Search Results (6,374)

Background: Most patients with hepatocellular carcinoma (HCC) present with advanced disease and have limited systemic treatment options. Oxaliplatin shows clinical activity in HCC but its effectiveness is frequently curtailed by intrinsic and acquired resistance. We sought to systematically identify genetic vulnerabilities that increase oxaliplatin sensitivity in HCC. Methods: Genome-scale negative-selection CRISPR–Cas9 screens were conducted in two genetically distinct HCC cell lines (Hep3B and MHCC-97H) under low-dose oxaliplatin to discover conserved determinants of sensitivity. Selected DNA damage response (DDR) hits were validated. An oxaliplatin-resistant MHCC-97H subline was generated for transcriptomic profiling to characterize resistance-associated programs. Screen results were integrated with TCGA-LIHC expression and survival data to evaluate clinical relevance. Additionally, we analyzed bulk RNA-seq data from biopsy specimens collected from 36 HCC patients prior to initiation of hepatic arterial infusion chemotherapy (HAIC), comparing expression levels of the DDR genes between patients with objective response and non-responders. Results: Screens in both cell lines converged on DDR pathways, particularly nucleotide excision repair (NER) and the Fanconi anemia/interstrand crosslink repair network; shared sensitizers included ERCC4 (XPF), FANCE and SLX4. Validation experiments showed that disruption of representative DDR factors (POLH and XPA) synergistically increased oxaliplatin efficacy at concentrations as low as 0.5 μM. Transcriptomic analysis of the resistant MHCC-97H subline revealed coordinated upregulation of DNA repair programs, G2/M checkpoint and E2F target signatures, and epithelial–mesenchymal transition features. Integration with TCGA-LIHC data demonstrated frequent overexpression of many screen-identified DDR genes in primary HCC and an association between higher expression of selected factors and poorer patient survival. In the HAIC cohort, several DDR genes, including ATR, BRCA2, CDK7, MUS81, MUTYH, PARG, POLH, POLK and XPA, were significantly lower in the objective response group. Conclusions: DDR components represent candidate biomarkers and therapeutic targets whose inhibition may enhance oxaliplatin efficacy in HCC. Full article

Bovine viral diarrhea virus (BVDV) is a major cattle pathogen associated with significant economic losses worldwide. In Brazil, the high genetic diversity of circulating strains represents an additional challenge for disease control. To update the molecular epidemiology of BVDV in southern Brazil, 16,198 bovine serum samples collected in 2020 through a national surveillance program were screened for pestivirus RNA by RT-qPCR. Forty-nine samples (0.36%) were positive and subjected to partial sequencing of the 5′UTR and N^pro regions. Phylogenetic analysis identified BVDV-1a (25/49; 51%), BVDV-1b (1/49; 2%), BVDV-1d (7/49; 14%), and BVDV-2b (16/49; 33%), with no detection of HoBiPeV. When compared descriptively with data from 2010 in the same region, BVDV-1a remained the most frequent subgenotype, while BVDV-2b also represented a substantial proportion of detections, contrasting with other regions worldwide. Although the two datasets are not directly comparable, and no statistically significant differences were observed, these findings provide an updated overview of circulating BVDV subgenotypes in Rio Grande do Sul. The absence of HoBiPeV contrasts with reports from other regions of Brazil and suggests a distinct regional pattern of pestivirus circulation. Overall, the results reinforce the importance of continuous genomic surveillance to monitor changes in viral diversity and support control strategies in cattle populations. Full article

