Biology subjects

Guo, X.

Publications and source records attributed to Guo, X..

At least 91 records · Page 5Linked to original sources

bioRxiv2025

Betrixaban Activates cGAS and ERVs to Promote Dual Nucleic-Sensing Antiviral Immunity

Broad-spectrum host-directed antivirals are urgently needed, as virus-targeted drugs often suffer from narrow specificity and rapid resistance. Here, we reported that Betrixaban (BT), an FDA-approved oral Factor Xa inhibitor, induced a robust antiviral state through dual innate immune pathways. Mechanistically, we identified BT directly bound and activated the DNA sensor cGAS to induce cGAMP production, representing BT as the first small-molecule cGAS agonist. Concurrently, BT inhibited histone deacetylases (HDACs), leading to chromatin de-repression of endogenous retroviruses (ERVs) and production of immunostimulatory double-stranded RNA (dsRNA) that engaged RIG-I/MDA5. These combined signal cascades triggered strong type I interferon responses and conferred broad-spectrum antiviral protection against RNA and DNA viruses in vitro and in vivo. These findings unveil a unique host-directed antiviral strategy wherein a small molecule drug engage dual nucleic acid-sensing pathway, and suggest repurposing BT as an orally broad-spectrum antiviral. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/669242v1_ufig1.gif" ALT="Figure 1"> View larger version (68K): org.highwire.dtl.DTLVardef@aa153dorg.highwire.dtl.DTLVardef@585d43org.highwire.dtl.DTLVardef@152ec59org.highwire.dtl.DTLVardef@11ada33_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Scalable automated segmentation quantifies mitochondrial proteins and morphology at the nanoscale

Ultrastructural changes of mitochondria are closely associated with metabolic dysfunction, leading to a variety of human disorders. These changes can be visualized by pan-Expansion Microscopy (pan-ExM), a 3D microscopy technique requiring only standard fluorescence microscopes, at a throughput exceeding that of the current gold standard, 3D electron microscopy, by orders of magnitude. However, a lack of tools that enable the characterization and quantification of the observed ultrastructural features in the acquired 3D datasets at a comparable throughput has hindered the widespread adoption of pan-ExM for quantitative imaging. Here, we present an automated deep-learning based segmentation approach that utilizes pan-ExMs power to acquire multi-channel images and uses specific labeling as the annotation for the training of the segmentation network. This molecular annotation reduces the required manual annotation effort for mitochondria substructures to just a few hours when setting up the experiment and thereby provides access to 3D suborganellar morphology of mitochondria at an unprecedented throughput. Our approach, which we term MAPS (Mitochondrial Automated Pan-ExM Segmentation), enables for the first time to quantify mitochondrial ultrastructural morphology at scale. We demonstrate this power by characterizing the 3D mitochondrial morphology at the organelle and sub-organelle level in tens of HeLa cells under different treatments and localizing mitochondrial proteins in the sub-organellar context. To demonstrate our technology in tissue, we compare the ultrastructural morphology of mitochondria in proximal tubules of kidneys of mice exhibiting acute kidney injury (AKI) with those of untreated mice, revealing striking differences in their cristae structure. MAPS can easily be adapted to different cell and tissue types, allowing the analysis of tens of samples per day, and therefore provides a versatile tool for a comprehensive understanding of mitochondrial ultrastructural changes in many disease contexts. Requiring only standard fluorescence microscopes and computer infrastructure, MAPS is readily adoptable by any lab. HighlightsO_LIMAPS utilizes specific labeling to train a segmentation model for 3D super-resolution pan-ExM images C_LIO_LISuborganellar mitochondrial features and their changes in disease models are quantified at high throughput C_LIO_LI3D protein distributions are correlated to ultrastructural features in mitochondria C_LIO_LIRequiring only standard lab infrastructure, MAPS is readily adoptable C_LI

cell biology↗

bioRxiv2025

Dual-Mode Microfluidic Immunostaining (Dumi) Device for Diagnostic Biomarkers Detection and Tumor Microenvironment Evaluation

