Biology subjects

Guo, J.

Publications and source records attributed to Guo, J..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

Tripleknock: predicting lethal effect of three-gene knockout in bacteria by deep learning

Investigating the lethal effect of multi-gene knockout is essential for discovering novel antibiotics targets and metabolic engineering. Unlike single genes or gene pairs, three-gene combinations involve more intricate interactions, making experimental screening time-consuming. Computational methods, particularly Genome-scale metabolic Model (GEM)-based Flux Balance Analysis (FBA), requires constructing new GEMs from experimental data, limiting its use for new species. Moreover, using FBA for three-gene knockout screening could take several years. Therefore, a faster and GEMs-independent approach is needed to facilitate genome-wide three-gene knockout screening. Here, we introduce Tripleknock, for predicting the lethal effects of three-gene knockouts. Tripleknock was trained using whole-genome data from Escherichia coli K-12 MG1655, and three-gene knockout simulations using FBA. The model uses a threshold of 90% reduction in cell growth to define lethal effect as the prediction output. Compared to FBA, Tripleknock achieves predictions approximately 20 times faster, reaching an average cross-species F1 score of 0.77 on six pathogenic species within the Enterobacteriaceae family. For closely related species such as pathogenic E. coli and Shigella, Tripleknock reaches F1 scores exceeding 0.83. To our knowledge, Tripleknock is the first end-to-end model for predicting lethal effects of three-gene knockout in bacteria. Data availabilityTripleknock is publicly available at: https://github.com/Peneapple/Tripleknock

bioinformatics↗

bioRxiv2025

Learning the native-like codons with a 5'UTR and secondary RNA structure aided species-informed transformer model

Efficient protein expression across heterologous hosts remains a major challenge in synthetic biology, largely due to species-specific differences in codon usage and regulatory sequence context. A key difficulty lies in reconstructing the codon landscape of the target expression system within a foreign host with a native-like codon preference. To address this, we present TransCodon, a Transformer-based deep learning model that leverages both 5 untranslated regions (5UTRs) and coding sequences (CDS), along with explicit species identifiers and RNA secondary structure information, to learn nuanced codon usage patterns across diverse organisms. By incorporating multisource genomic data and modeling sequence dependencies in a masked language modeling paradigm, TransCodon effectively captures both local and global determinants of codon preference. Our experiments demonstrate that integrating species-level information during training significantly improves the models ability to predict optimal synonymous codons when considering different evaluation metrics. More importantly it identifies native-like codons with less divergence from natural sequences compared to other methods. Besides, TransCodon could capture more low-frequency codons which are often omitted by other deep learning-based methods. The results thus indicate that TransCodon as a robust codon language model has the potential for generating native-like CDS with high translational efficiency in target hosts.

bioinformatics↗

bioRxiv2025

Homozygous kinase-dead Csf1r mutation in outbred mice reveals essential and redundant functions of tissue resident macrophages

The proliferation, differentiation and survival of cells of the macrophage lineage depends on signals from the macrophage colony-stimulating factor receptor (CSF1R). On a C57BL/6J background homozygous kinase-dead Csf1r mutation (Csf1rE631K/E631K - E631Km/m) causes perinatal lethality. Here we demonstrate that E631Km/m mice on a mixed genetic background (C57 x BALB/c F2) are osteopetrotic and growth retarded but viable as adults with no other gross developmental deficits. They lack osteoclasts, microglia and most peripheral tissue resident macrophages and exhibit perturbed hematopoiesis. Although CD169+ tissue resident macrophages in bone marrow are considered an essential component of the hematopoietic niche, CD169 is undetectable in E631Km/m marrow and F4/80+ macrophages are depleted. These changes are associated with expansion of mature and immature granulocytes and reduced B cells, whereas monocytes and stem and progenitor populations are unaffected as a proportion of total cells. Erythropoiesis in bone marrow is maintained in E631Km/m mice, associated with a residual population of CSF1R-independent CD169-ve/F4/80+ macrophages. Nevertheless, splenic extramedullary hematopoiesis in E631Km/m mice indicates a degree of bone marrow insufficiency. Red pulp macrophages are retained but CD169+ marginal metallophil macrophages are absent and CD209b+ (SIGNR1) macrophages are present but disorganized. Circulating white blood cell count is unchanged in E631Km/m mice, but the proportion of neutrophils is greatly increased whilst B cells and monocytes are reduced. This novel model reveals the essential roles of CSF1R-dependent macrophages in hematopoiesis and demonstrates that many developmental and homeostatic functions attributed to murine resident tissue macrophages are redundant and/or specific to inbred mouse strains.