Measles remains one of the most contagious infectious diseases globally and continues to pose substantial public health risks despite decades of effective vaccination. This narrative review examines both classical and contemporary computational approaches used for measles monitoring, prediction, and control, with particular attention given to the emerging role of artificial intelligence (AI). We synthesized findings from 46 studies; 31 focused directly on measles and 15 on methodologically relevant studies from related infectious diseases (COVID-19, influenza, malaria), selected through searches of PubMed, Scopus, Web of Science, IEEE Xplore, and preprint servers, conducted between June and December 2025. Traditional compartmental models (SIR, SEIR, MSEIR), statistical tools (ARIMA, SARIMA), and seroepidemiological analysis provide transparent, well-characterized frameworks for estimating transmission dynamics and simulating intervention scenarios. Spatial modeling, network analysis, and Monte Carlo simulations have added geographic granularity to outbreak characterization. More recently, AI and machine learning (ML) methods, including supervised algorithms (Random Forest, XGBoost, SVM), deep learning architectures (CNN, LSTM), and hybrid mechanistic ML models, have shown improved predictive performance by integrating multiple data sources: epidemiological records, demographic profiles, mobility patterns, and behavioral indicators. AI-based approaches appear most valuable for high-dimensional risk prediction and image-based diagnostic tasks, while classical models retain clear advantages for policy-oriented scenario analysis. However, no AI-based or hybrid model identified in this review has been adopted into routine national measles surveillance or used for vaccination policy decisions at scale. Important challenges remain: data quality varies across settings, model generalizability cannot be assumed, and computational infrastructure disparities limit deployment in high-burden regions. Explainable AI, federated learning, workforce training for model interpretation, and integration of vaccination registries with mobility and genomic surveillance data represent concrete future directions for strengthening computational support for measles elimination. Full article

Wastewater surveillance is an effective approach for monitoring populations of antibiotic-resistant bacteria and tracking the spread of antimicrobial resistance (AMR) across different settings. In this study, hospital and municipal wastewater were collected monthly for 12 months from multiple locations in the greater Pittsburgh area to quantify the presence of antibiotic-resistant bacteria and investigate their genomic diversity. After quantitative culturing on six different selective media types, a total of 150 isolates were speciated by 16S rRNA sequencing, which revealed diverse pathogenic and non-pathogenic taxa, including Klebsiella spp. (n = 28), Pseudomonas spp. (n = 20) and Aeromonas spp. (n = 37). A subset of isolates (n = 46) underwent whole genome sequencing, which identified several antibiotic resistance genes of clinical concern, such as bla_KPC (n = 17), bla_NDM (n = 6) and bla_IMP (n = 6), and revealed genetic similarities between wastewater isolates and clinical isolates collected from infected patients at a Pittsburgh-area medical center. In addition, analysis of plasmids carried by wastewater isolates revealed closely related plasmids present in isolates from different species and sampling locations. Overall, these findings suggest that both hospital and municipal wastewater act as interconnected reservoirs of antimicrobial resistance. Integrating wastewater surveillance with clinical and genomic data could enable the early detection of emerging resistance threats and support proactive infection-control strategies. Full article

Helicobacter pylori (H. pylori) is a highly adaptable gastric pathogen with marked genomic plasticity. Whilst functional CRISPR-Cas systems provide adaptive immunity in many bacteria, they have not been identified in H. pylori, unlike CRISPR-like sequences. In this study, eight H. pylori genomes were analysed using the bioinformatics tools CRISPRCasFinder, CRISPRCasTyper, and CRISPRloci. A total of 25 CRISPR-like arrays were identified, showing high conservation (88%) both between and within strains, suggesting that these arrays are not random remnants but rather organised structures possibly involved in cellular processes. Notably, a structural association was observed between the CRISPR-like sequences and the cag pathogenicity island (CagA-PAI). Conversely, CagA-PAI instability in specific strains was observed in the presence of the TnpA and TnpB transposons. Furthermore, in strain 29CaP, CRISPR-like assemblies were located in genomic proximity to the prophage Helico 1961P, leading to the hypothesis of a compensatory or regulatory effect in the absence of CagA-PAI. Taken together, these findings indicate that CRISPR-like arrays in H. pylori characterise a genomic architecture within regions of high plasticity. This study provides a solid exploratory foundation for future functional research on the adaptive and pathogenic evolution of H. pylori. Full article