The limited availability of tissue samples from rare tumors poses a major barrier to advances in precise diagnosis, prognostic evaluation, and therapeutic research--challenges exemplified by primary central nervous system diffuse large B-cell lymphoma (PCNS-DLBCL). However, both immunohistochemical (IHC) subtyping and preliminary studies of the tumor microenvironment (TME) using multiplex immunolabeling rely on conventional staining workflows that consume numerous serial sections per case, leading to irreversible depletion of precious samples from rare tumors. Here, we developed a dual-mode microfluidic immunostaining (Dumi) device, which integrates diagnostic and research workflows into a single, automated platform. Clinical validation shows that > 90% reduction in section usage, complete detection of subtyping diagnostic biomarkers on just 1-2 slides, and displays the TME maps incorporating six immune markers for analysis research. This microfluidic innovative offers an efficient, cost-effective, and multifunctional alternative immunostaining methods, overcoming the limitations of scarce tissue resources and providing a clinically accessible solution to the diagnostic and therapeutic challenges with rare tumors. TeaserDumi device enables multiplex IHC & TME mapping on 1-2 slides, reducing tissue use by >90% for rare tumors like PCNS-DLBCL.

bioengineering↗

bioRxiv2025

Auditory-cognitive determinants of speech-in-noise perception: structural equation modelling of a large sample

Problems understanding speech-in-noise (SIN) are commonly associated with peripheral hearing loss. But pure tone audiometry (PTA) alone fails to fully explain SIN ability. This is because SIN perception is based on complex interactions between peripheral hearing, central auditory processing (CAP) and other cognitive abilities. We assessed interaction between these factors and age using a multivariate approach that allows the modelling of directional effects on theoretical constructs: structural equation modelling. We created a model to explain SIN using latent constructs for sound segregation, auditory (working) memory, and SIN perception, as well as PTA, age and measures of non-verbal reasoning. In a sample of 207 participants aged 18-81 years old, age was the biggest determinant of SIN ability, followed by auditory memory. PTA did not contribute to SIN directly, although it modified sound segregation ability, which covaried with auditory memory. A second model, using a CAP latent structure formed by measures of sound segregation, auditory memory, and temporal processing, revealed CAP to be the largest determinant of SIN ahead of age. Furthermore, we demonstrated the impact of PTA and non-verbal reasoning on SIN are mediated by their influence on CAP. Our results highlight the importance of central auditory processing in speech-in-noise perception.

neuroscience↗

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 Timescale of Adolescents Major Depressive Disorder

Adolescent major depressive disorder (MDD) is characterized by heterogeneous symptomatology and complex neurodevelopmental underpinnings. Here, we investigated whether cortical intrinsic neural timescales (INT)--a measure of temporal stability in neural activity--are altered in adolescents with MDD and whether these alterations relate to clinical symptoms, suicidality, early-life adversity, and underlying neurobiological mechanisms. Using resting-state fMRI in adolescents with MDD and healthy controls (HCs), we found widespread reductions in INT in frontal, parietal, and sensorimotor regions, with a notable prolongation in the left temporoparietal junction. Disrupted timescales significantly impaired network modularity and clustering, and SVR-based machine learning revealed that altered INT patterns predicted individual depression and anxiety severity. INT abnormalities were further associated with suicidal ideation and childhood trauma, particularly emotional and physical neglect. Biophysical modeling linked INT variations to local recurrent excitation and external input strength, differing between HCs and MDD. Spatial correlations with PET-derived neurotransmitter receptor maps demonstrated that INT alterations colocalize with serotonergic, dopaminergic, and cholinergic systems. Transcriptomic enrichment analysis revealed associations with genes involved in mitochondrial function, synaptic signaling, and metabolic regulation. Together, these findings identify neural timescale disruption as a core pathophysiological feature of adolescent MDD, bridging macroscale dynamics with microcircuit and molecular architecture. INT may serve as a promising biomarker and mechanistic target for precision psychiatry in youth depression.

neuroscience↗

bioRxiv2025

Visual-like Template Diffusion: Boosting Single-Sequence Protein Structure Prediction by Adapting Image Diffusion Models