immunology↗

bioRxiv2025

Spliformer-v2 predicts multi-tissue RNA splicing and reveals functional genomic links with neurodegenerative diseases

Precise regulation of pre-mRNA splicing underlies transcriptomic diversity and is disrupted in aging and disease, yet tissue-specific splice-altering genetic variants remain poorly resolved. Here, we present Spliformer-V2, a SegmentNT-based deep learning model for predicting and interpreting variant effects on RNA splicing across human tissues. We generated a diploid sequence resolved RNA splice map from paired whole-genome-sequencing and RNA-seq data across 12 central nervous system (CNS) and 6 peripheral tissues for model development. Spliformer-V2 outperformed SpliceTransformer, Pangolin and AlphaGenome in predicting splice-site usage, identified tissue-specific splicing regulatory motifs, and revealed tissue vulnerability to pathogenic splice-altering variants. Analyses of loci associated with 8 neurological diseases prioritized CNS-specific mis-splice-vulnerable genes. In 1,405 amyotrophic lateral sclerosis (ALS) genomes, Spliformer-V2 nominated rare splice-altering variants enriched in PTPRN2, which showed reduced expression in TDP-43-depleted neurons. PTPRN2 overexpression rescued C9ORF72-patient derived motor neuron degeneration and modulated TDP-43 mislocalization, indicating it as a potential therapeutic modifier in ALS.

bioinformatics↗

bioRxiv2025

Multi-omics analyses reveal the effect of DNA methylation on iridoid glycosides biosynthesis in Rehmannia glutinosa

Rehmannia glutinosa roots produce a group of lipophilic bioactive components known as iridoid glycosides. However, the molecular mechanisms by which DNA methylation regulates the biosynthesis of iridoid glycosides in R. glutinosa remain unknown. Herein, the development of R. glutinosa roots and the content of iridoid glycosides in the Wenxian region were significantly higher than those in Xinxiang. Low methylation level contributed to the accumulation of iridoid glycosides and the expression of related enzyme genes. Demethylation promoted both roots growth and development, as well as the accumulation of iridoid glycosides. Up-regulated RgALDH13, RgHDR1, RgG10H4, RgDXR1, RgG10H3, and RgUPD1, along with transcription factors (TFs), form the regulatory network for the biosynthesis of iridoid glycosides. Furthermore, the primary active region of the RgG10H4 promoter is located in the -164 bp region, where the RgMYB2 protein specifically binds to the TAACCA motif in the RgG10H4 promoter. Collectively, low levels of DNA methylation enhance the expression of core genes, followed by inducing the accumulation of iridoid glycosides, which suggests that RgMYB2-RgG10H4 plays a positive role in this process. These findings will contribute to a deeper understanding of the role of DNA methylation in the accumulation of iridoid glycosides. HighlightLow levels of DNA methylation contribute to the accumulation of iridoid glycosides and the expression of key enzyme genes in R. glutinosa.

molecular biology↗

bioRxiv2025

PPIKB: A Comprehensive Knowledge Base and Analysis Platform for Protein Peptide Interactions Based on Literature and Patents

Protein-protein interactions (PPIs) underpin a multitude of fundamental biological processes and represent critical therapeutic targets. Peptide compounds--owing to their structural flexibility, high target specificity, and the combined advantages of small molecules and biologics--have emerged as powerful modulators of these interactions. However, the exponential growth of scientific publications and patent filings poses a significant challenge for the systematic integration of protein-peptide interaction (PPepI) data. To address this, we implemented a high-throughput extraction pipeline powered by large language models (LLMs) to harvest affinity data from both research articles and patents, followed by rigorous manual curation. Applying this approach to 4,241 research papers and 483 patents published between 1972 and 2025, we compiled 21,760 manually validated quantitative affinity entries encompassing 2,013 unique protein targets derived from human, mouse, rat, viral, bacterial, and other species. In parallel, we consolidated 18,005 structural entries of protein-peptide complexes from the Protein Data Bank and associated literature. Leveraging these comprehensive datasets, we developed PPIKB, a user-friendly database and analysis platform that enables efficient querying, visualization, and in-depth analysis of protein-peptide interactions. PPIKB also provides a suite of tools tailored to facilitate peptide-based drug discovery, protein engineering, and systems-level biological research. By combining state-of-the-art text-mining technologies with expert validation, PPIKB sets a new standard for accessibility and utility in the field of protein-peptide interaction data resources. Access PPIKB at: https://ppikb.duanlab.ac

