Search Results (1,325)

The genetic material in living systems is mainly stored in DNA molecules, which in turn play a dominant biological role in relation to the coding and transfer of genetic information, the biosynthesis of proteins and RNA and the packaging and regulation of DNA expression and accessibility. These features, strictly dictated by the three-dimensional structure of DNA, are governed by non-covalent chemical interactions that drive the folding process of these biological macromolecules. The Main Mechanical Forces (MMFs) approach is a recently formulated calculation method, based on the accurate prediction of structural features of biomolecules through an in-depth assessment of the interplay between specific non-covalent chemical interactions and related mechanical forces developed during the folding process. By adopting the MMFs method in the context of nucleic acids, we report here the results obtained in terms of predicting three-dimensional DNA oligomer tertiary structures. To this end, we have developed tailored nucleic acid-specific equations, enabling to predict the torsion angles (with a relevant level of agreement with experimental values) of the phosphate-sugar backbone of the three model molecules A-, B- and Z- DNA used in this study. To increase the validity of this methodology, we have conducted RMSD measurements, indicating that there is a weak but rather acceptable match between the calculated vs. predicted A-DNA structure, whereas the prediction of the BII-DNA and Z-DNA tertiary structures was fully correct. Full article

The Mongolian wild ass (Equus hemionus hemionus) is a flagship species of the desert-steppe ecosystem in Asia, and understanding its strategies for coping with cold environments is vital for both revealing its survival mechanisms and informing conservation efforts. In this study, we employed metagenomic sequencing to characterize the composition and functional potential of the gut microbiota, and applied DNA metabarcoding of the chloroplast trnL (UAA) g–h fragment to analyze dietary composition, aiming to reveal seasonal variations and the interplay between dietary plant composition and gut microbial communities. In the cold season, Bacteroidota and Euryarchaeota were significantly enriched, suggesting enhanced fiber degradation and energy extraction from low-quality forage. Moreover, genera such as Bacteroides and Alistipes were also significantly enriched and associated with short-chain fatty acid (SCFA) metabolism, bile acid tolerance, and immune modulation. In the cold season, higher Simpson index values and tighter principal coordinates analysis (PCoA) clustering indicated a more diverse and stable microbiota under harsh environmental conditions, which may represent an important microecological strategy for the host to cope with extreme environments. Functional predictions based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) further indicated upregulation of metabolic and signaling pathways, including ABC transporters, two-component systems, and quorum sensing, suggesting multi-level microbial responses to low temperatures and nutritional stress. trnL-based plant composition analysis indicated seasonal shifts, with Tamaricaceae detected more in the warm season and Poaceae, Chenopodiaceae, and Amaryllidaceae detected more in the cold season. Correlation analyses revealed that dominant microbial phyla were associated with the degradation of fiber, polysaccharides, and plant secondary metabolites, which may help maintain host energy and metabolic homeostasis. Despite the limited sample size and cross-sectional design, our findings highlight that gut microbial composition and structure may be important for host adaptation to cold environments and may also serve as a useful reference for future studies on the adaptive mechanisms and conservation strategies of endangered herbivores, including the Mongolian wild ass. Full article

Background: This narrative review summarizes recent progress in artificial intelligence (AI), especially radiomics and deep learning, for non-invasive diagnosis and molecular profiling of gliomas. Methodology: A thorough literature search was conducted on PubMed, Scopus, and Embase for studies published from January 2020 to July 2025, focusing on clinical and technical research. In key areas, these studies examine AI models’ predictive capabilities with multi-parametric Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Results: The domains identified in the literature include the advancement of radiomic models for tumor grading and biomarker prediction, such as Isocitrate Dehydrogenase (IDH) mutation, O6-methylguanine-dna methyltransferase (MGMT) promoter methylation, and 1p/19q codeletion. The growing use of convolutional neural networks (CNNs) and generative adversarial networks (GANs) in tumor segmentation, classification, and prognosis was also a significant topic discussed in the literature. Deep learning (DL) methods are evaluated against traditional radiomics regarding feature extraction, scalability, and robustness to imaging protocol differences across institutions. Conclusions: This review analyzes emerging efforts to combine clinical, imaging, and histology data within hybrid or transformer-based AI systems to enhance diagnostic accuracy. Significant findings include the application of DL to predict cyclin-dependent kinase inhibitor 2A/B (CDKN2A/B) deletion and chemokine CCL2 expression. These highlight the expanding capabilities of imaging-based genomic inference and the importance of clinical data in multimodal fusion. Challenges such as data harmonization, model interpretability, and external validation still need to be addressed. Full article

