Search Results (1,877)

The development of grapevine varieties combining powdery mildew (Erysiphe necator) resistance with acceptable wine quality represents an important goal for sustainable viticulture. This study evaluated the oenological potential of five advanced breeding lines carrying Run1 or Run1Ren1 resistance loci, developed through marker-assisted selection to achieve 99.2–99.6% Vitis vinifera genome content. Genotypes were assessed under Chilean conditions during the 2024–2025 seasons, analyzing disease resistance, berry characteristics, and wine chemical parameters. All resistant genotypes exhibited complete powdery mildew resistance (OIV scores 9) without fungicide applications. Wine analyses showed pH 3.4–3.9, titratable acidity 3.7–7.8 g/L, and total phenolics 229.2–1356.1 mg GAE/L, values within ranges reported in the literature for commercial wines. Two genotypes evaluated across both seasons showed different patterns of year-to-year variation, with AJ-T2 showing 4.7% variation in anthocyanin content, while AJ-T6 exhibited greater variation in phenolic parameters. HPLC analysis revealed anthocyanin profiles dominated by malvidin-3-glucoside without diglucoside forms, consistent with V. vinifera patterns. These preliminary results from single-plant evaluations suggest that marker-assisted breeding may contribute to developing disease-resistant varieties with wine chemical parameters within commercial ranges, though multi-plant trials with appropriate controls are essential for validation. Full article

Rheumatoid arthritis (RA) is a heterogeneous autoimmune disease characterized by variable clinical manifestations and a complex, often unpredictable disease trajectory, which hinders early diagnosis and personalized treatment. This review highlights recent breakthroughs in biomarker discovery, emphasizing the transformative impact of multi-omics technologies and deep profiling of the synovial microenvironment. Advances in genomics and transcriptomics have identified key genetic variants and expression signatures associated with disease susceptibility, progression, and therapeutic response. Complementary insights from proteomics and metabolomics have elucidated dynamic molecular patterns linked to inflammation and joint destruction. Concurrently, microbiome research has positioned gut microbiota as a compelling source of non-invasive biomarkers with both diagnostic and immunomodulatory relevance. The integration of these diverse data modalities through advanced bioinformatics platforms enables the construction of comprehensive biomarker panels, offering a multidimensional molecular portrait of RA. When coupled with synovial tissue profiling, these approaches facilitate the identification of spatially resolved biomarkers essential for localized disease assessment and precision therapeutics. These innovations are transforming RA care by enabling earlier detection, improved disease monitoring, and personalized treatment strategies that aim to optimize patient outcomes. Full article

Acid mine drainage (AMD) environments feature extreme acidity (pH ≤ 2) and high heavy metal concentrations. Acidophiles survive these conditions through unique genetic adaptations and secondary metabolite (SM) pathways. Leptospirillum ferriphilum, known for its acid and heavy metal resistance, serves as a model for AMD bioremediation, though systematic multi-omics studies on its key SMs and biosynthesis pathways remain underexplored. In this study, L. ferriphilum YR01 was isolated and identified from the AMD of the Zijinshan copper mine, China. Pangenomic analysis revealed that YR01 possesses the largest number of genes (2623) among the eight sequenced L. ferriphilum strains. Comparative genomics, antiSMASH, BiG-SCAPE, and metabolomic analyses (LC-MS and HPLC-MS) were integrated to comprehensively explore its biosynthetic capacity. A total of 39 biosynthetic gene clusters (BGCs) were identified, of which 60% shared <50% similarity with known clusters, indicating substantial novel biosynthetic potential. The sequence alignment of SM biosynthetic gene clusters (BGCs) demonstrated the potential of L. ferriphilum to synthesize conserved clusters for ectoine, choline, carotenoids, terpenoids, and terpene precursors. YR01 harbors complete BGCs for all five SM types. Notably, key nonribosomal peptide synthetase (NRPS) modules implicated in N-acyl homoserine lactone (AHL) synthesis were identified. Untargeted metabolomics (LC-MS) revealed the production of diverse SMs (18 types) putatively involved in environmental adaptation, including phosphocholine, carotenoids (e.g., anteraxanthin), cholera autoinducer-1 (CAI-1), and multiple AHLs. Targeted detection (HPLC-MS) further confirmed that YR01 could produce ectoine (0.10 ng/mL) and specific AHLs (C₁₄-HSL, C₁₂-HSL, C₁₂-OH-HSL), which were beneficial for the survival of the strain in extremely acidic environments and interspecies communication through SMs. This study represents the first comprehensive multi-omics characterization of BGCs in L. ferriphilum and experimentally validates the production of key SMs. Collectively, this study provides a comprehensive elucidation of the SM biosynthetic repertoire and environmental adaptation strategies in L. ferriphilum, advancing our understanding of microbial adaptation and interspecies communication in AMD systems, and offering potential implications for biomining applications. Full article