bioinformatics↗

bioRxiv2025

The metabolic and molecular mechanisms underlying running-induced energy compensation

Exercise not only regulates energy expenditure but also appetite, yet the underpinnings remain unclear. We describe that increased energy intake is a defense against energy loss that depends on initial running distance and operates independently of diet and age. Running caused a rapid circadian-dependent fat remodeling leading to a decline in circulating leptin accompanied by the activation of hypothalamic neurons. We discovered that the activation of the {beta}3-adrenergic receptor drives running-induced fat loss and the lower leptin triggers energy compensation by upregulating Neuropeptide Y. Once energy compensation is achieved, running is associated with molecular changes in hypothalamic signaling related to appetite and functional adaptations, such as enhanced sensitivity to hunger and satiety signals and increased responsiveness to appetite suppression induced by -Melanocyte-Stimulating Hormone. The increased food intake persisted without fat rebound beyond running in both lean and obese young mice, uncovering a new homeostatic balance in young mice.

physiology↗

bioRxiv2025

SMURF Reconstructs Single-Cells from Visium HD Data to Reveal Zonation of Transcriptional Programs in the Intestine

High-resolution spatial transcriptomics requires new computational methods to accurately assign transcripts to individual cells. We developed SMURF (Segmentation and Manifold UnRolling Framework), a novel, cross-platform, soft-segmentation algorithm that maps mRNAs from barcoded capture spots to nearby nuclei. SMURF also "unrolls" complex tissue architectures by projecting cells onto Cartesian coordinates, enabling analysis of cell-type organization and gene-expression gradients in intact tissues. We benchmarked SMURF and found it assigns mRNAs to single cells more accurately than existing approaches. We evaluated SMURFs ability to unroll complex tissues at cell-type resolution across multiple tissues and platforms. This analysis revealed previously unrecognized zonation of gene-expression programs, identified the transcription factors that regulate these patterns, and provided evidence that regional gene expression at the intestinal tip is reprogrammed by luminal environmental signals. Together, these results establish SMURF as a powerful framework for analyzing gene expression of cells within their native tissue contexts.

genomics↗

bioRxiv2025

AlphaFold3 for Non-canonical Cyclic Peptide Modeling: Hierarchical Benchmarking Reveals Accuracy and Practical Guidelines

Despite the revolutionary impact of AlphaFold3 on structural biology, this models capability in predicting non-canonical cyclic peptides remains unexplored. Given the clinical significance of cyclic peptides containing unnatural residues as a therapeutic modality, we present the first systematic evaluation of AlphaFold3 for this class of molecules. To facilitate benchmarking, we developed an automated input pipeline to streamline AlphaFold3 predictions for cyclic peptides. Our study aims to: (1) quantify the hierarchical accuracy (all-atoms, C atoms, and atoms of unnatural residue levels) of AlphaFold3 in predicting both non-canonical cyclic peptide monomers and complexes; (2) assess the reliability of AlphaFold3s confidence metrics; (3) evaluate the influence of multiple sequence alignment (MSA) and structural templates; and (4) identify systematic biases in AlphaFold3s predictions. Based on these analyses, we provide practical guidelines for applying AlphaFold3 in cyclic peptide structure prediction for facilitating the related research of bioactive cyclic peptides.