Recent progress in tumor immunotherapy highlights the important role of the immune system in combating various cancers. Traditionally designed to protect against infectious diseases, vaccines are now being adapted to stimulate immune responses against tumor-specific neoantigens. Both preclinical studies and clinical trials have explored innovative approaches for identifying neoantigens and optimizing vaccine design, advancing the field of personalized oncology. Among these, DNA-based vaccines have become a particularly attractive approach for cancer immunotherapy. This evolution has been driven by improvements in molecular biology techniques, including more precise methods for detecting tumor-specific mutations, computational tools for predicting immunogenic antigens, and novel platforms for delivering nucleic acid vaccines. Personalized DNA vaccines are typically developed through a complex, multi-step process that involves sequencing a patient’s tumor, computational analysis to identify potential targets, and custom vaccine production. In this review, we examine the use of both shared tumor antigens and individualized neoantigens in cancer vaccine development. We outline strategies for neoantigen identification that provide insights into tumor-specific alterations. Furthermore, we highlight recent advances in DNA vaccine technologies, address the current limitations facing cancer vaccines, propose strategies to overcome these challenges, and consider key clinical and technical factors for successful implementation. Full article

Immunogenic cell death (ICD) is a specialized form of cell death that triggers antitumor immune responses. In tumors, ICD promotes the release of tumor-associated and tumor-specific antigens, thereby reshaping the immune microenvironment, restoring antitumor immunity, and facilitating tumor eradication. However, the regulatory mechanisms of ICD and its immunological effects vary across tumor types, and a comprehensive understanding remains limited. We systematically analyzed the expression of 34 ICD-related regulatory genes across 33 tumor types. Differential expression at the RNA, copy number variation (CNV), and DNA methylation levels was assessed in relation to clinical features. Associations between patient survival and RNA expression, CNVs, single-nucleotide variations (SNVs), and methylation were evaluated. Patients were stratified into immunological subtypes and further divided into high- and low-risk groups based on optimal prognostic models built using a machine learning framework. We explored the relationships between ICD-related genes and immune cell infiltration, stemness, heterogeneity, immune scores, immune checkpoint and regulatory genes, and subtype-specific expression patterns. Moreover, we examined the influence of immunotherapy and anticancer immune responses, applied three machine learning algorithms to identify prognostic biomarkers, and performed drug prediction and molecular docking analyses to nominate therapeutic targets. ICD-related genes were predominantly overexpressed in ESCA, GBM, KIRC, LGG, PAAD, and STAD. RNA expression of most ICD-related genes was associated with poor prognosis, while DNA methylation of these genes showed significant survival correlations in LGG and UVM. Prognostic models were successfully established for 18 cancer types, revealing intrinsic immune regulatory mechanisms of ICD-related genes. Machine learning identified several key prognostic biomarkers across cancers, among which NT5E emerged as a predictive biomarker in head and neck squamous cell carcinoma (HNSC), mediating tumor–immune interactions through multiple ligand–receptor pairs. This study provides a comprehensive view of ICD-related genes across cancers, identifies NT5E as a potential biomarker in HNSC, and highlights novel targets for predicting immunotherapy response and improving clinical outcomes in cancer patients. Full article

Emerging evidence highlights the critical role of phagocytosis-related genes in CRC progression, underscoring the need for novel phagocytosis-based prognostic models to predict clinical outcomes. In this study, a four-gene (SPHK1, VSIG4, FCGR2B and FPR2) signature associated with CRC prognosis was developed using single-sample gene set enrichment analysis (ssGSEA), least absolute shrinkage and selection operator (LASSO) regression, and univariate Cox analysis. Pathway enrichment analysis was conducted on the prognostic genes, along with evaluations of the tumor microenvironment and sensitivity to immunotherapy and chemotherapy across the high- and low-risk groups. Prognostic gene validation was performed via quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) using CRC cDNA and tissue microarrays. High-risk patients showed enhanced responsiveness to immunotherapy, while chemotherapy sensitivity varied across risk subgroups. qRT-PCR results revealed upregulation of SPHK1 and FPR2 in cancer tissues, whereas FCGR2B and VSIG4 were downregulated. IHC assays confirmed increased SPHK1 and FPR2 expression in cancer samples. Single-cell RNA sequencing analysis demonstrated a decrease in SPHK1 and FCGR2B, while VSIG4 and FPR2 progressively increased during macrophage differentiation. These findings provide a potential framework for targeted therapy. Full article

