Search Results (197)

Cancer remains a significant challenge to global public health. Preliminary studies indicate that the protein Golgi-associated, Gamma-adaptin Ear Containing, ARF Binding Protein 2 (GGA2) may influence various cancers. However, the potential role of GGA2 in oncogenesis remains unknown. We utilized data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) projects to analyze GGA2 expression levels. Genetic variations and protein expression of GGA2 in human tissues were assessed using the cBioPortal. Gene Set Enrichment Analysis (GSEA) provided deeper insights into GGA2’s oncogenic functions. Comprehensive analysis of TCGA datasets combined with ESTIMATE and TIMER tools demonstrated significant correlations between GGA2 expression levels and clinical outcomes, survival metrics, genomic instability markers (microsatellite instability (MSI)/tumor mutational burden (TMB)), and immune microenvironment composition. Functional validation in prostate cancer models employed qRT-PCR quantification, immunoblotting verification, and cellular behavior assessments through colony formation, Transwell migration, and wound closure assays. Our findings suggest GGA2 could serve as a prognostic biomarker in various cancers. Abnormal levels of GGA2 promoter methylation and genetic alterations may contribute to its dysregulated expression in some cancers. Distinctly, GGA2 expression correlates with MSI and TMB across different cancers and is linked to the expression of immune checkpoint genes. Functionally, GGA2 is instrumental in inhibiting oncogenic mechanisms by diminishing the proliferation, colony formation, invasion, and migratory capabilities of prostate cancer cells. Our study shows that the oncogenic role of GGA2 in various cancers and GGA2 could be served as a biomarker of PARD. Full article

Background/Objectives: Lipid metabolism is fundamental to energy homeostasis and cellular structural integrity, and its dysregulation is a hallmark of biological aging. While DNA methylation clocks are well-established, it remains unclear whether epigenetic sites associated with specific lipid markers—High-Density Lipoprotein (HDL), Total Cholesterol (TCH), and Triglycerides (TGY)—are evolutionarily conserved across mammals and how they manifest across different metabolic tissues. Methods: We identified lipid-associated CpG sites in humans using the Korean Genome and Epidemiology Study (KoGES) cohort and projected these sites onto the Mammalian Methylation Consortium (GSE223748) dataset. Using the Hybrid Pi (HyPi) score, we selected robust markers to analyze their evolutionary conservation, tissue specificity, and age-related dynamics across over 300 mammalian species. Specifically, we examined the phylogenetic concordance between blood and three major metabolic organs (Liver, Adipose, Muscle) in five representative species. Results: Lipid-related CpGs were highly conserved across diverse mammals. t-SNE analysis revealed that these epigenetic signatures clustered samples by tissue identity and species. Methylation levels of these CpGs showed significant correlations with maximum lifespan and distinct aging rates across tissues. Notably, phylogenetic tanglegram analysis revealed a high degree of concordance between blood and key metabolic organs, suggesting that blood methylation profiles mirror the evolutionary trajectory of internal metabolic tissues. Furthermore, these patterns were consistent between sexes, indicating a fundamental, non-dimorphic regulation of lipid epigenetics. Conclusions: Our findings suggest that epigenetic mechanisms governing lipid metabolism are deeply conserved to maintain tissue identity and regulate biological aging, with blood serving as a reliable evolutionary proxy for internal metabolic states. Full article

Synthetic plastics are polymers that are largely produced worldwide, impacting ecosystems and human health. Microplastics are produced from fragmentation and degradation of larger plastics, as a consequence of environmental factors. Low-density polyethylene (LDPE) and polypropylene (PP) are plastic polymers acting as environmental hazards. Challenges in effective plastic waste management include sustainable and environmentally responsible approaches like microbial degradation. In this work, a shotgun metagenomic approach has been applied to analyze the response of the microorganisms living on plastic surfaces (plastispheres) of LDPE and PP to biodeterioration of these plastics (BioProject-NCBI, PRJNA1378224). Low-density polyethylene and polypropylene materials were collected from a waste landfill of intensive greenhouse agriculture. A further functional analysis supported putative roles of enzymes that could be involved in the initial steps of biodeterioration of LDPE and PP, including sarcosine oxidases; bromo- and chloro-peroxidases; cytochrome P450 and alkane monooxygenases; and multicopper oxidases. A CheckM analysis of genes that code for these oxidative enzymes revealed that they were mainly from the bacterial Phyllobacterium genus (Rhizobiaceae family) and, in less abundance, from the archaeon Methanoculleus genus (Methanoculleaceae family). This study supports putative roles of sarcosine oxidases and bromoperoxidases, and other relevant enzymes, in bacterial and archaeal LDPE and PP biodeterioration, highlighting the genomic potential of the microbiomes under study in biodeterioration of these synthetic plastics. Full article

