Search Results (1,247)

Background: Mycoplasma pneumoniae pneumonia (MPP) is a common community-acquired pneumonia in children. Increasing drug resistance highlights the need for more effective treatments with fewer side effects. The Qingfei Tongluo Jiedu formula (QTJD) has demonstrated clinical efficacy against MPP; however, its underlying mechanisms remain unclear. This study aimed to explore the mechanism of QTJD on MPP using network pharmacology and in vitro experiments. Methods: Network pharmacology was used to identify the active compounds and signaling pathways of QTJD in MPP. QTJD-containing serum was prepared, and primary mouse lung and bone marrow cells were isolated to examine the effects of QTJD on macrophage polarization through butyric acid. Cell viability assays, flow cytometry, and quantitative reverse transcription-polymerase chain reaction were performed. GPR109^−/− cells were used to confirm the receptor mediating butyric acid’s action, and Western blotting was employed to assess the MAPK signaling pathway. Results: QTJD promoted macrophage polarization and alleviated the inflammatory response caused by Mycoplasma pneumoniae. High-performance liquid chromatography-electrospray ionization mass spectrometry combined with network pharmacology identified 20 active compounds. Protein-protein interaction analysis revealed 10 core target, including JUN and Tumor Necrosis Factor (TNF), while enrichment analysis highlighted pathways such as Mitogen-Activated Protein Kinase (MAPK) and Phosphoinositide 3-Kinase-Protein Kinase B. Experimental validation demonstrated that QTJD reduced M1 markers (CD86, CXCL10) by increasing butyrate levels (p < 0.01) and enhanced M2 markers (CD206, Arg-1, MRC-1), promoting M2 polarization. QTJD inhibited ERK1/2, p38, and JNK1/2 (p < 0.01). In GPR109A^−/− mice macrophages, QTJD suppressed p38 and JNK1/2 (p < 0.01) but showed no effect on ERK1/2 (p > 0.05), confirming involvement of the butyrate-GPR109A-MAPK pathway. Conclusions: QTJD effectively alleviates MPP by regulating macrophage polarization through the butyrate-GPR109A-MAPK pathway. Future studies should explore how QTJD modulates pulmonary immunity through gut microbiota and butyrate production and elucidate its immunoregulatory mechanisms along the gut-lung axis using multi-omics approaches. Full article

Intracellular metabolites markedly change in yeast during fermentation, especially under various stresses in beer post-fermentation. To address the current limitations in understanding the regulatory mechanisms in this complex environment, industrial brewing yeast was analyzed using integrated transcriptomics and proteomics across the post-fermentation phases, dynamically profiling the transcriptional levels and protein abundances of differentially expressed genes. As a result, 6110 differentially expressed genes (DEGs) and 3533 differentially expressed proteins (DEPs) were identified. Additionally, transcriptomics showed the induced expression of low-pH- and oxidative stress-related genes (HAL1, HAL4, YAP5), gluconeogenesis- and sugar transport-related genes (HXT, MAL, FBP), and mannan synthetic genes (FSK, MNN) during early post-fermentation. Moreover, heat-shock-related genes were upregulated throughout post-fermentation. Furthermore, proteomics revealed the sustained upregulation of glucosidase Scw, mannoprotein Pir, hexose transporter Hxt, and heat-shock proteins (Hsp). These findings indicate that yeast adapts to stress in the wort environment during post-fermentation by enhancing cell wall biosynthesis, activating heat-shock responses, and modulating metabolic pathways. These integrated omics analyses provide guidance for selecting robust, tolerant strains to industrial-scale stresses and improving beer flavor profiles, establishing a theoretical foundation for optimizing brewing and enhancing beer quality. Full article