Background: Angioimmunoblastic T-cell lymphoma (AITL) and peripheral T-cell lymphomas (PTCL), not otherwise specified (NOS), share overlapping histology and T-follicular helper (TFH) biology but often show divergent outcomes and treatment needs. The clinical significance of KMT2A rearrangement (KMT2A-r) in nodal PTCL remains undefined. We aimed to investigate the clinicogenomic features and prognostic impact of KMT2A-r in AITL and PTCL-NOS. Methods: We retrospectively analyzed consecutive patients diagnosed with AITL or PTCL-NOS between 2021 and 2024 at two centers. All patients underwent 523-gene DNA/RNA next-generation sequencing. Gene co-variation and diagnostic splits were summarized using network and decision-tree analyses. Results: Overall, 37 patients were included (AITL: 14; PTCL-NOS: 23), with similar baseline clinical characteristics. In AITL, TFH markers were more frequently expressed, and RHOA mutations were enriched. KMT2A-r occurred in 24% of cases without histology-specific enrichment. AITL showed better 2-year overall survival (OS) than PTCL-NOS (70.7% vs. 38.8%; p = 0.040) but similar progression-free survival (PFS). Univariate analysis revealed that KMT2A-r, lactate dehydrogenase elevation, and bone-marrow involvement predicted inferior PFS (Hazard ratio for KMT2A-r: 2.56). Median PFS was 5.9 versus 12.5 months in the KMT2A-r and non-KMT2A-r groups, respectively (p = 0.039). Brentuximab vedotin (BV) plus cyclophosphamide, doxorubicin, and prednisone did not significantly improve OS or PFS overall; however, exploratory analysis indicated improved PFS in the KMT2A-r subset. Conclusions: KMT2A-r delineates an adverse-risk biology in nodal PTCL, aligns with non-TFH genomic hubs and markers of tumor burden, and may serve as a stratifier and hypothesis-generating target for BV-based strategies. Full article

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with a 5-year overall survival below 12% and high recurrence rates even after R0 resection. Traditionally managed with a “surgery-first” approach, two consistent observations—the near-universal presence of micrometastatic disease at diagnosis and the frequent inability to complete adjuvant therapy—have driven the integration of neoadjuvant therapy (NAT) into clinical practice. NAT offers several theoretical and practical advantages: early systemic control of occult disease, improved delivery and completion of multimodal treatment, biological selection of surgical candidates, and increased R0 resection rates. While in borderline resectable PDAC, randomized trials have consistently demonstrated improved margin-negative resection rates and early survival benefits compared with upfront surgery, in resectable PDAC, evidence is more heterogeneous. Real-world studies corroborate trial findings, reporting higher R0 rates and reduced lymph node positivity without increased perioperative risk, but also highlight substantial heterogeneity in regimens, duration, and radiotherapy use. Limitations to universal NAT adoption include reliance on anatomy-based resectability criteria, absence of validated predictive biomarkers, challenges in response assessment, and concerns over disease progression during preoperative treatment. Future developments will focus on integrating molecular profiling, circulating tumor DNA dynamics, and advanced imaging into patient selection and treatment adaptation, supported by biomarker-enriched and adaptive trial designs. NAT is thus evolving from a selective strategy for borderline disease to an innovative framework to optimize multimodal treatment delivery and refine patient selection in PDAC, with the potential to improve surgical outcomes and inform systemic therapy decisions in both resectable and borderline resectable settings Full article

Background/Objectives: The evolution of gene expression responses is a critical component of population adaptation to variable environments. Predicting how DNA sequence influences expression is challenging because the genotype-to-phenotype map is not well resolved for cis-regulatory elements, transcription factor binding, regulatory interactions, and epigenetic features, not to mention how these factors respond to the environment. Methods: We tested if flexible machine learning models could learn some of the underlying cis-regulatory genotype-to-phenotype map to predict expression response to a specific environment. We tested this approach using cold-responsive transcriptome profiles in five Arabidopsis thaliana natural accessions. Results: We first tested for evidence that cis regulation plays a role in environmental response, finding 14 and 15 motifs that were significantly enriched within the up- and downstream regions of cold-responsive differentially regulated genes (DEGs). We next applied convolutional neural networks (CNNs), which learn de novo cis-regulatory motifs in DNA sequences to predict expression response to cold. We found that CNNs predicted differential expression with moderate accuracy, with evidence that predictions were hindered by the biological complexity of regulation and the large potential regulatory code. Conclusions: Overall, approaches for predicting DEGs between specific environments based only on proximate DNA sequences require further development. It may be necessary to incorporate additional biological information into models to generate accurate predictions that will be useful to population biologists. Full article