Single-sequence protein structure prediction has drawn increasing attention due to the high computational costs associated with obtaining homologous information. Here, we propose a visual-like 2D geometric template* diffusion method, named TDFold, to generate high-quality pairwise geometries (including pairwise distances and orientations) for achieving accurate and highly efficient single-sequence 3D structure prediction for proteins. Given a protein sequence, TDFold initially generates high-quality inter-residue geometries from a probabilistic diffusion perspective. Since inter-residue geometries can be encoded as multi-channel feature matrices, analogous to image feature maps, we construct an image-level 2D geometric template diffusion module by adapting the stable diffusion (SD) model from text-vision generation to sequencegeometry diffusion for proteins. Subsequently, a lightweight sequencegeometry collaborative learning (SCL) network is constructed to facilitate accurate and efficient protein structure prediction. As a result, TDFold possesses three highlights: (i) better single-sequence prediction performance: TDFold greatly outperforms existing protein language models (PLMs, e.g. ESMFold and OmegaFold) and homology-based methods (e.g. AlphaFold2, AlphaFold3 and RoseTTAFold) on homologyinsufficient datasets such as Orphan and Orphan25, while also achieving promising results on the popular CASP14, CASP15 and CASP16 benchmarks; (ii) low resource consumption: By utilizing the lightweight SCL architecture, the GPU memory consumption of TDFold is generally lower than that of popular methods such as AlphaFold2 and ESMFold; (iii) higher efficiency in training and inference: TDFold can be trained within a week using a single NVIDIA 4090 GPU. Furthermore, the inference time of TDFold is significantly shorter (about 10x to 100x) than that of existing methods (ESMFold, AlphaFold2 and AlphaFold3) for long protein sequences. This work demonstrates the effectiveness of leveraging powerful vision diffusion models to enhance protein 2D geometric template generation, thereby establishing a new paradigm for single-sequence protein structure prediction. It also accelerates protein-related research, particularly for resource-limited universities and academic institutions. The code has been released to speed up biological research.

bioinformatics↗

bioRxiv2025

TaPGS1 Driven Flavonol Accumulation Delays Endosperm Cellularization and Enlarges Wheat Grains

Flavonols play a crucial role in seed development by regulating multiple physiological processes, including seed coat pigmentation, dormancy, fertilization, and endosperm formation. Notably, flavonols influence the polar transport of auxin, thereby affecting seed growth dynamics. In our previous work, we found that overexpression of the wheat bHLH transcription factor TaPGS1 results in increased grain size; however, the underlying mechanism remained unclear. In this study, metabolomic and transcriptomic analyses of TaPGS1 overexpressing wheat lines revealed enhanced flavonol accumulation and upregulation of key flavonol biosynthetic genes. Further investigations suggested that flavonol accumulation in the seed coat may disrupt auxin transport, leading to localized auxin buildup, delayed endosperm cellularization, and an increase in endosperm cell number. These changes collectively contribute to grain enlargement. Our findings uncover a TaPGS1 flavonol regulatory module that links auxin distribution to endosperm development and seed size control in wheat.

plant biology↗

bioRxiv2025

Phase-separating enzyme-responsive condensates for inhalable delivery of biomacromolecules

Inhaled delivery of biomacromolecules shows extensive promise for treating a large variety of diseases, especially pulmonary and respiratory diseases. Although nanoparticle-based vehicles enhance intracellular uptake efficiency, it still remains challenging for biomacromolecules inhaled delivery to overcome cytotoxicity, cargo leakage and off-target effect. Here, we present an enzyme-responsive condensates based highly biocompatible and controllable platform for inhaled delivery of biomacromolecules. The condensates that can be modularly designed for rapid recruitment and enrichment of biomolecules, including Cas9 ribonucleoprotein (RNP) and mRNA, were internalized by cells via lysosomal-independent endocytosis, and subsequently released payloads triggered with endogenous physicochemical stimulus for controlled regulation of genes and proteins expression. Additionally, the micrometer sized condensates with highly structural and biological stability during lyophilization and nebulization enable inhalable delivery of biomacromolecules to lung. This condensate-based vehicle provides a promising platform for biomacromolecules inhaled delivery and controlled release, targeted cell regulation, and precise therapy.