Background: We aimed to identify key molecular drivers of gastric cancer progression and poor prognosis by integrating multi-omics analyses with experimental validation. Methods: Single-cell RNA-seq data were clustered to delineate major cell types. InferCNV identified tumor epithelial cells, and reclustering revealed a malignant subset with poor prognosis. The overlap between subset markers and The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) upregulated differentially expressed genes (DEGs) was modeled with univariate, LASSO-, and multivariate Cox to derive a prognostic signature. Patients were stratified according to signature scores, and group differences in survival and immunologic features were compared. Spatial transcriptomics defined the localization patterns of key signature genes. In vitro functional assays (CCK-8, colony formation, EdU incorporation, flow cytometry, Transwell migration and invasion, and wound healing) confirmed the pivotal role of SRI. Results: Reclustering of tumor epithelial cells yielded seven subsets (C0–C6), with C5 displaying marked malignant features and correlating with poor prognosis in multiple cohorts. Intersecting 208 genes yielded a five-gene signature (ASCL2, REPIN1, CXCL3, TMEM176A, SRI). The signature stratified patients into high- and low-risk groups. The high-risk cohort exhibited significantly poorer survival, distinct immune-infiltration patterns, elevated immune-evasion scores, and a reduced predicted response to immunotherapy. Single-cell and spatial transcriptomics localized TMEM176A to fibroblasts and SRI to the tumor epithelium. Finally, in vitro knockdown of SRI inhibited tumor cell proliferation, migration and invasion. Conclusions: Our multi-omics approach identified a malignant epithelial subset, C5, and a five-gene signature that stratifies gastric cancer prognosis and immune response. Functional assays showed that SRI knockdown impairs tumor cell growth, migration and invasion. Full article

This article provides a comprehensive analysis of Virus-Induced Gene Silencing (VIGS), which is an effective tool for studying the functional genomics of organisms that are poorly amenable to genomic editing. The VIGS method is grounded in the plant’s post-transcriptional gene silencing (PTGS) machinery and utilizes recombinant viral vectors to trigger systemic suppression of endogenous plant gene expression, leading to visible phenotypic changes that enable gene function characterization. This article details the application of VIGS in model organisms (Arabidopsis thaliana, Nicotiana benthamiana) and a wide range of crops, with a special focus on the Solanaceae family, particularly pepper (Capsicum annuum L.). This review analyzes the design and structural elements of viral vectors used for VIGS, such as Tobacco Rattle Virus (TRV), Broad Bean Wilt Virus 2 (BBWV2), Cucumber Mosaic Virus (CMV), geminiviruses (CLCrV, ACMV), and satellite virus-based systems. It also critically examines the key factors that determine silencing efficiency. These factors encompass insert design, agroinfiltration methodology, plant developmental stage, agroinoculum concentration, plant genotype, and environmental factors (temperature, humidity, photoperiod). Particular attention is given to optimization strategies, such as the use of viral suppressors of RNA silencing (VSRs). This article concludes with the achievements in using VIGS to identify pepper genes governing fruit quality (color, biochemical composition, pungency), resistance to biotic (bacteria, oomycetes, insects) and abiotic (temperature, salt, osmotic stress) factors, as well as genes regulating plant architecture and development. The results obtained demonstrate the advantages and limitations of VIGS, alongside future perspectives for its integration with multi-omics technologies to accelerate breeding and advance functional genomics studies in pepper. Full article

