Search Results (7,413)

Spinocerebellar ataxia type 1 (SCA1) is an autosomal dominant neurodegenerative dis-ease caused by the expansion of cytosine–adenine–guanine (CAG) repeats in the ataxin-1 (ATXN1) gene, leading to toxic gain-of-function of the ataxin-1 (ATXN1) protein. This narrative review systematizes the clinical and genetic aspects of SCA1 and discusses key molecular and cellular mechanisms: the ATXN1-CIC ataxin-1-Capicua complex (ATXN1-CIC), the role of serine 776 (Ser776) phosphorylation, interactions with 14-3-3 proteins, transcriptional dysregulation, and critically analyzes experimental models of the disease in vivo and in vitro. In addition, it presents a descriptive quantitative analysis of the literature on in vivo SCA1 models, conducted using a defined search methodology with a cut-off date of 23 November 2025. For each model, phenotypic markers, molecular signatures, and applicability to preclinical testing tasks are summarized. A comparison of the models reveals their complementarity and outlines optimal research trajectories, including omics approaches and prospects for targeted antisense oligonucleotide (ASO) therapy, RNA interference (RNAi), and genome editing. The result is a practical guide for selecting a model in accordance with specific hypotheses and translational objectives. Full article

The genus Kalanchoe (Crassulaceae) comprises approximately 125 species of succulents distributed across Madagascar, Africa, Arabia, Australia, Southeast Asia, and tropical America. Traditionally regarded as “miracle plants”, Kalanchoe species are employed for treating inflammatory, infectious, metabolic, and cardiovascular conditions; this is associated with their abundant content of polyphenols, including phenolic acids and flavonoids such as quercetin, kaempferol, luteolin, rutin, and patuletin. However, robust clinical evidence remains limited. This review integrates pharmacological and bioinformatic perspectives by analyzing more than 70 studies published since 2000 on 15 species, including Bryophyllum. As an in silico complement, the genome of Kalanchoe fedtschenkoi was used to predict genes (AUGUSTUS), perform homology searches against Arabidopsis thaliana, and model three key enzymes: CHS, CYP90, and VEP1. The AlphaFold2/ColabFold models showed conserved catalytic motifs, and molecular docking with representative ligands supported the plausibility of biosynthetic pathways for flavonoids, brassinosteroids, and bufadienolides. The available evidence highlights chemopreventive, antibacterial, anti-inflammatory, antiviral, antioxidant, and cytotoxic activities, primarily associated with flavonoids and bufadienolides. Significant gaps remain, such as the lack of gene–metabolite correlations and the absence of standardized clinical trials. Overall, Kalanchoe represents a promising model that requires multi-omics approaches to enhance its phytopharmaceutical potential. Full article

Background: Heritable breast cancer (BC) predisposition is strongly influenced by high-penetrance genes such as BRCA1 and BRCA2, but many moderate- and low-penetrance genes remain poorly characterized. Although over 100 loci have been reported, the causal genes often include false positives or uncertain associations. Methods: We applied a gene-centric, integrative approach to multi-ethnic genomic datasets, including the UK Biobank (UKB) and FinnGen (FG). We assessed consistency across multiple GWAS in Open Targets (OT) and additional complementary genetic association approaches, including ExPheWAS, TWAS, and PWAS. Collapsing variant-level effects to a gene-level view enhanced confidence and reaffirmed contributions from genes such as BRCA1, BRCA2, PALB2, CHEK2, and other DNA repair genes. Results: Using this integrative framework, we identified 38 high-confidence BC predisposition genes, including 8 previously reported drivers, 13 supported by multiple lines of evidence, and additional candidates (e.g., APOBEC3A, TNS1, PEX14) with emerging evidence. PWAS revealed several genes with potential recessive effects often missed by standard GWAS. Multi-cohort replication showed robust findings in European ancestry populations, while transferability to other populations was more limited. Conclusions: This work demonstrates the value of a gene-centric, integrative framework for prioritizing high-confidence BC predisposition genes, highlighting associated cellular pathways, and uncovering new candidates for further functional study, providing a reliable foundation for future research. Full article