bioengineering↗

bioRxiv2025

Triad-LMF: A Hierarchical Low-Rank Multimodal Fusion Framework for Robust Cancer Subtype Classification Using Multi-Omics Data

Cancer heterogeneity poses a major challenge for accurate molecular subtype classification. Conventional methods often fail to exploit complementary information across multiple omics modalities, leading to overfitting on high-dimensional data and limited representation of subtype heterogeneity. To address this, we propose Triad-LMF, a multiomics integration framework based on low-rank multimodal fusion to improve classification accuracy. Triad-LMF harmonizes heterogeneous omics data and integrates information through a two-stage hierarchical fusion strategy. Local Pairwise Fusion and Global Triadic Fusion are combined via the Two-Feature and Three-Way LMF modules, enabling a gradual transition from local modality interactions to global feature integration. Experimental results show that Triad-LMF consistently outperforms existing methods. UMAP visualization confirms enhanced subtype separability, and SHAP-based analysis highlights biologically meaningful features. Across independent datasets, Triad-LMF demonstrates strong generalization, offering a robust and interpretable framework for multiomics-driven cancer subtype classification.

bioinformatics↗

bioRxiv2025

Saddle Curvature Association of nsP1 Facilitates the Replication Complex Assembly of Chikungunya Virus in Cells

The replication of positive-sense RNA viruses, including the pathogenic SARS-CoV-1 and -2, DENV, and ZIKV, often takes place in curved cellular membrane compartments of tens to hundreds of nanometers in diameter within host cells. The non-structural proteins (nsPs) of viruses are found to critically control the formation and maintenance of such unique nanoscale membrane structures. However, the molecular mechanism underlying the association and assembly of nsPs around curved membranes has remained elusive, due to the technical difficulties of imaging the nanoscale interaction in live cells. In this study, we leveraged vertically aligned nanostructures to define membrane curvatures in live cells and investigated the assembly of viral nsPs on these curved membranes. Using Chikungunya virus as a model system, we found that its nsP1 was preferentially bound to and stabilized around positively curved membrane sites and the preference became more apparent as the radius of curvature decreased to 150 nm or smaller. This preferential accumulation was mainly attributed to the hydrophobic residues on recently identified membrane association loops (MA loops) of each nsP1 monomer. Molecular dynamics simulations further confirmed the improved binding kinetics and stability of nsP1 on positively curved membranes, especially when a 12-mer ring was formed by the nsP1 dodecamer. More interestingly, a saddle curvature association was enabled by the 3D coordination of nsP1 monomers along the dodecamer ring, where the MA loops of individual nsP1 monomers sensed a wide range of the positively curved membrane in the x-z plane while the rigid nsP1 dodecamer ring stabilized the negative curvature in the x-y plane. Its strong coupling to the saddle curvature fulfilled the need to constrain the neck of the membrane spherule for viral replication. Strikingly, productive CHIKV replication exhibited strong enrichment on patterned nanostructure array, proving the effectiveness of membrane curvature-guided assembly of the functional replication complex. Our findings revealed that the cell membrane facilitated the local enrichment of viral nsPs in a curvature- dependent manner. It opens up membrane curvature modulation as a new dimension to dissect the formation and regulation of membrane compartments for viral replication.

biophysics↗

bioRxiv2025

SCRIPT: predicting single-cell long-range cis-regulation based on pretrained graph attention networks

Single-cell cis-regulatory relationships (CRRs) are essential for deciphering transcriptional regulation and understanding the pathogenic mechanisms of disease-associated non-coding variants. Existing computational methods struggle to accurately predict single-cell CRRs due to inadequately integrating causal biological principles and large-scale single-cell data. Here, we present SCRIPT (Single-cell Cis-regulatory Relationship Identifier based on Pre-Trained graph attention networks) that infers single-cell CRRs from transcriptomic and chromatin accessibility data. SCRIPT incorporates two key innovations: graph causal attention networks supported by empirical CRR evidence, and representation learning enhanced through pretraining on atlas-scale single-cell chromatin accessibility data. Validation using cell-type-specific chromatin contact data demonstrates that SCRIPT achieves a mean AUC of 0.9, significantly outperforming state-of-the-art methods (AUC: 0.68). Notably, SCRIPT obtains a threefold improvement in predicting long-range CRRs (>100 Kb) compared to existing methods. Applying SCRIPT to Alzheimers disease and schizophrenia, we establish a framework for prioritizing disease-causing variants and elucidating their functional effects in a cell-type-specific manner. By uncovering molecular genetic mechanisms undetected by existing computational methods, SCRIPT provides a roadmap for advancing genetic diagnosis and target discovery.