Cancer exhibits diverse and complex phenotypes driven by multifaceted molecular interactions. Recent biomedical research has emphasized the comprehensive study of such diseases by integrating multi-omics datasets (genome, proteome, transcriptome, epigenome). This approach provides an efficient method for identifying genetic variants associated with cancer and offers a deeper understanding of how the disease develops and spreads. However, it is challenging to comprehend complex interactions among the features of multi-omics datasets compared to single omics. This study investigates multi-omics lung cancer data obtained from The Cancer Genome Atlas (TCGA) repository. Differentially expressed genes were identified using four statistical approaches: LIMMA, T-test, Canonical Correlation Analysis (CCA), and the Wilcoxon test applied across gene expression (GE), DNA methylation, and microRNA (miRNA) datasets. Kernel Machine Regression (KMR) was subsequently employed to perform data fusion across the multi-modal datasets. The empirical results highlight notable interactions among GE, miRNA expression, and DNA methylation in lung cancer. Our analysis identified 38 genes that show significant associations with lung cancer. Among these, 8 genes of highest ranking (PDGFRB, PDGFRA, SNAI1, ID1, FGF11, TNXB, ITGB1, and ZIC1) were highlighted by rigorous statistical analysis. Furthermore, in silico studies identified three top-ranked potential candidate drugs (Selinexor, Orapred, and Capmatinib) that may offer promising therapeutic potential against lung cancer. The effectiveness of these candidate drugs is further reinforced by evidence from independent research studies, which emphasize their potential in lung cancer treatment. Full article

Background: Diarrheagenic Escherichia coli (DEC) remains relevant to public health and agri-food chains. The context in Brazil, as a major food producer and exporter, reinforces the need for genomic surveillance. Objective: We aimed to characterize Brazilian diffusely adhering (DAEC), enteropathogenic (EPEC), and Shiga toxin-producing E. coli (STEC) sequences in silico across O-serogroups, in addition to sequence-type (ST), virulence, resistome, and phylogenomic relationships. Methodology: We retrieved 973 genomes assigned to Brazil from NCBI Pathogen Detection Database and performed virtual-PCR screening for key DEC-genes. We then typed O-serogroups (ABRicate/EcOH), Multi-Locus Sequencing Type (MLST), virulome (Ecoli_VF), resistome (ResFinder), and characterized stx genes. Results: DEC represented 18.7% of genomes, driven primarily by EPEC. In EPEC, the eae β-1 subtype was most common; we detected, for the first time in Brazilian sequences, ξ-eae subtype and ST583/ST301. Seventy-eight percent of DAEC isolates were multidrug-resistant (MDR), and two ST were newly reported in the country (ST2141/ST500). In STEC, O157 formed a largely susceptible clade with uniform eae γ-1, whereas 57% of non-O157 were MDR. New STs (ST32/ST1804) were observed, and three genomes were closely related to international isolates. Conclusions: Despite the low DEC representation in the dataset, new STs and eae subtypes were detected in Brazil. Also, MDR in DAEC and non-O157 STEC reinforces the need for antimicrobial-resistance genomic surveillance. Full article

Gastric diseases represent a significant challenge to global health. A comprehensive understanding of their complex molecular mechanisms, particularly the pathways of molecular progression in precancerous lesions, is essential for enhancing diagnosis and treatment. StomachDB, the first comprehensive multi-omics database dedicated to gastric diseases, has been developed to address these research needs. This database integrates 6 types of biological data: genomics, transcriptomics, emerging single-cell and spatial transcriptomics, proteomics, metabolomics, and therapeutic-related information. It encompasses 44 gastric-related pathologies, including various forms of gastric cancer, gastric ulcers, and gastritis, primarily involving humans and mice as model organisms. The database compiles approximately 2.5 million curated and standardized profiles, along with 268,394 disease-gene associations. The user-friendly analytics platform provides tools for browsing, querying, visualizing, and downloading data, facilitating systematic exploration of multi-omics features. This integrative approach addresses the limitations of single-omics analyses, such as data heterogeneity and insufficient analytical dimensions. Researchers can investigate the clinical significance of target genes (e.g., CDH1) across different omics levels and explore potential regulatory mechanisms. Furthermore, StomachDB emphasizes the discovery of therapeutic targets by cataloging interactions among chemical drugs, traditional herbal medicines, and probiotics. As an open-access resource, it serves as a powerful tool for studying complex biological interactions and regulatory mechanisms. Full article

