Biology subjects

Yang, Q.

Publications and source records attributed to Yang, Q..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

Erucamide regulates retinal neurovascular crosstalk

Neurovasculoglial crosstalk is critical in establishing and maintaining a functional neurovascular unit. Breakdown in the unit is central to many neurodegenerative disorders of the CNS of which the retina is a component. A growing literature indicated that primary fatty acid amides (PFAMs) can regulate this crosstalk between vasculature and neuronal tissues. In this study we describe a central role for erucamide, a 22:1 mono-unsaturated omega-9 fatty acid amide, in degenerating retinal tissues. Using high-resolution global mass spectrometry-based metabolomics, we cataloged metabolites in murine models of retinal degeneration and show that while PFAMs, in general, are highly dysregulated, erucamide is the one most significantly diminished during photoreceptor atrophy. Using rodent models of retinal degeneration and novel organosilane-modified porous silicon nanoparticles (pSiNPs) for the in vivo delivery of erucamide, we demonstrate that erucamide activates CD11b+ myeloid cells, leading to the upregulation of angiogenic and neurotrophic cytokines that stabilize retinal degeneration. We identified TMEM19 as a novel binding protein for erucamide that is crucial for human iPSC-derived macrophage precursor cells activation and subsequent neurotrophic and angiogenic factor production. These findings reveal a previously unknown PFAM pathway that is modulated during retinal degenerative diseases, demonstrating that erucamide or functional analogues and their action through TMEM19 may be useful as a therapeutic alternative to neuroprotective and stem cell-based approaches for the treatment of retinal degenerative diseases.

neuroscience↗

bioRxiv2025

Identification and quantification of alternative polyadenylation sites in single cell RNA-seq data using scPAISO

AbstractsAlternative polyadenylation (APA) is a critical posttranscriptional mechanism that generates transcriptomic diversity through the production of mRNA isoforms with distinct 3 UTRs or coding sequences. Current APA analysis based on single-cell RNA sequencing (scRNA-seq) for establishing cell type-specific APA landscapes primarily rely on Read2 data, which lacks precise cleavage site (CS) information. This limitation restricts their ability to achieve precise de novo mapping of polyadenylation sites (PASs). Here, we present single-cell PolyAdenylation ISOform quantification (scPAISO), a computational pipeline designed for de novo identification of PAS and quantification of PAS isoforms in scRNA-seq data, by leveraging the often-discarded Read1 from 3 tag-based scRNA-seq protocols. Unlike existing tools, scPAISO directly captures mRNA 3 end cleavage sites, enabling superior performance in motif enrichment (stronger AAUAAA signal) and peak precision (sharper PAS peaks). Moreover, the smaller peak widths enhance the spatial resolution, enabling more accurate detection of closely spaced PASs in the genome. By integrating Read1 and Read2 data, scPAISO achieves isoform-level quantification with an assignment accuracy exceeding 95%. We demonstrate the robustness of scPAISO in identifying PASs and quantifying APA events across diverse biological contexts, including hematopoiesis, systemic sclerosis (SSc), and mouse tissues. We identified stage-specific 3 UTR lengthening in hematopoietic progenitors, global 3 UTR remodeling in SSc and tissue-specific polyadenylation (PA) preference along with RNA-binding proteins in mice. Overall, scPAISO represents a significant advancement in the analysis of APA at single-cell resolution and provides a powerful tool for exploring the regulatory landscape of APA, offering new insights into transcriptome complexity and gene regulation in both health and disease.

bioinformatics↗

bioRxiv2025

Voltage Imaging of CA1 Pyramidal Cells and SST+ Interneurons Reveals Stability and Plasticity Mechanisms of Spatial Firing

Hippocampal place cells (PCs) are important for spatial coding and episodic memory. PCs representations are modulated upon transitioning between environments (global remapping) but also change with repeated exposure to familiar spaces (representational drift). To gain insights into the mechanistic basis for this unique balance between circuit plasticity and stability, we used voltage imaging to longitudinally record the subthreshold and spiking activity of pyramidal neurons (PNs) and somatostatin-positive (SST) interneurons in CA1 during virtual navigation. A fraction of cells from both populations showed spatial representations, but many SSTs were speed-tuned or fired uniformly across space. Intracellular recordings revealed increased theta power and asymmetric ramp-like depolarization in PN place fields, while SSTs exhibited symmetric depolarization with no theta increase. Longitudinal recordings across weeks demonstrated representational drifts in both populations, although SSTs exhibited remarkably stable firing and subthreshold properties. Transition to a novel environment induced remapping in both populations, accompanied by increase in SST activity and reduction in PNs. These results provide new insights into how hippocampal circuits balance representational stability with experience-dependent plasticity.