Human adenoviruses (HAdVs) are globally distributed pathogens capable of causing a wide range of clinical manifestations, particularly acute respiratory infections. However, their genomic diversity remains insufficiently characterized, with substantial geographic gaps in available sequence data, including for Russia, where only a few complete genomes have been deposited prior to this work. In this study, we analyzed more than 1200 PCR-positive respiratory specimens collected from hospitalized patients within routine surveillance projects and the Global Influenza Hospital Surveillance Network (GIHSN) across plenty of Russian regions during 2023–2024. Virus isolation followed by next-generation sequencing yielded 128 complete HAdV genomes representing species B, C, and D. The dataset included 27 B3, 9 B7, 44 B55, 12 C1, 16 C2, 4 C5, 7 C89, 5 C108, and one D109 genome, as well as three unassigned recombinant viruses with p89h5f5, p5h6f6 and p5h57f6 genomic structures (p, penton base; h, hexon; f, fiber). Phylogenetic analyses of whole genomes and capsid genes revealed extensive variability in immunogenic regions, particularly in species C, and identified clusters within B3 viruses. Notably, HAdV-D109 was identified in Russia, marking only the second reported detection of this genotype worldwide. Together, these findings substantially expand the currently available genomic landscape of HAdVs, highlighting the circulation of diverse and recombinant strains in Russia. Full article

Germplasm collections are a treasure trove of humanity. The accessions constituting those collections (wild crop relatives, landraces, cultivars, etc.) contain genes and allelic variants, which evolved prior to or post domestication, in the course of adaptation and selection, and can be used in breeding to address current and future needs. Precise characterisation of genetic diversity is essential for the efficient conservation of genetic resources and their effective utilisation in crop improvement. Detailed genetic profiles resulting from DNA genotyping constitute a basis for establishing the level of genetic diversity of a collection, analysing population structure, identifying redundancies, performing genome-wide association scans (given the availability of phenotypic information), detecting loci under selection, and many other applications. To obtain an accurate picture of genetic diversity (at the DNA sequence level), robust, high-density, high-throughput, and cost-effective methods are needed. With the advances in the next-generation sequencing, new genotyping approaches emerged (such as genotyping-by-sequencing, whole genome resequencing), which provide excellent genome coverage and low cost per datapoint (with tens of thousands to millions of loci analysed in a single assay). Crop-specific, custom, microarray-based genotyping solutions were also developed. The aim of this review is to provide a comparative description of the genome-wide, high-density genotyping technologies that are most frequently used nowadays, comprising their advantages and drawbacks, as well as factors that determine, which of the methods will best suit the particular germplasm characterisation project. Further, we characterise the current role of these methods in addressing the challenges related to the effective management and use of genetic resources and present recent examples of their application in selected crop plant groups. Finally, we briefly describe constraints to germplasm characterisation and future prospects. Full article

Stem cell transplants are a common treatment for leukemia, and close donor–recipient matching improves their success. Machine learning models like support vector machine (SVM), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) can have difficulty handling the complexity of genomic and immune data, which then lowers the accuracy of clinical predictions. This study looks at using graph neural networks (GNNs) in a different way. This method combines data such as single-nucleotide polymorphisms (SNPs), human leukocyte antigen (HLA) typing, and clinical details to create a graph that shows the relationship between donor and recipient pairs. The framework uses graph attention networks (GATs) to focus on key compatibility traits and Dynamic GNNs (DGNNs) to monitor changes in the immune system and the disease’s progression. With data from the 1000 Genomes Project, the model correctly identified matches with 97.68% to 99.74% accuracy and classified them with 98.76% to 99.4% accuracy, outperforming standard machine learning models. The model uses SNP similarity and HLA mismatches to assess compatibility, which enhances its match prediction and compatibility explanation capabilities. The results suggest that GNNs offer a helpful and understandable way to model donor–recipient matching, potentially assisting in early leukemia detection and personalized stem cell transplant plans. Full article

