Search Results (257)

Phytophthora infestans is the causal agent of late blight, one of the most destructive diseases of potato and tomato worldwide. Although qPCR-based methods are widely used to estimate pathogen biomass in infected tissues, methods for specifically assessing sporangial proliferation remain limited. In this study, we developed an RT-qPCR-based assay using PITG_13011, which encodes a predicted major facilitator superfamily transporter, as a sporangia-associated molecular marker in P. infestans. Among five candidate genes selected from transcriptomic data, PITG_13011 showed the strongest association with sporangia-associated samples in our validation assays. PITG_13011 transcripts were detectable from cDNA and genomic DNA derived from as few as 100 sporangia, and transcript abundance showed a strong positive correlation with sporangial number under controlled experimental conditions. In detached leaf inoculation assays, PITG_13011 transcript levels were associated with differences in sporangia-associated proliferation during infection. These results indicate that PITG_13011-based RT-qPCR can serve as a complementary molecular approach for estimating sporangia-associated proliferation of P. infestans in laboratory experiments. This method will be useful when sporangial production, rather than total pathogen biomass alone, is the parameter of interest. Full article

Psoriasis is an immune-mediated inflammatory disease with a genetic component, characterized by dysregulation of cytokine signaling and activation of T lymphocytes. This study investigated genetic variants associated with psoriasis, psoriatic arthritis (PsA), and response to tumor necrosis factor alpha (TNF-α) inhibitors (adalimumab, infliximab, and etanercept) in a Russian cohort. A genome-wide association study (GWAS) was conducted in 1026 psoriasis patients and 9212 controls using Infinium Global Screening Array-24 v3.0 microarrays. Exploratory analyses of treatment response (n = 48) and PsA (n = 96) were performed without covariate adjustment or explicit modeling of population structure. Polygenic risk scores (PRS) were derived from internally estimated effect sizes in a split-sample design. The GWAS replicated a robust association in the major histocompatibility complex (MHC) region (rs12189871 near HLA-C, p = 3.2 × 10⁻⁵⁰, OR = 2.99 [2.59–3.45]). Additional loci included variants in ZC3H8 and PLCL2. Nominal signals were observed for IL18R1/IL18RAP in treatment response (including rs17027071) and for RCL1 and FBLIM1 in PsA; these findings remain exploratory. PRS demonstrated moderate predictive performance (AUC = 0.6355) and should be interpreted with caution given the study design. Overall, the results highlight a strong MHC signal in psoriasis, while findings for PsA and treatment response remain hypothesis-generating and require independent validation. Full article

Cancer represents a major global health challenge, contributing to an estimated 19 million new cases annually. While conventional chemotherapeutic approaches continue to advance, target-based therapeutic strategies are increasingly recognized as effective pathways in modern drug development. A prominent biological target in current anticancer research is the selective inhibition of Topoisomerase II alpha (TOP2A). TOP2A, a crucial DNA topoisomerase, is vital for maintaining genomic integrity by mediating the cleavage and re-ligation of double-stranded DNA during essential cellular processes, such as DNA replication and transcription. Inhibiting TOP2A effectively disrupts these processes, leading to cell death. This study employed computer-aided drug design approaches to virtually screen a library of 3000 xanthone derivatives against the TOP2A target, and the results were preliminarily validated through cytotoxicity assays on the A549 and HepG2 cancer cell lines. The computational methods utilized included molecular docking, pharmacological modeling, molecular dynamics simulations, and steered molecular dynamics simulations. The virtual screening identified two highly promising HIT compounds, CID162372098 and CID156619937, that exhibited the most favorable interactions and stability profiles in relation to the TOP2A active site. The experimental results demonstrated that both hit compounds effectively exhibited significant anti-proliferative activities against the HepG2 cell line, with IC50 values of 9.54 ± 0.26 µg mL⁻¹ (CID162372098) and 10.03 ± 0.36 12.69 ± 0.31 µg mL⁻¹ (CID156619937), respectively. Collectively, these findings demonstrate the potential of xanthone-based scaffolds as inhibitors of TOP2A and provide a rational framework for the screening and development of novel anticancer agents. Full article

