Search Results (1,420)

Transposable elements (TEs), once regarded as genomic “junk,” are now recognized as powerful regulators of gene expression, genome stability, and innate immunity. In the context of neurodegeneration, particularly Amyotrophic Lateral Sclerosis (ALS), accumulating evidence implicates TEs as active contributors to disease pathogenesis. ALS is a fatal motor neuron disease with both sporadic and familial forms, linked to genetic, epigenetic, and environmental factors. While coding mutations explain a subset of cases, advances in long-read sequencing and epigenomic profiling have unveiled the profound influence of non-coding regions—especially retrotransposons such as LINE-1, Alu, and SVA—on ALS onset and progression. TEs may act through multiple mechanisms: generating somatic mutations, disrupting chromatin architecture, modulating transcriptional networks, and triggering sterile inflammation via innate immune pathways like cGAS-STING. Their activity is normally repressed by epigenetic regulators, including DNA methylation, histone modifications, and RNA interference pathways; however, these controls are compromised in ALS. Taken together, these insights underscore the translational potential of targeting transposable elements in ALS, both as a source of novel biomarkers for patient stratification and disease monitoring, and as therapeutic targets whose modulation may slow neurodegeneration and inflammation. This review synthesizes the current knowledge of TE biology in ALS; integrates findings across molecular, cellular, and systems levels; and explores the therapeutic potential of targeting TEs as modulators of neurodegeneration. Full article

The stimulator of interferon genes (STING) serves as a pivotal signaling hub in innate immunity, orchestrating type I interferon (IFN-I) and pro-inflammatory responses upon detection of cytosolic DNA. While the canonical cyclic GMP-AMP synthase (cGAS)-STING axis has been extensively studied in host defense and sterile inflammation, increasing evidence indicates that STING can also be activated through a variety of both pattern recognition receptors (PRRs)-dependent and PRRs-independent mechanisms. In this review, we comprehensively summarize the molecular pathways through which PRRs—including cGAS, interferon gamma inducible protein 16 (IFI16), DEAD-box helicase 41 (DDX41), and DNA-dependent protein kinase (DNA-PK)—engage and regulate STING activation. Beyond PRRs-triggered pathways, we explore emerging evidence of PRRs-independent STING activation, driven by genetic mutations, endoplasmic reticulum (ER) stress, dysregulated intracellular trafficking, and impaired protein degradation. These mechanisms contribute to the pathogenesis of a broad spectrum of inflammatory and autoimmune disorders affecting multiple organ systems, including the digestive, cardiovascular, renal, pulmonary, and nervous systems. We also highlight the current landscape of pharmacological inhibitors targeting cGAS and STING, categorized according to their mechanisms of action and therapeutic potential. The redundancy and complexity of components within the STING signaling network present challenges in effectively suppressing inflammatory overactivation by targeting a single molecule. Nevertheless, the central role of STING offers multiple opportunities for therapeutic intervention, whether by modulating upstream or downstream signaling elements. This review not only provides a systematic framework for understanding the intricacies of STING signaling, but offers insights into the development of next-generation therapeutics aimed at selectively modulating STING activity in disease contexts. Full article

The diagnosis of behavioral variant of frontotemporal dementia (bvFTD)—a common cause of early-onset dementia—remains challenging due to a lack of determined biomarkers. 18F-fluorodeoxyglucose-positron emission tomography (FDG–PET) scan detects early glucose metabolism alterations in specific brain regions. The detection of distinct hypometabolic patterns in early stages of bvFTD has established FDG–PET as an indispensable adjunctive diagnostic tool in inconclusive cases, as well as in distinguishing between different types of dementia. Moreover, its role in the differential diagnosis of the often overlapping bvFTD and primary psychiatric disorders (PPD) is being studied by exploring disease-specific hypometabolic areas. Finally, the identification of early metabolic alterations and even earlier alterations in distinct metabolic brain networks may assist the diagnosis of presymptomatic carriers of disease-related gene mutations and lead to the development of novel biomarkers. The aim of our review is to underscore the role of FDG–PET as an approved yet promising tool that may lead to a new era in the diagnosis of bvFTD by establishing novel biomarkers and integrating AI as an assistant modality to inform diagnosis and decision-making. Full article