The Hedgehog (HH) signaling pathway is an evolutionarily conserved, multi-component signaling pathway. Its activation is initiated by the Hh protein, which signals upstream regulators PATCH and SMO to activate the transcription factor GLI. Upon activation, GLI translocates to the nucleus to induce the transcription of Hh/GLI target genes. Under normal conditions, the HH pathway plays a crucial role in embryogenesis, development, tissue patterning, and stem cell maintenance. Deregulation of the HH signaling pathway leads to various diseases, including cancer. However, in many human cancers, GLI1 is upregulated through a non-canonical pathway (independent of the HH pathway). This aberrant regulation of GLI1 via a non-canonical pathway is linked to the increased expression of various oncogenes. Aberrant expression of GLI not only affects the genes of several DNA repair pathways but also cancer stem cell pathways, which can contribute to genome instability and ultimately lead to cancer. The ineffectiveness of current HH pathway inhibitors in clinical trials necessitates the discovery of new HH pathway inhibitors. In this review, we will discuss our current understanding of the aberrant signaling of the HH-GLI pathway and focus on GLI1-mediated HH signaling in cancers, cancer stem cells, and carcinogenesis. We will also discuss the effectiveness of current HH inhibitors/drugs and combination therapies based on recent advances in this field. Furthermore, we will also review the role of HH-GLI in cancer stem cell markers, DNA damage response, gene regulation, tumor initiation, metastasis, cancer pathogenesis, and the role of drugs/inhibitors on this pathway. Full article

Seed-related traits such as seed size, germination, vigour, dormancy, biochemical composition, and stress resistance are critical to ensuring agricultural productivity and global food security, particularly in current scenarios of climate change and environmental unpredictability. This review examines the transformative potential of omics technologies, encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics, in enhancing our understanding of seed biology and its applications in crop improvement. Genomics and transcriptomics are key technologies in future plant breeding and gene editing to optimise seed yield and quality. We reviewed the role of metabolomic approaches in uncovering the molecular mechanisms behind seed germination, vigour, dormancy, and the proteomic advances to elucidate markers of seed quality, combining these omic technologies to decipher DOG1 as a marker of dormancy. Both biotic and abiotic stress resistance in seeds were reviewed from a multi-omics perspective to determine the best avenues for improving the resilience of seeds against drought, salinity and pathogens. Moreover, omics approaches have been reviewed to optimise plant–microbe interactions, particularly in enhancing symbiotic relationships within the soil microbiome. Full article

Background: Neurodegenerative diseases, including spastic paraplegia type 11 (SPG11), are complex disorders characterized by progressive neurological decline and significant metabolic disturbances. Spatacsin, the protein encoded by the SPG11 gene, plays a critical role in autophagy and lysosomal homeostasis, which are essential for neuronal health. Its impairment leads to defective cellular clearance and neurodegeneration. Recently, personalized and precision nutrition have emerged as promising approaches to enhance clinical outcomes by tailoring dietary interventions to individual genetic, metabolic, and phenotypic profiles. Objectives: This mini scoping review aimed to synthesize current evidence on the application of personalized and precision nutrition in SPG11 and to explore how insights from related neurodegenerative diseases could inform the development of future dietary and metabolic interventions for this rare disorder. Methods: Following PRISMA-ScR guidelines, a scoping review was conducted using PubMed, Scopus, and Web of Science databases (2020–2024). Eligible studies included investigations addressing nutritional, genomic, or metabolic interventions in neurodegenerative diseases. Of 30 screened papers, nine met the inclusion criteria, primarily focusing on nutritional and metabolic interventions related to neurodegenerative and neuromuscular conditions. Results: To date, no dietary intervention trials have been conducted specifically for SPG11. However, evidence from studies on related neurodegenerative diseases suggests that antioxidant, mitochondrial-supportive, and microbiota-targeted dietary approaches may beneficially influence key pathological processes such as oxidative stress, lipid dysregulation, and autophagy—core mechanisms that are also central to SPG11 pathophysiology. Conclusions: Although current evidence remains preliminary, personalized nutrition is a promising supplementary strategy for managing neurodegenerative diseases, including SPG11. Future research should incorporate systems-based approaches that combine dietary, metabolic, and neuroimaging assessments, with sex and comorbidity-stratified analyses, multi-omics profiling, and predictive modeling. These frameworks could help design safe, effective, and personalized nutritional interventions aimed at enhancing metabolic resilience and slowing disease progression in SPG11. Full article