Background and Objective: Colorectal cancer (CRC) liver metastasis (CRLM) represents a major clinical challenge, and acquired resistance to radiotherapy (RT) significantly limits therapeutic efficacy. A deep and comprehensive understanding of the cellular and molecular mechanisms driving RT resistance is urgently required to develop effective combination strategies. Here, we aimed to dissect the dynamic cellular landscape of the tumor microenvironment (TME) and identify key epigenetic regulators mediating radioresistance in CRLM by integrating cutting-edge single-cell and spatial omics technologies. Methods and Results: We performed integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) on matched pre- and post-radiotherapy tumor tissues collected from three distinct CRLM patients. Employing a robust machine-learning framework on the multi-omics data, we successfully identified MORF4L1 (Mortality Factor 4 Like 1), an epigenetic reader, as a critical epigenetic mediator of acquired radioresistance. High-resolution scRNA-seq analysis of the tumor cell compartment revealed that the MORF4L1-high subpopulation exhibited significant enrichment in DNA damage repair (DDR) pathways, heightened activity of multiple pro-survival metabolic pathways, and robust signatures of immune evasion. Pseudotime trajectory analysis further confirmed that RT exposure drives tumor cells toward a highly resistant state, marked by a distinct increase in MORF4L1 expression. Furthermore, cell–cell communication inference demonstrated a pronounced, systemic upregulation of various immunosuppressive signaling axes within the TME following RT. Crucially, high-resolution ST confirmed these molecular and cellular interactions in their native context, revealing a significant spatial co-localization of MORF4L1-expressing tumor foci with multiple immunosuppressive immune cell types, including regulatory T cells (Tregs) and tumor-associated macrophages (TAMs), thereby underscoring its role in TME-mediated resistance. Conclusions: Our comprehensive spatial and single-cell profiling establishes MORF4L1 as a pivotal epigenetic regulator underlying acquired radioresistance in CRLM. These findings provide a compelling mechanistic rationale for combining radiotherapy with the targeted inhibition of MORF4L1, presenting a promising new therapeutic avenue to overcome treatment failure and improve patient outcomes in CRLM. Full article

Background: Coronavirus disease 2019 (COVID-19) vaccines deliver mRNA packaged in lipid nanoparticles via intramuscular injection. This study investigated several factors influencing antibody production patterns and adverse reactions after vaccination with COVID-19 vaccines. Methods: Among the participants of the Iwaki Health Promotion Project (IHPP), 211 individuals who consented to this study were surveyed regarding antibody titers and adverse reaction symptoms following vaccination. A machine learning approaches such as ridge regression, elastic-net, light gradient boosting, and neural network were applied to extract the variables, and Bayesian network analysis was applied to explore causal relationships between health data and the multi-omics dataset obtained from the IHPP health checkups. Results: Females with lower levels of free testosterone experienced more adverse reactions than males. Moreover, the immune system is more active in younger individuals, causing adverse reactions and higher antibody production. The Spikevax vaccine induced adverse reaction symptoms with higher antibody production in cases of fever. Meanwhile, drinking 2–3 cups of green tea daily seemed to be effective in increasing antibody production. Factors increasing side effect risk include blood natural killer cell count and muscle quality in the vaccinated arm. Plasma metabolome metabolite concentrations, tongue coating bacterial colonization, and folate intake were also identified as factors influencing side effect risk. Furthermore, characteristics of participants at risk for fever symptoms included longer telomere length, higher antibody production patterns, and higher CD4-positive T cell counts. Conclusions: Further investigation of these identified influencing factors is expected to clarify the rationale for new vaccine development and identify lifestyle and dietary habits that enhance vaccine efficacy. Full article

