Search Results (879)

Multiple myeloma (MM) is a malignancy of plasma cells that is characterized by a complex and spatially heterogeneous genomic landscape. Despite this complexity, clinical monitoring remains largely dependent on localized bone marrow (BM) assessments. This dependence creates a significant diagnostic gap, as the primary monitoring tools fail to account for the spatial and temporal heterogeneity that drives tumor relapse. Liquid biopsy can serve as an adjunctive approach in assessing the pan-clonal landscape in MM through the molecular profiling of circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs). In this review, we examine the clinical utility of liquid biopsy components in capturing mutational profiles, clonal evolution, treatment resistance mechanisms, and minimal residual disease (MRD), including early detection of relapse and extramedullary progression. We will further discuss current limitations, including variability in assay sensitivity, lack of standardization, and the need for prospective validation. Full article

Background/Objectives: Prostate adenocarcinoma exhibits substantial inter-patient heterogeneity, limiting the accuracy of current prognostic tools. Prostate-specific antigen-based assessment remains insufficient for reliable survival prediction. There is a clear need for integrative, data-driven approaches that leverage multi-dimensional clinical and molecular data to improve outcome stratification. This study aimed to develop and evaluate an explicable machine learning framework for predicting overall survival in prostate adenocarcinoma. Methods: A comprehensive machine learning pipeline was constructed using clinical and laboratory data from 494 patients in the TCGA PanCancer Atlas cohort. Following data curation, 16 clinically relevant features were selected through expert-guided filtering and feature selection techniques. Missing values were addressed using imputation strategies, and class imbalance was mitigated using SMOTE. Eight machine learning models were evaluated, including a novel hybrid ensemble model combining Gradient Boosting Machine and random forest (GBM + RF). Model performance was assessed using stratified 10-fold cross-validation and quantified via accuracy, precision, recall, F1-score, and ROC-AUC. Model interpretability was examined using LIME, and prognostic relevance was validated through Cox proportional hazards regression. Results: The hybrid GBM + RF model demonstrated superior performance, achieving 97% accuracy and a ROC-AUC of 0.95 under mode imputation with SMOTE balancing. Ensemble-based models consistently outperformed single classifiers, particularly in handling missing data and class imbalance. Key predictors of survival included progression-free survival, hypoxia-related scores, genomic instability markers, and immune-associated variables. Cox regression analysis confirmed the independent prognostic significance of these features, supporting the biological plausibility of the model. Conclusions: An explainable ensemble machine learning approach enables accurate and clinically interpretable prediction of overall survival in prostate adenocarcinoma. The proposed framework provides a robust foundation for precision urology decision-support systems and warrants validation in independent cohorts. Full article

Background/Objectives: Overexpression of transferrin receptor (TFR1) is common in cancer and may be associated with inferior treatment outcomes. Due to these patterns and TFR1’s essential role in iron metabolism, the protein has been targeted for cytotoxic drug delivery. More recently, increased TFR1 expression has been linked to tumor microenvironment (TME) infiltration by immune effectors in selected tumors, but a comprehensive assessment of the genomic landscape associated TFRC (the gene encoding TFR1) expression has not been conducted. Methods: By utilizing a pan-cancer database of 93,248 patients with whole-exome and whole-transcriptome sequencing, we assessed TFRC-associated multiomic patterns. Results: We found that high TFRC expression correlates with significantly worse overall survival in multiple common solid tumor types, a higher tumor mutational burden (TMB), an increase in infiltrating effector cells with upregulated immune checkpoint markers within the TME, and increased frequency of specific high-risk genomic alterations. Further assessment in cell line models revealed increased susceptibility to cytotoxic T cells when iron metabolism is elevated, despite upregulation of the checkpoint ligand PD-L1. Conclusions: High TFRC expression, therefore, indicates worse clinical risk across multiple common tumor types but potentially increased susceptibility to cytotoxic immune effectors, informing the development of TFR1 biomarker-driven therapeutic strategies. Full article

The emergence of pangenome graphs represents a paradigm shift in genomics, moving beyond linear reference genomes to embrace the full spectrum of genetic diversity within and across species. These graph-based models provide a unified framework for representing alternative haplotypes, structural variants, and complex genomic rearrangements that are often missed by traditional approaches. This paper reviews the latest developments in pangenome graph construction, data structures, alignment algorithms, and variant inference. We explore recent human, plant, and microbial genomics applications, highlighting the advantages of graph representations in capturing population diversity and improving read mapping accuracy. We briefly discuss emerging directions such as machine learning-assisted graph analysis, although current applications remain limited and exploratory. Furthermore, we examine the emerging field of clinical genomics, where pangenome references have demonstrated measurable improvements in diagnostic yield—specifically increasing variant calling sensitivity for complex structural variants by up to 10–40% compared to linear models. We conclude by addressing ongoing challenges in graph scalability and standardization, aiming to guide future research and implementation in this rapidly evolving field. Full article