Obesity is a highly complex, multifactorial disease influenced by dynamic interactions among genetic, epigenetic, environmental, and behavioral determinants that explicitly position genetics as the core. While advances in multi-omic integration have revolutionized our understanding of adiposity pathways, translation into personalized clinical nutrition remains a critical challenge. This review systematically consolidates emerging insights into the molecular and nutrigenomic architecture of obesity by integrating data from large-scale GWAS, functional epigenomics, nutrigenetic interactions, and microbiome-mediated metabolic programming. The primary aim is to systematically organize and synthesize recent genetic and genomic findings in obesity, while also highlighting how these discoveries can be contextualized within precision nutrition frameworks. A comprehensive literature search was conducted across PubMed, Scopus, and Web of Science up to July 2024 using MeSH terms, nutrigenomic-specific queries, and multi-omics filters. Eligible studies were classified into five domains: monogenic obesity, polygenic GWAS findings, epigenomic regulation, nutrigenomic signatures, and gut microbiome contributions. Over 127 candidate genes and 253 QTLs have been implicated in obesity susceptibility. Monogenic variants (e.g., LEP, LEPR, MC4R, POMC, PCSK1) explain rare, early-onset phenotypes, while FTO (polygenic) and MC4R (monogenic mutations as well as common polygenic variants) represent major loci across populations. Epigenetic mechanisms, dietary composition, physical activity, and microbial diversity significantly recalibrate obesity trajectories. Integration of genomics, functional epigenomics, precision nutrigenomics, and microbiome science presents transformative opportunities for personalized obesity interventions. However, translation into evidence-based clinical nutrition remains limited, emphasizing the need for functional validation, cross-ancestry mapping, and AI-driven precision frameworks. Specifically, this review systematically identifies and integrates evidence from genomics, epigenomics, nutrigenomics, and microbiome studies published between 2000 and 2024, applying structured inclusion/exclusion criteria and narrative synthesis to highlight translational pathways for precision nutrition. Full article

Radiomics has emerged as a promising tool for non-invasive tumour phenotyping in breast cancer, providing valuable insights into tumour heterogeneity, response prediction, and risk stratification. However, traditional radiomic approaches often rely on correlative patterns of image analysis to clinical data and lack direct biological interpretability. Combining information provided by radiomics with genomics or other multi-omics data can be important to personalise diagnostic and therapeutic work up in breast cancer management. This review aims to explore the current progress in integrating radiomics with multi-omics data—genomics and transcriptomics—to establish biologically grounded, multidimensional models for precision management of breast cancer. We will review recent advances in integrative radiomics and radiogenomics, highlight the synergy between imaging and molecular profiling, and discuss emerging machine learning methodologies that facilitate the integration of high-dimensional data. Applications of radiogenomics, including breast cancer subtype and molecular mutation prediction, radiogenomic mapping of the tumour immune microenvironment, and response forecasting to immunotherapy and targeted therapies, as well as lymph nodes involvement, will be evaluated. Challenges in technical limitations including imaging modalities harmonization, interpretability, and advancing machine learning methodologies will be addressed. This review positions integrative radiogenomics as a driving force for next-generation breast cancer care. Full article

A determination and mathematical analysis of the statistics of gene numbers in genomes was proposed. It establishes sampling ranges and provides an analytical description of the probability density function, which represents the likelihood of the number of genes in sequenced genomes falling within a specific range of values. The components of the developed statistical multi-Poissonian model revealed the fundamental mechanisms underlying the evolution of life and identified the specific ranges of their dominant influence. The quantitative relations between the statistics of the number of genes and the genome size were shown. A mathematical model of genome size evolution was proposed, identifying subpopulations of intensive and extensive genes associated with protein-coding genes, pseudogenes, and non-coding genes. Full article

The simultaneous occurrence of tuberculosis (TB) and COVID-19 posed a major public health challenge, particularly in regions heavily impacted by both diseases, due to their shared effects on the lungs, immune system dysfunction, and the possibility of more severe clinical outcomes. The role of immunopathogenesis is crucial in influencing the progression of co-infection, which is marked by heightened inflammation, immune exhaustion, weakened T-cell responses, and unregulated cytokine production. To better understand the intricate interactions between host and pathogen and the immune disruptions associated with this dual epidemic, multi-omics approaches such as genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiomics have proven to be effective methods. These comprehensive strategies provide detailed insights into the mechanisms of disease, help identify potential biomarkers, and aid in the identification of therapeutic targets. This review emphasizes the importance of immune responses and systems biology in comprehending the TB-COVID-19 syndemic and highlights the promise of multi-omics in advancing precision medicine and enhancing disease management. Full article