Background/Objectives: Clear cell renal cell carcinoma is the most common subtype of kidney cancer and exhibits marked biological heterogeneity, even among tumors of the same histological grade. Although tumor grade remains a key prognostic parameter, the molecular alterations associated with tumor differentiation are not fully understood. This study aimed to evaluate grade-dependent tissue-level expression patterns of proteins involved in cellular stress response, growth regulation, stemness, and apoptosis in clear cell renal cell carcinoma. Methods: Protein expression of heat shock protein 70, insulin-like growth factor 1, octamer-binding transcription factor 4, and apoptosis-inducing factor were analyzed in human clear cell renal cell carcinoma samples and normal renal cortex. Low-grade and high-grade tumors were compared using immunofluorescence staining combined with semi-quantitative and quantitative image analysis. The proportion of positive signals and the number of positive cells were assessed across tissue compartments. In addition, publicly available transcriptomic data from The Cancer Genome Atlas kidney renal clear cell carcinoma cohort were analyzed to explore associations between gene expression levels and overall survival. Results: Distinct grade-dependent expression patterns were observed for all investigated proteins. Heat shock protein 70, insulin-like growth factor 1, and octamer-binding transcription factor 4 showed a higher expression in normal renal tissue with a progressive reduction across tumor grades. In contrast, apoptosis-inducing factor exhibited increased expression in tumor tissue, particularly in low-grade tumors, with a relative decrease in high-grade carcinomas. Stromal compartments of tumor tissue showed minimal or no expression for most markers. Transcriptomic survival analysis did not reveal significant differences in overall survival between high- and low-expression groups for any of the investigated genes. Grade-stratified transcriptomic analysis of the TCGA KIRC cohort revealed consistent patterns for HSP70 family members and OCT4, with progressive grade-dependent mRNA reduction toward higher grades, while IGF1 showed an inverse mRNA trend and AIFM1 showed a uniform reduction across all tumor grades without a clear inter-grade pattern. Conclusions: The findings demonstrate that stress response, growth-related, stemness-associated, and apoptotic proteins display distinct grade-dependent tissue-level expression patterns in clear cell renal cell carcinoma, with the expression profiles of high-grade tumors being of particular translational interest given the aggressive clinical behavior and therapeutic resistance characteristic of this disease stage. These alterations appear to reflect tumor differentiation and biological behavior rather than independent prognostic value, highlighting the complexity of molecular regulation in renal tumorigenesis. Full article

Genome Analysis Toolkit (GATK) is a rigorously maintained collection of 430 analysis tools and a core bioinformatics engine. First released in 2010 as a toolkit for next-generation sequencing (NGS) data analysis, GATK remains one of the least celebrated yet foundational tools of the NGS era. By employing state-of-the-art approaches and continuously adapting to the evolving demands of NGS analysis, it has effectively unified the variant calling process worldwide. In a field as rapidly evolving as genomics, it is remarkable that, over a decade later, the same toolkit remains the gold standard. This critical review explores the pre-history of GATK, the reasons for its broad and enduring adoption by the scientific community, its developmental evolution, contributions to science, and future prospects. Full article

Chicken manure and its potential to contaminate water systems through the dispersal of pathogenic bacteria are major concerns in environmental and public health. In this study, a metagenomic analysis was employed to systematically identify and compare bacterial assemblages in chicken manure (CM) and in a contaminated sample of chicken manure wastewater (CMW). Whole DNA was extracted from CM and CMW, followed by whole-genome shotgun sequencing; data analysis was done using online Galaxy software (ver. 26.0.1.dev1). Metagenomic analysis reveals a complex One Health challenge. Data showed that CM and CMW are different in their microbiota, as indicated by a distinct separation of beta diversity values and limited overlapping of species between sample types. In the current study, we found a greatly significant common functional set of adapted bacterial masses, including major pathogenic bacterial groups as well as opportunistic and environmental bacterial species, indicative of a direct contamination from CM and CMW. Notably, in both CM and CMW, a plethora of opportunistic, enteric, and environmental pathogens like Escherichia coli, Salmonella enterica, and Acinetobacter baumannii were found, coupled with an indication of a direct functional flow between both ecosystems as tangled reservoirs. Chicken manure samples showed differences in taxonomic composition and inferred functional profiles at the time of sampling: CM1 was pathogen-enriched, CM2 exhibited strong nitrogen-supportive metabolism, CM3 was dominated by fiber-degrading decomposers, and CM4 showed high methane-producing potential with environmental risk. Such findings underscore the raising of chickens as a potential source of harmful bacteria for the environment. It is important to note that this study represents a preliminary investigation with certain limitations, including the absence of biological replicates, lack of temporal sampling, and limited capacity to infer dynamic ecological interactions. Yet this metagenomic report is more about describing the taxonomy and functional potential of the bacteria, rather than discussing the actual ecological processes of these microorganisms in the environment. Future studies will be required to explore these aspects. Full article