Staphylococcus pseudintermedius is an opportunistic bacterium previously associated with dogs but has recently been found in human infections, raising zoonotic concerns. Genomic characterization of human S. pseudintermedius isolates can provide preliminary information on antibiotic resistance, pathogenicity, and genomic features relevant to host range. Two S. pseudintermedius isolates (hereafter referred to as S. pseudintermedius EGH1 and S. pseudintermedius EGH2) from human clinical samples in Egypt were sequenced using the Illumina NovaSeq X Plus platform. To assess genetic relatedness to human S. pseudintermedius isolates worldwide, multilocus sequence typing (MLST), pangenome analysis, and antimicrobial resistance gene profiling were performed. The sequencing produced a total of 9,499,989 reads for S. pseudintermedius EGH1 and 9,567,531 reads for S. pseudintermedius EGH2. Sequences were assembled with Geneious Prime^® 2025 and annotated using NCBI Prokaryotic Genome Annotation Pipeline v6.10. Pangenome analysis identified 9574 genes, comprising 1681 core genes (17.56%), 180 soft-core genes (1.88%), 837 shell genes (8.74%), and 6876 cloud genes (71.82%). MLST was conducted on human S. pseudintermedius genome assemblies using MLST v2.23.0. The analysis revealed both isolates as novel sequence types: S. pseudintermedius EGH1 was assigned ST-3037 with a new allele (purA-107), and S. pseudintermedius EGH2 was assigned ST-2874. Clonal relationships among S. pseudintermedius isolates were evaluated using the eBURST algorithm. This study presents the first next-generation genome sequencing and comparative genomic analysis of S. pseudintermedius isolates from humans in Egypt. Future studies integrating genomic, epidemiological, and phenotypic data are required. Full article

Lysobacter antibioticus Hz25 is a novel strain that was isolated from the rhizosphere of the relict endemic plant Hedysarum zundukii Peschkova (Fabaceae), which grows on carbonate soils in the Baikal region of Russia. This work presents the complete genome sequence of Hz25 (5.98 Mb, 66.94% GC), which was obtained using a hybrid assembly method combining Oxford Nanopore and Illumina sequencing. Phylogenetic analysis based on 47 Lysobacter genomes and an average nucleotide identity (ANI) value of 96% confirmed its affiliation with L. antibioticus. A comparative pan-genome analysis with three closely related strains (13-6, 76, and ATCC 29479) identified 554 strain-specific genes. This significant genomic plasticity likely reflects adaptation to the sharply continental climate, high insolation, and low free iron content of the native soil. The genome encodes a comprehensive micropredator arsenal, including: seven chitinase genes (GH18 and GH19 families); bacteriolytic enzymes (Blp, L1, L4, Ami); a complete type III secretion system (T3SS) with predicted effectors; type IV pili (including the PilZ-PilB regulatory complex); and siderophore biosynthesis genes (lysochelin). The genome contains genes ars of an arsenic resistance system, but lacks the ACR3 efflux pump, suggesting that these genes may have alternative functions. Genes involved in calcium homeostasis (Excalibur domain, Na⁺/Ca²⁺ antiporter) were also identified. These features make Hz25 a promising candidate for biocontrol applications in cold climates and metal-contaminated environments. Full article

Shigella spp., and Escherichia coli exhibit notable genomic and phenotypic similarities, including serologically and genetically related somatic antigens. For example, the relationship among pathogenic strains E. coli 64474, O179, O188, and S. boydii O16 suggests a shared clonal origin. To evaluate their genomic proximity, a comparative genomics study was conducted using whole-genome sequencing. Comparative genomics involved rfb gene cluster regions and whole-genome comparisons. Phylogenomic inferences were performed using the virtual genome fingerprint (VGF) method with bootstrap support. The results revealed a high degree of genomic similarity and a close evolutionary relationship among E. coli strains, which also demonstrated genetic associations with clinically relevant pathotypes through the presence of virulence genes. Furthermore, serogroups 64474, O188, and S. boydii O16 exhibited close genetic relationships, suggesting that serotype 64474 could represent a novel serogroup, although its similarity to O188 indicates the influence of divergent factors. These findings support the hypothesis that these E. coli strains originated from a common clonal lineage, enhancing our understanding of serogroup diversity and the evolutionary dynamics within enteric pathogens. Full article