neuroscience↗

bioRxiv2025

RNA m6A Methylation Control Salt Response by Affecting Photosynthesis Capacity

Soil salinization is a major abiotic stress constraining global plant growth. Epigenetic regulation particularly RNA modifications like N6-methyladenosine (m6A) is crucial for plant stress responses, the specific role of m6A in salt tolerance remains unclearly defined. Here, we show that Arabidopsis mutants deficient in m6A writers or readers exhibit heightened salt sensitivity at both growth and physiological levels. Global m6A modification levels increase following salt stress which results in a significant elevation of m6A signature throughout the Arabidopsis transcriptome. Spatiotemporal analysis through standardized read density of MeRIP-seq uncovered a significant salt-induced redistribution of m6A modification patterns. More than 80% m6A reads locate around the stop codon within 3-untranslated regions and tend to increase under salt stress. A total of 9,986 peaks aligned into 8,667 coding gene showing significant changes in m6A modification levels. By associating with gene expression profiling, 840 salt-responsive genes display significant alterations in m6A enriched level and negative regulation pattern happens in most of genes. The salt responsive genes showing increased m6A methylation but significantly decreased expression are focused which representatively enriched in photosynthesis pathway, and this suppression due to mRNA decay mediated by m6A modification. Photosynthetic capacity and chloroplast apparatus are impaired in m6A-dependent manner under salt stress. These results clarify the biological functions of m6A modification in plant response to salinity and uncover the specific role of RNA metabolism based on dynamic change pattern of m6A modification to cope with salt stress.

plant biology↗

bioRxiv2025

VO: The Vaccine Ontology

With the widespread use of vaccines in research and clinical settings, there is an urgent need to standardize vaccine representation, integrate information across diverse vaccine types, and support computer-assisted reasoning. Accordingly, we have since 2007 developed the community-based Vaccine Ontology (VO), which aligns with the Basic Formal Ontology and adheres to OBO Foundry principles. VO models ontologically vaccines, vaccine components, vaccine immune responses, vaccine investigation studies and other vaccine-related topics. VO represents more than 10,000 vaccines targeting 289 infectious pathogens and cancers in humans and over 30 nonhuman animal species. VO provides mappings to external resources such as RxNorm, CVX, FDA, and USDA. Various VO use cases exist. VO facilitates vaccine standardization in resources such as the VIOLIN vaccine database, ImmPort, and the Vaccine Adjuvant Compendium (VAC). Semantic queries can be made to query VO. VO has been shown to enhance experimental and clinical vaccine data analysis and vaccine literature mining. Overall, VO standardizes vaccine modeling and representation and greatly supports vaccine AI research in the Semantic Web era.

bioinformatics↗

bioRxiv2025

GIN-CRC-Pareto: A graph-based Pareto-optimal multi-task learning framework to identify miRNA-target interactions in colorectal cancer

Colorectal cancer (CRC) ranks as the third highest incidence among malignancies in humans and the second most common cause of cancer-related mortality in the United States. Accumulating evidence has established microRNAs (miRNAs) as critical regulators of cancer development and therapeutic response. Understanding miRNA-mRNA interactions is critical for elucidating the molecular mechanisms driving CRC and other malignancies. In this study, we proposed GIN-CRC-Pareto, a graph-based, Pareto-optimal multi-task learning framework that simultaneously predicts miRNA-mRNA binding pairs, identifies seed match pairings, and classifies seed match subtypes. By leveraging the power of graph neural networks and Pareto-optimal gradient balancing strategy, GIN-CRC-Pareto dynamically adjusted the task weights during training to optimize each task without compromising the others. Experimental results demonstrated that our framework consistently outperforms traditional deep learning models and existing state-of-the-art tools across multiple evaluation metrics, with 0.909 in accuracy, 0.909 in precision and 0.968 in AUC in the miRNA-mRNA binding pairs prediction task. Additionally, we further validated the generalizability of the framework in combination with transfer learning techniques to identify miRNA-target interactions across other cancers. These findings highlight the effectiveness of the proposed framework to comprehensively identify the miRNA-target interactions in CRC, with the potential to serve as a scalable and generalizable tool across diverse cancer types, ultimately facilitating the development of miRNA-based therapeutics for cancer treatment.