bioinformatics↗

bioRxiv2025

cellSTAAR: Incorporating single-cell-sequencing-based functional data to boost power in rare variant association testing of non-coding regions

Whole genome sequencing (WGS) studies have identified hundreds of millions of rare variants (RVs) and have enabled RV association tests (RVATs) of these variants with complex traits and diseases. Analysis of non-coding variants is challenged by the considerable variability in regulatory function which candidate Cis-Regulatory Elements (cCREs) exhibit across cell types. We propose cellSTAAR, which integrates WGS data with single-cell ATAC-seq data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and variant sets. To reflect the uncertainty in cCRE-gene linking, cellSTAAR also links cCREs to their target genes using an omnibus framework which aggregates results from a variety of popular linking approaches. We applied cellSTAAR on Freeze 8 (N = 60,000) of the NHLBI Trans-Omics for Precision Medicine (TOPMed) consortium data to four lipids phenotypes: LDL cholesterol, a binary variable corresponding to high LDL cholesterol, HDL cholesterol, and triglycerides. We also provide replication results for all four phenotypes using UK Biobank (N = 190,000). Evidence from simulation studies and our real data analysis demonstrates that cellSTAAR boosts power and improves interpretation of RVATs of cCREs.

genetics↗

bioRxiv2025

Trans-ancestry Mendelian Randomization Discovers Novel Causal Genes for Autoimmune Disease Traits

Mendelian randomization (MR) using summary statistics from genome-wide association studies (GWAS) has become a powerful tool for dissecting the causal relationships between exposure and outcomes. As GWAS begins to incorporate samples of diverse ancestries, the optimal strategy to perform MR analysis integrating these GWAS results remains to be determined. To fill in this gap, we proposed a robust method that aggregates the genetic variant-risk factor association summary statistics of multiple ancestries by modelling the genetic effects of the exposure as a function of the principal components of genome-wide allele frequency. This allows borrowing information from different ancestry groups and accounting for potential between-ancestry heterogeneities. The method is general and can be used with different MR methods. We demonstrated that our method improves accuracy and power compared to the inverse-variance weighting method based on only ancestry-stratified samples, with additional benefits in correcting winners curse bias. We also illustrated the flexibility and potential of discovering novel causal genes for autoimmune disease traits of our method in practice.

genetics↗

bioRxiv2025

Catastrophic drought effects on water quality and phytoplankton biomass in Nanwan Reservoir (Xinyang, China)

Climate change has led to increasingly frequent and unpredictable droughts and high-temperature events, creating extreme conditions that profoundly impact the productivity of freshwater ecosystems. This study investigates the Nanwan Reservoir, a large and deep reservoir in Xinyang, China, to evaluate the effects of extreme drought events on water quality and phytoplankton production. Field investigations were conducted during both high-temperature and drought (HTD) conditions in 2019, and during non-high-temperature and drought (NHTD) conditions in 2020. Results showed that HTD conditions significantly disrupted the thermocline and oxycline structures, leading to prolonged stratification during the HTD period. Although phosphorus concentrations remained relatively stable across both periods, nitrogen levels were markedly lower during HTD events--likely due to uptake by phytoplankton--indicating a shift in nutrient limitation from phosphorus to nitrogen. Additionally, a complex relationship between environmental variables and phytoplankton biomass was observed under HTD conditions. These findings advance our understanding of primary production responses to extreme weather events in the Nanwan Reservoir and highlight the importance of incorporating such knowledge into water resource management and ecological conservation strategies.

ecology↗

bioRxiv2025

Aicardi-Goutieres Syndrome Associated ADAR G1007R mutation dominantly induces neuroinflammation in mouse brain

