Search Results (1,593)

Background: The candidate therapeutic peptide TnP demonstrates broad, system-level regulatory capacity, revealed through integrated network analysis from transcriptomic data in zebrafish. Our study primarily identifies TnP as a multifaceted modulator of drug metabolism, wound healing, proteolytic activity, and pigmentation pathways. Results: Transcriptomic profiling of TnP-treated larvae following tail fin amputation revealed 558 differentially expressed genes (DEGs), categorized into four functional networks: (1) drug-metabolizing enzymes (cyp3a65, cyp1a) and transporters (SLC/ABC families), where TnP alters xenobiotic processing through Phase I/II modulation; (2) cellular trafficking and immune regulation, with upregulated myosin genes (myhb/mylz3) enhancing wound repair and tlr5-cdc42 signaling fine-tuning inflammation; (3) proteolytic cascades (c6ast4, prss1) coupled to autophagy (ulk1a, atg2a) and metabolic rewiring (g6pca.1-tg axis); and (4) melanogenesis-circadian networks (pmela/dct-fbxl3l) linked to ubiquitin-mediated protein turnover. Key findings highlight TnP’s unique coordination of rapid (protease activation) and sustained (metabolic adaptation) responses, enabled by short network path lengths (1.6–2.1 edges). Hub genes, such as nr1i2 (pxr), ppara, and bcl6aa/b, mediate crosstalk between these systems, while potential risks—including muscle hypercontractility (myhb overexpression) or cardiovascular effects (ace2-ppp3ccb)—underscore the need for targeted delivery. The zebrafish model validated TnP-conserved mechanisms with human relevance, particularly in drug metabolism and tissue repair. TnP’s ability to synchronize extracellular matrix remodeling, immune resolution, and metabolic homeostasis supports its development for the treatment of fibrosis, metabolic disorders, and inflammatory conditions. Conclusions: Future work should focus on optimizing tissue-specific delivery and assessing genetic variability to advance clinical translation. This system-level analysis positions TnP as a model example for next-generation multi-pathway therapeutics. Full article

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

Background: Salmonella infections pose a serious threat to both animal and human health worldwide. Notably, there is an increasing trend in the resistance of Salmonella to fluoroquinolones, the first-line drugs for clinical treatment. Methods: Utilizing Salmonella Typhimurium CICC 10420 as the test strain, ciprofloxacin was used for in vitro induction to develop the drug-resistant strain H1. Changes in the minimum inhibitory concentrations (MICs) of various antimicrobial agents were determined using the broth microdilution method. Transcriptomic and metabolomic analyses were conducted to investigate alterations in gene and metabolite expression. A combined drug susceptibility test was performed to evaluate the potential of exogenous metabolites to restore antibiotic susceptibility. Results: The MICs of strain H1 for ofloxacin and enrofloxacin increased by 128- and 256-fold, respectively, and the strain also exhibited resistance to ceftriaxone, ampicillin, and tetracycline. A single-point mutation of Glu469Asp in the GyrB was detected in strain H1. Integrated multi-omics analysis showed significant differences in gene and metabolite expression across multiple pathways, including two-component systems, ABC transporters, pentose phosphate pathway, purine metabolism, glyoxylate and dicarboxylate metabolism, amino sugar and nucleotide sugar metabolism, pantothenate and coenzyme A biosynthesis, pyrimidine metabolism, arginine and proline biosynthesis, and glutathione metabolism. Notably, the addition of exogenous glutamine, in combination with tetracycline, significantly reduced the resistance of strain H1 to tetracycline. Conclusion: Ciprofloxacin-induced Salmonella resistance involves both target site mutations and extensive reprogramming of the metabolic network. Exogenous metabolite supplementation presents a promising strategy for reversing resistance and enhancing antibiotic efficacy. Full article

Background/Objectives: Listeria monocytogenes is a foodborne pathogen of major public health concern due to its ability to invade host cells and cause severe illness. This study aimed to develop and validate a multi-tiered screening pipeline to identify Bacillus strains with probiotic potential against L. monocytogenes. Methods: A total of 26 Bacillus isolates were screened for antimicrobial activity, gastrointestinal resilience, and host cell adhesion. A fluorescence-based infection assay using mCherry-expressing HCT 116 cells was used to assess cytoprotection against L. monocytogenes NCTC 7973. Eight strains significantly improved host cell viability and were validated by quantification of intracellular CFU. Two top candidates were tested in a murine model of listeriosis. The genome of the lead strain was sequenced to evaluate safety and biosynthetic potential. Results: B. subtilis CECT 8266 completely inhibited intracellular replication of L. monocytogenes in HCT 116 cells, reducing bacterial recovery to undetectable levels. In vivo, it decreased splenic bacterial burden by approximately 6-fold. Genomic analysis revealed eight bacteriocin biosynthetic clusters and silent antibiotic resistance genes within predicted genomic islands, as determined by CARD and Alien Hunter analysis. The strain also demonstrated bile and acid tolerance, as well as strong adhesion to epithelial cells. Conclusions: The proposed pipeline enables efficient identification of probiotic Bacillus strains with intracellular protective activity. B. subtilis CECT 8266 is a promising candidate for translational applications in food safety or health due to its efficacy, resilience, and safety profile. Full article

