Search Results (1,483)

Prostate cancer (PC) remains a leading cause of malignancy in men worldwide, with current diagnostic methods such as prostate-specific antigen (PSA) testing and tissue biopsies facing limitations in specificity, invasiveness, and ability to capture tumor heterogeneity. Liquid biopsy, especially analysis of circulating tumor DNA (ctDNA), has emerged as a transformative tool for non-invasive detection, real-time monitoring, and treatment selection for PC. This review examines the role of ctDNA in both localized and metastatic PCs, focusing on its utility in early detection, risk stratification, therapy selection, and post-treatment monitoring. In localized PC, ctDNA-based biomarkers, including ctDNA fraction, methylation patterns, fragmentation profiles, and mutations, demonstrate promise in improving diagnostic accuracy and predicting disease recurrence. For metastatic PC, ctDNA analysis provides insights into tumor burden, genomic alterations, and resistance mechanisms, enabling immediate assessment of treatment response and guiding therapeutic decisions. Despite challenges such as the low ctDNA abundance in early-stage disease and the need for standardized protocols, advances in sequencing technologies and multimodal approaches enhance the clinical applicability of ctDNA. Integrating ctDNA with imaging and traditional biomarkers offers a pathway to precision oncology, ultimately improving outcomes. This review underscores the potential of ctDNA to redefine PC management while addressing current limitations and future directions for research and clinical implementation. Full article

Acute lymphoblastic leukemia (ALL) is a heterogeneous hematologic malignancy defined by the uncontrolled proliferation of lymphoid precursors. Accurate diagnosis and effective therapeutic strategies hinge on a comprehensive understanding of the genetic and molecular landscape of ALL. This review synthesizes the latest updates in cytogenetic and molecular classifications, emphasizing the 2022 World Health Organization (WHO) and International Consensus Classification (ICC) revisions. Key chromosomal alterations such as BCR::ABL1 and ETV6::RUNX1 and emerging subtypes including Ph-like ALL, DUX4, and MEF2D rearrangements are examined for their prognostic significance. Furthermore, we assess novel diagnostic tools, notably next-generation sequencing (NGS) and optical genome mapping (OGM). While NGS excels at identifying point mutations and small indels, OGM offers high-resolution structural variant detection with 100% sensitivity in multiple validation studies. These advancements enhance our grasp of leukemogenesis and pave the way for precision medicine in both B- and T-cell ALL. Ultimately, integrating these innovations into routine diagnostics is crucial for personalized patient management and improving clinical outcomes. Full article

Nephrogenic diabetes insipidus (NDI) is a rare hereditary disorder characterized by renal resistance to arginine vasopressin (AVP), resulting in the kidneys’ inability to concentrate urine. Approximately 90% of NDI cases follow an X-linked inheritance pattern and are associated with pathogenic variants in the AVPR2 gene, which encodes the vasopressin receptor type 2. The remaining 10% are attributed to mutations in the AQP2 gene, which encodes aquaporin-2, and may follow either autosomal dominant or recessive inheritance patterns. We present the case of a male infant, younger than nine months of age, who was clinically diagnosed with NDI at six months. The patient presented recurrent episodes of polydipsia, polyuria, dehydration, hypernatremia, and persistently low urine osmolality. Despite adjustments in pharmacologic treatment and strict monitoring of urinary output, the clinical response remained suboptimal. Given the lack of improvement and the radiological finding of an absent posterior pituitary (neurohypophysis), the possibility of coexistent central diabetes insipidus (CDI) was raised, prompting a therapeutic trial with desmopressin. Nevertheless, in the absence of clinical improvement, desmopressin was discontinued. The patient’s management was continued with hydrochlorothiazide, ibuprofen, and a high-calorie diet restricted in sodium and protein, resulting in progressive clinical stabilization. Whole-exome sequencing identified a novel homozygous missense variant in the AQP2 gene (c.398T > A; p.Val133Glu), classified as likely pathogenic according to the American College of Medical Genetics and Genomics (ACMG) criteria: PM2 (absent from population databases), PP2 (missense variant in a gene with a low rate of benign missense variation), and PP3 (multiple lines of computational evidence supporting a deleterious effect)]. NDI is typically diagnosed during early infancy due to the early onset of symptoms and the potential for severe complications if left untreated. In this case, although initial clinical suspicion included concomitant CDI, the timely initiation of supportive management and the subsequent incorporation of molecular diagnostics facilitated a definitive diagnosis. The identification of a previously unreported homozygous variant in AQP2 contributed to diagnostic confirmation and therapeutic decision-making. The diagnosis and comprehensive management of NDI within the context of polyuria-polydipsia syndrome necessitates a multidisciplinary approach, integrating clinical evaluation with advanced molecular diagnostics. The novel AQP2 c.398T > A (p.Val133Glu) variant described herein was associated with early and severe clinical manifestations, underscoring the importance of genetic testing in atypical or treatment-refractory presentations of diabetes insipidus. Full article