bioinformatics↗

bioRxiv2025

Ferret model of bleomycin-induced lung injury shares features of human idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a debilitating lung disease with limited therapeutic options. The development of effective therapies has been hindered by the lack of models that recapitulate key features of human disease. Here we report a bleomycin-induced ferret PF model characterized by an irreversible decrease in pulmonary compliance and an increase of opacification, accompanied by "honeycomb cyst-like" structures and "proximalization" of distal lung epithelium. Cellular and molecular analysis by single-nucleus RNA sequencing revealed a significant shift in distal lung epithelium towards proximal epithelial phenotype. Importantly, a histopathological pattern of bronchiolization encompassing divergent atypical epithelial cells and KRT17+/TP63+/KRT5low "basaloid-like" cells was present in the distal fibrotic lung lesions. Trajectory analysis revealed AT2 cells transition through multiple cell-states in bleomycin injured ferret lungs, particularly AT2 to KRT8high/KRT7low/SOX4+ and eventually to KRT8high/KRT7high/SFN+/TP63+/KRT5low "basaloid-like" cells. Further, immunofluorescence analyses demonstrated KRT7 and KRT8 populations reside overlaying the ACTA2-positive myofibroblasts in fibrotic foci, implying their pro-fibrogenic activity similar to human IPF lungs. Collectively, our results provide evidence that bleomycin-induced lung injury in ferrets recapitulates pathophysiological, cellular, and molecular features of human IPF, suggesting that they may be a reliable model for understanding mechanisms of IPF pathogenesis and for testing therapeutic strategies for treatment of IPF. Take Home MessageBleomycin-induced acute lung injury in the ferret recapitulates pathophysiological, cellular, and molecular features of human IPF; thus the ferret may be a reliable species for studying mechanisms of IPF pathogenesis and testing therapeutic strategies.

pathology↗

bioRxiv2025

Specific Pathogen Free Ten Gene-Edited Pig Donor for Xenotransplantation

Xenotransplantation has entered the clinical phase to fulfill the global organ shortage. However, recent clinical studies revealed that the xenograft from current gene-edited (GE) pigs still poses the risk of immune rejection, and biosafety concerns. In this study, we successfully constructed a large batch of 10- (GTKO/CMAHKO/ {beta}4GalNT2KO/hCD46/hCD55/hCD59/hTBM/hEPCR/hCD39/hCD47) GE cloned (GEC) donor pigs by utilizing gene editing and somatic cell cloning technology, and successfully obtained F1 generation. Phenotypic characterization of 10-GEC pigs showed the deletion of three xenoantigens along with expression of seven human transgenes in various tissues. Digital droplet polymerase chain reaction, and whole genome sequencing indicated 2 copies of hCD46/hCD55/hCD59/hTBM/hCD39 and 1 copy of hEPCR/hCD47 in pig genome without significant off-target and damage to the porcine own functional genes. The validation results showed that 10-GEC pigs effectively inhibited the attacks of human antibodies, complement, and macrophages on porcine endothelial cells and alleviated the coagulation abnormalities between pigs and humans. 10-GEC pigs were negative for all zoonotic pathogens (48) including cytomegalovirus, except streptococcal infections. Kidney, heart, and liver xenografts from these 10-GE pigs were transplanted to non-human primates (NHP), which started working normally without hyperacute rejection. Among them, the heart and liver transplant recipient died without resuscitation due to unexpected interruption of oxygen supply, while the 2 kidney transplant recipients survived for 23 and 16 days, respectively. Pathological analysis showed that 10-GE pig kidney xenografts showed mesenchymal congestion, and fibrosis, cellular hyperplasia, with minor antibody and complement deposition, and significantly reduced the infiltration of CD68+ macrophage. In summary, we successfully produced a group of specific pathogen free GEC donor pigs that effectively mitigated immune rejection upon multi-organ transplantation to NHP.

immunology↗

bioRxiv2025

GenVS TBDB: An Open AI-Generated andVirtual Screened Small-Molecule Database for Tuberculosis Drug Discovery