Background: In recent years, comprehensive analyses using a genome-wide association study (GWAS) have been conducted to identify genetic factors related to athletic performance. In this study, we investigated the association between genetic variants and elite wrestling status across multiple ethnic groups using a genome-wide genotyping approach. Methods: This study included 168 elite wrestlers (64 Japanese, 67 Turkish, and 36 Russian), all of whom had competed in international tournaments, including the Olympic Games. Control groups consisted of 306 Japanese, 137 Turkish, and 173 Russian individuals without elite athletic backgrounds. We performed a GWAS comparing allele frequencies of single-nucleotide polymorphisms (SNPs) between elite wrestlers and controls in each ethnic cohort. Cross-population analysis comprised (1) identifying SNPs with nominal significance (p < 0.05) in all three groups, then (2) meta-analyzing overlapped SNPs to assess effect consistency and combined significance. Finally, we investigated whether the most significant SNPs were associated with gene expression in skeletal muscle in 23 physically active men. Results: The GWAS identified 328,388 (Japanese), 23,932 (Turkish), and 30,385 (Russian) SNPs reaching nominal significance. Meta-analysis revealed that the ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms were associated (p < 0.0001) with elite wrestling status across all three populations. Both variants are located in intronic regions and influence the expression of their respective genes in skeletal muscle. Conclusions: This is the first study to investigate gene polymorphisms associated with elite wrestling status in a multi-ethnic cohort. ATP2A3 rs6502758 and UNC5C rs265061 polymorphisms may represent important genetic factors associated with achieving an elite status in wrestling, irrespective of ethnicity. Full article

High-temperature stress has become an important factor that has restricted the aquaculture industry. Huso dauricus is a high-economic-value fish that has faced the threat of thermal stress. Based on this point, our investigation aimed to explore the detailed mechanism of the negative impacts of heat stress on the liver metabolism functions in Huso dauricus. In this study, we set one control group (19 °C) and four high-temperature treatment groups (22 °C, 25 °C, 28 °C, 31 °C) with 40 fish in each group for continuous 53-day heat exposure. Histological analysis, biochemical detection, and transcriptome technology were used to explore the effects of heat stress on the liver structure and functions of juvenile Huso dauricus. It suggested heat-stress-induced obvious liver injury and reactive oxygen species accumulation in Huso dauricus with a time/temperature-dependent manner. Serum total protein, transaminase, and alkaline phosphatase activities showed significant changes under heat stress (p < 0.05). In addition, 6433 differentially expressed genes (DEGs) were identified based on the RNA-seq project. Gene Ontology enrichment analysis showed that various DEGs could be mapped to the lipid-metabolism-related terms. KEGG enrichment and immunohistochemistry analysis showed that ferroptosis and FoxO signaling pathways were significantly enriched (p < 0.05). These results demonstrated that thermal stress induced oxidative stress damage in the liver of juvenile Huso dauricus, which triggered lipid metabolism disorder and hepatocyte ferroptosis to disrupt normal liver functions. In conclusion, chronic thermal stress can cause antioxidant capacity imbalance in the liver of Huso dauricus to mediate the ferroptosis process, which would finally disturb the lipid metabolism homeostasis. In further research, it will be necessary to verify the detailed cellular signaling pathways that are involved in the heat-stress-induced liver function disorder response based on the in vitro experiment, while the multi-organ crosswalk mode under the thermal stress status is also essential for understanding the comprehensive mechanism of heat-stress-mediated negative effects on fish species. Full article