Glioma prognosis is shaped by molecular markers such as IDH mutation, WHO grade, and MGMT methylation, yet heterogeneity persists within defined subgroups. Sidekick Cell Adhesion Molecule 1 (SDK1), an immunoglobulin superfamily member mediating homophilic adhesion, has been documented in glioma tissue but lacks systematic prognostic evaluation. I assessed SDK1’s prognostic value using the Chinese Glioma Genome Atlas (CGGA, N = 503) and The Cancer Genome Atlas (TCGA, N = 572) through multivariate Cox regression, subgroup analyses, differential gene expression, pathway enrichment, ssGSEA-based immune profiling, and molecular subtype association. High SDK1 expression was independently associated with poor overall survival in both cohorts (CGGA: adjusted HR = 1.48, 95% CI 1.16–1.89, p = 0.002; TCGA: HR = 1.76, 95% CI 1.19–2.61, p = 0.005; pooled HR = 1.55, I² = 0%). Effect estimates varied across subgroups, with significant associations in WHO grade IV and IDH-wildtype strata but not in grade II or older patients. Cross-validated differentially expressed genes were enriched in extracellular matrix organization and focal adhesion pathways. Notably, SDK1 expression showed weak but statistically significant correlations with COL1A1-associated mesenchymal program scores (CGGA: R = 0.12, p = 0.008; TCGA: R = 0.15, p < 0.001) and oncostream-related gene signatures (CGGA: R = 0.16, p < 0.001; TCGA: R = 0.086, p = 0.039), suggesting a modest association with mesenchymal invasion programs. SDK1-high tumors showed elevated M2 macrophage and Treg signatures with upregulated immune checkpoints, though cohort-dependent differences were observed. Multivariate Cox analysis demonstrated that the prognostic significance of SDK1 is independent of tumor mutational burden (TMB), with no significant correlation or interaction observed between them (p > 0.05). SDK1 is a candidate prognostic biomarker in glioma co-occurring with ECM remodeling and immunosuppressive features, warranting experimental validation for clinical translation. Full article

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder caused by the interaction between genetic and environmental influences, potentially mediated by epigenetic mechanisms such as DNA methylation. Genome-wide DNA methylation profiling was performed using the Infinium MethylationEPIC v2.0 array on peripheral blood mononuclear cells (PBMCs) from 100 children with ASD and 50 typically developing controls. Differential methylation analyses were conducted by adjusting for age, sex, and estimated blood-cell-type composition as covariates. Functional enrichment, SFARI gene-overlap analysis, and cross-cohort validation were performed. We identified 3507 differentially methylated positions (DMPs) in the ASD cohort. Functional enrichment revealed pathways involved in neuronal signaling, synaptic activity, and immune regulation, suggesting coordinated neurodevelopmental and immune processes in ASD. Stratification by clinical severity demonstrated common and unique biological characteristics between the moderate and severe ASD groups. Furthermore, DMP-associated genes significantly overlapped with high-confidence ASD risk genes from the SFARI database and established transcriptomic signatures of neurodevelopmental disorders. Comparisons with independent post mortem brain tissue and peripheral blood datasets revealed partial overlap and directional concordance. However, the strength of concordance varied across datasets and was limited in the most directly comparable peripheral blood cohort. Our findings suggested that DNA methylation profiling of PBMCs provided peripheral epigenetic signatures and candidate loci for further validation in larger independent cohorts. Full article