Bloodstream infections (BSIs) pose a significant global health challenge, particularly due to the increasing prevalence of antimicrobial resistance (AMR). Timely and accurate identification of pathogens and resistance determinants is critical for guiding appropriate therapy and improving patient outcomes. Traditional culture-based diagnostics are limited by prolonged turnaround times and reduced sensitivity, especially in culture-negative or polymicrobial infections. This review systematically examined current and emerging sequencing technologies for AMR detection in BSIs, including whole-genome sequencing (WGS), targeted next-generation sequencing (tNGS), metagenomic next-generation sequencing (mNGS), and long-read sequencing platforms (Oxford Nanopore, PacBio). We compared their clinical performance using key metrics such as diagnostic sensitivity, turnaround time, and cost, highlighting contexts in which each technology is most effective. For example, tNGS can achieve the rapid detection of known resistance genes within 8–24 h, while WGS provides comprehensive genome-wide resistance profiling over 24–48 h. mNGS offers broader detection, including rare or unexpected pathogens, although at higher cost and longer processing times. Our analysis identifies specific strengths and limitations of each approach, supporting the use of context-specific strategies, such as combining rapid targeted sequencing for common pathogens with broader metagenomic approaches for complex cases, to improve diagnostic yield and guide antimicrobial therapy. Quantitative comparisons indicate that sequencing technologies can complement conventional methods, particularly in cases where culture-based approaches fail. In conclusion, sequencing-based diagnostics offer measurable improvements in sensitivity and speed over traditional methods for AMR detection in BSIs. Future work should focus on optimizing workflows, integrating sequencing data into clinical decision-making, and validating approaches in prospective studies. Full article

Analysis of coding areas has long been used to study monogenic illnesses, but despite the extensive use of whole-exome sequencing (WES), up to half of suspected cases remain genetically unexplained. Variants outside coding areas can alter splicing, transcript stability, or gene regulation, compromising normal gene activity. These include mutations in noncoding RNAs, promoters, enhancers, deep intronic sequences, and untranslated regions (UTRs). Several well-known disorders have been linked to these mechanisms, including β-thalassemia caused by deep intronic mutations leading to aberrant splicing, familial hypercholesterolemia caused by promoter defects affecting LDLR expression, and inherited retinal diseases driven by noncoding variants influencing retinal gene regulation. These instances show that pathogenic variation is not limited to the exome and can have significant clinical implications. This review summarizes current understanding of noncoding and regulatory variants in monogenic diseases, discusses how they influence diagnosis and therapy, and highlights integrative approaches combining genomic, transcriptomic, and epigenomic data. Multi-layered research has increased diagnostic accuracy and unveiled new therapeutic potentials, although noncoding variations make the connection between genotype and phenotype more complex. Noncoding regions will need to be incorporated into standard diagnostic procedures to convert molecular insights into concrete therapeutic applications in the future. Predictive algorithms, patient-derived model systems, and functional validation testing will all help to simplify this process. Full article