Objectives: Light intensity is a critical environmental factor regulating plant growth, development, and stress adaptation. However, the physiological and molecular mechanisms underlying light responses in Aconitum kusnezoffii, a valuable alpine medicinal plant, remain poorly understood. This study aimed to elucidate the adaptive strategies of A. kusnezoffii under different light intensities through integrated physiological and transcriptomic analyses. Methods: Two-year-old A. kusnezoffii plants were exposed to three controlled light regimes (790, 620, and 450 lx). Leaf anatomical traits were assessed via histological sectioning and microscopic imaging. Antioxidant enzyme activities (CAT, POD, and SOD), membrane lipid peroxidation (MDA content), osmoregulatory substances, and carbon metabolites were quantified using standard biochemical assays. Transcriptomic profiling was conducted using Illumina RNA-seq, with differentially expressed genes (DEGs) identified through DESeq2 and functionally annotated via GO and KEGG enrichment analyses. Results: Moderate light (620 lx) promoted optimal leaf structure by enhancing palisade tissue development and epidermal thickening, while reducing membrane lipid peroxidation. Antioxidant defense capacity was elevated through higher CAT, POD, and SOD activities, alongside increased accumulation of soluble proteins, sugars, and starch. Transcriptomic analysis revealed DEGs enriched in photosynthesis, monoterpenoid biosynthesis, hormone signaling, and glutathione metabolism pathways. Key positive regulators (PHY and HY5) were upregulated, whereas negative regulators (COP1 and PIFs) were suppressed, collectively facilitating chloroplast development and photomorphogenesis. Trend analysis indicated a “down–up” gene expression pattern, with early suppression of stress-responsive genes followed by activation of photosynthetic and metabolic processes. Conclusions: A. kusnezoffii employs a coordinated, multi-level adaptation strategy under moderate light (620 lx), integrating leaf structural optimization, enhanced antioxidant defense, and dynamic transcriptomic reprogramming to maintain energy balance, redox homeostasis, and photomorphogenic flexibility. These findings provide a theoretical foundation for optimizing artificial cultivation and light management of alpine medicinal plants. Full article

The protein p53, often referred to as the “guardian of the genome,” is essential for preserving cellular balance and preventing cancerous transformations. As one of the most commonly altered genes in human cancers, its impaired function is associated with tumor initiation, development, and resistance to treatment. Exploring the diverse roles of p53, which include regulating the cell cycle, repairing DNA, inducing apoptosis, reprogramming metabolism, and modulating immunity, provides valuable insights into cancer mechanisms and potential treatments. This review integrates recent findings on p53′s dual nature, functioning as both a tumor suppressor and an oncogenic promoter, depending on the context. Wild-type p53 suppresses tumors by inducing cell cycle arrest or apoptosis in response to genotoxic stress, while mutated variants often lose these functions or gain novel pro-oncogenic activities. Emerging evidence highlights p53′s involvement in non-canonical pathways, such as regulating tumor microenvironment interactions, metabolic flexibility, and immune evasion mechanisms. For instance, p53 modulates immune checkpoint expression and influences the efficacy of immunotherapies, including PD-1/PD-L1 blockade. Furthermore, advancements in precision diagnostics, such as liquid biopsy-based detection of p53 mutations and AI-driven bioinformatics tools, enable early cancer identification and stratification of patients likely to benefit from targeted therapies. Therapeutic strategies targeting p53 pathways are rapidly evolving. Small molecules restoring wild-type p53 activity or disrupting mutant p53 interactions, such as APR-246 and MDM2 inhibitors, show promise in clinical trials. Combination approaches integrating gene editing with synthetic lethal strategies aim to exploit p53-dependent vulnerabilities. Additionally, leveraging p53′s immunomodulatory effects through vaccine development or adjuvants may enhance immunotherapy responses. In conclusion, deciphering p53′s complex biology underscores its unparalleled potential as a biomarker and therapeutic target. Integrating multi-omics analyses, functional genomic screens, and real-world clinical data will accelerate the translation of p53-focused research into precision oncology breakthroughs, ultimately improving patient outcomes. Full article

Asia minor bluegrass (Polypogon fugax), a widespread Poaceae weed, exhibits broad tolerance to abiotic stresses. Validated reference genes (RGs) for reliable RT-qPCR normalization in this ecologically and agriculturally significant species remain unidentified. This study identified eight candidate RGs using transcriptome data from seedling tissues. We assessed the expression stability of these eight RGs across various abiotic stresses and developmental stages using Delta Ct, BestKeeper, geNorm, and NormFinder algorithms. A comprehensive stability ranking was generated using RefFinder, with validation performed using the target genes COR413 and P5CS. Results identified EIF4A and TUB as the optimal RG combination for normalizing gene expression during heat stress, cold stress, and growth stages. EIF4A and ACT were most stable under drought stress, EIF4A and 28S under salt stress, and EIF4A and EF-1 under cadmium (Cd) stress. Furthermore, EIF4A and UBQ demonstrated optimal stability under herbicide stress. Additionally, application of validated RGs revealed higher acetyl-CoA carboxylase gene (ACCase) expression in one herbicide-resistant population, suggesting target-site gene overexpression contributes to resistance. This work presents the first systematic evaluation of RGs in P. fugax. The identified stable RGs provide essential tools for future gene expression studies on growth and abiotic stress responses in this species, facilitating deeper insights into the molecular basis of its weediness and adaptability. Full article