Background: Pangolins are critically endangered mammals, and a comprehensive understanding of their genetic diversity is crucial for effective conservation. The mitochondrial genome serves as a vital molecular marker for phylogenetic and population genetic studies. Obtaining genetic material from these elusive animals non-invasively remains a challenge. This study aimed to sequence and characterize the complete mitochondrial genome of Manis javanica and explore the phylogenetic relationships among pangolin species. Methods: The complete mitochondrial genome was sequenced from a saliva-derived sample. Standard procedures for DNA extraction, amplification, and sequencing were employed. The genome was assembled and annotated using bioinformatic tools. Phylogenetic analysis was conducted based on the cytochrome c oxidase subunit I (COXI) gene sequences from nine pangolin species, with the resulting tree constructed using the maximum-likelihood method. Results: The complete mitochondrial genome of M. javanica (GenBank accession: PP110760) is a circular molecule of 16,573 bp, containing 13 protein-coding genes, 22 tRNA genes, 2 rRNA genes, and a control region. The overall base composition showed a lower GC content (43.83%) than AT content (56.17%). Phylogenetic analysis based on COXI sequences delineated the nine species into three distinct genera: Manis, Phataginus, and Smutsia. Within the genus Manis, Manis pentadactyla was identified as the closest relative to M. javanica. The newly described species Manis mysteria was found to be closer to Manis culionensis and Manis crassicaudata than to other congeners. Furthermore, the analysis indicated that African pangolins diverged earlier than Asian pangolins. Conclusions: This study successfully demonstrates the feasibility of extracting and sequencing the complete mitochondrial genome from saliva samples, providing a valuable non-invasive method for future genetic studies on pangolins. The genomic data and phylogenetic results offer significant molecular insights that will benefit the genetic management and conservation of critically endangered pangolin resources. Full article

Candidozyma auris (C. auris) is an emerging healthcare-associated yeast of major epidemiological concern because of its multidrug resistance and outbreak potential. We report the recovery of a single C. auris isolate from a used face mask collected in May 2025 during a blinded dental medicine quality-control programme assessing microbial contamination in the working environment. To contextualise this finding, we analysed routine diagnostic laboratory data from 2017 to 2025. The isolate underwent whole-genome sequencing for molecular characterisation, including analysis of the ERG11 gene, and antifungal susceptibility testing by EUCAST broth microdilution. In addition, 53,802 patient-related Candida spp. isolates collected between 2017 and 2025 were reviewed retrospectively; species identification had been performed by MALDI-TOF. The environmental isolate belonged to clade III and carried the V125A/F126L substitutions in ERG11, consistent with African clade isolates and associated with intrinsically high fluconazole minimum inhibitory concentrations. No C. auris was detected in routine patient specimens during the study period, whereas Candida albicans remained the predominant species in clinical samples. These findings provide no evidence of ongoing C. auris transmission at the Medical University of Innsbruck, but highlight the need for continued vigilance and robust infection-prevention measures to limit the risk posed by isolated introductions. Full article