Tuberculosis (TB) remains a leading global health threat, with over 10 million new cases and 1.25 million deaths reported in 2023. Current TB therapies rely on a limited drug repertoire and prolonged treatment courses that have changed little in four decades. Here, we present GenVS-TBDB, a target-aware AI-generated and virtual-screened small-molecule database, which expands the chemical space against Mycobacterium tuberculosis (M.tb) essential proteins. We first identified 460 probable small-molecule binding pockets across 377 essential M.tb proteins by integrating multiple sources. Then, by leveraging the target-aware molecule generative model, over 1.2 million novel small molecules tailored to these pockets were produced. The key physicochemical properties were computed for all compounds to ensure medicinal chemistry tractability. These compounds were also evaluated using molecular docking and anti-TB specific graph neural network model, yielding binding propensity ranking and whole-cell activity prioritization for each target. To substantiate the integrated AI-driven workflow for anti-TB discovery, 30 compounds were obtained, including 22 AI-designed molecules synthesized de novo and 8 commercially available analogs. In validation, 2 synthesized compounds demonstrated significant thermal stabilization of FtsZ, confirming target engagement. 6 compounds exhibited cellular inhibition below 50 {micro}M, the most potent at 12 {micro}M. Furthermore, GDI-11785 showed binding to the cell wall biosynthesis pathway with 35 {micro}M cellular activity, establishing a promising starting point for tuberculosis drug discovery. Our findings contribute a significant library of bioactive molecules which may hasten the preliminary phase of tuberculosis drug discovery. The GenVS-TBDB small library is publicly accessible and can be downloaded freely at https://datascience.ghddi.org/database/view.

pharmacology and toxicology↗

bioRxiv2025

PIBE: Parallel ion beam etching of sections collected on wafer for ultra large-scale connectomics

We developed a parallel ion beam etching device that simultaneously thins hundreds of sections on a whole 4-inch wafer at 20 nm steps, enabling sub-centimeter volumetric imaging with high axial resolution. Compared with traditional volume electron microscopy, PIB preserves in-situ continuity within sections and simplifies inter-section registration. Its high axial resolution facilitates reliable neuron segmentation and tracing with reduced proofreading, providing an efficient route toward ultra-large-scale connectomics, such as whole-mouse-brain reconstruction.

neuroscience↗

bioRxiv2025

The influence of ceftriaxone, ceftazidime-avibactam, and piperacillin-tazobactam on the gut microbiota

PurposeThe administration of antibiotics can induce dysbiosis of the gut microbiota, leading to diseases, e.g., irritable bowel syndrome, metabolic syndrome, and Clostridium difficile infection. However, the specific effects of different {beta}-lactam antibiotics on the dysbiosis of the gut microbiota remain poorly understood, particularly in human studies. MethodsThis study assessed the impacts of ceftriaxone, ceftazidime-avibactam, and piperacillin-tazobactam on the diversity, composition, and bacterial interactions within the gut microbiota at days 1, 6, and 37 after administration. ResultsAll three antibiotics significantly altered beta diversity of the gut microbiota by day 6, with ceftriaxone showing the most prolonged effects. Changes in the composition of the gut microbiota were more similar between the ceftazidime-avibactam and piperacillin-tazobactam groups and differed markedly from those in the ceftriaxone group. Consistent with beta diversity changes, bacterial interaction networks showed greater and longer-lasting disruptions of bacterial interaction in the gut microbiota in the ceftriaxone group compared to the ceftazidime-avibactam and piperacillin-tazobactam groups. ConclusionThese findings highlight distinct patterns of microbiota disruption following ceftriaxone, ceftazidime-avibactam, and piperacillin-tazobactam treatments and provide insights for mitigating dysbiosis of the gut microbiota during {beta}-lactam therapy.

microbiology↗

bioRxiv2025

MeDCycFold: A Rosetta Distillation Model to Accelerate Structure Prediction of Cyclic Peptides with Backbone N-methylation and D-amino Acids

Cyclic peptides with backbone N-methylated amino acids(BNMeAAs) and D-amino acids(D-AAs) have gained attention for their stability, membrane permeability, and other therapeutic potentials. Currently, Rosetta can predict their structures using energy calculations, but this method is heavily time-consuming. Moreover, structural data for cyclic peptides containing BNMeAAs and D-AAs are extremely insufficient to build a data-driven structure prediction model. To address these problems, we propose MeDCycFold, a deep learning-based Rosetta distillation model by fine-tuning the AlphaFold model. First, a cyclic peptide structure dataset is constructed using Rosetta by sampling massive conformations for cyclic peptides with BNMeAAs and D-AAs and evaluating their energy scores. Then, the AlphaFold model is fine-tuned with the extended 56 BNMeAAs and D-AAs. Besides, a relative position cyclic matrix is introduced for head-to-tail cyclization in the cyclic peptides. Finally, a force field is employed to reduce clashes in the predicted structures. Empirical experiments show that our proposed MeDCycFold speeds up structure prediction by 49 times while maintaining the prediction accuracy comparable to Rosetta, which can greatly accelerate the development of cyclic peptide drugs.

