Biology subjects

Zhang, J.

Publications and source records attributed to Zhang, J..

At least 271 records · Page 15Linked to original sources

bioRxiv2026

Population Structure and Antimicrobial Resistance Gene Transfer of Respiratory Escherichia coli Isolated from Swine in China

ABSTRACPorcine respiratory diseases caused by extraintestinal pathogenic Escherichia coli (ExPEC) pose a severe threat to swine production and public health; however, research on respiratory tract-isolated ExPEC remains limited. This study comprehensively analyzed the genomic characteristics and antibiotic resistance gene (ARG) transfer potential of 441 ExPEC strains isolated from porcine lungs across 21 Chinese provinces (including 53 newly isolated strains from 2022-2024 and 388 NCBI-deposited strains). Phylogenetic analysis revealed that 84% of the isolates belonged to phylogroups A, B1, and C, with ST410, ST101, and ST88 as the predominant STs. The strains exhibited extensive ARG diversity, harboring 111 distinct ARG subtypes, with sul2 (81.4%), floR (73.5%), and tet (A) (68.0%) being the most prevalent. Importantly, critical "last-resort" antibiotic resistance genes (e.g., blaNDM-1/5, the mcr family, and tet (X4)) were also detected. Notably, 77.2% of the ARGs presented horizontal transfer potential, with plasmids (especially IncF family replicons) serving as core vectors, followed by integrons and transposons. Cooccurrence network analysis identified aph (3)-Ib, aph (6)-Id, sul2, and floR as core subnetworks driving multidrug resistance dissemination. Pangenomic analysis confirmed an open genome architecture, with core genes accounting for only 6%, reflecting the strains capacity to acquire exogenous genetic material via horizontal transfer. From the One Health perspective, these transferable ARGs can spread to the environment and humans through fecal discharge and the food chain. These findings underscore the importance of monitoring and controlling ExPEC infections in swine, as such strains can as reservoirs of ARGs, pose potential risks to human health, and may even act as sources of pathogenic agents responsible for human infections. IMPORTANCEPorcine respiratory ExPEC-induced diseases threaten swine production and public health, yet respiratory tract-isolated ExPEC research remains scarce. This study comprehensively analyzed 441 porcine lung ExPEC strains across 21 Chinese provinces, uncovering their dominant phylogroups, high ARG diversity (111 subtypes) and the presence of "last-resort" antibiotic resistance genes. We identified 77.2% of ARGs with horizontal transfer potential, plasmids (especially IncF family) as core vectors, and a core ARG subnetwork driving multidrug resistance. The open pangenome (6% core genes) highlights ExPECs strong capacity to acquire exogenous genes. These findings fill the research gap of respiratory ExPEC, clarify ARG transmission mechanisms in swine ExPEC, and provide critical genomic data for One Health-based AMR surveillance and control, guiding targeted strategies to mitigate ARG spread from swine to humans and the environment.

microbiology↗

bioRxiv2026

Multi-omics analysis of the potential of MACC1 as a biomarker and therapeutic target for colonic adenocarcinoma

Colonic adenocarcinoma (COAD) is a major cause of cancer-related mortality worldwide. Various tumors are linked to metastasis-associated in colon cancer 1 (MACC1). This study aimed to analyze public datasets to examine MACC1 expression, signaling pathways, copy number variations, and associations with immune cell subsets in COAD employing bioinformatics. MACC1 expression was elevated in COAD, especially in Wnt signaling and chromatin modifier pathways. Analysis of somatic copy number alterations in The Cancer Genome Atlas-COAD dataset revealed a link between MACC1 and DNA damage repair. MACC1 also showed a negative correlation with genes involved in immune cell infiltration in patients with COAD, including cluster of differentiation (CD)8+ T cells, activated dendritic cells, CD8 T cells, and cytotoxic cells. Collectively, these findings suggest MACC1 as a potential prognostic biomarker and therapeutic target for COAD.

cancer biology↗

bioRxiv2026

Integrating Long-Read Structural Variant Analysis with single-nucleus RNA-seq to Elucidate Gene Expression Effects in Disease

