Search Results (459)

With the rapid development of cancer treatment, immunotherapy has revolutionized renal cell carcinoma (RCC) treatment, yet patient responses remain heterogeneous. Here, a computational pipeline was constructed by integrating single-cell and bulk RNA sequencing data to identify immune-related candidate driver genes and characterize their impact on RCC immunotherapy. Based on gene regulatory networks (GRN), 25 immune-related candidate driver genes were identified, leading to the stratification of patients into three clusters (C1–C3). Compared to the C2/C3 cluster, the C1 cluster exhibited elevated immune infiltration, tumor mutation burden and checkpoint expression, which may represent immunotherapy responders. Dynamic analysis of GRNs revealed the critical role of candidate driver genes in predicting the efficacy of immunotherapy. IRF1, IRF9 and STAT1 in lymphoid cells of C1 participated in anti-tumor immune response by impacting target genes CD8A, HLA-A/E, TAP1 and PD-1. JUN, FOS, STAT3, JUND and NR2F1 were up-regulated in clusters C2 and C3, leading to tumor progression and immune evasion by influencing target genes HSPA1A, CXCL9 and PDGFR. In conclusion, integration of the transcriptome with molecular networks provided a network-based framework to uncover immune-related candidate driver genes for stratifying RCC patients, thereby serving as potential therapeutic targets to improve the outcome of RCC immunotherapy. Full article

Background/Objectives: Spondylarthritis (SA) is characterized by high clinical heterogeneity, often resulting in a discrepancy between systemic inflammation and patient-reported symptoms. While hematologic indices are emerging as cost-effective biomarkers, their role in phenotypic differentiation remains unclear. We investigated the utility of traditional inflammatory markers, including the novel fibrinogen-to-platelet ratio (FPR), in differentiating SA subtypes and predicting patient-reported disease activity. Methods: This cross-sectional study included 64 patients with spondylarthritis: axial SA (n = 32), peripheral SA (n = 8), and psoriatic SA (n = 24). Clinical assessments included the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Functional Index (BASFI). Systemic inflammation was evaluated via C-reactive protein (CRP), fibrinogen, and calculated ratios (NLR, PLR, MLR, and FPR). Principal Component Analysis (PCA) was employed to map the inflammatory architecture, while Receiver Operating Characteristic (ROC) curves evaluated the predictive power for high disease activity (BASDAI ≥ 4). Results: Significant phenotypic differences were observed with the FPR demonstrating superior discriminative capacity (p = 0.003). Patients with peripheral SA exhibited significantly higher FPR (median 1.88) compared to axial (1.33) and psoriatic (1.32) subtypes, and the dedicated ROC analysis for phenotypic discrimination yielded an AUC of 0.866 (95% CI: 0.745–0.987) (1.36, p = 0.039). HLA-B27 prevalence was low overall (31.3%) and in psoriatic SA (4.2%, p = 0.012). Correlation analysis revealed strong associations between BASDAI and BASFI (ρ = 0.79), NLR and MLR (ρ = 0.78), and CRP and fibrinogen (ρ = 0.66). PCA identified two independent inflammatory dimensions explaining 74.8% of variance: neutrophil-hypercoagulable axis (41.4%, driven by NLR, PLR, and MLR), and an acute-phase/fibrinogen axis (33.4%, driven by CRP, fibrinogen, and FPR). Notably, FPR clustered with acute-phase reactants rather than leukocyte-derived ratios, supporting its role as a marker of systemic inflammatory burden. Although fibrinogen is involved in the coagulation cascade, the absence of direct coagulation markers precludes definitive characterization of this component as hypercoagulable. ROC analysis revealed that fibrinogen showed the highest discriminative ability for disease activity (BASDAI ≥ 4), with an AUC of 0.690 (95% CI: 0.519–0.861), followed by NLR (0.621), MLR (0.592), and FPR (0.583). However, overall discriminative performance remained modest, with most 95% confidence intervals crossing 0.5. Conclusions: FPR emerges as a robust phenotypic biomarker capable of discriminating against SA subtypes, particularly identifying peripheral SA with high accuracy and excellent negative predictive value. In contrast, its ability to predict patient-reported disease activity remains limited, reinforcing the distinction between trait and state biomarkers. Exploratory PCA revealed that FPR clusters with acute-phase reactants rather than leukocyte-derived ratios, supporting its biological link to systemic inflammatory burden. These findings advocate for a dual-purpose biomarker approach in SA: FPR for phenotypic stratification and fibrinogen for activity assessment, while clinical indices remain indispensable for symptom monitoring. Validation in larger, prospective cohorts is warranted. Full article