Climate change is jeopardizing global food security, with at least 713 million people facing hunger. To face this challenge, legumes as common beans could offer a nature-based solution, sourcing nutrients and dietary fiber, especially for rural communities in Latin America and Africa. However, since common beans are generally heat and drought susceptible, it is imperative to speed up their molecular introgressive adaptive breeding so that they can be cultivated in regions affected by extreme weather. Therefore, this study aimed to couple an advanced panel of common bean (Phaseolus vulgaris L.) × tolerant Tepary bean (P. acutifolius A. Gray) interspecific lines with Bayesian regression algorithms to forecast adaptation to the humid and dry sub-regions at the Caribbean coast of Colombia, where the common bean typically exhibits maladaptation to extreme heat waves. A total of 87 advanced lines with hybrid ancestries were successfully bred, surpassing the interspecific incompatibilities. This hybrid panel was genotyped by sequencing (GBS), leading to the discovery of 15,645 single-nucleotide polymorphism (SNP) markers. Three yield components (yield per plant, and number of seeds and pods) and two biomass variables (vegetative and seed biomass) were recorded for each genotype and inputted in several Bayesian regression models to identify the top genotypes with the best genetic breeding values across three localities on the Colombian coast. We comparatively analyzed several regression approaches, and the model with the best performance for all traits and localities was BayesC. Also, we compared the utilization of all markers and only those determined as associated by a priori genome-wide association studies (GWAS) models. Better prediction ability with the complete SNP set was indicative of missing heritability as part of GWAS reconstructions. Furthermore, optimal SNP sets per trait and locality were determined as per the top 500 most explicative markers according to their β regression effects. These 500 SNPs, on average, overlapped in 5.24% across localities, which reinforced the locality-dependent nature of polygenic adaptation. Finally, we retrieved the genomic estimated breeding values (GEBVs) and selected the top 10 genotypes for each trait and locality as part of a recommendation scheme targeting narrow adaption in the Caribbean. After validation in field conditions and for screening stability, candidate genotypes and SNPs may be used in further introgressive breeding cycles for adaptation. 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

Structural variations (SVs) represent genomic variations that involve breakage and rejoining of DNA segments. SVs can alter normal gene dosage, lead to rearrangements of genes and regulatory elements within a topologically associated domain, and potentially contribute to physical traits, genomic disorders, or complex traits. Recent advances in sequencing technologies and bioinformatics have greatly improved SV detection and interpretation at unprecedented resolution and scale. Despite these advances, the functional impact of SVs, the underlying SV mechanism(s) contributing to complex traits, and the technical challenges associated with SV detection and annotation remain active areas of research. This review aims to provide an overview of structural variations, their mutagenesis mechanisms, and their detection in the genomics era, focusing on the biological significance, methodologies, and future directions in the field. Full article

