Search Results (5,003)

Prostate cancer accounts for approximately 1.5 million new diagnoses and 400,000 deaths every year worldwide, and demographic projections indicate a near-doubling of both figures by 2040. Despite existing treatments, 10–20% of patients eventually progress to metastatic castration-resistant disease (mCRPC). The median overall survival (OS) after progression to mCPRC drops to 24 months, and efficacy drops severely after each additional line of treatment. Omics platforms have reached advanced levels and enable the acquisition of high-resolution large datasets that can provide insights into the molecular mechanisms underlying PCa pathology. Genomics, especially DDR (DNA damage response) gene alterations, detected via tissue and/or circulating tumor DNA, efficiently guides therapy in advanced prostate cancer. Given recent developments, we have performed a comprehensive literature search to cover recent research and clinical trial reports (over the last five years) that integrate omics along three converging trajectories in therapeutic development: (i) predicting response to approved agents with demonstrated survival benefits, (ii) stratifying patients to receive therapies in clinical trials, (iii) guiding drug development as part of drug repurposing frameworks. Collectively, this review is intended to serve as a comprehensive resource of recent advancements in omics-guided therapies for advanced prostate cancer, a clinical setting with existing clinical needs and poor outcomes. Full article

The premature senescence of red raspberry leaves severely affects plant growth. In this study, the double-season red raspberry cultivar ‘Polka’ was used, with N₁₅₀ (0.10 g N·kg⁻¹) selected as the treatment group (T150) and N₀ (0 g N·kg⁻¹) set as the control (CK). This study systematically investigated the mechanism of premature senescence in red raspberry leaves under different nitrogen application levels by measuring physiological parameters and conducting a combined multi-omics analysis of transcriptomics and metabolomics. Results showed that T150 plants had 8.34 cm greater height and 1.45 cm greater ground diameter than CK. The chlorophyll, carotenoid, soluble protein, and sugar contents in all leaf parts of T150 were significantly higher than those in CK, whereas soluble starch contents were lower. Malondialdehyde (MDA) content and superoxide anion (O₂⁻) generation rate in the lower leaves of T150 were significantly lower than those in CK. Superoxide sismutase (SOD) and peroxidase (POD) activities in the middle and lower functional leaves of T150 were higher than in CK, while catalase (CAT) activity was lower. Transcriptomic analysis identified 4350 significantly differentially expressed genes, including 2062 upregulated and 2288 downregulated genes. Metabolomic analysis identified 135 differential metabolites, out of which 60 were upregulated and 75 were downregulated. Integrated transcriptomic and metabolomic analysis showed enrichment in the phenylpropanoid biosynthesis (ko00940) and flavonoid biosynthesis (ko00941) pathways, with the former acting as an upstream pathway of the latter. A premature senescence pathway was established, and two key metabolites were identified: chlorogenic acid content decreased, and naringenin chalcone content increased in early senescent leaves, suggesting their pivotal roles in the early senescence of red raspberry leaves. Modulating chlorogenic acid and naringenin chalcone levels could delay premature senescence. Optimizing fertilization strategies may thus reduce senescence risk and enhance the productivity, profitability, and sustainability of the red raspberry industry. Full article

Microalgae, with their unparalleled capabilities for sunlight-driven growth, CO₂ fixation, and synthesis of diverse high-value compounds, represent sustainable cell factories for a circular bioeconomy. However, industrial deployment has been hindered by biological constraints and the inadequacy of conventional genetic tools. The advent of CRISPR-Cas systems initially provided precise gene editing via targeted DNA cleavage. This review argues that the true transformative potential lies in moving decisively beyond cutting to harness CRISPR as a versatile synthetic biology “Swiss Army Knife”. We synthesize the rapid evolution of CRISPR-derived tools—including transcriptional modulators (CRISPRa/i), epigenome editors, base/prime editors, multiplexed systems, and biosensor-integrated logic gates—and their revolutionary applications in microalgal engineering. These tools enable tunable gene expression, stable epigenetic reprogramming, DSB-free nucleotide-level precision editing, coordinated rewiring of complex metabolic networks, and dynamic, autonomous control in response to environmental cues. We critically evaluate their deployment to enhance photosynthesis, boost lipid/biofuel production, engineer high-value compound pathways (carotenoids, PUFAs, proteins), improve stress resilience, and optimize carbon utilization. Persistent challenges—species-specific tool optimization, delivery efficiency, genetic stability, scalability, and biosafety—are analyzed, alongside emerging solutions and future directions integrating AI, automation, and multi-omics. The strategic integration of this CRISPR toolkit unlocks the potential to engineer robust, high-productivity microalgal cell factories, finally realizing their promise as sustainable platforms for next-generation biomanufacturing. Full article