Background: A major technical challenge in single-cell transcriptomics is the absence of an integrative analytic pipeline that can simultaneously leverage gene regulatory network (GRN) architecture, AI-assisted gene panel discovery, and functional relevance analyses to generate coherent biological insights. Existing approaches often treat these components independently, focusing on clusters, marker genes, or predictive features without integrating them into a mechanistically grounded framework. Consequently, comprehensive screening that links regulatory association, gene signature screening, and functional interpretation within single-cell datasets remains limited, underscoring the need for an integrated strategy. Methods: We developed an integrative bioinformatics pipeline based on Gene regulatory network–AI–Functional Analysis (GAFA), combining latent-space integration, unsupervised clustering, diffusion pseudotime analysis, lineage-resolved generalized additive modeling, GRN inference, and machine learning-based gene panel discovery. This framework enables systematic mapping of cell-state structure, reconstruction of differentiation and effector trajectories, and identification of transcriptional and regulatory features strongly associated with clinical outcomes. As a case study, we applied the pipeline to NK cell transcriptomes from six CML patients (two early relapse, two late relapse, two durable treatment-free remission—TFR; 15 samples) collected at TKI discontinuation and 6–12 months after therapy cessation. Results: We reanalyzed publicly available scRNA-seq data from a previously published CML cohort to evaluate NK-cell transcriptional programs associated with treatment-free remission and relapse. We resolved six transcriptionally distinct NK cell states spanning CD56^bright-like cytokine-responsive, early activated, terminally mature, cytotoxic, lymphoid trafficking, and HLA-DR⁺ immunoregulatory populations, each exhibiting outcome-specific compositional differences. Pseudotime analysis revealed two major NK cell lineages—a maturation trajectory and a cytotoxic effector trajectory. TFR samples displayed balanced occupancy of both lineages, whereas early relapse samples showed marked depletion of the maturation branch and preferential accumulation in cytotoxic end states. AI-guided feature selection and random forest modeling identified an 18-gene panel that distinguished NK cells from TFR and relapse samples in an exploratory manner. Among them, CST7, FCER1G, GNLY, GZMA, and HLA-C were conventional NK-associated genes, whereas ACTB, CYBA, IFITM2, IFITM3, LYZ, MALAT1, MT2A, MYOM2, NFKBIA, PIM1, S100A8, S100B, and TSC22D3 were novel. The GRN inference further uncovered outcome-specific regulatory modules, with RUNX3, EOMES, ELK4, and REL regulons enriched in TFR, whereas FOSL2 and MAF regulons were enriched in relapse, and their downstream targets linked to IFN-γ signaling, metabolic reprogramming, and immunoregulatory feedback circuits. Conclusions: This AI-enabled single-cell analysis demonstrates how NK cell state composition, differentiation trajectories, and regulatory network rewiring collectively shape TFR versus relapse following TKI discontinuation in CML. The integrative pipeline provides a modular framework that could be extended to additional datasets for data-driven biomarker discovery and mechanistic stratification, and highlights candidate transcriptional regulators and NK cell programs that may be leveraged to improve remission durability, pending validation in larger patient cohorts. Full article