Background: Intervertebral disc degeneration (IVDD) is driven by the interplay between inflammatory signaling, extracellular matrix (ECM) degradation, and impaired cellular adaptation. Although several nutraceutical compounds have been reported to exert protective effects in IVDD-related models, their multitarget mechanisms within integrated molecular networks remain incompletely characterized. Methods: An in silico framework integrating molecular docking with network-based analyses was employed to evaluate resveratrol, quercetin, melatonin, curcumin, and baicalein against a predefined panel of IVDD-associated targets, within an exploratory in silico framework. Binding affinities and interaction profiles were assessed using molecular docking, followed by protein–protein interaction (PPI) network construction, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, and hub gene identification. Results: Docking analyses revealed binding energies ranging from −4.59 to −13.25 kcal/mol, with curcumin and quercetin showing plausible docking poses across a subset of selected targets under the applied protocol. Network analysis showed a highly interconnected structure centered on key inflammatory regulators, including NFKB1, IL6, TNF, IL1B, STAT3, and NLRP3, together with ECM-associated components such as ACAN, COL2A1, SOX9, MMP13, and ADAMTS5. Enrichment analyses further suggested significant associations with inflammatory signaling pathways, cytokine regulation, and ECM organization. Conclusions: These findings are compatible with a distributed, multitarget interaction pattern of nutraceutical compounds within IVDD-associated molecular networks. By integrating molecular docking with network-based analyses, this study offers a system-level framework for interpreting previously reported effects within a disease-specific context. Docking-derived interaction patterns should be interpreted as qualitative and exploratory observations, as docking scores represent model-dependent estimates and do not establish comparable pharmacological effects across heterogeneous targets. The results should be considered hypothesis-generating and require experimental validation. Full article

Genome-wide association studies (GWASs) are powerful and flexible tools for identifying single nucleotide polymorphisms (SNPs) associated with quantitative traits (yield, stress resistance) in plants. Variable selection and machine learning are two effective approaches in GWAS. However, both face limitations in complex, noisy data analysis in the big-data era. In this study, we integrated variable selection and machine learning under the mixed linear model framework, proposing a novel method, the improved LASSO screening and sparse Bayesian learning algorithm (ILSBL). The ILSBL first corrects the polygenic and environmental noise, then reduces genotypic dimensionality by LASSO-based variable selection, and finally performs parameter estimation using sparse Bayesian learning. Two simulation experiments and association analyses of three flowering-time-related traits in Arabidopsis thaliana were conducted to validate the new algorithm. The results showed that, compared to established methods, the ILSBL exhibited flexibility in simulation studies and maintained robust performance under complex genetic backgrounds, achieving a favorable balance among statistical power, parameter estimation accuracy, runtime efficiency, and false-positive rate. The analysis of the real Arabidopsis datasets further confirmed the advantages of ILSBL for GWASs, with 30 candidate genes adjacent to significant quantitative trait nucleotides (QTNs) associated with flowering-related traits. These results provide valuable insights for a better understanding of the genetic basis underlying flowering-related traits in Arabidopsis. Full article