The tumour suppressor protein p53 plays a central role in safeguarding genomic integrity through the regulation of DNA repair, cell cycle arrest, and apoptosis. Mutations in TP53, particularly within its DNA-binding domain, are among the most frequent genetic alterations in human cancers and are strongly associated with chemoresistance and poor prognosis. In this study, all TP53 mutations reported in the COSMIC database were systematically mapped onto all experimentally resolved TP53 three-dimensional structures available in the Protein Data Bank, supplemented with AlphaFold-predicted models to achieve full structural coverage. Mutations were classified according to their structural context—protein core, interface regions, ligand- and zinc-binding sites, and intrinsically disordered regions—and evaluated using complementary sequence- and structure-based predictive tools. The analysis revealed distinct mutational hotspots, differential distribution across structural regions, and context-dependent effects on stability and DNA-binding capacity. Notably, a subset of mutations exhibited consistent predictions of high destabilisation across all structural contexts, underscoring their potential as drivers of functional inactivation. By providing a comprehensive structural map of TP53 alterations, this work offers a valuable resource for understanding mutation-specific mechanisms of p53 dysfunction and for guiding the development of precision therapeutic strategies aimed at restoring its tumour-suppressive functions. Full article

Background/Objectives: During cancer development, tumor cells accumulate somatic mutations, which could generate tumor-specific neoantigens. The aberrant protein can be recognized by the immune system as no-self, triggering an immune response against cells expressing this aberrant protein which could mediate tumor control or rejection. Since the expression of this mutated protein is exclusive to tumor cells, great efforts are being made to identify neoantigens of relevance in the development of new cancer treatment strategies. In comparison to adulthood tumors, pediatric malignancies present fewer mutations and thus fewer potential neoantigens. Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy worldwide that can be benefited by the identification of neoantigens for immunotherapy approaches, the landscape of neoantigens in ALL is not well known, therefore the aim of our study was to identify potential neoantigens in ALL pediatric patients. Methods: To identify neoantigens in ALL, whole-exome sequencing of matched tumor-normal cells from pediatric cases was performed, with these data HLA-I alleles predicted and somatic mutations identified to propose potential neoantigens based on binding affinity of mutated peptide-HLA-I. Results: We found a strong correlation between tumor mutational burden (TMB) and neoantigen load (p < 0.001) but no correlation with prognosis. Furthermore, TMB and neoantigens were greater in ALL patients with at least one mutated DNA mismatch repair gene (p < 0.001). Also, differences between B- and T-cell ALL were found but statistical significance did not remain after permutation. Conclusions: The presence of neoantigens in pediatric cases with ALL makes the neoantigen-based immunotherapy a promising new strategy for the treatment of this malignancy, especially for patients with relapse. Full article

Isocitrate dehydrogenase 1 (IDH1) mutations are key drivers of glioma biology, influencing tumor aggressiveness and treatment response. To elucidate their molecular impact, we performed proteome analysis on patient-derived (PD) and U87MG glioma cell models with either mutant or wild-type IDH1. We quantified over 6000 protein groups per model, identifying 1594 differentially expressed proteins in PD-AS (IDH1_MUT) vs. PD-GB (IDH1_WT) and 904 in U87_MUT vs. U87_WT. Both IDH1_MUT models exhibited enhanced MHC antigen presentation and interferon signaling, indicative of an altered immune microenvironment. However, metabolic alterations were model-dependent: PD-AS cells shifted toward glycolysis and purine salvage, while U87_MUT cells retained oxidative phosphorylation, potentially due to D2-hydroxyglutarate (2OHG)-mediated HIF1A stabilization. We also observed a predominance of downregulated DNA repair proteins in IDH1_MUT models, particularly those involved in homologous recombination. In contrast, RB1 and ASMTL were strongly upregulated in both IDH1_MUT models, implicating them in DNA repair and cellular stress responses. We also found distinct expression patterns of proteins regulating histone methylation in IDH1_MUT cells, favoring increased methylation of H3K4, H3K9, and H3K36. A key driver of this may be the upregulation of SETD2 in PD-AS, an H3K4 and H3K36 trimethyltransferase linked to the recruitment of HIF1A as well as DNA mismatch repair proteins. This study uncovers candidate biomarkers and pathways relevant to glioma progression and therapeutic targeting, but also underscores the complexity of predicting glioma pathogenesis and treatment responses based on IDH1 mutation status. While proteome profiling provides valuable insights, a comprehensive understanding of IDH1_MUT gliomas will likely require integrative multi-omics approaches, including DNA/RNA methylation profiling, histone and protein post-translational modification analyses, and targeted DNA damage and repair assays. Full article