Drought stress severely limits the productivity of sweet potato (Ipomoea batatas L.), yet the stage-specific molecular mechanisms of its adaptation remain poorly understood. Therefore, we integrated transcriptomics and extensive targeted metabolomics analysis to investigate the drought responses of the sweet potato cultivar ‘Luoyu 11’ during the branching and tuber formation stage (DS1) and the storage root expansion stage (DS2) under controlled drought conditions (45 ± 5% field capacity). Transcriptome analysis identified 8292 and 13,509 differentially expressed genes in DS1 and DS2, respectively, compared with the well-watered control (75 ± 5% field capacity). KEGG enrichment analysis revealed the activation of plant hormone signaling, carbon metabolism, and flavonoid biosynthesis pathways, and more pronounced transcriptional changes were observed during the DS2 stage. Metabolomic analysis identified 415 differentially accumulated metabolites across the two growth periods, with flavonoids being the most abundant (accounting for 30.3% in DS1 and 23.7% in DS2), followed by amino acids and organic acids, which highlighted their roles in osmotic regulation and oxidative stress alleviation. Integrated omics analysis revealed stage-specific regulation of flavonoid biosynthesis under drought stress. Genes such as CYP75B1 and IF7MAT were consistently downregulated, whereas flavonol synthase and glycosyltransferases exhibited differential expression patterns, which correlated with the selective accumulation of trifolin and luteoloside. Our findings provide novel insights into the molecular basis of drought tolerance in sweet potato and offer actionable targets for breeding and precision water management in drought-prone regions. Full article

Background: Type 2 diabetes (T2D) is a global health epidemic with rising prevalence within Asian populations, particularly amongst individuals with high visceral adiposity and ectopic organ fat, the so-called Thin-Outside, Fat-Inside phenotype. Metabolomic and microbiome shifts may herald T2D onset, presenting potential biomarkers and mechanistic insight into metabolic dysregulation. However, multi-omics datasets across ethnicities remain limited. Methods: We performed cross-sectional multi-omics analyses on 171 adults (99 Asian Chinese, 72 European Caucasian) from the New Zealand-based TOFI_Asia cohort at 4-years follow-up. Paired plasma and faecal samples were analysed using untargeted metabolomic profiling (polar/lipid fractions) and shotgun metagenomic sequencing, respectively. Sparse multi-block partial least squares regression and discriminant analysis (DIABLO) unveiled signatures associated with ethnicity, glycaemic status, and sex. Results: Ethnicity-based DIABLO modelling achieved a balanced error rate of 0.22, correctly classifying 76.54% of test samples. Polar metabolites had the highest discriminatory power (AUC = 0.96), with trigonelline enriched in European Caucasians and carnitine in Asian Chinese. Lipid profiles highlighted ethnicity-specific signatures: Asian Chinese showed enrichment of polyunsaturated triglycerides (TG.16:0_18:2_22:6, TG.18:1_18:2_22:6) and ether-linked phospholipids, while European Caucasians exhibited higher levels of saturated species (TG.16:0_16:0_14:1, TG.15:0_15:0_17:1). The bacteria Bifidobacterium pseudocatenulatum, Erysipelatoclostridium ramosum, and Enterocloster bolteae characterised Asian Chinese participants, while Oscillibacter sp. and Clostridium innocuum characterised European Caucasians. Cross-omic correlations highlighted negative correlations of Phocaeicola vulgatus with amino acids (r = −0.84 to −0.76), while E. ramosum and C. innocuum positively correlated with long-chain triglycerides (r = 0.55–0.62). Conclusions: Ethnicity drove robust multi-omic differentiation, revealing distinctive metabolic and microbial profiles potentially underlying the differential T2D risk between Asian Chinese and European Caucasians. Full article