Homologous recombination deficiency (HRD) is a clinically relevant biomarker that predicts sensitivity to PARP inhibitors and enables personalized cancer therapy. Validated local HRD testing solutions are essential to ensure timely and equitable access, ultimately improving treatment outcomes. We evaluated a shallow whole-genome sequencing (sWGS) approach for genomic instability (GI) assessment combined with a 52-gene targeted panel in ovarian cancer. Validation used reference materials and 24 archival samples with prior HRD characterization, comparing performance with the Myriad myChoice^® HRD test. A prospective cohort of 124 newly diagnosed ovarian cancer patients was then analyzed. sWGS-derived GI status showed strong concordance with the reference test (95.8% overall agreement; κ = 0.913; NPV 100%, PPV 93.3%). Pathogenic BRCA1/2 variants were detected in 30 patients (24.19%). An additional 22.76% were BRCA1/2-negative but GI-positive, giving an overall HRD prevalence of 47.15%. Platinum sensitivity occurred in 90.0% (18/20) of HRD-positive patients with follow-up. Among 12 patients assessed for PARP-inhibitor response, the overall response rate was 66.7% (95% CI 39.1–86.2) and disease control rate 83.3% (95% CI 55.2–95.3). TP53 alterations were most frequent (62.90%), followed by BRCA1 (19.35%) and BRCA2 (4.83%). Pathogenic variants in other HR-pathway genes (ATM, CHEK2, BRIP1, RAD51C, BARD1) appeared in 9.57% of BRCA-wild-type cases, with heterogeneous GI impact. Two cases showed concurrent BRCA2 variants and microsatellite instability, indicating possible eligibility for anti-PD-1/PD-L1 therapy in addition to PARPi. This first comprehensive analysis of Romanian ovarian cancer patients suggests that integrating sWGS-based genomic instability assessment with BRCA testing can improve HRD detection and reflects the heterogeneity of HR-pathway variants. Preliminary clinical observations were consistent with known HRD-associated treatment responses, although larger studies are needed to confirm these findings. Full article

Proline-, glutamic acid-, and leucine-rich protein 1 (PELP1) is a proto-oncogene that serves as a nuclear and cytoplasmic scaffolding protein. PELP1 plays a critical role in nuclear receptor signaling, ribosome biogenesis, chromatin modifications, cell cycle progression, non-genomic signaling, and DNA damage response. PELP1 expression is upregulated in a variety of cancers, including breast, ovarian, endometrial, prostate, and liver cancers and serves as a prognostic factor for poor survival. PELP1’s structural motifs, unique scaffolding function, and oncogenic activity make it a potential target for a range of therapeutic approaches. This review summarizes the most recent advancements in PELP1 biology, with a particular focus on the emergent oncogenic functions of PELP1 and its inhibitors for the treatment of cancer. Full article

Recent advancements in neuroimaging and genetics have generated a rapid proliferation of primary studies in these fields, leading to the development and application of meta-analytic methods, which have contributed substantially to our understanding of psychiatric and neurological disorders. The current narrative review discusses four such innovations and applications in meta-analytic techniques and how they have advanced our understanding of clinical conditions: (1) multilevel kernel density analysis (MKDA) of functional magnetic resonance imaging (fMRI) studies, (2) meta-analyses of positron emission tomography (PET) imaging of neuroinflammation, (3) Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium neuroimaging protocols, and (4) meta-genome-wide association studies (Meta-GWASs) and polygenic risk scores (PRSs). These meta-analytic methods have contributed substantially to our understanding of psychiatric and neurological disorders by refining robust neural models, identifying transdiagnostic and disease-specific biomarkers of inflammation, uncovering numerous genetic risk variants with improved prediction models, and underscoring the polygenic and pleiotropic architecture of these conditions. Future research should continue to develop techniques for harmonizing multimodal data analysis, pursue both biomarker- and mechanism-driven approaches to discovery, and leverage biological discoveries to advance development of precision treatments and diagnostic frameworks. Full article