Moringa oleifera (MO), a nutritionally and pharmacologically potent species, is emerging as a sustainable candidate for applications across bioenergy, agriculture, textiles, pharmaceuticals, and biomedicine. This review explores recent advances in MO-based biotechnologies, highlighting novel extraction methods, green nanotechnology, and clinical trial findings. Although MO’s resilience offers promise for climate-smart agriculture and public health, challenges remain in standardizing cultivation and verifying therapeutic claims. This work underscores MO’s translational potential and the need for integrative, interdisciplinary research. MO is used in advanced materials, like electrospun fibers and biopolymers, showing filtration, antibacterial, anti-inflammatory, and antioxidant properties—important for the biomedical industry and environmental remediation. In textiles, it serves as an eco-friendly alternative for wastewater treatment and yarn sizing. Biotechnological advancements, such as genome sequencing and in vitro culture, enhance traits and metabolite production. MO supports green biotechnology through sustainable agriculture, nanomaterials, and biocomposites. MO shows potential for disease management, immune support, metabolic health, and dental care, but requires further clinical trials for validation. Its resilience is suitable for land restoration and food security in arid areas. AI and deep learning enhance Moringa breeding, allowing for faster, cost-effective development of improved varieties. MO’s diverse applications establish it as a key element for sustainable development in arid regions. Full article

Penicillium astrolabium is a primary pathogenic fungus that causes grape blue mold during postharvest, leading to substantial losses in the grape industry. Nevertheless, hypovirulence-associated mycoviruses can attenuate the virulence of postharvest grape-rot pathogens, thereby offering a promising biocontrol tool. Characterizing the mycovirus repertoire of P. astrolabium is imperative for grape protection, yet remains largely unexplored. Here, we screened six strains harboring viruses in 13 P. astrolabium isolates from rotted grapes. Using high-throughput sequencing, four novel dsRNA viruses and two +ssRNA viruses were identified from the six P. astrolabium strains. The dsRNA viruses belonged to two families—Chrysoviridae and Partitiviridae—and were designated to Penicillium astrolabium chrysovirus 1 (PaCV1), Penicillum astrolabium partitivirus 1′ (PaPV1′), Penicillum astrolabium partitivirus 2 (PaPV2), and Penicillum astrolabium partitivirus 3 (PaPV3). For the +ssRNA viruses, one was clustered into the Alphaflexiviridae family, while the other one was clustered into the Narnaviridae family. The two +ssRNA viruses were named Penicillium astrolabium alphaflexivirus 1 (PaAFV1) and Penicillium astrolabium narnavirus 1 (PaNV1), respectively. Moreover, several viral genomic contigs with non-overlapping and discontinuous sequences were identified in this study, which were probably representatives of five viruses from four families, including Discoviridae, Peribunyaviridae, Botourmiaviridae, and Picobirnaviridae. Taken together, our findings could expand the diversity of mycoviruses, advance the understanding of mycovirus evolution in P. astrolabium, and provide both potential biocontrol resources and a research system for dissecting virus–fungus–plant interactions. Full article

The potential of a protein to cause an allergic reaction is often assessed using a variety of computational techniques. Leveraging advances in high-throughput protein sequence data coupled with in silico or computational methods can be used to systematically analyze large proteomes for allergenic potential. Despite mango’s widespread consumption and growing clinical reports of hypersensitivity, the full extent of their allergenicity is yet unknown. In this study, for the first time, we conducted a genome-wide in silico analysis by analyzing a total of 54,010 protein sequences to identify the complete spectrum of potential mango allergens. These proteins were analyzed using various bioinformatics tools to predict their allergenic potential based on sequence similarity, structural features, and known allergen databases. In addition to the known mango allergens, including Man i 1, Man i 2, and Man i 3, our findings demonstrated that several isoforms of cysteine protease, non-specific lipid-transfer protein (LTP), legumin B-like, 11S globulin, vicilin, thaumatin-like protein, and ervatamin-B family proteins exhibited strong allergenic potential, with >80% 3D epitope identity, >70% linear 80 aa window identity, and matching with >80 known allergens. Thus, a genome-wide in silico study provided a comprehensive profile of the possible mango allergome, which could help identify the low-allergen-containing mango cultivars and aid in the development of accurate assays for variety-specific allergic reactions. Full article