Background: Drug-resistant tuberculosis remains a major challenge in resource-limited settings, particularly in rural regions of the Eastern Cape Province, where limited laboratory infrastructure, constrained access to advanced molecular diagnostics, shortages of specialized healthcare personnel, and prolonged diagnostic turnaround times can delay appropriate treatment initiation. This study examined whether routinely detectable genomic resistance markers could be integrated with parsimonious machine learning approaches to support early risk stratification for isoniazid (INH) and/or rifampicin (RIF) resistance and multidrug-resistant tuberculosis (MDR-TB). Methods: We conducted a retrospective analysis of clinical, demographic, and genomic data from 207 Mycobacterium tuberculosis isolates representing 207 unique patients. Resistance was classified as INH and/or RIF resistance or MDR-TB (concurrent resistance to both drugs). Predictors included age, sex, and canonical resistance-associated mutations (katG S315T, inhA −15C>T, and rpoB codon substitutions). Logistic regression was used to estimate adjusted odds ratios (aORs), while Random Forest models were applied to assess non-linear feature importance. Internal validation was performed using 10-fold cross-validation. A systems network analysis mapped the integration of model-derived risk bands into Clinical Governance structures and Community-Engaged Education pathways, including interventions delivered by Community Health Workers (CHWs). Results: INH and/or RIF resistance was identified in 58.9% of isolates, with 21.7% classified as MDR-TB. The most frequently detected mutations were katG S315T (29.0%) and rpoB S450L (26.6%). Logistic regression identified rpoB S450L (aOR 4.20; 95% CI: 2.10–8.45) and katG S315T (aOR 2.85; 95% CI: 1.40–5.80) as the strongest independent predictors, while age and sex were not statistically significant. Models demonstrated strong internal discrimination (AUCs of 0.96 for INH and/or RIF resistance and 0.99 for MDR-TB). Risk stratification categorized 18% of patients as high risk. Scenario-based modelling suggested that prioritizing high-risk patients for reflex Line Probe Assay testing could reduce the median time to appropriate treatment from 14 to 3 days and may reduce progression from isoniazid-resistant TB to MDR-TB under specified operational assumptions. Conclusions: Mutation-informed predictive modelling demonstrates strong internally validated discrimination and provides a structured framework for risk-stratified intervention. Integrating probability-based risk thresholds within Clinical Governance systems and community-level support structures, including CHW-led adherence and education strategies, may support earlier treatment optimization in high-burden rural settings. External validation and prospective implementation studies are required before broader programmatic adoption. Full article

Background: Comprehensive genomic profiling (CGP) is increasingly used in precision oncology to identify actionable genomic alterations and guide targeted therapies in solid tumors. However, the clinical implementation of CGP assays requires rigorous analytical validation to ensure accurate and reproducible detection of diverse genomic alterations across heterogeneous tumor samples. Despite rapid advancements in next-generation sequencing technologies, there remains a need for validated CGP platforms that demonstrate reliable performance and readiness for routine clinical use. Methods: This study evaluated the analytical and clinical performance of a CGP assay capable of detecting multiple genomic alteration types, including single nucleotide variants (SNVs), insertions/deletions (Indels), copy number variations (CNVs), gene fusions, and tumor mutational burden (TMB). Validation was conducted using patient-derived 117 FFPE tumor samples, external proficiency testing materials, and reference standards. Assay performance was assessed through comparison with orthogonal methods and through evaluation of reproducibility, limit of detection, and TMB concordance. Results: The assay demonstrated excellent analytical performance, achieving 100% sensitivity, specificity, and accuracy for variant detection across evaluated samples. Strong concordance was observed for TMB estimation (R² = 0.9925), with consistent classification of TMB-high cases. The assay showed robust inter- and intra-run reproducibility and reliable detection of low-frequency variants. Limit-of-detection (LOD) analysis confirmed accurate SNV detection at approximately 1% variant allele frequency and reliable RNA fusion detection at low input levels. Conclusions: The validated CGP assay provides accurate, reproducible, and comprehensive detection of clinically relevant genomic alterations in solid tumors. These results support its suitability for routine clinical deployment, enabling reliable genomic profiling to inform precision oncology treatment decisions. Full article