The success and impact of biological invasions depend on adaptations to novel abiotic and biotic selective pressures. However, the genetic mechanisms underlying adaptations in invasive species are inadequately understood. Taenioides sp. is an invasive worm goby, originally endemic to brackish waters in the estuaries of Southeastern China, and now colonizes multiple inland freshwaters of North China within decades as a byproduct of the East Route of South-to-North Water Transfer (ESNT) project. However, the molecular mechanisms underlying their adaptations to the climate of North China, especially the temperature regime, are unknown. Here, we performed genomic resequencing analysis to assess genetic diversity and population genetic structure, and further investigated the genomic signatures of local adaptation in the invasive population of Taenioides sp. during their northward invasion. We revealed that all invasive populations exhibited no genetic differentiation but low gene flow and an obvious signal of population bottleneck. Yangtze River estuary may serve as the source population, while Gaoyou Lake serves as a potential bridgehead of the invasion. Selective sweep analyses revealed 117 genomic regions, containing 673 candidate genes, under positive selection in populations at the invasive front. Redundancy analysis suggested that local temperature variables, particularly the monthly minimum temperature, represent critical evolutionary forces in driving adaptive divergence. Functional enrichment analyses revealed that multiple biological processes, including metabolism and energy production, substance transmembrane transport, and neural development and synaptic transmission, may play important roles in adaptation to regional temperature conditions. Our findings revealed a scenario of adaptive evolution in teleost species that underpins their regional climate adaptation and successful establishment of invasive populations in a human-facilitated invasion context. Proper management strategies should be established to manage Taenioides sp invasion as soon as possible. Full article

Tumor-initiating cells (TICs) constitute a subpopulation of cancer cells with stem-like properties contributing to tumorigenesis, progression, recurrence, and therapeutic resistance. Despite their biological importance, their molecular signatures that distinguish them from non-TICs remain incompletely characterized. This study aimed to comprehensively analyze transcriptomic differences between TICs and non-TICs, identify TIC-specific gene expression patterns, and construct a machine learning-based classifier that could accurately predict TIC status. RNA sequencing data were obtained from four human cell lines representing TIC (TS10 and TS32) and non-TIC (32A and Epi). Transcriptomic profiles were analyzed via principal component, hierarchical clustering, and differential expression analysis. Gene-Ontology and Kyoto-Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted for functional interpretation. A logistic-regression model was trained on differentially expressed genes to predict TIC status. Model performance was validated using synthetic data and external projection. TICs exhibited distinct transcriptomic signatures, including enrichment of non-coding RNAs (e.g., MIR4737 and SNORD19) and selective upregulation of metabolic transporters (e.g., SLC25A1, SLC16A1, and FASN). Functional pathway analysis revealed TIC-specific activation of oxidative phosphorylation, PI3K-Akt signaling, and ribosome-related processes. The logistic-regression model achieved perfect classification (area under the curve of 1.00), and its key features indicated metabolic and translational reprogramming unique to TICs. Transcriptomic state-space embedding analysis suggested reversible transitions between TIC and non-TIC states driven by transcriptional and epigenetic regulators. This study reveals a unique transcriptomic landscape defining TICs and establishes a highly accurate machine learning-based TIC classifier. These findings enhance our understanding of TIC biology and show promising strategies for TIC-targeted diagnostics and therapeutic interventions. Full article

Background: Pharmacogenomics (PGx), a pivotal branch of personalized medicine, studies how genetic variations influence drug responses. Despite its transformative potential, the adoption of PGx in Indian clinical practice faces challenges, such as the lack of population-specific data, evidence-based guidelines, and complexities in interpreting genomic reports. Comprehensive datasets tailored to Indian patients are essential to facilitate the integration of PGx into clinical settings. Methodology: The study collates pharmacogenomic data from multiple sources, including essential drugs listed by the World Health Organization (WHO), drugs used in neonatal intensive care units (NICUs), minimum sets of alleles recommended by the Association for Molecular Pathology (AMP), and catalogs the allele frequencies from the IndiGenomes database to address gaps in actionable PGx for the Indian population. Curated datasets were used to identify pharmacogenomic variants relevant to clinical practice. Results: Overall, 24 prime genes are essential for the outcomes of 57 drugs. In adults, 18 genes influence the metabolism of 44 drugs whereas, in pediatric populations, genotypes of 18 genes significantly impact the metabolism of 18 drugs. Two over-the-counter drugs with actionable PGx variants were identified: ibuprofen and omeprazole. These findings emphasize the clinical relevance of PGx for commonly used drugs, underscoring the need for population-specific data. Conclusions: As the data of several Indian human genome projects become available, an overarching need exists to establish and regulate the dynamic actionable PGx in Indian clinical practice. This will facilitate the integration of pharmacogenomic data into healthcare, enabling effective and personalized drug therapies. Full article