Structural variants (SVs) are a major source of genetic diversity, yet how they impact cell types in complex brain diseases remains largely unexplored, partially due to limitations of short-read sequencing. Here, we addressed this fundamental question in Parkinsons disease (PD). generating long-read whole-genome sequencing (WGS) data for 100 post-mortem brain samples from a PD cohort, constructing a high-confidence catalog of 74,552 SVs. To resolve their functional impact, we integrated single-nucleus RNA-sequencing data from two brain regions from the same samples and focused functional analyses on SVs proximal to genes previously nominated as cis-regulated, potential causal targets of PD-associated GWAS loci. Using expression quantitative trait locus and allele-specific expression analyses, we uncovered SVs significantly associated with expression in specific cell types as well as effects shared across cell types. This study demonstrates the power of uniting long-read WGS with transcriptomics to uncover SVs underlying complex disease architecture with cell type resolution.

genomics↗

bioRxiv2026

Designing mRNA coding sequence via multimodal reverse translation language modeling with Pro2RNA

mRNA coding sequence design is a critical component in the development of mRNA vaccines, nucleic acid therapeutics, and heterologous gene expression systems. While large language models have recently been successfully applied to protein design and RNA modeling, designing optimal mRNA coding sequences for a given protein, particularly in a species-specific manner, remains a major challenge. Here, we present Pro2RNA, a multimodal reverse-translation language model that generates mRNA coding sequences from their corresponding protein sequences while explicitly conditioning on host organism taxonomy information. Pro2RNA integrates multiple pretrained language models across different modalities, including ESM2 for protein representation, SciBERT for taxonomy understanding, and a generative RNA language model for mRNA codon-level sequence generation. By training on mRNA-protein pairs from eukaryote and bacteria datasets respectively, Pro2RNA learns species-dependent genetic codes and codon usage patterns, enabling the generation of host-adapted and natural-like mRNA coding sequences. Across multiple benchmark evaluations, Pro2RNA matches or surpasses existing optimization methods, demonstrating its potential as a powerful and flexible framework for species-aware mRNA coding sequence design.

bioinformatics↗

bioRxiv2026

Bacterial endosymbionts enhance fungal virulence by disrupting the disease-suppressive rhizobiome

The mechanisms by which bacterial endosymbionts (bacteria living within fungal cells) enhance the fitness and virulence of fungal pathogens remain poorly understood. Here, we report that the tomato Fusarium wilt pathogen Fusarium oxysporum f. sp. lycopersici (FOL) hosts Achromobacter sp. endosymbionts that enhance fungal virulence. This virulence potentiation is partially dependent on interactions with the native rhizosphere microbiota. We show that bacterial endosymbiont-harboring FOL reshapes the rhizosphere bacterial community during pathogen infection and decreased the abundance of disease{square}suppressive bacteria, including Streptomyces sp. taxa. This inhibitory effect is mediated by bacterial endosymbiont{square}stimulated production of beauvericin (an antibacterial cyclic hexadepsipeptide) by FOL. Together, our findings reveal a tripartite interaction in which a fungal pathogen leverages its bacterial endosymbiont to weaken rhizosphere microbiome{square}based disease suppression by inhibiting plant-protective bacterial taxa. This work highlights how cross{square}kingdom symbioses can modulate pathogen ecology and virulence in soil environments.

microbiology↗

bioRxiv2026

Cerebellum-Inspired Kernel for Robust OOD Detection

Detecting novel stimuli is a fundamental neural function, yet its machine learning counterpart--out-of-distribution (OOD) detection--remains challenging, with models often making overconfident predictions on unseen inputs. Inspired by the strong pattern-separation capabilities of cerebellum-like circuits, we introduce a cerebellum-inspired kernel with an efficient closed-form implementation. Combining random Gaussian projection with Top-k sparsification, the kernel reshapes similarities in high-dimensional space to enhance separability between in-distribution (ID) and OOD samples. On OpenOOD benchmarks, our kernel consistently improves multiple baseline methods, and pairing it with the energy score achieves performance comparable to or exceeding current state-of-the-art approaches. The closed-form design also avoids the high computational cost of large-expansion explicit mapping. These results demonstrate the generality and potential of cerebellar kernels for OOD detection and other tasks requiring efficient pattern separation under limited computational resources.