bioinformatics↗

bioRxiv2025

Targeting CyclinD1-CDK6 to Mitigate Senescence-Driven Inflammation and Age-Associated Functional Decline

Cellular senescence contributes to aging and age-related diseases by driving chronic inflammation through the Senescence Associated Secretory Phenotype (SASP) and interferon-stimulated genes (ISGs). Cyclin D1 (CCND1), a key cell cycle regulator, is paradoxically upregulated in these non-proliferating cells. We show that CCND1 and its kinase partner CDK6 drive SASP and ISG expression in senescent cells by promoting DNA damage accumulation. This leads to the formation of cytoplasmic chromatin fragments (CCFs) that activate pro-inflammatory CGAS-STING signaling. The tumor suppressor p53 (TP53) and its target p21 (CDKN2A) antagonize this CCND1-CDK6-dependent DNA damage accumulation pathway to suppress the SASP. In aged mouse livers, senescent hepatocytes show increased Ccnd1 expression. Hepatocyte-specific Ccnd1 knockout or treatment with the Cdk4/6 inhibitor Palbociclib reduces DNA damage and ISGs in aged mouse liver. Notably, Palbociclib also suppresses frailty and improves physical performance of aged mice. These findings reveal a novel role for CCND1/CDK6 in regulating DNA damage and inflammation in senescence and aging, highlighting it as a promising therapeutic target.

cell biology↗

bioRxiv2025

Benchmarking RNA velocity methods across 17 independent studies

RNA velocity techniques offer great potential for unveiling trajectories of cell state transitions in different biological contexts. While diverse computational methods have been developed, there is no evidence-based guidelines for best-practice in RNA velocity inference. Here we conducted a benchmark study for 14 existing RNA velocity methods in 17 independent datasets. Many validations were done for the first time. We evaluated the performances of each method by measuring accuracy, stability, and usability. Our data showed no single method exhibited superior performance in all the assessments, and unexpected underperformance was observed in certain cases. Especially, the lack of uniformity in the inference results highlights the necessity to compare and control of multiple methods in a single analysis. Our study revealed current limitations and challenges in the RNA velocity methods and informed the best-practice for future studies.

bioinformatics↗

bioRxiv2025

GeoGAT-site: A Face-Centered Geometric Graph Attention Network for Protein-Protein Interface Prediction

Protein-protein interactions (PPIs) underpin the intricate machinery of cellular life, orchestrating processes from signal transduction to metabolic regulation, yet their precise interface prediction remains a cornerstone challenge in structural biology, demanding scalable computational paradigms that balance accuracy and efficiency. Herein, we present GeoGAT-site, a pioneering geometric graph attention network that leverages face-centered surface fingerprints extracted from three-dimensional protein architectures to forecast interaction sites. Diverging from conventional vertex-centric approaches, our face-centered methodology achieves a 3.64-fold acceleration in patch generation, mitigating computational bottlenecks while preserving granular surface descriptors. At its core, GeoGAT-site incorporates a bespoke attention mechanism that adaptively modulates inter-facial distances and normal vector orientations, synergistically integrating spatial geometries with physicochemical attributes for nuanced interface delineation. Harnessing a meticulously curated dataset of 150 million face-centered fingerprints from over 20,000 structurally diverse proteins, the model attains robust generalization across heterogeneous interaction motifs. Empirical validation on an independent cohort of 167 protein complexes yields a ROC AUC of 0.89, surpassing established benchmarks including MaSIF-site (0.845), SPPIDER (0.65), and PSIVER (0.63). By furnishing high-fidelity interface annotations, GeoGAT-site augments downstream structural modeling paradigms through targeted constraints, thereby providing a versatile scaffold for unraveling PPI dynamics with profound implications for therapeutic discovery, protein redesign, and molecular epistemology.

bioinformatics↗

bioRxiv2025

Predicting TCR antigen specificity at proteome-scale with synthetic immune cells and machine learning