Although giant viruses have introduced new perspectives on the definition and evolution of viruses and are increasingly recognized for their significant biological roles within marine ecosystems, systematic evaluations of development trends and scientific contributions in this research field remain limited. This study conducted a bibliometric analysis of the global academic literature on marine giant viruses (MGVs), focusing on nucleocytoplasmic large DNA viruses (NCLDVs), from 1996 to 2024. Using the Web of Science Core Collection, 1544 publications related to giant viruses were identified. After filtering using marine-related keywords and manual review, 300 studies specifically addressing marine giant viruses were selected for the final analysis. This study comprehensively examined the structural characteristics and evolutionary trends in this field by analyzing annual publication productivity, citation patterns, contributions by countries and institutions, author collaboration networks, and keyword co-occurrence patterns. The results show that research on MGVs has steadily increased since the mid-2000s, with a notable surge after 2018 driven by advancements in metagenomics, next-generation sequencing technologies, and global ocean exploration initiatives. The United States and France have taken leading positions in terms of research productivity and impact, with key institutions such as the CNRS (Centre National de la Recherche Scientifique) and Aix-Marseille Université playing central roles. A multipolar network of international collaborations between countries and institutions has been formed. Research topics have evolved from an early focus on virus classification and genome analysis to more diverse themes, including interactions with marine microbiota, viral ecological functions, infection dynamics, virophage research, and metagenome-based ecosystem-level studies. This study provides an overview of the chronological and structural evolution of the marine giant virus research field by systematically presenting key research themes and collaborative networks. The results provide a valuable foundation for determining future academic directions and planning strategic research initiatives. Furthermore, it is expected to facilitate interdisciplinary research in marine biology, environmental science, systems biology, and artificial intelligence-based functional predictions. Full article

Brain-derived neurotrophic factor (BDNF) plays a crucial role in cognitive functions and dementia. In individuals with mild cognitive impairment (MCI) and dementia, we have investigated BDNF Val66Met genotype distribution, peripheral BDNF DNA methylation, mRNA and protein levels, and cognitive performance using the Mini-Mental State Examination (MMSE) and Clock Drawing Test (CDT). Lower BDNF_IV1 methylation had predictive value for dementia. Patients with mild-to-moderate dementia had lower levels of BDNF_IV2 methylation, whereas patients with severe dementia had higher levels than the MCI group, while BDNF_IV2 methylation positively correlated with CDT scores. An insignificant decline in BDNF mRNA levels in dementia patients positively correlated with significantly lower BDNF plasma levels, especially pronounced in severe dementia patients. BDNF mRNA and protein levels were positively correlated with CDT and MMSE scores, respectively. BDNF Val66Met polymorphism was associated with methylation of the BDNF_IX amplicon, but not with methylation in BDNF promoters I and IV, peripheral BDNF gene and protein expression, MMSE and CDT scores, or dementia. Methylation at the BDNF Val66Met site was positively correlated with overall BDNF_IX methylation and methylation at 5 BDNF_IX CpG loci but negatively correlated with methylation of BDNF_IV1, BDNF_IV3, and BDNF_I1 amplicons. Further studies should evaluate the translational potential of these peripheral BDNF-based biomarkers for dementia. Full article

Nucleoside analogues are used as drugs and as labels in laboratory-based research. However, the effect of different nucleoside analogue mechanism(s) on cell sensitivity or mutagenesis is unclear. This is particularly important in cancer treatments where checkpoint proteins and DNA damage factors are often mutated. We tested six nucleoside analogues in fission yeast, Schizosaccharomyces pombe. We found that the mutations in the DNA replication checkpoint cause unique sensitivity profiles towards chemotherapeutic nucleoside analogues (gemcitabine, 5-fluorouracil, cytarabine) and the non-clinical analogue bromodeoxyuridine. Antiretroviral compounds, zidovudine and lamivudine, did not alter cell growth. We compared half-maximal inhibitory concentration (IC50) doses between checkpoint deficient yeast strains, examining culture growth and DNA mis-segregation. Intriguingly, gemcitabine and bromodeoxyuridine doses above the IC50 promoted better growth. Above each compound’s IC50 dose we saw that cells were insensitive to nucleoside analogue re-exposure, particularly in DNA replication checkpoint mutants (cds1∆, rad3∆). Thus, pairing nucleoside analogue use with personal genomics may inform drug choice, dose, and schedule. Finally, these data indicate that resistance may be predictable, informing clinical strategy. Full article