Search Results (675)

Background/Objectives: Myelodysplastic syndrome (MDS) is a heterogeneous clonal hematopoietic disorder characterized by ineffective hematopoiesis and leukemic transformation risk. Current therapies show limited efficacy, with ~50% of patients failing hypomethylating agents. This review aims to synthesize recent discoveries through an integrated network model and examine translation into precision therapeutic approaches. Methods: We reviewed breakthrough discoveries from the past three years, analyzing single-cell multi-omics technologies, epitranscriptomics, stem cell architecture analysis, and precision medicine approaches. We examined cell-type-specific splicing aberrations, distinct stem cell architectures, epitranscriptomic modifications, and microenvironmental alterations in MDS pathogenesis. Results: Four interconnected mechanisms drive MDS: genetic alterations (splicing factor mutations), aberrant stem cell architecture (CMP-pattern vs. GMP-pattern), epitranscriptomic dysregulation involving pseudouridine-modified tRNA-derived fragments, and microenvironmental changes. Splicing aberrations show cell-type specificity, with SF3B1 mutations preferentially affecting erythroid lineages. Stem cell architectures predict therapeutic responses, with CMP-pattern MDS achieving superior venetoclax response rates (>70%) versus GMP-pattern MDS (<30%). Epitranscriptomic alterations provide independent prognostic information, while microenvironmental changes mediate treatment resistance. Conclusions: These advances represent a paradigm shift toward personalized MDS medicine, moving from single-biomarker to comprehensive molecular profiling guiding multi-target strategies. While challenges remain in standardizing molecular profiling and developing clinical decision algorithms, this systems-level understanding provides a foundation for precision oncology implementation and overcoming current therapeutic limitations. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. 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

Plant diseases resulting from pathogens and pests constitute a persistent threat to global food security. Pathogenic infections of plants are influenced by environmental factors; a concept encapsulated in the “disease triangle” model. It is important to elucidate the complex molecular mechanisms underlying the interactions among plants, their pathogens and various environmental factors in the disease triangle. This review aims to highlight recent advancements in the application of systems biology to enhance understanding of the plant disease triangle within the context of microbiome rising to become the 4th dimension. Recent progress in microbiome research utilizing model plant species has begun to illuminate the roles of specific microorganisms and the mechanisms of plant–microbial interactions. We will examine (1) microbiome-mediated functions related to plant growth and protection, (2) advancements in systems biology, (3) current -omics methodologies and new approaches, and (4) challenges and future perspectives regarding the exploitation of plant defense mechanisms via microbiomes. It is posited that systems biology approaches such as single-cell RNA sequencing and mass spectrometry-based multi-omics can decode plant defense mechanisms. Progress in this significant area of plant biology has the potential to inform rational crop engineering and breeding strategies aimed at enhancing disease resistance without compromising other pathways that affect crop yield. Full article

The identification of microRNA (miRNA) biomarkers is crucial in advancing disease research and improving diagnostic precision. Network-based analysis methods are powerful for identifying disease-related biomarkers. However, it is a challenge to generate a robust molecular network that can accurately reflect miRNA interactions and define reliable miRNA biomarkers. To tackle this issue, we propose a disease-related miRNA biomarker identification method based on the knowledge-enhanced bio-network (BIM-Ken) by combining the miRNA expression data and prior knowledge. BIM-Ken constructs the miRNA cooperation network by examining the miRNA interactions based on the miRNA expression data, which contains characteristics about the specific disease, and the information of the network nodes (miRNAs) is enriched by miRNA knowledge (i.e., miRNA-disease associations) from databases. Further, BIM-Ken optimizes the miRNA cooperation network using the well-designed GAE (graph auto-encoder). We improve the loss function by introducing the functional consistency and the difference prompt, so as to facilitate the optimized network to keep the intrinsically important characteristics of the miRNA data about the specific disease and the prior knowledge. The experimental results on the public datasets showed the superiority of BIM-Ken in classification. Subsequently, BIM-Ken was applied to analyze renal cell carcinoma data, and the defined key modules demonstrated involvement in the cancer-related pathways with good discrimination ability. Full article