bioinformatics↗

bioRxiv2025

Splicing factor SRSF1 is a pH-stat to restore nucleolar integrity and function

Nucleolus is a multiphase biomolecular assembly containing three distinct subcompartments. Evidence is just emerging that nucleolar proteins with characteristic electrochemical properties condense to generate a pH gradient, which serves as a key driving force to organize the nucleolar sub-phases. Given the indispensable functionality of nucleolus in most, if not all, of the cellular activities, it is vital for a cell to sense potential fluctuation in the nucleolar pH and subsequently maintain the pH homeostasis. The mechanisms how a cell achieve this task remains poorly decoded. Here, we show that splicing factor SRSF1 is shuttled from nuclear speckles (NSs) to the nucleolus in times of stress that interrogates the nucleolar pH. SRSF1 nucleolar localization is reliant on an acidic patch and molecular interactions with a nucleolar-resident protein, DDX18. Loss of SRSF1 impedes pH homeostasis in the nucleolus, and subsequently obstructs the restoration of the nucleolar multiphase and function. The arginine residues in the RS (arginine/serine rich) domain of SRSF1, endowed by high isoelectric point (pI), directly alkalize the nucleolar microenvironment. Interestingly, synthetic arginine-rich dipeptides derivative of SRSF1 RS domain safeguard nucleolus from pH and functional disturbance. Our findings uncover unprecedented mechanistic insights into nucleolar pH-sensing and regulation.

cell biology↗

bioRxiv2025

Exploring Population Differences in the Human Gut Microbiome: from Microbial Abundance to Single Nucleotide Polymorphisms

The human gut microbiota exhibits significant diversity across populations, influenced by factors such as geography, diet, and lifestyle, particularly between the Han Chinese and non-Chinese populations. While previous studies have predominantly focused on the taxonomic abundance of the gut microbiome, the impact of single nucleotide polymorphisms (SNPs) in driving population-specific differences remains largely underexplored. In this study, we systematically investigated gut microbial differences between the Han Chinese and non-Chinese populations using the Human Gut Microbiome Reference Genome Catalog (HGMRGC). HGMRGC includes 271,480 non-redundant genomes from 5,785 prokaryotic species, which was generated by metagenomic sequencing data from 9,320 publicly available samples across 22 countries and 3,584 newly sequenced samples from Han Chinese individuals across 29 provinces and regions in China. We observed geography was the primary driver of microbial variation of abundance and SNPs. We identified 625 novel population-specific genome clusters from HGMRGC with functional differences in carbohydrate utilization and 126 species exhibiting distinct prevalence related to vitamin biosynthesis, antibiotic resistance, and carbohydrate metabolism. Beta diversity analysis highlighted significant inter-population differences in both microbial abundance and SNPs, while alpha diversity analysis revealed that non-Chinese populations exhibited higher diversity in microbial abundance, and Han Chinese populations displayed greater diversity in SNPs. These results provide valuable insights into population-specific microbial diversity, laying the groundwork for future research on its functional and health implications.

microbiology↗

bioRxiv2025

Suite3D: Volumetric cell detection for two-photon microscopy

In two-photon imaging of neuronal activity it is increasingly common to acquire 3-dimensional volumes. However, these volumes are typically processed plane by plane, leading to uncorrected axial movement, duplicated cells across planes, reduced signal-to-noise ratio per cell, and missed cells. To overcome these limitations, we introduce Suite3D, a volumetric cell detection pipeline. Suite3D corrects for rigid and non-rigid 3D brain motion, both lateral and axial. It detects neurons using 3D correlation, improving detectability. Finally, it performs 3D segmentation, identifying cells across imaging planes. We validated Suite3D with data from conventional multi-plane microscopes and advanced volumetric microscopes, at multiple resolutions and in multiple brain regions, and with ground truth anatomical labelling. Suite3D successfully detected cells appearing on multiple imaging planes, improving cell detectability and signal quality, avoiding duplications, and running faster than a prior volumetric pipeline. Suite3D offers a powerful solution for analyzing volumetric two-photon data.

neuroscience↗