The rapid advancement of sequencing technologies has led to the generation of complex multi-omics data, which are often high-dimensional, noisy, and imbalanced, posing significant challenges for traditional machine learning methods. The novelty of this work resides in the architecture-level integration of autoencoders with Generative Adversarial Network (GAN) and Conditional Tabular Generative Adversarial Network (CTGAN) models, where the autoencoder is employed for latent feature extraction and noise reduction, while GAN-based models are used for realistic sample generation and class imbalance mitigation in multi-omics cancer datasets. This study proposes a novel framework that combines an autoencoder for dimensionality reduction and a CTGAN for generating synthetic samples to balance underrepresented classes. The process starts with selecting the most discriminative features, then extracting latent representations for each omic type, merging them, and generating new minority samples. Finally, all samples are used to train a neural network to predict specific cancer outcomes, defined here as clinically relevant biomarkers or patient characteristics. In this work, the considered outcome in the bladder cancer is Tumor Mutational Burden (TMB), while the breast cancer outcome is menopausal status, a key factor in treatment planning. Experimental results show that the proposed model achieves high precision, with an average precision of 0.9929 for TMB prediction in bladder cancer and 0.9748 for menopausal status in breast cancer, and reaches perfect precision (1.000) for the positive class in both cases. In addition, the proposed AE–CTGAN framework consistently outperformed an autoencoder combined with a standard GAN across all evaluation metrics, achieving average accuracies of 0.9929 and 0.9748, recall values of 0.9846 and 0.9777, and F1-scores of 0.9922 for bladder and breast cancer datasets, respectively. A comparative fidelity analysis in the latent space further demonstrated the superiority of CTGAN, reducing the average Euclidean distance between real and synthetic samples by approximately 72% for bladder cancer and by up to 84% for breast cancer compared to a standard GAN. These findings confirm that CTGAN generates high-fidelity synthetic samples that preserve the structural characteristics of real multi-omics data, leading to more reliable class balancing and improved predictive performance. Overall, the proposed framework provides an effective and robust solution for handling class imbalance in multi-omics cancer data and enhances the accuracy of clinically relevant outcome prediction. Full article

Reproductive performance in livestock and poultry is a core determinant of economic efficiency in the animal industry. While traditional research has primarily focused on genetics, endocrinology, and immune regulation, emerging microbiome studies reveal that commensal microbiota within the gut and reproductive tracts play an underestimated yet pivotal role in host reproductive health. This review systematically synthesizes recent advances regarding the relationship between the microbiome and reproductive functions in major livestock species (cattle, pigs, sheep, and chickens). We first delineate the theoretical basis and mechanisms of the “gut-reproductive axis,” highlighting cross-system communication mediated by microbial metabolites, including short-chain fatty acids (SCFAs), indoles, and bile acids. Subsequently, we provide an in-depth comparative analysis of the microecological features of both female (vagina/uterus) and male (semen/epididymis) reproductive systems, examining their impacts on fertility, sperm quality, and pregnancy outcomes. Furthermore, we explore the molecular and systemic mechanisms governing microbial regulation of reproduction, encompassing the modulation of the hypothalamic-pituitary-gonadal (HPG) axis, the balance of local mucosal immunity and inflammation, and epigenetic regulation. Finally, we address current challenges—such as causal validation and the scarcity of multi-species databases—and propose future directions, including spatial multi-omics, AI-integrated analysis, and microbial intervention strategies. Ultimately, this review aims to offer a theoretical foundation and translational insights for elucidating reproductive regulatory networks and developing microbiome-driven precision strategies to enhance reproductive performance. Full article

The neurofibromin 1 (NF1) splice-site mutation c.61-2A>G (rs1131691100) is a rare, pathogenic, autosomal dominant variant that disrupts NF1 tumor-suppressor function, causing neurofibromatosis type 1 (NF1). Its pathogenic mechanism is poorly understood, and the potential for personalized therapeutic genome editing remains unknown due to the absence of a standard framework for investigating splicing disorders. Here, we performed a comprehensive multi-omics analysis of a de novo c.61-2A>G case from South Korea, integrating short- and long-read whole genome sequencing, whole transcriptome sequencing, and methylation profiling. We confirm that c.61-2A>G abolishes the canonical splice acceptor site, activating a cryptic splice acceptor 16 nucleotides downstream in exon 2. This splicing shift generates a 16-nucleotide deletion, causing a frameshift and premature stop codon that truncates the protein’s N-terminal region. Long-read sequencing further reveals that the mutation creates a novel CpG dinucleotide, which is methylated in the majority of reads. Finally, we assessed therapeutic correction strategies, revealing that CRISPR-Cas9 prime editing is the only viable approach for in vivo correction. This study provides the first comprehensive multi-omics characterization of the NF1 c.61-2A>G mutation and establishes a minimal framework for precision therapeutic development in silico in monogenic splicing disorders. Full article