The global population is rising at an alarming rate and is projected to reach 10 billion by 2050, necessitating a substantial increase in food production. However, the overuse of chemical pesticides, including antibacterial agents and synthetic fertilizers, poses a major threat to sustainable agriculture. This review examines the ecological and health impacts of antibacterial agents (e.g., streptomycin, oxytetracycline, etc.) in horticultural crops, focusing on their effects on non-target organisms such as beneficial microbes involved in plant growth promotion and resistance development. Certain agents (e.g., triclosan, sulfonamides, and fluoroquinolones) leach into water systems, degrading water quality, while others leave toxic residues in crops, leading to human health risks like dysbiosis and antibiotic resistance. To mitigate these hazards, sustainable alternatives such as integrated plant disease management (IPDM) and biotechnological solutions are essential. Advances in genetic engineering including resistance-conferring genes like EFR1/EFR2 (Arabidopsis), Bs2 (pepper), and Pto (tomato) help combat pathogens such as Ralstonia solanacearum and Xanthomonas campestris. Additionally, CRISPR-Cas9 enables precise genome editing to enhance inherent disease resistance in crops. Emerging strategies like biological control, plant-growth-promoting rhizobacteria (PGPRs), and nanotechnology further reduce dependency on chemical antibacterial agents. This review highlights the urgent need for sustainable disease management to safeguard ecosystem and human health while ensuring food security. Full article

Age-related macular degeneration (AMD) is a common cause of blindness worldwide, and it is projected to affect several million individuals by 2040. The human retinal pigment epithelium (hRPE) degenerates in dry AMD, prompting the need to develop stem cell therapies to replace the lost tissue by autologous transplantation and restore the visual function. Nevertheless, the molecular factors behind the hRPE cell fate determination have not been elucidated. Here we identify all molecular determinants of the hRPE cell fate identity by comprehensive and unbiased screening of predicted pioneer factors in the human genome: such TFs mediate coordinated transitions in chromatin accessibility and transcriptional outcome along three major stages of the hRPE genesis. Furthermore, we compile a complete census of all transcription factor-specific binding sites by footprinting analysis of the human epigenome along the RPE developmental trajectory. Gene regulatory networks were found to be involved in cellular responses to glucose and hypoxia, RPE nitrosative stress, type II epithelial-to-mesenchymal transition (EMT), and type III tumorigenic EMT, providing routes for therapeutic intervention on pleiotropic targets dysregulated in AMD, diabetic retinopathy, and cancer progression. Genome editing technologies may leverage this repository to devise functional screenings of regulatory elements and pharmacogenomic therapies in complex diseases, paving the way for strategies in precision medicine. Full article

Background: The tissues of origin and molecular mechanisms underlying human complex diseases remain incompletely understood. Previous studies have leveraged transcriptomic data to interpret genome-wide association studies (GWASs) for identifying disease-relevant tissues and fine-mapping causal genes. However, according to the central dogma, proteins more directly reflect cellular molecular activities than RNA. Therefore, in this study, we integrated proteomic data with GWAS to identify disease-associated tissues and genes. Methods: We compiled proteomic and paired transcriptomic data for 12,229 genes across 32 human tissues from the GTEx project. Using three tissue inference approaches—S-LDSC, MAGMA, and DESE—we analyzed GWAS data for six representative complex diseases (bipolar disorder, schizophrenia, coronary artery disease, Crohn’s disease, rheumatoid arthritis, and type 2 diabetes), with an average sample size of 260 K. We systematically compared disease-associated tissues and genes identified using proteomic versus transcriptomic data. Results: Tissue-specific protein abundance showed a moderate correlation with RNA expression (mean correlation coefficient = 0.46, 95% CI: 0.42–0.49). Proteomic data accurately identified disease-relevant tissues, such as the association between brain regions and schizophrenia and between coronary arteries and coronary artery disease. Compared to GWAS-based gene association estimates alone, incorporating proteomic data significantly improved gene association detection (AUC difference test, p = 0.0028). Furthermore, proteomic data revealed unique disease-associated genes that were not identified using transcriptomic data, such as the association between bipolar disorder and CREB1. Conclusions: Integrating proteomic data enables accurate identification of disease-associated tissues and provides irreplaceable advantages in fine-mapping genes for complex diseases. Full article