The rapid accumulation of genome-scale data has transformed plant biology from descriptive genetics to predictive and increasingly mechanistic genomics. Longan (Dimocarpus longan Lour.) is an economically important subtropical fruit tree in China and Southeast Asia, but compared with model plants and major temperate fruit crops, its genomic resources and functional studies have developed relatively late. Here, we review recent progress in longan genomics with emphasis on three interrelated areas: genome assembly and annotation, transcriptomic resources, and representative gene family studies associated with flowering, somatic embryogenesis, and transporter-mediated stress tolerance. The progression from the first draft genome of ‘Honghezi’ to the chromosome-scale assemblies of ‘Jidanben’ and ‘Shixia’ has substantially improved contiguity and gene annotation, thereby enabling population-genomic analysis, genome-wide gene family identification, and candidate-gene discovery. Available transcriptomic datasets further support studies of reproductive development, stress responses, and embryogenic competence, although cross-study integration remains limited. We also summarize how gene family analyses have advanced the current understanding of floral induction, continuous flowering, somatic embryogenesis, mineral transport, and sugar transport in longan. Importantly, the field is still dominated by cataloguing and expression-based inference, whereas causal validation, pan-genomic analysis, and multi-omics integration remain insufficient. We therefore argue that future progress in longan molecular breeding will depend on integrating high-quality genomic resources with functional validation, standardized comparative annotation, and improved transformation or regeneration systems. Full article

Background and Objectives: Gastric cancer remains a leading cause of cancer-related mortality worldwide and develops through complex interactions between environmental factors, microbial dysbiosis, and host molecular pathways. Although Helicobacter pylori infection is a well-established risk factor, emerging evidence suggests that broader alterations in the gastric microbiome may also contribute to carcinogenesis. However, the associations between gastric cancer-associated microbial taxa and host gene expression profiles remain insufficiently characterized. This study aimed to identify host gene signatures associated with gastric cancer-related microbial taxa through a descriptive analysis integrating microbiome-derived taxa with transcriptome data. Materials and Methods: Microbial taxa associated with gastric cancer were systematically retrieved from the Disbiome database. Taxon set enrichment analysis (TSEA) was performed using the MicrobiomeAnalyst platform to identify host genes associated with gastric cancer-associated taxa. Importantly, TSEA relies on healthy reference data from the Human Microbiome Project and does not establish gastric cancer-specific interactions or causal relationships. Gene expression levels were subsequently evaluated using The Cancer Genome Atlas (TCGA) PanCancer stomach adenocarcinoma (STAD) dataset by comparing tumor and matched normal gastric tissues. Gene interaction network and transcription factor (TF) enrichment analyses were conducted to explore predicted regulatory relationships. Results: Among 64 microbial taxa associated with gastric cancer, 43 were reported as elevated. After removing overlapping taxa across studies, 37 elevated and 21 reduced taxa were retained for analysis. TSEA identified 11 host genes associated with gastric cancer-related microbial taxa. Transcriptomic analysis demonstrated significant downregulation of DPP6 and DLG2, while KDM4D, USP34, and VDR were significantly upregulated in gastric cancer tissues compared with normal controls. Network and TF enrichment analyses revealed predicted co-expression and co-localization patterns among these genes, suggesting their potential involvement in immune-related processes, epigenetic regulation, and cellular organization. Conclusions: This descriptive study identifies distinct host gene expression signatures associated with gastric cancer-associated microbial dysbiosis. This study is purely associative and hypothesis-generating; no causal or mechanistic inferences are made. TSEA used healthy reference data and therefore does not reflect gastric cancer-specific host–microbe interactions. The findings provide a basis for future hypothesis-driven research but require validation in independent cohorts. Full article