Background: Cellular senescence is a key regulatory mechanism in tumor initiation and progression, influencing cancer development through modulation of the cell cycle, the immune microenvironment, and inflammatory responses. However, the molecular characteristics and potential clinical value of senescence-related genes in lung adenocarcinoma (LUAD) have not been systematically elucidated. This study aimed to comprehensively characterize the expression patterns, molecular subtypes, and prognostic significance of cellular senescence-related genes in LUAD, and to identify key regulatory determinants. Methods: Transcriptomic data of cellular senescence-related genes were obtained from The Cancer Genome Atlas (TCGA) cohort, and integrated analyses were performed to characterize their mutational landscape, copy number variations, and differential expression profiles. Senescence-related molecular subtypes were established using consensus clustering, followed by gene set variation analysis (GSVA) for pathway enrichment and immune infiltration analyses. A prognostic risk model was subsequently constructed using LASSO-penalized Cox regression, and its predictive performance was systematically evaluated. Candidate key regulators were further prioritized through bioinformatic screening, identifying FOLR1 as a hub gene. The biological function of FOLR1 was validated by qRT–PCR, Western blotting, assessment in clinical specimens, and a subcutaneous xenograft tumor model in mice. Results: Cellular senescence-related genes in LUAD exhibited a high frequency of somatic mutations and copy number alterations, accompanied by marked transcriptional dysregulation. Based on the expression profiles of these genes, LUAD patients could be stratified into three distinct molecular subtypes with significantly different clinical outcomes. These subtypes displayed pronounced heterogeneity in pathway enrichment patterns and immune cell infiltration. The subsequently developed prognostic signature demonstrated robust predictive performance in both the training and validation cohorts. Functional assays showed that FOLR1 was significantly downregulated in LUAD tissues and cell lines; FOLR1 knockdown promoted tumor cell proliferation, whereas restoration of its expression or pharmacological intervention markedly suppressed tumor progression. Consistently, in vivo xenograft experiments further corroborated the tumor-suppressive role of FOLR1 in lung adenocarcinoma. Conclusions: This study systematically delineated the molecular landscape of cellular senescence-related genes in LUAD and elucidated their associations with the tumor immune microenvironment and patient prognosis. Moreover, FOLR1 was identified as a potential tumor suppressor and therapeutic target. These findings provide a theoretical basis for senescence-informed molecular stratification and the development of precision treatment strategies in lung adenocarcinoma. Full article

This study investigates shared molecular mechanisms between pyoderma gangrenosum (PG) and inflammatory bowel disease (IBD) and systematically evaluates the therapeutic potential of JAK inhibitors targeting this pathway. Despite the clear clinical comorbidity, the core inflammatory pathways driving cross-tissue associations between the two diseases remain unclear. Furthermore, systematic mechanistic evidence is lacking regarding whether JAK inhibitors act by regulating shared pathological pathways in patients with comorbidities. To address this, this study integrated PG skin and IBD intestinal transcriptome data, single-cell transcriptomic data, and genome-wide association study (GWAS) meta-data from public databases. It employed a multi-level computational biology approach combining Mendelian randomization, weighted gene co-expression network analysis, protein interaction network construction, molecular docking simulations, and system dynamics modeling. The results revealed that genetic analysis confirmed IBD as a causal risk factor for PG, precisely identifying six shared genetic loci. Transcriptomic analysis identified a cross-tissue conserved inflammatory module centered on the JAK-STAT pathway, with JAK2 and STAT3 identified as network hubs. Molecular docking predicted high affinity of baricitinib for both JAK1 and JAK2, while system dynamics modeling demonstrated that its intervention effectively suppresses signaling in the shared inflammatory network. This study reveals the molecular basis of the “gut–skin axis” comorbidity between PG and IBD from a multi-omics integration perspective. It provides predictive computational evidence for the use of JAK inhibitors in targeted comorbidity therapy. Baricitinib is identified as a particularly promising candidate. These findings advance the transition from empirical drug use to mechanism-guided precision treatment strategies. Although this study provides multiscale computational simulation evidence, the lack of direct experimental validation of these predicted results necessitates further confirmation through in vitro and in vivo experiments. Full article