This study addresses the performance of deep learning models for predicting human DNA sequence classification through an exploration of ideal feature representation, model architecture, and hyperparameter tuning. It contrasts traditional machine learning with advanced deep learning approaches to ascertain performance with respect to genomic data complexity. A hybrid network combining long short-term memory (LSTM) and convolutional neural networks (CNN) was developed to extract long-distance dependencies as well as local patterns from DNA sequences. The hybrid LSTM + CNN model achieved a classification accuracy of 100%, which is significantly higher than traditional approaches such as logistic regression (45.31%), naïve Bayes (17.80%), and random forest (69.89%), as well as other machine learning models such as XGBoost (81.50%) and k-nearest neighbor (70.77%). Among deep learning techniques, the DeepSea model also accounted for good performance (76.59%), while others like DeepVariant (67.00%) and graph neural networks (30.71%) were relatively lower. Preprocessing techniques, one-hot encoding, and DNA embeddings were mainly at the forefront of transforming sequence data to a compatible form for deep learning. The findings underscore the robustness of hybrid structures in genomic classification tasks and warrant future research on encoding strategy, model and parameter tuning, and hyperparameter tuning to further improve accuracy and generalization in DNA sequence analysis. Full article

Apomixis-mediated fixation of heterosis could transform hybrid breeding in alfalfa (Medicago sativa), a globally important forage crop. The parthenogenesis-inducing morphogenetic regulator BABY BOOM (BBM) represents a promising candidate for enabling this advancement. Here, we identified BBM homologs from three alfalfa genomes, characterized their promoter regions, and cloned a 2082 bp MsBBM gene encoding a 694-amino acid nuclear-localized protein. Three alfalfa BBM gene promoters primarily contained light- and hormone-responsive elements. Phylogenetic and conserved domain analyses of the MsBBM protein revealed a high sequence similarity with M. truncatula BBM. Expression profiling demonstrated tissue-specific accumulation of MsBBM transcripts, with the highest expression in the roots and developing pods. Hormonal treatments differentially regulated MsBBM. Expression was upregulated by GA₃ (except at 4 h) and SA, downregulated by NAA, MeJA (both except at 8 h), and ABA (except at 4 h), while ETH treatment induced a transient expression peak at 2 h. As an AP2/ERF family transcription factor showing preferential expression in young embryos, MsBBM likely participates in reproductive development and may facilitate apomixis. These findings establish a molecular framework for exploiting MsBBM to enhance alfalfa breeding efficiency through heterosis fixation. Full article

Background/Objectives: Next-generation-sequencing-based genomic profiling (GP) of advanced breast cancer (BC) has been increasingly integrated into clinical practice. The growing number of biomarker-based therapies in BC increasingly complicates treatment decisions. As a result, molecular tumor boards (MTBs) have become pivotal. However, real-world data on the utility of MTBs in advanced BC remain limited. This study evaluates the translation of molecular findings in BC patients into MTB recommendations and examines their implementation and outcomes in real-world clinical practice. Methods: This retrospective, single-center study included 103 BC patients who received GP between January 2018 and December 2023. Patients were discussed at the weekly multidisciplinary MTB of our institution. Data retrieved included patient characteristics, GP results, and MTB recommendations, which were consecutively matched with treatment outcomes, namely the proportion of patients receiving an MTB treatment recommendation, proportion of patients receiving molecularly matched targeted therapy (MTT), and best treatment response. Results: The MTB reviewed 94 patients and provided 155 recommendations to 68 patients (72.3%), including systemic anti-cancer treatment (n = 123), clinical study participation (n = 4), genetic counseling (n = 12), and additional molecular testing (n = 16) recommendations. Treatment recommendations were provided to 63 patients (67%), of whom 38 (60.3%) received MTT. Of the 35 patients eligible for response assessment, 16 (45.7%) demonstrated clinical benefit: three achieved a complete response, six a partial response, and ten a stable disease > 6 months. Conclusions: GP and MTBs expand biomarker-matched treatment options to BC patients beyond the standard of care. Around half of the patients who receive MTT experience a clinical benefit. The standardization of procedures, the development of multi-biomarker-based prediction, and the enhancement in MTT delivery to patients are key challenges, which should be addressed in future initiatives. Full article