Staphylococcus aureus is a globally distributed bacterium that spans interconnected human, animal, and environmental niches and is a major driver of antimicrobial resistance. Environmental and wildlife-associated isolates from hospital-surrounding settings remain underrepresented in comparative genomic studies. To address this gap, we integrated a newly sequenced environmental isolate recovered from pigeon fecal samples collected around a hospital into a standardized pangenome framework composed of 99 reproducibly selected RefSeq genomes plus the environmental isolate S_S3. Using uniform genome annotation and orthologous gene family clustering, we identified an open pangenome of 8366 gene families (Heaps’ law γ = 0.275), consistent with the high genomic plasticity previously reported for S. aureus. The core genome stabilized at approximately 1757 genes, including 1651 genes conserved across all genomes. Gene frequency spectra showed a dominant cloud genome and a structured shell fraction contributing to interstrain differentiation. Jaccard-based gene content similarity resolved clusters shaped mainly by accessory gene composition. The environmental isolate retained the complete core genome, carried only 15 isolate-specific gene families (0.18% of the pangenome), and clustered within an established lineage. Its unique content included a lincosamide resistance-associated locus and efeB, a gene potentially related to heme or iron metabolism and oxidative stress response. These findings highlight a conserved genomic backbone over a dynamic accessory reservoir and support One Health genomic surveillance that includes wildlife-associated niches, while indicating that the environmental isolate fits within the broader gene content diversity observed in the analyzed dataset. Full article

Generally, forest trees with medicinal value present diverse chemotypes considered key determinants of efficacy, safety, and commercial valuation. Such heterogeneity varies among tissues, genotypes, and seasons, and stress exposure. This review summarizes how regulatory controls and genome architecture affect the stability and synthesis of secondary metabolites in woody medicinally important taxa. Detailed haplotypic and chromosomal analyses have recently identified diverse and repeatable architectural drivers. Among these, LTR/transposon-mediated revamping, neofunctionalization, biosynthetic gene clusters, and tandem duplication play a special role in reshaping pathway capacity. The enzymatic regulation of these drivers translates this “capacity” into harvest-pertinent chemistry by employing conserved TF modules, hormone crosstalk, and emergent chromatin/epigenetic layers. Nevertheless, major parameters pertaining to the tissue-specific storage, transport, and compartmentalization of these chemotypes are contextualized with certain limitations. In this review, the integration of GWAS/eQTL/TWAS with multi-tissue is explained in addition to the replacement of a single reference with pangenome/haplotype frameworks, and explicit modeling of G × E further strengthen genotype-to-chemotype mapping. Therefore, in this review we summarize practical workflows for chemotype discovery utilizing staged validation models of heterologous reconstitution, isotope/spatial evidence, and chemistry. These findings were supported by data on saponins, alkaloids, iridoids, and defense response. Such an integration links mechanistic understanding to authentication, standardization, and sustainable utilization strategies in woody medicinal trees. Full article

This study evaluated the performance of Fourier-transform infrared (FTIR) spectroscopy for identifying spectral signatures associated with two key traits of Listeria monocytogenes: serogroup classification and biofilm-forming capacity. A total of 100 strains, previously serogrouped by PCR and categorized as high, intermediate, or low biofilm producers, were analyzed. Whole-genome sequencing was performed, and comparative genomics was conducted at core-genome, pangenome, and whole-genome (k-mer) levels to determine which genomic representation best reflected the phenotypes. Strains were typed using Fourier-Transform Infrared (FTIR Biotyper^® system from Bruker Daltonics GmbH and Co., Bremen, Germany) with five technical replicates. Spectral data from the polysaccharide region (1300–800 cm⁻¹) were extracted and used to train twelve statistical models within a machine learning pipeline combined with cross-validation to predict four serogroups and three biofilm clusters from 501 spectral variables. Genomic analyses showed strong concordance between population structure and serogroup, whereas biofilm formation displayed only weak genomic association, explaining less than 0.1% of genomic variance (PERMANOVA R² ≤ 0.001). Penalized discriminant analysis achieved the highest performance for serogroup prediction (overall accuracy 97.2%), while the k-nearest neighbor model performed best for biofilm prediction (74.8%). Two dedicated R Shiny applications were developed to facilitate model use. Overall, FTIR spectroscopy coupled with machine learning can provide a rapid and cost-effective alternative to PCR, genomic analyses, and in vitro assays for phenotypic trait prediction. Full article