Super-enhancers (SEs) orchestrate high-level transcription by integrating multiple regulatory elements and signals. Although chromatin accessibility and transcription factor binding within SEs are extensively studied, the role of non-canonical DNA structures, particularly Z-DNA, remains underexplored. In this study, genome-wide predictions of Z-DNA-forming sequences (generated by the Z-DNA-BERT model) were applied to systematically investigate their distribution within typical enhancers and SEs across multiple human cancer cell lines. Statistically significant enrichment of Z-DNA sequences within SE regions, compared to random genomic controls, was observed. Furthermore, genetic variants overlapping these Z-DNA regions, identified using data from the 1000 Genomes Project, were found to alter binding motifs of the SP/KLF transcription factor family. These mutations exhibited population-specific clustering and overlapped previously reported pathogenic copy-number variations (CNVs) associated with neurodevelopmental disorders, potentially affecting transcription factor binding motifs related to neuronal growth and differentiation pathways. Population-level phylogenetic analysis revealed distinct clustering patterns of these variants, suggesting frequency-specific genetic architecture. Overall, the computational findings indicate that Z-DNA structures within super-enhancers might play regulatory roles and potentially influence population-specific genetic variation, highlighting specific genomic targets and providing new avenues for future experimental research. Full article

Background/Objectives: Genodermatoses are genetic conditions with clinical symptoms manifesting in the skin and adjoining tissues, individually rare but comprising a large and heterogeneous group of disorders that represents 15% of genetic diseases. This article discusses the results of individuals with genodermatoses from a reference center for rare diseases studied through whole genome sequencing conducted by the Brazilian Rare Genomes Project between 2021 and 2023. Methods: A retrospective case series with data comprising sex, age at first assessment in the hospital, family history, clinical findings, and molecular results. Results: Excluding neurofibromatosis type 1, Ehlers–Danlos syndrome and RASopathies are discussed elsewhere. Diagnoses in this work comprised ectodermal dysplasias (n = 6), ichthyosis (n = 4), albinism (n = 4), tuberous sclerosis complex (n = 4), and incontinentia pigmenti (n = 3), in addition to 11 others with individual rare conditions. The sex ratio was 17:16 (M:F), consanguinity was present in 6/33 (18%), and the age at the first evaluation ranged from neonatal to 26 years (median 13.65 years). Negative results were 3/33 (9%), novel variants were 17/41 (41.4%), and 7/30 (23%) presented initially with a double molecular diagnosis, three confirming composed phenotypes. Conclusions: Besides reporting 17 novel variants in 14 genes (BLM, CACNA1B, EDA, ELN, ENG, ERC6, EVC2, PNPLA1, PITCH1, PORCN, SIN3A, TP63, TYR, and WNT10B), the study also identified three atypical clinical presentations due to dual diagnoses, and the c.454C>T variant in the SDR9C7 gene, previously reported only in dogs, was, for the first time, confirmed as causative for ichthyosis in humans. Full article

Educational farms provide students with hands-on experience in agricultural and animal practices. However, the close contact between humans and farm animals creates a significant interface for zoonotic disease transmission, yet research on infectious diseases in such settings remains limited. This study investigates the ongoing spillovers of human-origin influenza A virus (IAV) into swine at an educational farm in central Chile, describing IAV prevalence, outbreak dynamics, and the genomic characterization of detected strains. The Menesianos educational farm, located in Melipilla, central Chile, houses approximately 40 swine alongside other domestic animals, such as horses and cows. As part of an active IAV surveillance project, monthly nasal swab samples were collected from pigs between June 2019 and September 2023 for IAV detection via RT-qPCR targeting the M gene, with positive samples subsequently sequenced. During the study period, monthly IAV prevalence ranged from 0% to 52.5%, with a notable outbreak detected between May and June 2023. The outbreak lasted 5 weeks, peaking at 52.5% prevalence during week 3. Nine IAV strains were isolated over the study period, eight of which were obtained during weeks 2 and 3 of the outbreak. Phylogenetic analysis revealed that all strains were closely related to the pandemic H1N1 2009 influenza virus, with the closest related strains being those circulating in humans in Chile during the same years. These findings highlight the importance of conducting regular IAV surveillance on educational farms, where close interactions between animals and individuals—particularly children and young people—can facilitate viral spillovers and potential reverse zoonosis events. Full article