neuroscience↗

bioRxiv2026

Quinone reductase 2 reads H3 serotonylation to support neuronal maturation

Histone H3 Gln5 serotonylation (H3Q5ser) is a recently described posttranslational modification1 that plays important roles in guiding transcriptional permissiveness in brain and peripheral systems2-5. H3Q5ser has been implicated in diverse physiological and pathological processes ranging from neural differentiation1 to sensory processing6, circadian rhythmicity7, stress responsivity8, placental gene regulation9, and tumorigenesis10-19. Since H3Q5ser can occur in combination with H3 Lys4 trimethylation (H3K4me3), most mechanistic studies to date have focused on H3Q5sers roles in modulating H3K4me3 reader interactions, where it has been shown to potentiate TAF3/TFIID binding to H3K4me31,20,21 and inhibit the recruitment of K4me3 demethylases21; however, whether H3 serotonylation functions as an autonomous chromatin signaling mark through dedicated reader proteins has remained unknown. Here, using a combination of proteomic-, structural-, molecular-, epigenomic-, and cellular-based approaches, we demonstrate that the Quinone reductase 2 (QR2) enzyme reads H3Q5ser independently of H3K4me3. CRISPR-Cas9-mediated disruption of H3 serotonylation or QR2s binding to the mark in human induced pluripotent stem cell-derived neurons impairs the establishment of neuronal transcriptional programs, alters synaptic connectivity, and disrupts electrophysiological maturation. These findings thus uncover an H3 serotonylation-dependent chromatin signaling axis that is essential for human neurodevelopment.

molecular biology↗

bioRxiv2026

Genetically Engineered Biomimetic Nanozymes Reprogram Immune Niches to Intercept Colitis-Carcinoma Transition

Colitis-associated colorectal cancer (CAC) arises from chronic inflammatory niches characterized by persistent oxidative stress and dysregulated immune cell recruitment. Current anti-inflammatory therapies provide only transient symptom relief and fail to prevent malignant progression due to their inability to simultaneously mitigate oxidative injury and immune chemotaxis. Our analysis of clinical samples revealed markedly elevated C-X-C motif chemokine ligand 2 (CXCL2) in intestinal tissues from patients with inflammatory bowel disease (IBD) and CAC, implicating the CXCL2-CXCR2 axis in driving excessive neutrophil and macrophage infiltration and fostering tumorigenesis. Herein, we report a biomimetic nanozymes (PB@ECM) that reprogram inflammatory immune niches to intercept CAC progression. PB@ECM integrates a Prussian blue nanozyme core with genetically engineered macrophage membranes, enabling concurrent scavenging of reactive oxygen species through superoxide dismutase- and catalase-like activities and sequestration of CXCL2 via membrane-displayed CXCR2 receptors. In murine models of colitis and CAC, PB@ECM significantly alleviated intestinal inflammation, suppressed neutrophil and macrophage infiltration, and effectively inhibited colitis-carcinoma transition. By disrupting the pathological crosstalk between oxidative stress and immune chemotaxis, this work establishes a biomimetic nanozyme strategy for preventing inflammation-driven carcinogenesis.

bioengineering↗

bioRxiv2026

Programming Biomolecular Interactions with All-Atom Generative Model

Biomolecular interactions lie at the core of cellular life, spanning diverse molecular modalities from small molecules to nucleic acids and proteins. Nevertheless, design strategies remain separated despite shared physicochemical principles of molecular recognition. Here we present AnewOmni, a unified generative framework trained on more than 5 million biomolecular complexes, that enables transferable molecular design across molecular scales by assembling chemically meaningful building blocks at atomic resolution. We further introduce programmable graph prompts to support user-defined chemical, topological, and geometric steering during generation, exploring hybrid and unconventional chemistries beyond canonical structures. We demonstrate that transferable learning of interaction patterns and physical constraints across molecular modalities is possible, via an atom-to-block latent space capturing both atomic details and structural priors. The framework successfully designed small molecules, peptides, and nanobodies targeting the challenging KRAS G12D switch II pocket, as well as orthosteric peptides and allosteric small-molecule inhibitors for PCSK9 in the absence of known binding site, achieving 23%-75% success with only low-throughput validation, bypassing modality-specific high-throughput screening. AnewOmni is the first to succeed in functional molecular design across all scales, from small chemical entities to large biologics, and represents a stepstone towards general molecular reasoning engines, advocating a generative foundation model for biomolecular interactions to enter regimes where data and human intuition remain limited.