The global antimicrobial resistance crisis demands innovative strategies to combat bacterial infections, including those caused by drug-sensitive pathogens that evade treatment through biofilm formation or metabolic adaptations. Here, we demonstrate that Squama Manitis extract (SME)—a traditional Chinese medicine component—exhibits broad-spectrum bactericidal activity against clinically significant pathogens, including both Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) species (MIC = 31.25 mg/mL), achieving significant reduction in bacterial viability within 24 h. Through integrated multi-omics analysis combining scanning electron microscopy and RNA sequencing, we reveal SME’s unprecedented tripartite mechanism of action: (1) direct membrane disruption causing cell envelope collapse, (2) metabolic paralysis through coordinated suppression of TCA cycle and fatty acid degradation pathways, and (3) inhibition of DNA repair systems (SOS response and recombination downregulation). Despite its potent activity, SME shows low cytotoxicity toward mammalian cells (>90% viability) and can penetrate Gram-negative outer membranes. These features highlight SME’s potential to address drug-resistant infections through synthetic lethality across stress response, energy metabolism, and DNA integrity pathways. While advocating for synthetic alternatives to endangered animal products, this study establishes SME as a polypharmacological template for resistance-resilient antimicrobial design, demonstrating how traditional knowledge and modern systems biology can converge to guide sustainable anti-infective development. Full article

This study integrated non-targeted metabolomics and transcriptomics to investigate dynamic changes in Antheraea pernyi pupae across five developmental stages. Metabolomic analysis identified 1246 metabolites, primarily organic acids, lipids, heterocyclic compounds, and oxygen-containing organics. Principal component analysis revealed stage-specific metabolic profiles: amino acid derivatives (pyruvate, proline, lysine) declined, while pyrimidines (cytidine, uridine, β-alanine) and monosaccharides (glucose, mannose) increased. 18β-glycyrrhetinic and ursolic acids accumulated significantly in the middle and late stages. Transcriptomic analysis identified 7230 differentially expressed genes (DEGs), with 366, 1705, and 5159 significantly differentially expressed genes in the T1, T3, and T5 comparison groups, respectively. KEGG enrichment highlighted ABC transporters, amino acid/pyrimidine metabolism, and tyrosine pathways as developmentally critical, with aminoacyl-tRNA biosynthesis upregulated in later phases. Integrated multi-omics analysis revealed coordinated shifts in metabolites and genes across developmental phases, reflecting dynamic nutrient remodeling during pupal maturation. This study systematically delineates the molecular transitions driving pupal development in Antheraea pernyi pupae, uncovering conserved pathway interactions and mechanistic insights into nutrient metabolism. These findings provide a scientific foundation for leveraging pupal resources in functional food innovation and bioactive compound discovery for pharmaceutical applications. Full article

Background/Objectives: Obesity is increasingly recognized as a global health concern due to its association with metabolic disorders and gut microbiota dysbiosis. While probiotics offer promise in regulating gut microbiota and improving host metabolism, strain-specific effects remain underexplored, particularly for canine-derived probiotics. This study aimed to isolate and characterize a novel probiotic strain, Ligilactobacillus animalis LA-1, and evaluate its anti-obesity effects and underlying mechanisms using a high-fat diet (HFD)-induced obese mouse model. Methods: LA-1 was isolated from the feces of a healthy dog and assessed for probiotic potential in vitro, including gastrointestinal tolerance, bile salt hydrolase activity, cholesterol-lowering capacity, and fatty acid absorption. Male C57BL/6J mice were fed either a standard chow diet or an HFD for 16 weeks, with HFD mice receiving oral LA-1 supplementation (2 × 10⁹ CFU/day). Multi-omics analyses, including 16S rRNA gene sequencing, short-chain fatty acid (SCFA) quantification, and untargeted liver metabolomics, were employed to investigate the effects of LA-1 on gut microbiota composition, metabolic pathways, and obesity-related phenotypes. Results: LA-1 supplementation significantly alleviated HFD-induced weight gain, hepatic lipid accumulation, and adipose tissue hypertrophy, without affecting food intake. It improved serum lipid profiles, reduced liver injury markers, and partially restored gut microbiota composition, decreasing the Firmicutes/Bacteroidetes ratio and enriching SCFA-producing genera. Total SCFA levels, particularly acetate, propionate, and butyrate, increased following LA-1 treatment. Liver metabolomics revealed that LA-1 modulated pathways involved in lipid and amino acid metabolism, resulting in decreased levels of acetyl-CoA, triglycerides, and bile acids. Conclusions: L. animalis LA-1 exerts anti-obesity effects via gut microbiota modulation, enhanced SCFA production, and hepatic metabolic reprogramming. These findings highlight its potential as a targeted probiotic intervention for obesity and metabolic disorders. Full article