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

Arrhythmogenic cardiomyopathies (ACMs) are a phenotypically and etiologically heterogeneous group of myocardial disorders characterized by fibrotic or fibro-fatty replacement of ventricular myocardium, electrical instability, and an elevated risk of sudden cardiac death. Initially identified as a right ventricular disease, ACMs are now recognized to include biventricular and left-dominant forms. Genetic causes account for a substantial proportion of cases and include desmosomal variants, non-desmosomal variants, and familial gene-elusive forms with no identifiable pathogenic mutation. Nongenetic etiologies, including post-inflammatory, autoimmune, and infiltrative mechanisms, may mimic the phenotype. In many patients, the disease remains idiopathic despite comprehensive evaluation. Cardiac magnetic resonance imaging has emerged as a key tool for identifying non-ischemic scar patterns and for distinguishing arrhythmogenic phenotypes from other cardiomyopathies. Emerging classifications propose the unifying concept of scarring cardiomyopathies based on shared structural substrates, although global consensus is evolving. Risk stratification remains challenging, particularly in patients without overt systolic dysfunction or identifiable genetic markers. Advances in tissue phenotyping, multi-omics, and artificial intelligence hold promise for improved prognostic assessment and individualized therapy. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

The Fusarium genus includes some of the most economically and ecologically impactful fungal pathogens affecting global agriculture and human health. Over the past 15 years, rapid advances in molecular biology, genomics, and diagnostic technologies have reshaped our understanding of Fusarium taxonomy, host–pathogen dynamics, mycotoxin biosynthesis, and disease management. This review synthesizes key developments in these areas, focusing on agriculturally important Fusarium species complexes such as the Fusarium oxysporum species complex (FOSC), Fusarium graminearum species complex (FGSC), and a discussion on emerging lineages such as Neocosmospora. We explore recent shifts in species delimitation, functional genomics, and the molecular architecture of pathogenicity. In addition, we examine the global burden of Fusarium-induced mycotoxins by examining their prevalence in three of the world’s most widely consumed staple crops: maize, wheat, and rice. Last, we also evaluate contemporary management strategies, including molecular diagnostics, host resistance, and integrated disease control, positioning this review as a roadmap for future research and practical solutions in Fusarium-related disease and mycotoxin management. By weaving together morphological insights and cutting-edge multi-omics tools, this review captures the transition into a new era of Fusarium research where integrated, high-resolution approaches are transforming diagnosis, classification, and management. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Background: Colorectal cancer (CRC) is a prevalent gastrointestinal malignancy, ranking third in incidence and second in cancer-related mortality. Despite therapeutic advances, challenges such as chemotherapy toxicity and drug resistance persist. Thus, there is an urgent need for novel CRC treatments. However, developing new drugs is time-consuming and resource-intensive. As a more efficient approach, drug repurposing offers a promising alternative for discovering new therapies. Methods: In this study, we screened 1068 small molecular compounds from an FDA-approved drug library in CRC cells. Menadione was selected for further study based on its activity profile. Mechanistic analysis included a cell death pathway PCR array, differential gene expression, enrichment, and network analysis. Gene expressions were validated by RT-qPCR. Results: We identified menadione as a potent anti-tumor drug. Menadione induced three programmed cell death (PCD) signaling pathways: necroptosis, apoptosis, and autophagy. Furthermore, we found that the anti-tumor effect induced by menadione in CRC cells was mediated through a key gene: MAPK8. Conclusions: By employing methods of cell biology, molecular biology, and bioinformatics, we conclude that menadione can induce multiple forms of PCD in CRC cells by activating MAPK8, providing a foundation for repurposing the “new use” of the “old drug” menadione in CRC treatment. Full article

Background: There is growing interest in the potential role of manganese (Mn) in the development of Alzheimer’s Disease and related dementias (ADRD). Methods: In this nested pilot study of a ferroalloy worker cohort, we investigated the impact of chronic occupational Mn exposure on cognitive function through β-amyloid (Aβ) deposition and multi-omics profiling. We evaluated six male Mn-exposed workers (median age 63, exposure duration 31 years) and five historical controls (median age: 60 years), all of whom had undergone brain PET scans. Exposed individuals showed significantly higher Aβ deposition in exposed individuals (p < 0.05). The average annual cumulative respirable Mn was 329.23 ± 516.39 µg/m³ (geometric mean 118.59), and plasma Mn levels were significantly elevated in the exposed group (0.704 ± 0.2 ng/mL) compared to controls (0.397 ± 0.18 in controls). Results: LC-MS/MS-based pathway analyses revealed disruptions in olfactory signaling, mitochondrial fatty acid β-oxidation, biogenic amine synthesis, transmembrane transport, and choline metabolism. Simoa analysis showed notable alterations in ADRD-related plasma biomarkers. Protein microarray revealed significant differences (p < 0.05) in antibodies targeting neuronal and autoimmune proteins, including Aβ (25–35), GFAP, serotonin, NOVA1, and Siglec-1/CD169. Conclusion: These findings suggest Mn exposure is associated with neurodegenerative biomarker alterations and disrupted biological pathways relevant to cognitive decline. Full article