Background/Objectives: The Korean Genome and Epidemiology Study (KoGES) is a large population-based cohort designed to investigate chronic disease risk using long-term dietary and health data. However, comprehensive amino acid information for estimating long-term intake from food frequency questionnaire (FFQ) data remains limited. This study aimed to develop and validate a standardized, rule-based algorithm for food matching and substitution and to construct an amino acid database applicable to the KoGES FFQ. Methods: The algorithm sequentially evaluated food name concordance, preparation forms, substitutability of similar foods, and differences in energy, macronutrients, and moisture (±20%). Amino acid composition data were derived from domestic and international food composition tables and published literature, with protein–nitrogen conversion factors applied by food group. Results: Amino acid information was established for 475 FFQ food items covering 19 amino acids. Of the database values, 31.0% were analytical, 64.2% were calculated, and 4.8% were substituted. Overall database coverage across all amino acid–food item combinations was 98.8%. The constructed database was applied to dietary data from the second follow-up (Phase 3) of the KoGES Ansan and Ansung community-based cohorts, showing that total amino acid intake accounted for 86.7% of total protein intake, reflecting the inclusion of non-protein nitrogen in conventional protein estimates. Based on the Estimated Average Requirement (EAR) criteria, the proportions of participants with intakes below the EAR for protein and essential amino acids varied across age and sex groups. Overall and in both men and women, lysine showed the highest proportion of participants below the EAR, whereas tryptophan showed the lowest proportion. Conclusions: This standardized algorithm provides a reproducible framework for constructing amino acid databases and can be applied to large-scale cohort and dietary survey data. Full article

Melanoma is characterized by a high mutational burden making it an established model for studying tumor neoantigens and developing strategies for personalized immunotherapy. In this study, a reproducible bioinformatics pipeline was developed and implemented for the identification and prioritization of candidate neoantigens derived from non-synonymous somatic mutations in melanoma, using genomic data from the MSK-IMPACT cohort (mel-mskimpact-2020; n = 696) and comparative reference information from TCGA-SKCM. From the somatic mutation annotation file (MAF), 16,311 non-synonymous mutations were filtered, from which 50,480 mutant 8–11-mer peptides were generated using a sliding-window approach centered on the mutated position. Peptide–HLA class I binding affinity was predicted using MHCflurry 2.0 across six representative alleles (HLA-A*02:01, HLA-A*24:02, HLA-B*35:01, HLA-B*39:05, HLA-C*04:01, and HLA-C*07:02). Candidate prioritization was initially based on predicted binding percentile (rank ≤ 2), identifying 12,209 peptide–HLA combinations with high predicted binding affinity. To refine candidate selection, additional computational analyses were incorporated, including proteasomal cleavage prediction using NetChop 3.1 and estimation of T-cell epitope immunogenicity using the Immune Epitope Database (IEDB) immunogenicity predictor. Furthermore, a direct comparison between mutant (MUT) and corresponding wild-type (WT) peptides was performed using Δaffinity and Δrank metrics to evaluate the predicted impact of somatic mutations on HLA binding. The analysis revealed a predominance of peptides associated with the HLA-B locus, particularly the allele HLA-B*35:01, among the interactions with the lowest predicted binding percentiles. Several high-ranking peptide candidates were derived from genes with known roles in melanoma biology, including PLCG2, GATA3, AKT1, PTEN, PTCH1, and SMO. Overall, the integrative computational framework implemented in this study enables the systematic prioritization of candidate neoantigens derived from non-synonymous mutations in melanoma. This pipeline provides a reproducible strategy for exploring tumor neoantigen repertoires and may serve as a foundation for subsequent experimental validation and for studies related to neoantigen-based immunotherapies and immunopeptidomics. Full article

Upstream binding transcription factor (UBTF) is a nuclear transcription factor implicated in ribosome biogenesis, yet its pancancer relevance and immunological associations remain incompletely understood. We integrated datasets from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Human Protein Atlas (HPA), Cancer Cell Line Encyclopedia (CCLE), and cBioPortal databases to characterize UBTF expression, genomic alterations, and prognostic value across 33 cancer types. Immune microenvironment analyses were performed using ESTIMATE and multiple deconvolution algorithms. CRISPR-Cas9–mediated UBTF depletion was conducted in breast invasive carcinoma (BRCA) cell lines to evaluate functional roles. UBTF was broadly upregulated in multiple tumors with recurrent copy number gains. Survival analyses revealed cancer type–dependent prognostic associations. UBTF expression correlated with immune/stromal contexture, checkpoint features, and predicted immunotherapy response. In BRCA, UBTF depletion reduced proliferation and migration while increasing apoptosis. A UBTF-related prognostic signature effectively stratified patient outcomes and was associated with immune infiltration and predicted immunotherapy response. UBTF represents a pancancer biomarker linked to tumor immunity, with validated functional significance in BRCA and potential utility for risk stratification. Full article