The synergistic effects of glucose (Glu) concentration and clay mineral type (kaolinite [Kao], montmorillonite [Mon]) on abiotic humification via the polyphenol-Maillard reaction remain poorly understood. To address these scientific challenges, a series of controlled, sterile batch experiments was conducted. Specifically, a glucose concentration gradient (0, 0.03, 0.06, 0.12, and 0.24 mol/L) was established; Kao and Mon were separately introduced as mineral catalysts; and the Maillard reaction was facilitated in the presence of catechol and glycine under strictly abiotic conditions to preclude any potential biological interference. Comprehensive analyses were performed on the reaction products—namely, the supernatant and the dark-brown residue generated during the reaction process. These analyses included: the E₄/E₆ ratio and total organic carbon (TOC) content of the supernatant; the carbon-based ratio of humic-like acid to fulvic-like acid (C_HLA/C_FLA); and the structural characteristics of humic-like acid (HLA) isolated from the dark-brown residue. Results showed dynamic E₄/E₆ ratio and TOC changes in the supernatant were accurately described by the Logistic function. Kao favored soluble organic C accumulation and enhanced retention of early-stage, low-molecular-weight intermediates in the dark-brown residue, while Mon promoted humic-like substances (HLS) polymerization and aromatic condensation. FTIR spectroscopy analysis identified optimal Glu thresholds for maximal HLS formation—0.03 mol/L for Kao and 0.06 mol/L for Mon—indicating non-linear, rather than monotonic, dependence on Glu dosage. Comparative pre- and post-reaction Fourier-transform infrared (FTIR) spectroscopy further demonstrated that Mon, owing to Mg–OH octahedral sites arising from isomorphic substitution, formed more stable Cat chelates than Kao. These chelates effectively stabilized surface-bound hydroxyl-associated water molecules and modulated the electron cloud distribution around Si–O bonds. Collectively, this study clarified the dual regulatory role of Glu concentration and clay mineral identity in abiotic humification pathways, advanced mechanistic understanding of clay mineral-mediated polyphenol-Maillard reactions, and established a scientific foundation for optimizing humification efficiency in both engineered and natural systems. Full article

To date, Lyme vaccine development has largely overlooked the vaccinee’s human leukocyte antigen (HLA) genetic makeup on which antibody production critically depends. Here, we evaluated in silico the predicted binding affinities of 192 HLA-II alleles with all 15-mer peptide sequences of five Borrelia burgdorferi proteins to identify peptides with strong binding affinity, as they would be the best candidates for antibody production in response to vaccination. We found the following: (a) 226 of the 1067 peptides tested (21.2%) were found to bind strongly to HLA-II molecules; (b) decorin-binding protein A had the greatest number of strongly binding peptides; and (c) 69 HLA-II alleles (primarily of the DRB1 gene) bound with strong affinity to peptides from Borrelia burgdorferi proteins. Finally, we tested for possible susceptibility to autoimmunity by any one of the 226 peptides above by searching for their occurrence in ~84,000 proteins of the human proteome and found overlap with only two 8-mer peptide sequences (embedded within the 226 15-mer peptides), neither of which was characterized by strong binding to HLA-I, suggesting a reduced likelihood of autoimmunity. These findings emphasize the importance of a personalized vaccine approach based on the vaccinee’s human leukocyte antigen genetic makeup and offer specific vaccine-candidate peptides that are predicted to maximize vaccine effectiveness and safety. The results of this computational study provide novel directions for future development of Lyme vaccines. 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