Background/Objective: Oral lichen planus (OLP) is a chronic inflammatory disorder of the oral mucosa with unclear etiology. Increasing evidence implicates oral microbial dysbiosis in its pathogenesis, but little is known about supragingival plaque communities in relation to clinical subtypes. This cross-sectional case–control study aimed to characterize the supragingival plaque microbiota and microbial interaction networks in erosive OLP (E-OLP), non-erosive OLP (NE-OLP), and healthy controls (HCs), to elucidate microbial patterns associated with disease severity. Methods: Supragingival plaque samples were collected from 90 participants (30 per group) and analyzed using 16S rRNA gene sequencing. Alpha and beta diversity metrics, differential abundance, and co-occurrence network analyses were performed. Results: E-OLP exhibited pronounced dysbiosis, including the enrichment of pro-inflammatory taxa (e.g., Prevotella, Parvimonas) and depletion of health-associated commensals (e.g., Rothia, Capnocytophaga). Network analysis revealed the stepwise disintegration of microbial community structure from HC to NE-OLP to E-OLP, with reduced connectivity and increased dominance of pathogenic clusters in E-OLP. These microbial alterations aligned with clinical findings, as E-OLP patients showed significantly higher Reticulation/keratosis, Erythema, and Ulceration (REU) scores for erythema and ulceration compared to NE-OLP. Conclusions: Supragingival plaque dysbiosis and ecological disruption are strongly associated with OLP severity and subtype. This study highlights the utility of plaque-based microbial profiling in capturing lesion-proximal dysbiotic signals, which may complement mucosal and salivary analyses in future diagnostic frameworks. Multi-omics approaches incorporating fungal, viral, and metabolic profiling are warranted to fully elucidate host–microbe interactions in OLP. Full article

Epigenetics, as an important scientific field that bridges genomic function and phenotypic plasticity, increasingly demonstrates its value in bee research. In recent years, with the rapid development of omics technologies, there have been significant advancements in the study of epigenetics in honeybees. This article reviews the role of epigenetic regulation in the development, behavioral regulation, and immune response of honeybee larvae from the perspectives of DNA methylation, histone modification, and non-coding RNA. With the continuous deepening of related research, honeybee epigenetics not only opens new paths for understanding the formation mechanisms of complex traits in social insects but also provides solid theoretical support and innovative perspectives for the study of social insects and beekeeping practices. These insights also inform sustainable beekeeping practices. Full article

Background and Objectives: VPS26A, a core component of the retromer complex, is pivotal to endosomal trafficking and membrane protein recycling. However, its expression profile, prognostic significance, and clinical relevance in liver hepatocellular carcinoma (LIHC) remain unexplored. This study investigates the prognostic potential of VPS26A by extensively analyzing publicly available LIHC-related databases. Materials and Methods: Multiple databases, including TIMER, UALCAN, HPA, GSCA, KM Plotter, OSlihc, MethSurv, miRNet, OncomiR, LinkedOmics, GeneMANIA, and STRING, were used to evaluate VPS26A expression patterns, prognostic implications, correlations with tumor-infiltrating immune cells (TIICs), epigenetic modifications, drug sensitivity, co-expression networks, and protein–protein interactions in LIHC. Results: VPS26A was significantly overexpressed at both the mRNA and protein levels in LIHC tissues compared to that in normal tissues. This upregulation was strongly associated with a poor prognosis. Furthermore, VPS26A expression was both positively and negatively correlated with various TIICs. Epigenetic analysis indicated that VPS26A is regulated by promoter and regional DNA methylation. Additionally, VPS26A influences the sensitivity of LIHC cells to a broad range of anticancer agents. Functional enrichment and co-expression analyses revealed that VPS26A serves as a central regulator of the LIHC transcriptomic landscape, with positively correlated gene sets linked to poor prognosis. Additionally, VPS26A contributes to the molecular architecture governing vesicular trafficking, with potential relevance to diseases involving defects in endosomal transport and autophagy. Notably, miRNAs targeting VPS26A-associated gene networks have emerged as potential prognostic biomarkers for LIHC. VPS26A was found to be integrated into a highly interconnected signaling network comprising proteins in cancer progression, immune regulation, and cellular metabolism. Conclusions: Overall, VPS26A may serve as a potential prognostic biomarker and therapeutic target in LIHC. This study provides novel insights into the molecular mechanisms underlying LIHC progression, and highlights the multifaceted role of VPS26A in tumor biology. Full article