The emergence or mutation of aggressive viruses represents a latent threat to human health that could lead to new pandemics, so it is important to constantly monitor and analyze the behavior of the diseases they can cause. In this sense, the purpose of this work was to generate models to predict the behavior of recoveries and deaths from COVID-19 in Mexico in the post-pandemic era, applying machine learning techniques to data related to this disease, published by the Mexican government. Models based on artificial neural networks, logistic regression, and classification algorithms were generated and validated, yielding high rates of correct classification, accuracy, and recall, so that they could be used to make predictions about future cases of patients infected with the SARS-CoV-2 virus. Full article

Colorectal cancer (CRC) exhibits profound molecular heterogeneity between left-sided and right-sided tumors with distinct therapeutic responses that current static genomic analyses incompletely explain. We developed Dynamic Functional Influence Computation (DynaFIC), a computational framework modeling time-resolved signal propagation through biological networks to quantify functional influence beyond static expression levels. Using the GSE39582 dataset comprising 583 primary CRC samples, we performed confounder-adjusted differential expression analysis controlling for microsatellite instability status, BRAF mutations, Tumor Node Metastasis (TNM) stage, age, and sex, identifying 105 laterality-associated genes that underwent DynaFIC temporal network analysis. Right-sided tumors exhibited dramatically higher network connectivity density despite fewer nodes, creating distributed vulnerability patterns with HOXC6 as the dominant regulator, achieving 200-fold influence through network amplification. Left-sided tumors showed compartmentalized, hierarchical organization with PRAC1 as the primary regulator and predictable expression-influence scaling. Temporal clustering revealed distinct propagation kinetics: right-sided tumors demonstrated rapid signal saturation requiring early intervention, while left-sided tumors exhibited sustained propagation permitting sequential approaches. Stability Volatility Index analysis showed right-sided tumors maintain significantly higher systemic vulnerability. These findings establish anatomical location as a fundamental network organizational principle, suggesting that incorporating temporal dynamics into cancer analysis reveals therapeutically relevant differences for precision medicine applications in colorectal cancer. Full article

Germline mutations in the X-linked cohesin subunit gene SMC1A have been increasingly recognized as a cause of developmental and epileptic encephalopathy (DEE); however, the underlying basis of its marked phenotypic heterogeneity remains elusive. In our narrative review, starting from all literature-reported clinical cases of SMC1A-related DEE, we propose an integrative framework summarizing all the clinical and genetic features, stratified by mutation type, mosaic fraction, and X-chromosome inactivation (XCI) patterns to provide valuable support for genetic diagnosis and variants, found to date. Also, we discuss how somatic mosaicism and epigenetic variability underlie the clinical diversity of SMC1A-associated epilepsy and systematically describe the entire phenotypic spectrum, from early-onset, therapy-resistant seizures to milder intellectual disability profiles. We further examine how SMC1A mutations perturb cohesin’s canonical roles in chromatin loop formation and sister-chromatid cohesion, leading to widespread transcriptional dysregulation of neurodevelopmental gene networks. Evidence that XCI skewing can ameliorate or exacerbate neuronal cohesin deficits and, thus modulate seizure threshold, is presented. Full article

Cancer is a multifaceted disease characterized by uncontrolled cell division resulting from substantial disruptions of normal biological processes. Central to its development is cellular transformation, which involves a dynamic sequence of events including chromosomal translocations, genetic mutations, abnormal DNA methylation, post-translational protein modifications, and other genetic and epigenetic alterations. These changes compromise physiological regulatory mechanisms and contribute to accelerated tumor growth. A critical factor in this process is the dysregulation of transcription factors (TFs) which regulate gene expression and DNA transcription. Dysregulation of TFs initiates a cascade of biochemical events, such as abnormal DNA replication, that further enhance cell proliferation and increase genomic instability. This microenvironment not only sustains tumor growth but also promotes the accumulation of somatic mutations, thereby fueling tumor evolution and heterogeneity. In this study, we employed an in silico approach to identify TFs regulating 622 key genes whose mutations are implicated in carcinogenesis. Transcriptional regulatory networks were analyzed through bioinformatics methods to elucidate molecular pathways involved in cancer development. A thorough understanding of these processes may help to clarify the function of dysregulated TFs and facilitate the development of novel therapeutic approaches designed to make cancer treatments personalized and efficacious. Full article