Background: Dry Eye Disease (DED) is a multifactorial disorder characterized by tear film instability and ocular surface inflammation. Murine models based on environmental stress are widely used to mimic evaporative DED, although many focus on limited disease features. This study aimed to provide an integrated characterization of ocular surface alterations induced by chronic desiccating stress. Methods: Adult mice were housed in a Controlled-Environmental Chamber (CEC) with low humidity and increased airflow for up to 21 days and sacrificed after 14 or 21 days. Corneal damage was assessed by fluorescein staining. Conjunctival histology was evaluated for epithelial morphology, goblet cell (GC) size, and mucin composition. Complement fractions C3 and C5a were assessed by immunohistochemistry. Expression of inflammatory markers (Major Histocompatibility Complex, Class II, DR, HLA-DR; interleukin-1β, IL-1β; tumor necrosis factor-α, TNF-α) was quantified by Real-Time PCR (RT-PCR) in corneal and conjunctival epithelium. Results: Fluorescein staining revealed progressive corneal epithelial damage over time. Histological analysis demonstrated conjunctival epithelial alterations characterized by a significant reduction in GC size and in neutral mucin-positive GCs, consistent with mucin remodeling of the ocular surface epithelium. Increased epithelial deposition of complement fractions C3 and C5a was observed, while molecular analysis confirmed upregulation of inflammatory markers, including HLA-DR, IL-1β, and TNF-α. Collectively, these findings indicate that the model captures key pathophysiological components of DED. Conclusions: The CEC model reproduces major features of evaporative DED, including epithelial damage, GC remodeling, immune activation, and inflammation. As a non-invasive desiccating stress model, it represents a relevant experimental platform for studying ocular surface inflammation and for preclinical evaluation of therapeutic strategies. 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/Objectives: Ischemic sudden cardiac death (SCD) is a devastating event that often occurs in apparently healthy individuals. Genetic susceptibility may play a key role in the pathogenesis of such ischemic events. This study aimed to investigate the correlations between Human Leukocyte Antigen (HLA) alleles, genotypes, and haplotypes and SCD to identify potential risk factors. This study also investigated three Single-Nucleotide Polymorphisms (SNPs) in the MYBPC3 gene and their association with SCD. Methods: We conducted an exploratory study between 2022 and 2024 in North-Western Transylvania (Romania) on 81 autopsy-confirmed SCD cases, compared with 162 controls for HLA typing, and with 96 controls for SNPs. HLA analysis of the HLA-DRB1 and HLA-DQB1 genes was performed using low-resolution SSP-PCR. The three SNPs in the MYBPC3 gene: rs142317339 (C > T), rs148808089 (G > A), and rs11570076 (G > A) were performed using a Real-Time PCR System. Results: The HLA-DRB1*07 allele has reduced odds of SCD, after adjustment for age and sex, and the HLA-DRB1*08 allele showed a trend toward increased odds. No statistically significant associations were detected at the allele or genotype level for HLA-DQB1. Haplotype-based analyses further revealed that genetic susceptibility is driven predominantly by low-frequency protective haplotypes rather than by common risk haplotypes, with several combinations conferring strong or moderate protection (HLA-DRB1*07~HLA-DQB1*03, HLA-DRB1*07~HLA-DQB1*02, and HLA-DRB1*15~HLA-DQB1*05). No statistically significant association was found between the three SNPs studied in the two groups, and their frequencies were very low. Conclusions: Specific HLA-DRB1 and HLA-DQB1 alleles and haplotypes may be associated with protection against SCD, supporting a possible immunogenetic role in SCD and the identification of genetic risk markers. Full article

Background: Latent tuberculosis infection (LTBI) is the principal reservoir for active tuberculosis, with >85% of cases attributable to reactivation. Bacillus Calmette-Guérin fails to block this transition, leaving a critical gap in prevention. Methods: An immunoinformatics/reverse-vaccinology pipeline was applied to seven dormancy-related antigens retrieved from Mycobrowser. T-cell epitopes were predicted with NetMHCI/IIpan-4.1 and B-cell epitopes with ABCpred; antigenicity, allergenicity, and toxicity were evaluated with VaxiJen, AllerTOP, and ToxinPred. Secondary/tertiary structures were modeled with PSIPRED and AlphaFold-3; docking to Toll-like receptors (TLR) 2/4 and 100 ns molecular dynamics simulations assessed complex stability. Immune responses were simulated with C-ImmSim, and the mRNA sequence was human-codon-optimized using ExpOptimizer. Results: The resulting construct, RP14914P, encodes 14 cytotoxic T lymphocyte, 9 helper T lymphocyte, and 14 B-cell epitopes within an 866-aa, 90.4 kDa polypeptide. Antigenicity score = 0.7797, immunogenicity score = 8.58629. and no toxicity or allergenicity was predicted. Physicochemical analysis: instability index = 28.65, and solubility = 0.513. Estimated population coverage is 82.35% and 99.67% for Human Leukocyte Antigen (HLA)-I and HLA-II globally. Docking energies: −1477.8 kcal/mol (TLR2) and −1480.1 kcal/mol (TLR4). Molecular dynamics trajectories confirm stable binding. Immune simulation predicts potent activation of Natural Killer cells, macrophages, and dendritic cells, Th1 polarization, high interferon-γ/interleukin-2 secretion, and durable memory. Conclusions: In silico analyses predict that RP14914P exhibits favorable immunogenicity, safety, and broad population coverage, suggesting its potential as a promising mRNA vaccine candidate to prevent LTBI reactivation. However, these computational predictions require thorough experimental validation to confirm the vaccine’s immunogenicity and protective efficacy. Full article