Osteoporosis is a multifactorial, polygenic disease characterized by reduced bone mineral density (BMD) and increased fracture risk. Genome-wide association studies (GWASs) have identified numerous loci associated with BMD and/or bone fractures, but functional characterization of these target genes is essential to understand the biological mechanisms underlying osteoporosis. This review focuses on current methodologies and key examples of successful functional studies aimed at evaluating gene function in osteoporosis research. Functional evaluation typically follows a multi-step approach. In silico analyses using omics datasets expression quantitative trait loci (eQTLs), protein quantitative trait loci (pQTLs), and DNA methylation quantitative trait loci (mQTLs) help prioritize candidate genes and predict relevant biological pathways. In vitro models, including immortalized bone-derived cell lines and primary mesenchymal stem cells (MSCs), are used to explore gene function in osteogenesis. Advanced three-dimensional culture systems provide additional physiological relevance for studying bone-related cellular processes. In situ analyses of patient-derived bone and muscle tissues offer validation in a disease-relevant context, while in vivo studies using mouse and zebrafish models enable comprehensive assessment of gene function in skeletal development and maintenance. Integration of these complementary methodologies helps translate GWAS findings into biological insights and supports the identification of novel therapeutic targets for osteoporosis. Full article

Early diagnosis of lung cancer is crucial for improving patient prognosis. In this study, we developed a diagnostic model for lung cancer based on serum proteomic data from the GSE168198 dataset using four machine learning algorithms (nnet, glmnet, svm, and XGBoost). The model’s performance was validated on datasets that included normal controls, disease controls, and lung cancer data containing both. Furthermore, the model’s diagnostic capability was further validated on an independent external dataset. Our analysis identified SLC16A4 as a key protein in the model, which was significantly downregulated in lung cancer serum samples compared to normal controls. The expression of SLC16A4 was closely associated with clinical pathological features such as gender, tumor stage, lymph node metastasis, and smoking history. Functional assays revealed that overexpression of SLC16A4 significantly inhibited lung cancer cell proliferation and induced cellular senescence, suggesting its potential role in lung cancer development. Additionally, correlation analyses showed that SLC16A4 expression was linked to immune cell infiltration and the expression of immune checkpoint genes, indicating its potential involvement in immune escape mechanisms. Based on multi-omics data from the TCGA database, we further discovered that the low expression of SLC16A4 in lung cancer may be regulated by DNA copy number variations and DNA methylation. In conclusion, this study not only established an efficient diagnostic model for lung cancer but also identified SLC16A4 as a promising biomarker with potential applications in early diagnosis and immunotherapy. Full article

Porcine epidemic diarrhea virus (PEDV) infection induces severe, often fatal, watery diarrhea and vomiting in neonatal piglets, characterized by profound dehydration, villus atrophy, and catastrophic mortality rates approaching 100% in unprotected herds. This study developed a composite probiotic from Min-pig-derived Lactobacillus crispatus LCM233, Ligilactobacillus salivarius LSM231, and Lactiplantibacillus plantarum LPM239, which exhibited synergistic growth, potent acid/bile salt tolerance, and broad-spectrum antimicrobial activity against pathogens. In vitro, the probiotic combination disrupted pathogen ultrastructure and inhibited PEDV replication in IPI-2I cells. In vivo, PEDV-infected piglets administered with the multi-strain probiotic exhibited decreased viral loads in anal and nasal swabs, as well as in intestinal tissues. This intervention was associated with the alleviation of diarrhea symptoms and improved weight gain. Furthermore, the multi-strain probiotic facilitated the repair of intestinal villi and tight junctions, increased the number of goblet cells, downregulated pro-inflammatory cytokines, enhanced the expression of barrier proteins, and upregulated antiviral interferon-stimulated genes. These findings demonstrate that the multi-strain probiotic mitigates PEDV-induced damage by restoring intestinal barrier homeostasis and modulating immune responses, providing a novel strategy for controlling PEDV infections. Full article