bioengineering↗

bioRxiv2026

Synergistic Inhibition of Notch Signaling and Forced Cell Cycle Re-entry Drive Müller Glia Reprogramming in Uninjured Mouse Retina

In regenerative species, such as teleost fish, Muller glia (MG) autonomously re-enter the cell cycle after injury and give rise to functional retinal neurons. In contrast, the loss of retinal neurons in mammals is irreversible due to the limited proliferative and regenerative ability of MG. Various strategies have been developed to induce proliferation of mature mouse MG with or without injury, yet most MG daughter cells retain glial cell fate. Here, we found that MG progenies maintain high Notch signaling, which may constrain their neurogenic potential. Conditional deletion of Rbpj, the central transcriptional effector of Notch, induced limited MG-to-neuron conversion in mature MG without proliferation. However, Rbpj deletion, combined with forced MG proliferation by overexpressing cyclin D1 and suppressing p27Kip1, significantly promoted MG dedifferentiation and ectopic expression of the neuronal marker Otx2 in MG daughter cells in uninjured mouse retina. Combining Notch inhibition with MG cell cycle re-activation not only increased the numbers of bipolar- and amacrine-like cells generated from MG but also promoted the further differentiation toward ON-cone, OFF-cone, and rod-bipolar subtypes. Single-nucleus RNA and ATAC sequencing data revealed that Notch inhibition facilitated the formation of MG-derived progenitor-like cells while MG proliferation increased chromatin accessibility of neurogenic genes. Notably, most MG-derived cells survived long term despite incomplete maturation. Together, our findings delineate how Notch inhibition and MG proliferation, alone or in combination, influence the regenerative potential of MG in the mammalian retina.

cell biology↗

bioRxiv2026

PhosSight: a Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries

Protein phosphorylation is a key regulator of signaling, with mass spectrometry (MS) based phosphoproteomics serving as the premier technology for its analysis. However, phosphorylation profiling is hindered by acquisition biases: Data-Dependent Acquisition (DDA) suffers from stochastic undersampling and missing values, while Data-Independent Acquisition (DIA) faces computational bottlenecks and inefficiencies from vast spectral libraries. We present PhosSight, a unified deep learning framework designed to augment identification depth and accelerate search efficiency. PhosSight features PhosDetect, a model that explicitly encodes phosphorylation specific physicochemical features to accurately predict peptide detectability. For DDA, PhosSight leverages predicted retention time, fragment intensity, and detectability to refine site localization and rescoring, recovering marginal, low-abundance spectra. For DIA, PhosSight utilizes detectability-guided library pruning to remove non-detectable noise, accelerating search speeds without compromising sensitivity. Benchmarking on synthetic and real world datasets confirms PhosSights superior performance in both modes. Applying PhosSight to a large-scale Uterine Corpus Endometrial Carcinoma (UCEC) cohort significantly reduced missing values and expanded the quantifiable phosphoproteome. This enhanced completeness enabled the discovery of novel prognosis associated kinase targets, such as MARK2, underscoring PhosSight as a powerful tool for biological discovery in precision oncology.

bioinformatics↗

bioRxiv2026

Phosphorylation of a tumor-derived ASXL2 epitope remodels 1 peptide-HLA binding affinity and interaction dynamics

The expansion of cancer immunotherapy has made it increasingly important to identify tumor-specific HLA class I epitopes and to define the molecular determinants of peptide-HLA binding and stability. Post-translationally modified epitopes, particularly phosphorylation, can reflect oncogenic signaling states and offer highly tumor-selective targets. Despite their potential, the structural and dynamic mechanisms by which phosphorylation reshapes peptide-HLA affinity and stability remain unclear. Here, we investigate a phosphorylated ASXL2-derived epitope supported by confident MS/MS-based phosphosite localization and detected across multiple cancer types. We performed all-atom molecular dynamics simulations of peptide-HLA (pHLA) complexes to compare the phosphorylated peptide, its non-phosphorylated counterpart, and related variants. Our simulations indicate that phosphorylation reconfigures non-bonded interaction networks within the binding groove by introducing new local contacts, leading to an enhanced predicted binding affinity. Furthermore, principal component analysis revealed that phosphorylation increases the overall conformational flexibility and alters the collective backbone motions of the pHLA complex. Together, these findings provide a structural-dynamic basis for how phosphorylation can modulate pHLA stability and interaction dynamics, guiding the rational prioritization and targeting of cancer-specific pHLA complexes for immunotherapy.