Sudden cardiac death (SCD) is a major public health concern, being a leading cause of death worldwide. SCD is particularly alarming for individuals with apparently good health, as it often occurs without preceding warning signs. Unfortunately, traditional autopsy methods frequently fail to identify the precise cause of death in these cases, highlighting the need for advanced techniques to elucidate underlying mechanisms. Recent advances in molecular biology over the past few years, particularly in proteomics, transcriptomics, and metabolomics techniques, have led to an expanded understanding of gene expression, protein activity, and metabolic changes, offering valuable insights into fatal cardiac events. Combining multi-omics methods with bioinformatics and machine learning algorithms significantly enhances our ability to uncover the processes behind lethal cardiac dysfunctions by identifying new useful biomarkers (like cardiac myosin-binding protein C, acylcarnitines, or microRNAs) to reveal molecular pathways linked to SCD. This narrative review summarizes the role of multi-omics approaches in forensic diagnosis by exploring current applications in unexplained cases and the benefits of integrating merged techniques in otherwise negative autopsies. We also discuss the potential for developing personalized and preventive forensic medicine, the technical limitations of currently available methods, and the ethical considerations arising from these advancements. Full article

The relentless emergence of RNA viruses poses a perpetual threat to global public health, necessitating continuous efforts in surveillance, discovery, and understanding of these pathogens. This review provides a comprehensive update on recent advancements in RNA virus discovery, highlighting breakthroughs in technology and methodologies that have significantly enhanced our ability to identify novel viruses across diverse host organisms. We explore the expanding landscape of viral diversity, emphasizing the discovery of previously unknown viral families and the role of zoonotic transmissions in shaping the viral ecosystem. Additionally, we discuss the potential implications of RNA virus discovery on disease emergence and pandemic preparedness. Despite remarkable progress, current challenges in sample collection, data interpretation, and the characterization of newly identified viruses persist. Our ability to anticipate and respond to emerging respiratory threats relies on virus discovery as a cornerstone for understanding RNA virus evolution. We address these challenges and propose future directions for research, emphasizing the integration of multi-omic approaches, advanced computational tools, and international collaboration to overcome barriers in the field. This comprehensive overview aims to guide researchers, policymakers, and public health professionals in navigating the intricate landscape of RNA virus discovery, fostering a proactive and collaborative approach to anticipate and mitigate emerging viral threats. Full article

The zinc finger protein with KRAB and SCAN domains 3 (ZKSCAN3) has emerged as a critical regulator of diverse cellular processes, including autophagy, cell cycle progression, and tumorigenesis. Structurally, ZKSCAN3 is characterized by its conserved DNA-binding zinc finger motifs, a SCAN domain mediating protein–protein interaction, and a KRAB repression domain implicated in transcriptional regulation. Post-translational modifications, such as phosphorylation and ubiquitination, dynamically modulate its subcellular localization and activity, enabling context-dependent functional plasticity. Functionally, ZKSCAN3 acts as a master switch in autophagy by repressing the transcription of autophagy-related genes under nutrient-replete conditions, while its nuclear-cytoplasmic shuttling under stress conditions links metabolic reprogramming to cellular survival. Emerging evidence also underscores its paradoxical roles in cancer: it suppresses tumor initiation by maintaining genomic stability yet promotes metastasis through epithelial–mesenchymal transition induction. Furthermore, epigenetic mechanisms, including promoter methylation and non-coding RNA regulation, fine-tune ZKSCAN3 expression, contributing to tissue-specific outcomes. Despite these insights, gaps remain in understanding the structural determinants governing its interaction with chromatin-remodeling complexes and the therapeutic potential of targeting ZKSCAN3 in diseases. Future investigations should prioritize integrating multi-omics approaches to unravel context-specific regulatory networks and explore small-molecule modulators for translational applications. This comprehensive analysis provides a framework for advancing our mechanistic understanding of ZKSCAN3 and its implications in human health and disease. This review synthesizes recent advances in elucidating the regulatory networks and functional complexity of ZKSCAN3, highlighting its dual roles in physiological and pathological contexts. Full article

Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of global morbidity and mortality, prompting significant interest in individualized prevention and treatment strategies. This review synthesizes recent advances in genomic and precision medicine approaches relevant to ASCVD, with a focus on genetic risk scores, lipid metabolism genes, and emerging gene editing techniques. A structured literature search was conducted across PubMed, Scopus, and Web of Science databases to identify key publications from the last decade addressing genomic mechanisms, therapeutic targets, and computational tools in ASCVD. Notable findings include the identification of causal genetic variants such as PCSK9 and LDLR, the development of polygenic risk scores for early prediction, and the use of deep learning algorithms for integrative multi-omics analysis. In addition, we highlight current and future therapeutic applications including PCSK9 inhibitors, RNA-based therapies, and CRISPR-based genome editing. Collectively, these advances underscore the promise of precision medicine in tailoring ASCVD prevention and treatment to individual genetic and molecular profiles. Full article