The ADARG1007Rmutation is one of the most frequent mutations found in type six Aicardi-Goutieres Syndrome (AGS), a severe inflammatory encephalopathy in pediatric patients. We report here a mouse model bearing an human equivalent ADARG1007R mutation, and the heterozygous mice recapitulated some pathologic features of ADARG1007R AGS patients, including early-onset brain inflammation in heterozygous individuals and interferon-stimulated gene (ISG) expression within deep brain areas. Furthermore, we demonstrated that brain inflammation could be reversed by deletion of the cellular RNA receptor MDA5, which blocks the cellular RNA sensing signaling pathway. This model provides a unique tool for studying the molecular mechanisms underlying the heterozygous ADARWT/G1007R mutation in AGS brain pathogenesis. It may also be a valuable platform for developing personalized therapies for patients with this specific mutation.

neuroscience↗

bioRxiv2025

Reshaping epistatic network modules enhances wheat yield potential

Increasing wheat yield is a key approach to ensuring global food security and an enduring focus in crop breeding. Here, we assembled a panel of 3,030 wheat lines to dissect the genetic mechanisms of yield improvement. We conducted large-scale field trials across five ecological environments over two consecutive years to evaluate yield performance and constructed a haplotype atlas using the Wheat 660K genotype array. 234 quantitative trait loci (QTLs) and 522 haplotype blocks (HBs) were identified for 14 traits through genome-wide association studies (GWAS), including 10 QTLs and 35 HBs associated with grain yield. Genomic analysis revealed that wheat yield improvement is primarily driven by changes in the epistatic network rather than the introduction of new haplotype segments. SNPs or HBs explain 53.3%-62.9% of the phenotypic variation in yield, whereas epistasis explains 70.4%, highlighting the role of epistasis in yield improvement. Moreover, yield improvement is significantly correlated with the accumulation of favorable epistatic modules. Pedigree-based analysis revealed that the modules remained relatively stable under the pressure of breeding selection, while new favorable modules were also created during hybrid breeding, highlighting that crop design breeding should be based on genetic modules. This study provides insights into genetic mechanism of wheat yield improvement and guidance for designing future wheat in the era of artificial intelligence.

genetics↗

bioRxiv2025

Restoration of Susceptibility of blaNDM-bearing Escherichia. coli to Carbapenem Antibiotics by Exogenous N-acetylcysteine

The global spread of the New Delhi metallo-{beta}-lactamase (NDM) -producing carbapenem-resistant Enterobacteriaceae pose a serious threat to public health, as NDM and its variants enzymes efficiently hydrolyze almost {beta}-lactam antibiotics. The development of novel antibiotics or antibiotic adjuvants capable of eradicating antibiotic-resistance bacteria through multiple mechanisms represents a promising strategy for reversing antibiotic resistance and preventing the emergence of new resistance. In this study, we utilized high-performance liquid chromatography (HPLC) to demonstrate that N-acetylcysteine (NAC) is a prominent reducing metabolite in bacteria harboring NDM-5. Notably, NAC was able to restore carbapenem susceptibility in NDM-producing bacteria in vitro. Subsequently, we further investigated the underlying mechanisms involved. Our findings revealed that NAC exerts its effect through multiple mechanisms that collectively contribute to reversing meropenem resistance. Firstly, NAC may inhibit biofilm formation and reduces polysaccharide production in NDM-positive Escherichia coli. and this potential mechanism was verified by transcriptomic analysis, reverse transcription PCR (RT-PCR) and a biofilm restoration experiment involving the addition of citrate cycle intermediate metabolites. Secondly, RT-PCR analysis demonstrated that NAC significantly downregulates the expression of blaNDM-5. Additionally, NAC exhibited competitive inhibition of NDM activity in vitro. Thirdly, NAC was found to enhance the intracellular accumulation of meropenem, therapy increasing its bactericidal efficacy. These three independent mechanisms offer valuable insights for developing adjuvants. The discovery offers a promising prospect for combating the antibiotic resistance concern. This study paves the way for further exploration and potential clinical applications in the fight against antibiotic-resistant bacterial infections.

pharmacology and toxicology↗