Anaerobic fungi (phylum Neocallimastigomycota) play a crucial role in degrading forages and fibrous foods in the gastrointestinal tract of mammalian herbivores, particularly ruminants. Currently, they are classified into twenty-two genera; however, recent research suggests the occurrence of several novel taxa that require further characterization. Anaerobic rumen fungi play a pivotal role in lignocellulose degradation due to their unique enzymatic capabilities. This review explores the enzymatic systems of rumen anaerobic fungi, highlighting their ability to produce a diverse array of carbohydrate-active enzymes (CAZymes), such as cellulases, hemicellulases, and pectinases. These enzymes facilitate the breakdown of complex plant polymers, making anaerobic fungi essential contributors to fiber degradation in the rumen ecosystem and valuable resources for biotechnological applications. This review summarizes the structural and functional diversity of fungal CAZymes, and the mechanical disruption of plant cell walls by fungal rhizoidal networks is discussed, showcasing the ability of fungi to enhance substrate accessibility and facilitate microbial colonization. Recent studies using genomic, transcriptomic, and biochemical approaches have uncovered several novel CAZymes in anaerobic fungi, including multifunctional xylanases, β-glucosidases, and esterases. These findings highlight the continued expansion of fungal enzyme repertoires and their potential for biotechnology and feed applications. Continued research in this field will enhance our understanding of microbial ecology and enzyme function, paving the way for applications that address global challenges in energy, food security, and environmental sustainability. Full article

Diabetes mellitus and its microvascular complications, including diabetic retinopathy (DR), present significant health challenges. DR is a leading cause of vision impairment and blindness among working-age individuals in developed countries. The prevalence of DR continues to rise, underscoring the need for more precise diagnostic and therapeutic strategies. Due to its multifactorial nature and despite advancements in understanding DR pathophysiology, predicting its onset and progression remains challenging. Traditional screening and treatment methods often fall short of addressing the heterogeneous nature of the disease, underscoring the need for personalised therapeutic strategies. Recent research has highlighted the vital role of genetic biomarkers in the development and progression of DR, paving the way for a precision medicine approach. Personalised eye care in patients with diabetes aims to accurately predict the risk of DR progression and visual loss in real time. A precision medicine approach that utilises genetic biomarkers offers a promising pathway for personalised diagnosis and treatment strategies. Each DR case is distinct in phenotype, genotype, and therapeutic response, making personalised therapy crucial for optimising outcomes. Advancements in genomics, including genome-wide association studies (GWAS) and next-generation sequencing (NGS), have identified numerous genetic markers associated with DR susceptibility and severity. Emerging evidence underscores the critical role of genetic factors, which account for 25–50% of the risk of developing DR. Advances in identifying genetic markers, such as gene polymorphisms and human leukocyte antigen associations, along with the development of targeted drugs, highlight a promising future for personalised medicine in DR. By identifying specific genetic variants associated with DR, we can enhance prevention and early diagnosis, tailor personalised treatment plans, and more accurately predict disease progression. This represents a critical step toward personalised medicine in DR management. Integrating genetic and epigenetic biomarkers into clinical models may transform DR care through earlier diagnosis and precision-guided interventions, gearing it toward precision ophthalmology. Full article

Neurofibromatosis type 1 (NF1) predisposes to a spectrum of peripheral nerve sheath tumors, ranging from benign plexiform neurofibromas (PN) to atypical neurofibromatous neoplasms of uncertain biological potential (ANNUBP) and malignant peripheral nerve sheath tumors (MPNST). Tumorigenesis follows a multistep molecular cascade initiated by biallelic NF1 inactivation, followed by CDKN2A loss and disruption of the Polycomb Repressive Complex 2 (PRC2). These events guide chromatin remodeling, widespread epigenetic dysregulation, and activation of oncogenic pathways such as RAS/MAPK and PI3K/AKT. Here, we integrate genomic, transcriptomic, and epigenomic studies to delineate the molecular trajectories underlying tumor progression and to define promising biomarkers for the early detection of malignant transformation. Emerging liquid biopsy approaches, based on circulating tumor DNA (ctDNA) analyses, reveal distinctive copy number variations (CNVs) and methylation patterns that mirror tissue-derived profiles, enabling the detection of malignant transformation. Together, these findings support a model in which cumulative genetic and epigenetic alterations drive the PN–ANNUBP–MPNST continuum. They also underscore the value of multi-omics and liquid biopsy-based strategies to improve early diagnosis, patient risk stratification, and personalized management of NF1-associated tumors, thereby advancing precision medicine in this complex disease spectrum. Full article