Brachiaria decumbens is a high-yielding forage grass of major economic value in tropical regions. The root endophytic fungus Piriformospora indica is widely recognized for promoting plant growth and stress tolerance, yet its effects on B. decumbens remain poorly characterized. Here, we profiled root responses to P. indica colonization at 10 days after inoculation (dais; early stage) and 20 dais (late stage) during symbiosis establishment. Colonization was confirmed by phenotypic and physiological assessments, with inoculated plants showing enhanced root growth; colonized roots exhibited higher activities of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD), along with increased indole-3-acetic acid (IAA) levels, whereas malondialdehyde (MDA), jasmonic acid (JA), and the ethylene precursor 1-aminocyclopropane-1-carboxylic acid (ACC) were reduced. Transcriptome and metabolomic profiling identified 1884 and 1077 differentially expressed genes (DEGs) and 2098 and 1509 differentially accumulated metabolites (DAMs) at 10 dais (Pi10d vs. CK10d) and 20 dais (Pi20d vs. CK20d), respectively, and 3355 DEGs and 2314 DAMs between stages (Pi20d vs. Pi10d). Functional enrichment highlighted key pathways related to secondary metabolism, carbohydrate metabolism, and lipid biosynthesis. Differentially expressed transcription factors spanned multiple families, including MYB, AP2/ERF, MADS-box, and bZIP, consistent with broad transcriptional reprogramming during symbiosis establishment. Integrative multi-omics analysis further highlighted phenylpropanoid biosynthesis and α-linolenic acid metabolism as consistently co-enriched pathways, suggesting coordinated shifts in gene expression and metabolite accumulation across colonization stages. Collectively, these results provide a multi-layered resource and a framework for mechanistic dissection of the P. indica–B. decumbens interaction. Full article

Background: Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, characterized by high genomic instability, metabolic stress, and limited therapeutic options. Ferroptosis, an iron-dependent form of regulated cell death, has emerged as a promising vulnerability in TNBC, yet its subtype-specific regulatory landscape remains insufficiently defined. Methods: Using transcriptomic (METABRIC, TCGA, GEO) and proteomic (CPTAC) datasets, ferroptosis-related genes were profiled across PAM50 breast cancer subtypes. Differential expression, univariate Cox regression, LASSO modeling, survival analyses, GSEA, and dimensionality reduction (PCA, t-SNE) were applied. A Ferroptosis Index (FI) was calculated using β-coefficients from the Cox/LASSO regression model. Single-cell RNA-seq data was used to map ferroptosis-associated signature across tumor and microenvironmental compartments. Results: Basal-like tumors exhibited the strongest ferroptosis-associated transcriptional shift, characterized by upregulation of ACSL4 and EZH2 and downregulation of AR, GPX4, and CIRBP. Sixteen ferroptosis-related genes were associated with overall survival, forming a ferroptosis-associated signature. The FI was significantly higher in Basal-like tumors, indicating elevated ferroptosis-associated transcriptional state. GSEA revealed enrichment of cell cycle, mitotic, cytoskeletal, and metabolic stress pathways. Single-cell analysis demonstrated expression of ferroptosis markers across cancer epithelial, stromal, and myeloid populations. Conclusions: Basal-like tumors harbor a distinct ferroptosis-associated transcriptional state linked to tumor aggressiveness and poor prognosis. These findings provide a biologically grounded framework for ferroptosis-related stratification and support future functional and translational studies targeting ferroptosis vulnerabilities in aggressive breast cancer. Full article