bioinformatics↗

bioRxiv2026

Neurocognitive deficits in controlling aversive memory among insomnia disorders

BackgroundInsomnia disorder is a common sleep disturbance characterized by adverse daytime cognitive and emotional impairments, such as repetitive negative thinking and increased psychological distress. Memory control, a key self-regulatory ability to control or inhibit unwanted thoughts and memories, plays an essential role in supporting cognitive functions and emotional well-being. Here, we delineate the neurocognitive mechanisms underlying memory control among individuals with insomnia. Methods41 participants meeting DSM-5 criteria for insomnia disorder and 40 healthy sleepers completed an emotional Think/No-Think task, during which participants either retrieved (Think) or suppressed the retrieval (No-Think) of aversive memories in response to memory cues while electroencephalograms were recorded. ResultsLinear mixed model analyses with age and depression scores as covariates showed that participants with insomnia exhibited impaired memory control abilities, as evidenced by reduced suppression-induced forgetting in memory recall when compared to healthy sleepers. Electrophysiologically, healthy sleepers showed enhanced right prefrontal theta power in retrieval suppression than in retrieval, indicating elevated needs of inhibitory control during memory control. In sharp contrast, this difference was absent among those with insomnia. Notably, the greater the severity of insomnia symptoms, the smaller the retrieval vs. retrieval suppression theta power differences across participants, linking inefficient top-down control of unwanted memories with low sleep qualities. ConclusionIndividuals with insomnia showed impaired memory control of aversive memories and aberrant electrophysiological activities during retrieval suppression. Future research shall investigate the causal relationship between memory control abilities and insomnia symptoms.

neuroscience↗

bioRxiv2026

Circular RNA identification using a genomic language model and a small number of authenticated examples

Genomic language models (gLMs) hold great promise for deciphering biological sequences, yet their effectiveness is hindered by the limited number of experimentally verified examples available for model training, a ubiquitous bottleneck for supervised machine learning. To overcome this challenge, we developed circFormer, the first gLM-driven approach for circular RNA (circRNA) identification. circFormer integrates curriculum learning with gLM fine-tuning: a Nucleotide Transformer model is first trained on a small set of validated circRNAs, the resulting model is used as a teacher to score [~]2.3 million noisy candidates, and the model is then fine-tuned with the noisy candidates along with their scores to improve prediction. Operating either as a standalone predictor or as a filter for existing pipelines, circFormer consistently outperformed traditional machine-learning approaches in accuracy and robustness. Among 50 circFormer-selected candidates that were overlooked by most existing tools, experimental validation using RNase R digestion and RT-qPCR confirmed 94.1% (32/34) of the evaluable candidates as genuine circRNAs. To enhance interpretability, we introduced a model-agnostic, dual-level explainable AI strategy that reveals mechanistic signatures of circRNA formation. circFormer provides a scalable, interpretable, and generalizable framework for converting noisy high-throughput data into reliable functional annotations, highlighting a practical path forward for gLM-based genomics in data-scarce settings.

bioinformatics↗

bioRxiv2026

Genome-wide maps of transcription factor footprints identify noncoding variants rewiring gene regulatory networks with varTFBridge

Genome-wide association studies have identified millions of noncoding loci linked to human traits, yet how these variants alter gene regulation remains a major challenge, particularly for rare variants where whole-genome sequencing cohorts and high-resolution functional annotations remain limited. Here we show that single-molecule deaminase footprinting (FOODIE) in K562 cells captures up to 103-fold heritability enrichment for erythroid traits despite covering 0.12% of the genome. We introduce varTFBridge, integrating FOODIE footprinting with AlphaGenome variant effect prediction to identify causal noncoding variants altering transcription factor (TF)-mediated regulation. Applied to 490,640 UK Biobank genomes across 13 erythrocyte traits, varTFBridge prioritises 113 high-confidence regulatory variants (104 common, 9 rare), encompassing 2,173 linkages along the variant-TF binding- gene-trait cascade across 64 TFs and 108 genes. varTFBridge recapitulates rs112233623 and resolves its mechanism: GATA1/TAL1 co-binding disruption at a CCND3 enhancer altering red blood cell count and volume.