The rapid accumulation of genome-scale data has transformed plant biology from descriptive genetics to predictive and increasingly mechanistic genomics. Longan (Dimocarpus longan Lour.) is an economically important subtropical fruit tree in China and Southeast Asia, but compared with model plants and major temperate fruit crops, its genomic resources and functional studies have developed relatively late. Here, we review recent progress in longan genomics with emphasis on three interrelated areas: genome assembly and annotation, transcriptomic resources, and representative gene family studies associated with flowering, somatic embryogenesis, and transporter-mediated stress tolerance. The progression from the first draft genome of ‘Honghezi’ to the chromosome-scale assemblies of ‘Jidanben’ and ‘Shixia’ has substantially improved contiguity and gene annotation, thereby enabling population-genomic analysis, genome-wide gene family identification, and candidate-gene discovery. Available transcriptomic datasets further support studies of reproductive development, stress responses, and embryogenic competence, although cross-study integration remains limited. We also summarize how gene family analyses have advanced the current understanding of floral induction, continuous flowering, somatic embryogenesis, mineral transport, and sugar transport in longan. Importantly, the field is still dominated by cataloguing and expression-based inference, whereas causal validation, pan-genomic analysis, and multi-omics integration remain insufficient. We therefore argue that future progress in longan molecular breeding will depend on integrating high-quality genomic resources with functional validation, standardized comparative annotation, and improved transformation or regeneration systems. Full article

The life sciences are currently undergoing a serious transition from the reductive biochemical analysis of dissociated tissues to non-destructive “spatial forensics”. In addition to discovering new molecules, we are moving towards finding out their precise tissue localization and performing in situ interrogation to uncover a biological logic within preserved cellular “neighborhoods”. Our perspective is focused on exploring the spatial imperative, including the structural logic and “neighborhood effects” of the tissue microenvironment, which is a prerequisite to understanding cellular function in normal and in pathological conditions. Beginning with a historical foundation of the origins of histochemistry, dating back to the 19th century with pioneer botanist François-Vincent Raspail, we emphasize the technological metamorphosis, transitioning from classical immunohistochemistry to modern multi- and high-plex spatial multi-omics. A critical evaluation of the current operational landscape has been made, addressing the engineering strategies behind multiplexed immunofluorescence (mIF), the challenges of experimental design in spatial transcriptomics, and the functional symbiosis between targeted and unbiased spatial proteomics. There are many layers of genomic and proteomic information we have to consider in order to unravel the mechanisms underlying body function. If we learn how to combine all this information together, we will be able to better understand how cells communicate with each other and what disrupts their communication, leading to cancer and many other pathologies. It is obvious that by implementing spatial biology tools, it becomes possible to develop new medicines and treat diseases in the most efficient ways. At the same time, we realize that there is an urgent need to learn how to put data pieces together so that they blend seamlessly into a meaningful output, further transitioning spatial biology over time into a routine tool to cure for both common and rare diseases and improve our lives and health. Full article

Genotyping is a key step in molecular breeding. Due to its cost-effectiveness, accuracy, and flexibility, genotyping by target sequencing (GBTS) has become a preferred technology for medium-density genotyping. In this study, a new GBTS array for fresh edible maize was developed using resequencing data from 477 lines. The array contains 5759 SNPs evenly distributed across the maize genome, with average minor allele frequency (MAF) and polymorphism information content (PIC) values of 0.40 and 0.36, respectively. These SNPs are closely associated with 1566 functional genes. Cluster analysis of 198 maize lines based on the GBTS array was consistent with their pedigree relationships. Furthermore, 277 fresh waxy maize lines were genotyped and used for genomic selection analyses of hundred-kernel weight, kernel length, and kernel width. Comparative evaluation of different models indicated that Ridge Regression Best Linear Unbiased Prediction (rrBLUP) was the optimal model, with prediction accuracies of 0.33, 0.64, and 0.36, respectively. Additional analyses using different marker densities based on the rrBLUP model showed that prediction accuracy did not increase when the number of markers exceeded 2000, indicating that this array provides sufficient marker density for genetic analysis and genomic selection. Overall, this array provides a useful tool for genetic studies of fresh edible maize and facilitates the application of genomic selection in breeding programs. Full article