Background: Breast cancer, the most prevalent malignancy among women worldwide, exhibits significant heterogeneity, particularly in the tumor microenvironment (TME), which poses challenges for treatment. Spatial transcriptomics (ST) has emerged as a transformative technology, enabling gene expression analysis while preserving tissue spatial architecture. This provides unprecedented insights into tumor heterogeneity, cellular interactions, and disease mechanisms, offering a powerful tool for advancing breast cancer research and therapy. This review aims to synthesize the applications of ST in breast cancer research, focusing on its role in decoding tumor heterogeneity, characterizing the TME, elucidating progression and metastasis dynamics, and predicting therapeutic responses. We also explore how ST can bridge molecular profiling with clinical translation to enhance precision therapy. The key scientific concepts of review included the following: We summarize the technological advancements in ST, including imaging-based and sequencing-based methods, and their applications in breast cancer. Key findings highlight how ST resolves spatial heterogeneity across molecular subtypes and histological variants. ST reveals the dynamic interplay between tumor cells, immune cells, and stromal components, uncovering mechanisms of immune evasion, metabolic reprogramming, and therapeutic resistance. Additionally, ST identifies spatial prognostic markers and predicts responses to chemotherapy, targeted therapy, and immunotherapy. We propose that ST serves as a hub for integrating multi-omics data, offering a roadmap for precision oncology and personalized treatment strategies in breast cancer. Full article

The escalating sophistication of cyber threats underscores the critical need for intelligent and adaptive intrusion detection systems (IDSs) to identify known and novel attack vectors in real time. Feature selection is a key enabler of performance in machine learning-based IDSs, as it reduces the input dimensionality, enhances the detection accuracy, and lowers the computational latency. This paper introduces a novel optimization framework called Quantum Epigenetic Algorithm (QEA), which synergistically combines quantum-inspired probabilistic representation with biologically motivated epigenetic gene regulation to perform efficient and adaptive feature selection. The algorithm balances global exploration and local exploitation by leveraging quantum superposition for diverse candidate generation while dynamically adjusting gene expression through an epigenetic activation mechanism. A multi-objective fitness function guides the search process by optimizing the detection accuracy, false positive rate, inference latency, and model compactness. The QEA was evaluated across four benchmark datasets—UNSW-NB15, CIC-IDS2017, CSE-CIC-IDS2018, and TON_IoT—and consistently outperformed baseline methods, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Quantum Genetic Algorithm (QGA). Notably, QEA achieved the highest classification accuracy (up to 97.12%), the lowest false positive rates (as low as 1.68%), and selected significantly fewer features (e.g., 18 on TON_IoT) while maintaining near real-time latency. These results demonstrate the robustness, efficiency, and scalability of QEA for real-time intrusion detection in dynamic and resource-constrained cybersecurity environments. Full article

Background: Boiling histotripsy (BH) uses high-amplitude, short-pulse focused ultrasound to disrupt tissue mechanically. Oncolytic virotherapy using reovirus has shown modest clinical benefit in pancreatic cancer patients. Here, reovirus and BH were used to treat pancreatic tumours, and their effects on the immune transcriptome of these tumours were characterised. Methods: Orthotopic syngeneic murine pancreatic KPC tumours grown in immune-competent subjects, were allocated to control, reovirus, BH and combined BH and reovirus treatment groups. Acoustic cavitation was monitored using a passive broadband cavitation sensor. Treatment effects were assessed histologically with hematoxylin and eosin staining. Single-cell multi-omics combining whole-transcriptome analysis with the expression of surface-expressed immune proteins was used to assess the effects of treatments on tumoural leukocytes. Results: Acoustic cavitation was detected in all subjects exposed to BH, causing cellular disruption in tumours 6 h after treatment. Distinct cell clusters were identified in the pancreatic tumours 24 h post-treatment. These included neutrophils and cytotoxic T cells overexpressing genes associated with an N2-like and an exhaustion phenotype, respectively. Reovirus decreased macrophages, and BH decreased regulatory T cells compared to controls. The combined treatments increased neutrophils and the ratio of various immune cells to Treg. All treatments overexpressed genes associated with an innate immune response, while ultrasound treatments downregulated genes associated with the transporter associated with antigen processing (TAP) complex. Conclusions: Our results show that the combined BH and reovirus treatments maximise the overexpression of genes associated with the innate immune response compared to that seen with each individual treatment, and illustrate the anti-immune phenotype of key immune cells in the pancreatic tumour microenvironment. Full article