TCR specificity to peptide-HLA antigens is central to immunology, impacting responses in infection, autoimmunity and cancer. Achieving precise recognition while avoiding off-target reactivity is critical for effective immunity and safe therapeutic interventions. Comprehensive, proteome-wide specificity profiling of TCRs is challenging with current methods, which notably lack integrated machine learning for large-scale analysis. Here, we report a synthetic immune cell system coupled with machine learning to enable TCR functional and specificity mapping of peptide-HLA antigens at proteome-scale. Multi-step immunogenomic engineering of synthetic antigen-presenting cells (APCs) was performed to enable stable mono-allelic integration and precise display of peptide antigens and HLA class I from a defined genomic locus, ensuring genomically-encoded antigen presentation. Compatible with a synthetic TCR displaying T cell system, this platform incorporates a fluorescent reporter of cytokine-mediated signaling for real-time activation detection in both synthetic APCs and T cells. We combined this screening with diverse peptide antigen libraries and deep sequencing to train supervised machine learning models. These models were applied to predict TCR specificity to peptide-HLA antigens across the entire human proteome. Experimental validation confirmed novel off-targets for therapeutic TCR candidates, including for a clinically-approved TCR therapeutic. This integrated synthetic immune cell and machine learning approach provides unprecedented proteome-wide peptide-HLA specificity mapping to support the development of safer TCR-based therapies. One sentence summaryWe present a synthetic immune cell platform integrated with machine learning that enables prediction of TCR specificity to peptide-HLA antigens at proteome-scale.

immunology↗

bioRxiv2025

ETS1 Orchestrates a Hybrid EMT Program Driving in vivo Metastasis and Immune Evasion

Transcriptional Intratumoral heterogeneity (ITH) is a hallmark of aggressive cancers, yet how transcriptional ITH programs drive tumor metastasis and immune evasion in upper aerodigestive squamous cell carcinoma (UASCC) remains unclear. Through single-cell RNA sequencing analysis of UASCC cells and patient tumors, we uncovered a hybrid epithelial mesenchymal transition (hEMT) ITH program linked to metastatic dissemination. The transcription factor ETS1 was identified as a master regulator of the hEMT program, directly activating pro-metastatic genes and promoting distant spread in vivo. Unexpectedly, ETS1 also orchestrated an immune-cold tumor microenvironment by transcriptionally activating both STAT1 and PD-L1 (CD274) genes, suppressing T lymphocyte infiltration, and elevating immune checkpoint molecules. Clinically, ETS1-high tumors strongly correlated with poor survival and resistance to immune checkpoint blockade across multiple cohorts. Leveraging drug screens, we discovered that ETS1-high cancers are vulnerable to HSP90 inhibitors (e.g., Alvespimycin), which suppress ETS1 by disrupting HIF1A-mediated transcriptional activation. Together, our work reveals ETS1 as a dual driver of tumor distal metastasis and immune evasion in UASCC, while nominating HSP90 inhibition as a tailored treatment strategy for ETS1-driven tumors. These findings provide a roadmap for targeting aggressive ITH subsets and overcoming immunotherapy resistance.

cancer biology↗

bioRxiv2025

Mapping and Harmonization of CVX vaccine terms tothe Vaccine Ontology

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

bioinformatics↗

bioRxiv2025

BrainBeacon: A Cross-Species Foundation Model for Single-cell Resolved Brain Spatial Transcriptomics

The brains functional complexity emerges from spatiotemporal heterogeneity in molecular, cellular, and circuit organization, shaped by evolution, aging, and disease. Single-cell spatial transcriptomics provides a transformative framework to decode this complexity, resolving cell-type-specific expression patterns, region-specific regulation, evolutionary divergence, and disease-associated disruptions. Despite recent advances in spatial transcriptomics, a unified foundation model capturing cross-species, whole-brain cellular architecture remains lacking. Here, we introduce BrainBeacon, the first cross-species foundation model for whole-brain spatial transcriptomics. Trained on 133 million spatially resolved cells covering a total area of 210194 mm2 from human, macaque, marmoset and mouse brains profiled across five major platforms, BrainBeacon integrates gene expression rankings, spatial cellular organization, and evolutionarily conserved genetic relationships into a unified transformer-based architecture. A two-stage training strategy captures both intra-cellular transcriptional dependencies and inter-cellular spatial interactions, yielding biologically grounded and interpretable representations of brain structure. Subsequently, BrainBeacon enables cross-species alignment of brain cell types and anatomical regions, facilitating integrative analyses of conserved and divergent cellular architectures. Moreover, it uncovers spatial regulatory mechanisms of aging by predicting bidirectional perturbation responses between cells and their local niche. Together, BrainBeacon serves as a foundational cross-species model for whole-brain spatial transcriptomics, enabling virtual brain atlas construction, systematic in silico perturbation studies, and mechanistic insights into brain disease pathogenesis.