bioinformatics↗

bioRxiv2026

Traceless protein semi-synthesis in cells using the promiscuous ultra-fast split intein NrdJ-1

Chemistry-driven approaches that allow proteins to be manipulated in ways not permitted by standard genetics are indispensable in the basic and applied biomedical sciences. Of the various platform technologies in common use, those that exploit intein-mediated protein splicing have proven especially powerful owing to compatibility with both in vitro and in vivo applications. Here, we provide detailed biochemical characterization of the split intein, NrdJ-1. We demonstrate that the rapid splicing kinetics associated with this system are largely independent of splice junction primary sequence context. The promiscuity and high efficiency of this split intein makes it well suited to the generation of chemically modified proteins in vitro and in living cells, as demonstrated by the traceless generation of semisynthetic chromatin harboring various post-translational modifications. These studies highlight the utility of NrdJ-1 for traceless protein editing and suggest it is a superior choice for many intracellular biochemical applications.

biochemistry↗

bioRxiv2026

Phenotypic Bioactivity Prediction as Open-set Biological Assay Querying

The traditional drug discovery pipeline is severely bottlenecked by the need to design and execute bespoke biological assays for every new target and compound--a process that is both time-consuming and prohibitively expensive. While machine learning has accelerated virtual screening, current models remain confined to "closed-set" paradigms, unable to generalize to entirely novel biological assays without target-specific experimental data. Here, we present OpenPheno, a groundbreaking multimodal foundation model that fundamentally redefines bioactivity prediction as an open-set, visual-language question-answering (QA) task. By integrating chemical structures (SMILES), universal phenotypic profiles (Cell Painting images), and natural language descriptions of biological assays, OpenPheno unlocks the highly coveted "profile once, predict many" paradigm. Instead of conducting countless target-specific wet-lab experiments, researchers only need to capture a single, low-cost Cell Painting image of a novel compound. OpenPheno then evaluates this universal phenotypic "fingerprint" against the text-based description of any unseen assay, predicting bioactivity in a zero-shot manner. On 54 entirely unseen assays, it achieves strong zero-shot performance (mean AUROC 0.75), exceeding supervised baselines trained with full labeled data, and few-shot adaptation further improves predictions. In the most stringent setting where both compounds and assays are novel, OpenPheno maintains robust generalization (mean AUROC 0.66), opening up a new paradigm for a highly scalable, cost-effective, and universal engine for next-generation drug discovery.

bioinformatics↗

bioRxiv2026

iGS: A Zero-Code Dual-Engine Graphical Software for Polygenic Trait Prediction

Genomic selection (GS) has become the core driving force in modern plant and animal breeding. However, state-of-the-art comprehensive GS tools often rely on complex underlying environment configurations and command-line operations, posing significant technical barriers for breeders lacking programming expertise. To address this critical pain point, this study developed a fully "zero-code" graphical user interface (GUI) decision support system for genomic selection. The platform innovatively employs a "portable dual-engine architecture" (R-Portable and Python-Portable) to achieve completely dependency-free, "out-of-the-box" deployment, and integrates a standardized six-step end-to-end workflow from data quality control to result export. Furthermore, the platform comprehensively integrates 33 cutting-edge prediction models across four major paradigms, linear, Bayesian, machine learning, and deep learning, and features an original intelligent parameter configuration system that dynamically renders algorithm parameters to provide a minimalist UI interaction experience. Benchmark testing on the Wheat2000 dataset across six complex agronomic and quality traits, including thousand-kernel weight (TKW) and grain protein content (PROT), demonstrated that classic linear models remain highly robust for polygenic additive traits, while tree-based machine learning and hybrid deep learning architectures exhibit superior predictive potential and noise resilience when resolving complex epistatic effects and low-heritability traits. The successful deployment of this platform fundamentally liberates biologists from the constraints of computational science, providing robust digital infrastructure to accelerate the popularization and practical application of GS technologies in agricultural production.

bioinformatics↗