Background/Objectives: The emergence of antimicrobial-resistant (AMR) pathogens in aquaculture ecosystems poses a significant risk to both food security and human health. Shewanella species are recognized as significant AMR reservoirs, yet their prevalence and resistance mechanisms within a shrimp-related ecosystem remain poorly characterized. This study aimed to perform a genotypic and phenotypic characterization of S. algae VK101, isolated from wild-caught black tiger shrimp (Penaeus monodon) broodstock. Methods: A complete 5.21 Mb genome was generated using a hybrid Illumina and Oxford Nanopore sequencing approach. Antimicrobial susceptibility was evaluated for 21 antibiotics via Minimum Inhibitory Concentration (MIC) testing. Comparative pangenomics and genome-wide association studies (GWAS) across 125 S. algae genomes were conducted to identify novel resistance determinants. Results: MIC analysis revealed that VK101 was resistant to ampicillin (>16 µg/mL) and colistin (8 µg/mL), while showing intermediate susceptibility to imipenem and ciprofloxacin. In silico analysis identified 205 antimicrobial resistance genes (ARGs), including a perfect hit for the fluoroquinolone resistance gene qnrA3. Notably, no mcr genes were detected. Although VK101 exhibited moderate resistance (8 µg/mL), GWAS across the broader S. algae population linked a specific lptA mutation (K140N) to high-level resistance (64 µg/mL). Other GWAS-identified genes (e.g., czcA, ampC, and oprM) likely represent indirect associations driven by genetic linkage or clade-specific markers rather than direct causal factors. Conclusions: These findings highlighted the presence of multidrug-resistant S. algae in wild-caught P. monodon broodstock, reflecting the occurrence of antimicrobial resistance in aquatic environments. Colistin resistance in these isolates was primarily mediated by chromosomal variants rather than mobile mcr elements, indicating the need for integrated genomic surveillance within the aquaculture value chain. Full article

Soybean is a vital crop supporting global food, feed, and biofuel production. Soybean yields have surged, with record yields reaching 14,678 kg/ha⁻¹, though average farm yields remain stagnant at 2770–2790 kg ha⁻¹. The persistent yield gaps leave 44% of potential production unrealized due to climate change, threatening food security. To meet future caloric demands, which are projected to rise by 46.8% by 2050, soybean breeding must prioritize climate-resilient, high-yielding varieties with minimal ecological footprints. In this comprehensive and in-depth review, we synthesized existing literature and Google Patents and reviewed the multifaceted impacts of climate-change driven eCO₂ and stresses (heat, drought, flooding, salinity, and pathogens), revealing non-linear interactions where eCO₂ may not compensate yield losses under combined stresses. We then highlight key strategies for soybean breeding under climate-change scenario. To this regard, we provide a detailed trait-by-trait breeding roadmap covering seed number, seed size, seed weight, protein-oil balance and their metabolic trade-offs, above and below ground plant architecture, nitrogen fixation and nodulation dynamics, root system architecture, water use efficiency, canopy architecture, flowering time regulation, early maturity etc., in light of specific genes and validated strategies. We explicitly discuss the novel strategies including deeper understanding of traits, abiotic stress physiology, changing pathogen dynamics, phenomics, (multi-)omics, machine learning, and modern biotechnological techniques for developing future soybean varieties. We provide a future roadmap prioritizing specific actions, including engineering climate-resilient ideotypes through gene stacking, optimizing nitrogen fixation and nutrition under stresses leveraging omics data, pan-genome, wild soybean, speeding breeding hubs, and participatory farmer-network validation, while redefining the future soybean breeder would be a hybrid orchestrator of data and dirt. This review establishes a foundational framework for translating climate-adaptive morphological, biochemical, physiological, omics, agronomic, phenomics, and biotechnological insights into actionable breeding strategies, thereby guiding policy-driven investment in soybean improvement programs targeting 2050 and beyond. Full article

Providencia stuartii (Ps) is a clinically significant opportunistic pathogen often associated with “difficult-to-treat resistance” (DTR) infections due to pan-resistance to first-line antimicrobials. We report the clinical diagnosis and rapid genomic characterization of strain Ps-CMC-4104, recovered from a human splenic abscess in a patient with infected necrotizing pancreatitis. To resolve the complex genetic architecture of this strain, we utilized hybrid sequencing combining Oxford Nanopore (long-read) and Illumina (short-read) technologies. Analysis revealed a 4,504,925 bp circular chromosome featuring a unique genomic resistance island (GRI) closely related to Salmonella SGI1. Notably, the PsGRI contains multiple copies of NDM-1 and PER-1 carbapenem-resistance and -inhibitor genes, a repetitive structure typically unresolvable by standard short-read methods. Additionally, a large 278,489 bp low-copy circular plasmid harbored single copies of these carbapenemase and extended-spectrum β-lactamase genes alongside other antimicrobial resistance determinants and ISCR1 insertion sequences. Nanopore technology allowed us to precisely identify the duplications, providing critical insights into the strain’s pan-resistant phenotype. This study serves as proof-of-concept for the importance of integrating long-read sequencing into clinical workflows to identify complex resistance mechanisms in DTR pathogens, facilitating targeted antimicrobial stewardship and infection control. Full article