Genomic organization and its comparative analysis throughout all major kingdoms of life are extensively studied across multiple scales, ranging from individual gene-level analyses to system-wide investigations. This work introduces a novel framework for characterizing genetic architecture through a new integral genomic parameter. We propose the concept of a multidimensional Gene Space to enable holistic quantification of genome organization principles. Gene Space—a multidimensional space based on the frequencies of nucleotide tokens, such as individual nucleotides, codons, or codon pairs. We demonstrate that in this space, genes from each of the studied microorganism species occupy a limited region, and individual genes from different species can be effectively separated with more than 95% accuracy. Consequently, a specific Genome Subspace can be defined for each species, which constrains the organism’s evolutionary pathways, thereby determining the constraints on gene optimization for these species. Further in-depth analysis is required to test if it is true for other organisms as well. The Gene Space framework offers a novel and powerful approach for genome analysis at the most basic levels, with promising applications in comparative genomics, evolutionary biology, and gene optimization. Full article

The rhizosphere, a dynamic interface shaped by plant root exudates, fosters microbial communities with significant biochemical potential. This study investigated the interplay between soil properties and fungal bioactivity in the rhizospheres of Withania coagulans and Justicia adhatoda in Pakistan. Physicochemical analysis revealed silty loam textures with divergent phosphorus [25.7 vs. 71.5 mg/kg] and potassium [108 vs. 78 mg/kg] levels, alongside near-neutral pH, influencing microbial dynamics. Two fungal isolates, Aspergillus luchuensis and A. flavus, were identified through morphological traits and ITS-region sequencing. Gas chromatography-mass spectrometry [GC-MS] profiling of ethyl acetate extracts uncovered 30 and 25 previously uncharacterized metabolites in A. luchuensis and A. flavus, respectively, including bioactive compounds such as tetradecanoic acid and nonadecane. Bioassays demonstrated broad-spectrum efficacy against multidrug-resistant clinical isolates, with A. flavus exhibiting notable inhibition against Salmonella typhi [31.7 mm zone] and A. luchuensis against Shigella spp. [23 mm]. Both extracts suppressed Lemna minor growth by 70%, indicating phytotoxic potential, and displayed species-specific insecticidal activity, inducing 70% mortality by A. luchuensis against Blattodea and 50% by A. flavus against the same species. These findings underscore the rhizosphere’s role as a reservoir of bioactive fungi, with Aspergillus spp. producing metabolites of pharmaceutical and agrochemical relevance. The study highlights the necessity for advanced structural elucidation and ecotoxicological assessments to harness these compounds, advocating integrated approaches combining metabolomics and genomic mining to unlock novel biotechnological applications. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

(1) Background: Backcross (BC) breeding is a key technology of crop improvement, yet its efficiency largely depends on the precise assessment of the genetic background recovery. Conventional molecular marker-assisted techniques suffer from inadequate genomic coverage or an inability to resolve true chromosomal structure. (2) Methods: To address major issues in maize BC breeding, we devised a G2H block-scanning strategy. This approach converts high-density point markers into haplotype blocks, enabling precise evaluation of the genetic background in backcross progenies. A key innovation is the CFDI, which quantifies the distribution of unrecovered fragments, allowing for visual tracking of chromosomal recombination and identification of ideal individuals with both a high genetic background recovery rate and few small fragments retention. (3) Results: We validated the accuracy and effectiveness of the G2H strategy across multiple backcross generations. Through enabling a precise “point-to-line-to-area” panoramic assessment of genetic background, G2H provides a powerful tool for developing ideal breeding materials with pure genetic background and minimized linkage drag. (4) Conclusions: Notably, this strategy significantly shortens the breeding cycle by 2–3 generations compared to conventional background assessment methods, thereby accelerating precision molecular design breeding in crops. Full article