Mutations in the ACTN1 gene, which encodes the cytoskeletal protein α-actinin-1, have been implicated in the etiology of autosomal dominant congenital macrothrombocytopenia. α-Actinin-1 is a member of the spectrin superfamily and is essential for key physiological processes in megakaryocytes and platelets. The pathophysiological mechanisms by which α-actinin-1 mutations lead to macrothrombocytopenia have been attributed to alterations in actin organization, increased binding affinity of α-actinin-1 to actin filaments, and modulation of integrin αIIbβ3 signaling. In previous studies, we utilized megakaryocyte-specific α-actinin-1 knockout (PF4-ACTN1^−/−) mice to explore the influence of α-actinin-1 on megakaryocyte and platelet function. Despite these efforts, the precise mechanisms remain inadequately understood. To advance our understanding and clarify the role of α-actinin-1 in thrombopoiesis, we first delineated the functions of α-actinin-1 in megakaryocytes and platelets, followed by a comprehensive overview of the proteins known to interact with α-actinin-1. As a pivotal scaffold protein, α-actinin-1 interacts with a complex network of partners, including integrin αIIbβ3, and actin filaments, to modulate cytoskeletal dynamics, megakaryocyte maturation, and proplatelet formation. In addition to its well-documented proteins that interact with α-actinin-1 within megakaryocytes and platelets, α-actinin-1 also associates with proteins outside the megakaryocytic lineage, such as cytohesin-2 and MOB1, which have been predominantly examined in other cellular contexts. These varied interactions imply that α-actinin-1 may influence megakaryocyte and platelet functions through multiple mechanisms. This review provides a comprehensive synthesis of current knowledge regarding the structure, binding partners of α-actinin-1, and essential roles of α-actinin-1 in thrombopoiesis. Full article

Objective: This study conducted a comprehensive analysis of molecular transmission networks and pretreatment drug resistance (PDR) in newly diagnosed HIV-1 infections in Taizhou, China. Methods: From 2021 to 2023, we collected 1126 plasma samples from newly diagnosed HIV patients in Taizhou. The HIV pol gene was amplified, and the obtained sequence was used to construct a maximum likelihood (ML) phylogenetic tree and molecular transmission network. PDR-related mutations were analyzed based on the Stanford University HIV Resistance Database. We conducted genotyping analysis and analysis of factors related to the larger clusters (≥10). Results: We successfully amplified and sequenced the pol region from 937 (83.2%, 937/1126) treatment-naïve HIV-1 patients, each with comprehensive epidemiological documentation. Phylogenetic characterization revealed significant subtype heterogeneity, with CRF07_BC (42.1%, 395/937), CRF01_AE (27.6%, 259/937) and CRF08_BC (22.1%, 209/937) being the most prevalent. Notably, 11.4% of the sequenced population (107/937) presented detectable PDR mutations. Univariate analysis revealed that larger clusters (≥10) are more inclined to be aged ≥60, divorced or widowed, have high or technical secondary school education, and have sexual contact with homosexuality. Multivariate analysis revealed that age ≥60 years and not having a PDR mutation (p < 0.05) were factors associated with larger clusters (≥10). Conclusions: Molecular transmission networks suggest that CRF08_BC is spreading rapidly among the older male population. Consequently, targeted interventions aimed at this population are crucial for halting the ongoing rapid dissemination of this subtype. Full article