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

The aim of this review is to examine the contribution of genomic variation to preeclampsia susceptibility in Africans. PubMed/Medline, Scopus, African Index Medicus and Sabinet African Journals databases were used to access studies conducted in populations of African descent focussing on the genomics of preeclampsia. Studies were selected according to PRISMA guidelines and assessed for quality and risk of bias using the Critical Appraisal Skills Programme (CASP) and Joanna Briggs Institute (JBI) checklists. Meta-analysis was conducted using a random effects model, and publication bias was evaluated using the Eggers test and funnel plots. Grading of Recommendations, Assessment, Development and Evaluation (GRADE) was applied to evaluate the certainty of evidence outcomes. Sixty-six (66) studies reporting on genomics of preeclampsia were retrieved. Forty-four (44) studies had a quality assessment score ≥75%. Vascular pathway genes (GNB3, FLT1, NOS3 and VEGFC; OR (95% CI): 1.61 (1.38–1.88); I²: 0.0%, p = 0.87; GRADE: low certainty), immune/inflammatory pathway genes (APOL1, ERAP2, HLA-G, IL-1β, LEPR and TNF-α; OR (95% CI): 2.07 (1.68–2.54); I²: 42.2%, p = 0.04; GRADE: low certainty) and cellular homeostasis genes (GLUT9, URAT1, SLC4A1 and SLCO4C1; OR (95% CI): 1.65 (1.43–1.91); I²: 0.0%, p = 0.99; GRADE: low certainty) showed pooled effect estimates suggestive of moderate to increased preeclampsia risk. APOL1 G1 or G2 risk alleles seemed to contribute 1.70-fold (95% CI: 1.39–2.07; I²: 0.0%; p = 0.51; GRADE: low certainty), respectively, to overall preeclampsia risk. Vascular, immune/inflammatory and cellular homeostasis genes may be ideal starting points for future research, and further validation of the role of APOL1 G1 or G2 risk alleles in preeclampsia may be essential. Full article

Background: Perineural invasion (PNI) is associated with aggressive tumor behavior in several malignancies, but its independent prognostic value in early-stage cervical cancer remains uncertain. We evaluated the clinical significance of PNI and explored molecular and immune features associated with PNI. Methods: We retrospectively analyzed 499 patients with FIGO 2009 stage IB–IIA cervical cancer treated with radical hysterectomy and pelvic lymphadenectomy. Associations between PNI, clinicopathological variables, recurrence-free survival, and overall survival were assessed using Kaplan–Meier methods and Cox regression. An independent cohort of 286 cervical cancers from The Cancer Genome Atlas (TCGA) was analyzed to characterize PNI-associated transcriptomic patterns, pathway enrichment, immune cell composition, and microRNA profiles. Results: PNI was identified in 11.6% of cases and was associated with larger tumor size, deep stromal invasion, and lymphovascular space invasion. PNI was not an independent prognostic factor in the overall cohort; however, it was associated with increased recurrence risk in the subgroup without high-risk factors and not meeting Sedlis criteria, with a modest improvement in 5-year recurrence discrimination when incorporated into Sedlis-based models. In TCGA, PNI was associated with differential gene expression and enrichment of oncogenic and immune-related pathways, an increased estimated abundance of resting mast cells, and six differentially expressed microRNAs. Conclusions: In early-stage cervical cancer, PNI is strongly correlated with established adverse pathological features and shows a subgroup-specific association with recurrence in an otherwise low-risk postoperative population. The multi-omics findings are exploratory and support biological hypotheses regarding tumor–nerve–immune interactions; external validation is needed before PNI can be used to guide postoperative management. Full article