bioinformatics↗

bioRxiv2025

Neural mechanisms of structural inference: an EEG investigation of linguistic phrase structure categorization

Language comprehension is a complex cognitive process of understanding spoken or written language, during which the brain derives meanings and interprets messages by constructing hierarchical structures from word sequences. A key component underlying this process is structural inference. This computation determines the syntactic category ("head") of phrases, such as noun phrases (NP) and verb phrases (VP). Categorization confers distinct semantic and distributional properties. However, isolating neural correlates of structural inference is challenging, as modulations of syntactic category usually alter lexical content. Here, we collected the electroencephalography (EEG) data while participants read Mandarin NPs and VPs, which differ in their syntactic "headedness" but with lexical-semantic, lexical-syntactic categories and word order strictly conserved. We found significant theta power increases at left central-parietal scalp sites in NPs relative to VPs around the presentation (0-210 ms) of the nouns, where only the nouns in NPs project a head. Both NPs and VPs relative to one-word phrases exhibited spatiotemporally consistent low-frequency power increases at the point of linguistic composition, consistent with previous studies. Overall, our findings offer novel constraints on neurocomputational accounts of linguistic structure-building through these previously undocumented signatures of syntactic inference.

neuroscience↗

bioRxiv2025

An artificially selected cytokinin-pathway transcription factor balances soybean yield and pathogen resistance

Soybean contributes to sustainable food and feed production, but some modern varieties favor yield at the cost of disease resistance. Using a bottom-up approach, we demonstrate that the RR2b transcription factor activates cytokinin signaling for plant growth and reproductive fitness but represses disease-resistance and root-nodulation pathways. RR2b was artificially selected during domestication and improvement, and its expression levels correlate with ATT repeat polymorphisms in its promoter. Most wild soybeans and landraces have either more or less ATT repeats: more lead to weak RR2b expression with lower yield and enhanced resistance to soybean bacterial blight, and fewer cause strong RR2b expression, higher yield but reduced disease resistance. Elite cultivars instead balance yield and defense via moderate RR2b expression and several ATT repeats. This confers contrasting RR2b functionality across haplotypes in response to contrasting selection pressures and offers insights into decoupling trade-offs between yield and disease resistance to enhance crop productivity.

genetics↗

bioRxiv2025

Copper-Containing ROS-Scavenging Nanozyme Paradoxically Drives Alpha-Synucleinopathy by Amplifying Nitrosative Stress

Reactive oxygen and nitrogen species (RONS) are implicated in neurodegeneration, but their specific pathogenic roles remain unclear. Here, we developed a pair of iridium-based nanozymes with opposing functionalities to dissect these pathways. We show that a copper-tuned iridium nanozyme (Ir{square}Cu), despite being a superior ROS scavenger, paradoxically and dramatically exacerbated -synuclein (Syn) pathology in vivo. This pathology was causally linked to its ability to amplify RNS, as pharmacological inhibition of nitric oxide synthase (NOS) with L-NAME completely abrogated the pathology and reversed a human Parkinsons disease (PD)-like transcriptomic signature. In contrast, a copper-free, broad-spectrum RONS-scavenging iridium (Ir) nanozyme demonstrated substantial therapeutic efficacy across diverse brain-first, body-first, and Alzheimers disease with Lewy body co-pathology models. Our findings uncover the importance of the RNS pathway in driving -synucleinopathies and establish a critical design principle for nanomedicine, mandating caution in the use of redox-active copper for neuroprotective applications.

pathology↗

bioRxiv2025

Identification of senescence-related hub genes and potential therapeutic agents for chronic heart failure