In 2022, consecutive sweeps of highly transmissible SARS-CoV-2 Omicron-derived lineages (B.1.1.529*) maintained viral transmission despite extensive antigen exposure from both vaccinations and infections. To better understand Omicron variant emergence in the context of the dynamic fitness landscape of 2022, we aimed to explore putative drivers behind SARS-CoV-2 lineage replacements. Variant fitness is determined through its ability to either outrun previously dominant lineages or more efficiently circumvent host immune responses to previous infections and vaccinations. By analyzing data collected through our local genomic surveillance program from Connecticut, USA, we compared emerging Omicron lineages’ growth rates, estimated infections, effective reproductive rates, average viral copy numbers, and likelihood for causing infections in recently vaccinated individuals. We find that newly emerging Omicron lineages outcompeted dominant lineages through a combination of enhanced viral shedding or advanced immune escape depending on the population-level exposure state. This analysis integrates individual-level sequencing data with demographic, vaccination, laboratory, and epidemiological data and provides further insights into host–pathogen dynamics beyond public aggregate data. Full article

Nearly four decades have passed since fluorescence in situ hybridisation was first applied in plants to support molecular cytogenetic analyses across a wide range of species. Subsequent advances in DNA sequencing, bioinformatic analysis, and microscopy, together with the immunolocalisation of various nuclear components, have provided unprecedented insights into the cytomolecular organisation of the nuclear genome in both model and non-model plants, with crop species being perhaps the most significant. The ready availability of sequenced genomes is now facilitating the application of state-of-the-art cytomolecular techniques across diverse plant species. However, these same advances in genomics also pose a challenge to the future of plant molecular cytogenetics, as DNA sequence analysis is increasingly perceived as offering comparable insights into genome organisation. This perception persists despite the continued relevance of FISH-based approaches for the physical anchoring of genome assemblies to chromosomes. Furthermore, cytogenetic approaches cannot currently rival purely genomic methods in terms of throughput, standardisation, and automation. This review highlights the latest key topics in plant cytomolecular research, with particular emphasis on chromosome identification and karyotype evolution, chromatin and interphase nuclear organisation, chromosome structure, hybridisation and polyploidy, and cytogenetics-assisted crop improvement. In doing so, it underscores the distinctive contributions that cytogenetic techniques continue to offer in genomic research. Additionally, we critically assess future directions and emerging opportunities in the field, including those related to CRISPR/Cas-based live-cell imaging and chromosome engineering, as well as AI-assisted image analysis and karyotyping. Full article

Common carp (Cyprinus carpio) is one of the most significant fish species worldwide, with its natural distribution spanning Europe and Asia. To conduct a genome-wide association study (GWAS) and compare the prediction accuracy of genomic selection (GS) models for the growth traits of common carp in spring and autumn at 2 years of age, a total of 325 carp individuals were re-sequenced and phenotypic measurements were taken. Three GWAS methods (FarmCPU, GEMMA, and GLM) were applied and their performance was evaluated in conjunction with various GS models, using significance levels based on p-values. GWAS analyses were performed on eight traits (including the body length, body weight, fat content of fillet, and condition factor) for both spring and autumn seasons. Eleven different GS models (such as Bayes A, Bayes B, and SVR-linear) were combined to evaluate their performance in genomic selection. The results demonstrate that the FarmCPU method consistently exhibits superior stability and predictive accuracy across most traits, particularly under higher SNP densities (e.g., 5K), where prediction accuracies frequently exceed 0.8. Notably, when integrated with Bayesian approaches, FarmCPU achieves a substantial performance boost, with the prediction accuracy reaching as high as 0.95 for the autumn body weight, highlighting its potential for high-resolution genomic prediction. In contrast, GEMMA and GLM exhibited a more variable performance at lower SNP densities. Overall, the integration of FarmCPU with genomic selection (GS) models offers one of the most reliable and efficient frameworks for trait prediction, particularly for complex traits with substantial genetic variation. This approach proves especially powerful when coupled with Bayesian methodologies, further enhancing its applicability in advanced breeding programs. Full article