2.1-fold), P21 (3.1-fold), P53 (2.4-fold), and {gamma}-H2AX (1.8-fold). In vivo intervertebral disc injection confirmed a positive correlation between lactic acid concentration and intervertebral disc degeneration severity. This study clarifies lactic acids role in intervertebral disc degeneration via the "oxidative stress-cell cycle arrest-cellular senescence" axis, advancing understanding of intervertebral disc degeneration pathogenesis and providing a basis for targeted therapies against lactic acid metabolism.

cell biology↗

bioRxiv2026

Longitudinal Proteomics Analysis of Cerebrospinal Fluid in Survivors of Childhood Acute Lymphoblastic Leukemia

Mass spectrometry-based proteomic profiling of cerebrospinal fluid (CSF) in large patient cohorts, particularly among childhood cancer survivors, remains scarce, limiting opportunities for benchmarking, method development, and validation. Here, we present a longitudinal CSF proteomics dataset from survivors of childhood acute lymphoblastic leukemia (ALL). Samples were collected from 178 patients enrolled in the TOTXVI therapeutic protocol at two timepoints: diagnosis (pre-treatment) and during the consolidation phase of chemotherapy (totaling 356 samples). CSF proteomes were profiled using tandem-mass-tag (TMT) labeling coupled with extensive fractionation and high-resolution liquid chromatography-tandem mass spectrometry (LC/LC-MS/MS). The dataset includes quantitative profiles for more than 3,000 confidently identified unique proteins. This resource enables the investigation of chemotherapy-induced alterations in the CSF proteome and provides a valuable resource for studying the molecular mechanisms of neurotoxicity, identifying biomarkers of adverse late effects, and guiding the development of neuroprotective strategies in childhood cancer survivors.

systems biology↗