Sepsis is a life-threatening syndrome characterized by marked clinical heterogeneity and complex host–pathogen interactions. Although traditional mechanistic studies have identified key molecular pathways, they remain insufficient to capture the highly dynamic, multifactorial, and systems-level nature of this condition. The advent of high-throughput omics technologies—particularly integrative multi-omics approaches encompassing genomics, transcriptomics, proteomics, and metabolomics—has profoundly reshaped sepsis research by enabling comprehensive profiling of molecular perturbations across biological layers. However, the unprecedented scale, dimensionality, and heterogeneity of multi-omics datasets exceed the analytical capacity of conventional statistical methods, necessitating more advanced computational strategies to derive biologically meaningful and clinically actionable insights. In this context, artificial intelligence (AI) has emerged as a powerful paradigm for decoding the complexity of sepsis. By leveraging machine learning and deep learning algorithms, AI can efficiently process ultra-high-dimensional and heterogeneous multi-omics data, uncover latent molecular patterns, and integrate multilayered biological information into unified predictive frameworks. These capabilities have driven substantial advances in early sepsis detection, molecular subtyping, prognosis prediction, and therapeutic target identification, thereby narrowing the gap between molecular mechanisms and clinical application. As a result, the convergence of AI and multi-omics is redefining sepsis research, shifting the field from descriptive analyses toward predictive, mechanistic, and precision-oriented medicine. Despite these advances, the clinical translation of AI-driven multi-omics approaches in sepsis remains constrained by several challenges, including limited data availability, cohort heterogeneity, restricted interpretability and causal inference, high computational demands, difficulties in integrating static molecular profiles with dynamic clinical data, ethical and governance concerns, and limited generalizability across populations and platforms. Addressing these barriers will require the establishment of standardized, multicenter datasets, the development of explainable and robust AI frameworks, and sustained interdisciplinary collaboration between computational scientists and clinicians. Through these efforts, AI-enabled multi-omics research may progress toward reproducible, interpretable, and equitable clinical implementation. Ultimately, the synergy between artificial intelligence and multi-omics heralds a new era of intelligent discovery and precision medicine in sepsis, with the potential to transform both research paradigms and bedside practice. Full article

Background/Objectives: Prebiotics, which are non-digestible compounds that selectively modulate gut microbiota, are recognized for their potential to promote host health. Although their bifidogenic effect is well documented, a systematic synthesis of how this microbial modulation translates into clinical gastrointestinal (GI) and metabolic outcomes across diverse populations is needed. This review aims to integrate mechanistic insights with clinical evidence to elucidate the pathway from prebiotic structures to tangible health benefits. Methods: This comprehensive narrative review details the structural properties of major prebiotics (e.g., inulin, FOS, and GOS) that govern their fermentation and the production of short-chain fatty acids (SCFAs). To evaluate clinical efficacy, an analysis of 22 randomized controlled trials from the past decade was conducted, focusing on human studies that utilized ISAPP-recognized prebiotics as the sole intervention. Results: The analysis confirms that prebiotic supplementation consistently increased the abundance of beneficial bacteria (e.g., Bifidobacterium and Lactobacillus) and SCFA production. These changes are associated with significant clinical improvements, including enhanced stool frequency and consistency, strengthened intestinal barrier function, and modulated immune responses. Benefits have been documented in healthy individuals, children, the elderly, and those with conditions such as constipation, metabolic syndrome, and antibiotic-associated dysbiosis. However, significant inter-individual variability in response was evident, and the study designs showed notable heterogeneity in prebiotic type, dosage, and duration. Conclusions: Prebiotics are effective modulators of gut health, driving clinical benefits through selective microbial fermentation and SCFA production. The documented heterogeneity and variability highlight the need for future research to focus on personalized nutritional strategies. Key priorities include standardizing intervention protocols, elucidating dose–response relationships, integrating multi-omics data to link taxonomy to function, and exploring novel applications such as synbiotic formulations and gut–brain axis modulation. Full article