Background: Immunodeficiencies are a heterogeneous group of disorders classified etiologically as primary (congenital) or secondary (acquired). Primary immunodeficiencies (PIDs), such as common variable immunodeficiency (CVID), result from genetic mutations that impair the development and function of lymphocytes. Secondary immunodeficiencies (SIDs) arise as a consequence of chronic diseases, lymphoid malignancies, or immunosuppressive therapies. Aim of the study: The purpose of this study was to assess the serum expression profile of selected microRNAs (miRNAs) in patients with CVID and in those with chronic lymphocytic leukemia (CLL) and coexisting SID, compared to healthy individuals. Methods: Digital PCR (dPCR) was applied to quantify the serum expression levels of selected miRNAs in patients with CVID, patients with CLL and SID, and in healthy controls. Results: dPCR revealed significantly reduced levels of miR-16, miR-30c, miR-181a, miR-29a, miR-150, and miR-326 in the CVID group, potentially reflecting impaired regulatory mechanisms of the immune system. In contrast, elevated levels of miR-21, miR-125b, and miR-155 were observed in the CLL group with SID, suggesting their role in tumorigenesis and secondary immunosuppression. Correlations between miRNA levels and the expression of immune checkpoints (PD-1, CTLA-4, CD200) indicated the involvement of a complex regulatory network encompassing both humoral and cellular immune mechanisms. Conclusions: The results provide preliminary evidence that selected miRNAs could reflect disease-specific immune dysregulation patterns and may hold potential as diagnostic and prognostic biomarkers in both PIDs and SIDs. Full article

Studies have shown that the iron concentration in the peritoneal fluid of women is associated with the severity of endometriosis. Therefore, investigation of iron metabolism-related genes (IM-RGs) in endometriosis holds significant implications for both prevention and therapeutic strategies in affected patients. Differentially expressed IM-RGs (DEIM-RGs) were identified by intersecting IM-RGs with differentially expressed genes derived from GSE86534. Mendelian randomization analysis was employed to determine DEIM-RGs causally associated with endometriosis, with subsequent verification through sensitivity analyses and the Steiger test. Biomarkers associated with IM-RGs in endometriosis were validated using expression data from GSE86534 and GSE105764. Functional annotation, regulatory network construction, and immunological profiling were conducted for these biomarkers. Single-cell RNA sequencing (scRNA-seq) (GSE213216) was utilized to identify distinctively expressed cellular subsets between endometriosis and controls. Experimental validation of biomarker expression was performed via reverse transcription–quantitative polymerase chain reaction (RT-qPCR). BMP6 and SLC48A1, biomarkers indicative of cellular BMP response, were influenced by a medicus variant mutation that inactivated PINK1 in complex I, concurrently enriched by both biomarkers. The lncRNA NEAT1 regulated BMP6 through hsa-mir-22-3p and hsa-mir-124-3p, while SLC48A1 was modulated by hsa-mir-423-5p, hsa-mir-19a-3p, and hsa-mir-19b-3p. Immune profiling revealed a negative correlation between BMP6 and monocytes, whereas SLC48A1 displayed a positive correlation with activated natural killer cells. scRNA-seq analysis identified macrophages and stromal stem cells as pivotal cellular components in endometriosis, exhibiting altered self-communication networks. RT-qPCR confirmed elevated expression of BMP6 and SLC48A1 in endometriosis samples relative to controls. Both BMP6 and SLC48A1 were consistently overexpressed in endometriosis, reinforcing their potential as biomarkers. Moreover, macrophages and stromal stem cells were delineated as key contributors. These findings provide novel insights into therapeutic and preventive approaches for patients with endometriosis. Full article