Traditional ecotoxicology primarily investigates pollutant toxicity through physiological, biochemical, and single-molecular indicators. However, the limited data obtained through this approach constrain its application in the mechanistic analysis of pollutant toxicity. Omics technologies have emerged as a major research focus in recent years, enabling the comprehensive and accurate analysis of biomolecular-level changes. The integration of multi-omics technologies can holistically reveal the molecular toxicity mechanisms of pollutants, representing a primary emphasis in current toxicological research. This paper introduces the fundamental concepts of genomics, transcriptomics, proteomics, and metabolomics, while reviewing recent advancements in integrated omics approaches within aquatic toxicology. Furthermore, it provides a reference framework for the implementation of multi-omics strategies in ecotoxicological investigations. While multi-omics integration enables the unprecedented reconstruction of pollutant-induced molecular cascades and earlier biomarker discovery (notably through microbiome–metabolome linkages), its full potential requires experimental designs, machine learning-enhanced data integration, and validation against traditional toxicological endpoints within environmentally relevant models. Full article

Legumes are vital sources of protein, dietary fibre and nutrients, making them crucial for global food security and sustainable agriculture. However, their widespread acceptance and consumption are often limited by undesirable sensory characteristics, such as “a beany flavour”, bitterness or variable textures. Addressing these challenges requires a comprehensive understanding of the complex molecular mechanisms governing appearance, aroma, taste, flavour, texture and palatability in legumes, aiming to enhance their sensory appeal. This review highlights the transformative power of multi-omics approaches in dissecting these intricate biological pathways and facilitating the targeted enhancement of legume sensory qualities. By integrating data from genomics, transcriptomics, proteomics and metabolomics, the genetic and biochemical networks that directly dictate sensory perception can be comprehensively unveiled. The insights gained from these integrated multi-omics studies are proving instrumental in developing strategies for sensory enhancement. They enable the identification of key biomarkers for desirable traits, facilitating more efficient marker-assisted selection (MAS) and genomic selection (GS) in breeding programs. Furthermore, a molecular understanding of sensory pathways opens avenues for precise gene editing (e.g., using CRISPR-Cas9) to modify specific genes, reduce off-flavour compounds or optimise texture. Beyond genetic improvements, multi-omics data also inform the optimisation of post-harvest handling and processing methods (e.g., germination and fermentation) to enhance desirable sensory profiles and mitigate undesirable ones. This holistic approach, spanning from the genetic blueprint to the final sensory experience, will accelerate the development of new legume cultivars and products with enhanced palatability, thereby fostering increased consumption and ultimately contributing to healthier diets and more resilient food systems worldwide. Full article

Background/Objectives: Brain metastasis (BM) is a common and often early manifestation in lung adenocarcinoma (LUAD), yet its tumor microenvironment remains poorly defined at the time of initial diagnosis. This study aims to characterize early immune microenvironmental alterations in synchronous BM using spatial proteomic profiling. Methods: We performed digital spatial proteomic profiling using the NanoString GeoMx platform on formalin-fixed paraffin-embedded tissues from five treatment-naïve LUAD patients in whom BM was the initial presenting lesion. Paired primary lung and brain metastatic samples were analyzed across tumor and stromal compartments using 68 immune- and tumor-related protein markers. Results: Spatial profiling revealed distinct expression patterns between primary tumors and brain metastases. Immune regulatory proteins—including IDO-1, PD-1, PD-L1, STAT3, PTEN, and CD44—were significantly reduced in brain metastases (p < 0.01), whereas pS6, a marker of activation-induced T-cell death, was significantly upregulated (p < 0.01). These alterations were observed in both tumor and stromal regions, suggesting a more immunosuppressive and apoptotic microenvironment in brain lesions. Conclusions: This study provides one of the first spatially resolved proteomic characterizations of synchronous BM at initial LUAD diagnosis. Our findings highlight early immune escape mechanisms and suggest the need for site-specific immunotherapeutic strategies in patients with brain metastasis. Full article