BackgroundChronic heart failure (CHF), the terminal phase of cardiovascular disease progression, has emerged as an increasingly severe global public health concern. Despite current therapeutic approaches aimed at symptom relief, their long-term effectiveness remains limited, urgently necessitating the exploration of novel treatment strategies. This study endeavored to explore the role of cellular senescence in CHF, identify the characteristic genes linked to cellular senescence, and predict potential therapeutic agents. MethodsWe acquired CHF-related datasets from the Gene Expression Omnibus database and cell senescence-related genes from the CellAge database to identify differentially expressed cell senescence-related genes. We then conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to elucidate the functions of these differentially expressed genes, constructed a protein-protein interaction network, and screened hub genes. Using receiver operating curve (ROC) analysis, we developed a diagnostic model based on these hub genes. Furthermore, we constructed networks for the hub genes involving miRNAs, lncRNAs, transcription factors, and drugs. Subsequently, we explored the potential mechanism of action of metformin in the treatment of CHF through molecular docking studies. Lastly, we verified the expression of the hub genes in a doxorubicin-induced CHF model in rats. Results We ultimately identified nine hub genes associated with cellular senescence: STAT1, MMP9, MAP2K1, SOCS1, SDC1, MET, EIF4EBP1, ATF3, and NAMPT. These genes exhibited significant differential expression between CHF and normal tissues. The constructed diagnostic model demonstrated robust diagnostic performance in ROC curve analysis, with an area under the curve exceeding 0.7, thereby providing biomarker support for early CHF diagnosis. Furthermore, we identified a regulatory network comprising 94 lncRNAs, 63 miRNAs, and 11 transcription factors and screened 42 potential therapeutic drugs. Subsequent molecular docking simulations revealed that metformin could effectively bind to the hub genes. Finally, we validated the expression of some hub genes in a rodent CHF model, in which gene expression regulation varied across diverse experimental settings. ConclusionsOur research identified nine genes cellular senescence-related genes with potential roles in CHF pathogenesis, offering fresh perspectives concerning the diagnosis and treatment of CHF.

bioinformatics↗

bioRxiv2025

Pp6-Pfkfb1 axis modulates intracellular bacterial proliferation by orchestrating host-pathogen metabolic crosstalk

Intracellular bacterial pathogens exhibit heterogeneous replication rates within host macrophages, but the mechanisms by which they manipulate host factors for survival remain incompletely understood. Using a fluorescence-dilution reporter system in Salmonella typhimurium (Salmonella)-infected macrophages, we found that Protein Phosphatase 6 (Pp6) was downregulated in macrophages harboring growing bacteria. Conditional knockout of Pp6 elevated host susceptibility to Salmonella-mediated lethality due to compromised antimicrobial defenses. MicroRNA-31 (miR-31) was identified as a negative regulator of Pp6, and its conditional ablation enhanced bacterial clearance. Yeast two-hybrid screening identified 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (Pfkfb1), a metabolic regulator, as a substrate of Pp6. Pp6 deficiency resulted in significantly elevated expression of Pfkfb1, which was highly expressed in macrophages containing replicating Salmonella. Pfkfb1 deletion restricted bacterial proliferation by promoting nitric oxide (NO) production while concurrently suppressing arginase-1 (Arg-1) expression and impairing arginine metabolism in macrophages. Collectively, these results establish the Pp6-Pfkfb1 axis as a key regulator of host metabolic adaptation and intracellular bacterial survival, offering potential therapeutic targets against multidrug-resistant pathogens. Author summaryIntracellular pathogens secrete effector proteins that intercept and modify host cells to usurp host defenses and establish habitable intracellular niches, yet the host factors they manipulate for survival and virulence are not fully understood. Using a fluorescence-dilution reporter system in Salmonella typhimurium (Salmonella)-infected macrophages, we found Protein Phosphatase 6 (Pp6) was significantly decreased in macrophages harboring replicating Salmonella and its deficiency indulged bacterial growth in macrophages. However, the deletion of microRNA-31 (miR-31), a negative regulator of Pp6, enhanced Salmonella clearance. Furthermore, through yeast two- hybrid screening, we identified 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (Pfkfb1) is a substrate of Pp6. And we demonstrated Pfkfb1, abundantly expressed in macrophages containing growing bacteria, is crucial for Salmonella proliferation by regulating nitric oxide (NO) levels, arginase-1 (Arg-1) expression and arginine metabolism. Overall, the molecular changes in Pp6-Pfkfb1 axis drive host metabolic adaptations that enable intracellular bacterial survival.

microbiology↗