The Internet of Vehicles (IoV) is becoming increasingly vulnerable to zero-day (ZD) cyberattacks, which often bypass conventional intrusion detection systems. To mitigate this challenge, this study proposes Zero-Day Bidirectional Encoder Representations from Transformers approach (ZDBERTa), a zero-shot learning (ZSL)-based framework for ZD attack detection, evaluated on the CICIoV2024 dataset. Unlike conventional AI models, ZSL enables the classification of attack types not previously encountered during the training phase. Two dataset variants are formed: Variant 1, created through synthetic traffic generation using a mixture of pattern-based, crossover, and mutation techniques, and Variant 2, augmented with a Generative Adversarial Network (GAN). To replicate realistic zero-day conditions, denial-of-service (DoS) attacks were omitted during training and introduced only at testing. The proposed ZDBERTa incorporates a Byte-Pair Encoding (BPE) tokenizer, a multi-layer transformer encoder, and a classification head for prediction, enabling the model to capture semantic patterns and identify previously unseen threats. The experimental results demonstrate that ZDBERTa achieves 86.677% accuracy on Variant 1, highlighting the complexity of zero-day detection, while performance significantly improves to 99.315% on Variant 2, underscoring the effectiveness of GAN-based augmentation. To the best of our knowledge, this is the first research to explore ZD detection within CICIoV2024, contributing a novel direction toward resilient IoV cybersecurity. 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

Brassinosteroids (BRs) are essential regulators of plant development and stress responses, but the distinct contributions of BR biosynthesis and signaling to hormonal crosstalk remain poorly defined. Here, we investigated the effects of the BR biosynthesis inhibitor brassinazole (BRZ) and the BR-insensitive mutant bri1-6 on endogenous phytohormone profiles in Arabidopsis thaliana. Using multivariate analysis and targeted hormone quantification, we show that BRZ treatment and BRI1 disruption alter hormone balance through partially overlapping but mechanistically distinct pathways. Principal component analysis (PCA) and hierarchical clustering revealed that BRZ and the bri1-6 mutation do not phenocopy each other and that BRZ still alters hormone profiles even in the bri1-6 mutant, suggesting potential BRI1-independent effects. Both BRZ treatment and the bri1-6 mutation tend to influence cytokinins and auxin conjugates divergently. On the contrary, their effects on stress-related hormones converge: BRZ decreases salicylic acid (SA), jasmonic acid (JA), and abscisic acid (ABA) in the WT leaves; similarly, bri1-6 mutants show reduced SA, JA, and ABA. These results indicate that BR biosynthesis and BRI1-mediated perception may contribute independently to hormonal reprogramming, with BRZ eliciting additional effects, possibly via metabolic feedback, compensatory signaling, or off-target action. Hormone correlation analyses revealed conserved co-regulation clusters that reflect underlying regulatory modules. Altogether, our findings provide evidence for a partial uncoupling of BR levels and BR signaling and illustrate how BR pathways intersect with broader hormone networks to coordinate growth and stress responses. Full article

With the surge in urban logistics demand, traditional surface transportation faces challenges, such as traffic congestion and environmental pollution. Leveraging metro systems in metropolitan areas for both passenger commuting and underground logistics presents a promising solution. The metro-based underground logistics system (M-ULS), characterized by extensive coverage and independent right-of-way, has emerged as a potential approach for optimizing urban freight transport. However, existing studies primarily focus on single-line scenarios, lacking in-depth analyses of multi-tier network coordination and dynamic demand responsiveness. This study proposes an optimization framework based on mixed-integer programming and an improved ICSA to address three key challenges in metro freight network planning: balancing passenger and freight demand, optimizing multi-tier node layout, and enhancing computational efficiency for large-scale problem solving. By integrating E-TOPSIS for demand assessment and an adaptive mutation mechanism based on a normal distribution, the solution space is reduced from five to three dimensions, significantly improving algorithm convergence and global search capability. Using the Nanjing metro network as a case study, this research compares the optimization performance of independent line and transshipment-enabled network scenarios. The results indicate that the networked scenario (daily cost: CNY 1.743 million) outperforms the independent line scenario (daily cost: CNY 1.960 million) in terms of freight volume (3.214 million parcels/day) and road traffic alleviation rate (89.19%). However, it also requires a more complex node configuration. This study provides both theoretical and empirical support for planning high-density urban underground logistics systems, demonstrating the potential of multimodal transport networks and intelligent optimization algorithms. Full article