This review focuses on the research progress on natural products as β-lactam antibiotic adjuvants, aiming to address the escalating challenge of antibiotic resistance, particularly the inactivation of antibiotics caused by β-lactamases. The article provides an in-depth analysis of the mechanisms by which plant-derived (e.g., flavonoids, tannins, phenolics, terpenoids, and alkaloids) and microbial-derived (e.g., clavulanic acid, fungal metabolites, bacteriophages) natural products enhance antimicrobial efficacy. Key potentiation strategies discussed include efflux pump inhibition, membrane permeability alteration, biofilm disruption, PBP2a inhibition, and direct β-lactamase inhibition. Additionally, the review outlines in vitro methods (e.g., dilution and checkerboard assays) and in vivo models (e.g., mouse infection models) used to assess synergistic effects. It also addresses major challenges in identifying active compounds, elucidating mechanisms of action, and pharmacokinetic characterization. Looking forward, the article highlights the potential of multi-omics approaches, artificial intelligence, and nanotechnology to overcome existing bottlenecks, providing novel strategies for the development of effective and safe antibiotic adjuvants. These advances are expected to provide both theoretical insights and practical guidance for combating antibiotic-resistant bacterial infections. Full article

Background/Objectives: The arrangement and positioning of genes on chromosomes are non-random in plant genomes. Adjacent gene pairs often exhibit similar co-expression patterns and regulatory mechanisms. However, the genomic and epigenetic features influencing such co-expression, particularly in perennial crops like tea (Camellia sinensis), remain largely uncharacterized. Methods: Firstly, we identified 771 specific neighboring gene pairs (SNGs) in C. sinensis (YK10) and investigated the contributions of intergenic distance and gene length to SNGs’ co-expression. Secondly, we integrated multi-omics data including transcriptome, ATAC-seq, Hi-C and histone modification data to explore the factors influencing their co-expression. Thirdly, we employed logistic regression models to individually assess the contributions of nine factors—ATAC-seq, H3K27ac, Hi-C, GO, distance, length, promoter, enhancer, and expression level—to the co-expression of SNGs. Finally, by integrating co-expression networks with metabolic profiles, several transcription factors potentially involved in the regulation of catechin metabolic pathways were identified. Results: Intergenic distance was significantly negatively correlated with co-expression strength, while gene length showed a positive correlation. Furthermore, these two features exerted synergistic effects with threshold characteristics and functional significance. SNGs marked by either ATAC-seq or H3K27ac peaks displayed significantly higher expression levels, suggesting that epigenetic regulation promotes co-expression. In addition, correlation analysis revealed that the expression of certain SNGs was closely associated with catechin accumulation, particularly epicatechin gallate (EGC) and epigallocatechin gallate (EGCG), highlighting their potential role in modulating tissue-specific catechin levels. Conclusions: Collectively, this study reveals a multilayered regulatory framework governing SNG co-expression and provides theoretical insights and candidate regulators for understanding metabolic regulation in tea plants. Full article

RNA-directed DNA methylation (RdDM), a new plant breeding technology, induces epigenetic modifications that can be inherited even after segregation of the responsible transgene. The transgene-free descendants (null segregants) are potentially exempt from the regulation of genetically modified plants. To evaluate the risks of potential unintended molecular changes in the null segregants of RdDM-positive plants, we produced null segregants (S44end2-null) from a transgenic tobacco line in which RdDM targeting the promoter of the transgene was introduced. Comprehensive multi-omics analyses, including transcriptomics, proteomics, and metabolomics, were conducted using S44end2-null and wild-type (WT) plants. Principal component analysis demonstrated clear separation of the transcriptomic and proteomic profiles of the two groups. The metabolomic profiles of S44end2-null plants exhibited considerable overlap with those of WT plants. Proteomic analysis of the null segregants of tobacco plants transformed with an empty vector demonstrated distinct cluster separation from WT plants. Because only sporadic DNA methylation on the tobacco genome was expected by the RdDM construct used in this study, the observed differences in omics profiles are considered to be significantly influenced by genetic variation accumulated during the transformation and regeneration processes (somaclonal variation). The safety assessment points for null segregants using RdDM technology are discussed. Full article