Background and Aims: Sepsis drives mortality in cirrhosis, yet the gut antimicrobial resistance (AMR) landscape remains unmapped in high-burden settings like India. This study aimed to integrate shotgun metagenomics with deep immunophenotyping to define the gut–immune–resistome axis and correlate specific microbial and genetic signatures with clinical outcomes in decompensated cirrhosis. Methods: We analysed 78 hospitalized patients with cirrhosis using stool shotgun metagenomics, multiplex cytokine arrays, and flow cytometry. The microbiome and resistome (AMR genes) were mapped and correlated with disease severity, immune function (monocyte HLA-DR, neutrophil CD64), and clinical endpoints including mortality. Results: Disease severity was characterized by a “Gram-negative bloom” (Klebsiella) alongside pathogenic Enterococcus expansion and novel markers: Clostridium sp. C5-48 (severe decompensation) and Sutterella (ascites). A specific, dense resistome predicted adverse outcomes; the quinolone-resistance gene QnrB4 correlated with mortality and immune paralysis, while the carbapenemase OXA-833 gene was linked to gastrointestinal bleeding. Notably, the commensal Ligilactobacillus salivarius was associated with systemic inflammatory cytokines. Conclusions: This study reveals a “pathogenic ecosystem” in Indian decompensated cirrhosis where the resistome is intrinsically linked to host immune failure. The identification of specific prognostic markers (QnrB4, OXA-833) and inflammatory associations with L. salivarius challenges generic probiotic use and underscores the urgent need for precision, resistome-targeted therapies. Full article

Influenza’s pandemic threat is driven by antigenic drift, which limits the efficacy of conventional vaccines. To address this challenge, we established a clinical serum-anchored computational design pipeline for a broad-spectrum multi-epitope mRNA vaccine (MEMV), bridging the gap between pure in silico design and clinical applicability. Using 36 longitudinal sera (d0/d28/d365) from 12 well-characterized human cohorts (6 vaccine recipients and 6 influenza patients) and high-density antibody-peptide microarrays, we empirically identified 12 immunodominant B-cell linear epitopes from the nucleoprotein (NP) of influenza A (H1N1/H3N2) and B viruses. These experimentally validated epitopes were combined with in silico-predicted conserved helper T-lymphocyte (HTL)/cytotoxic T-lymphocyte (CTL) epitopes (from NP/HA/NA) to construct MEMVs candidates, ensuring high antigenicity, non-toxicity, and 95.63% global HLA coverage. Molecular docking and 100 ns molecular dynamics (MD) simulations confirmed favorable conformational compatibility between MEMVs and Toll-like receptor 3 (TLR3) in silico immunization via C-ImmSim predicted robust B/T-cell responses and protective cytokine (IFN-γ/IL-10) production. Collectively, this pipeline shortens the preliminary design cycle for influenza vaccines, provides a standard epitope-combination strategy, and offers direct targets for follow-up in vitro/in vivo experiments. Full article

Background/Objectives: Non-steroidal anti-inflammatory drugs (NSAIDs) are widely prescribed to help alleviate pain and treat inflammation, but they are also recognized as common causes of severe cutaneous adverse reactions (SCARs), including Stevens–Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). Despite their clinical importance, pharmacogenetic markers to predict individual susceptibility to NSAID-induced SJS/TEN remain insufficiently defined. This study investigated associations between HLA class I and II alleles, CYP2C9 polymorphisms, and NSAID-induced SJS/TEN in a Thai population. Methods: A total of 18 patients with NSAID-induced SJS/TEN and 54 NSAID-tolerant controls were enrolled. Genotype data from 183 unrelated Thai individuals without a history of drug allergy were included as a general population control group. Genotyping was performed for HLA class I and II alleles and the CYP2C9*3 variant. Results: HLA-DQB1*03:02 was significantly associated with NSAID-induced SJS/TEN (OR = 9.23, 95% CI = 2.19–38.83, p = 0.0024, Pc = 0.0312), particularly those triggered by piroxicam (OR = 13.71, 95% CI = 2.81–66.86, p = 0.0012, Pc = 0.0156). Additional associations were identified for HLA-B*56:01 and HLA-A*68:01 in the overall NSAID-induced SJS/TEN group. The subgroup analysis suggested that these alleles, along with HLA-DRB1*04:03, were associated with an increased risk of piroxicam-induced SJS/TEN. However, these associations did not remain statistically significant after Bonferroni’s correction. No significant association was identified for CYP2C9*3. Conclusions: This study identified specific HLA alleles, particularly HLA-DQB1*03:02, as candidate pharmacogenetic risk factors for NSAID-induced SJS/TEN in a Thai population, especially in piroxicam-associated cases. However, these associations should be considered exploratory. Larger, multicenter, multi-ethnic studies are required to validate these findings and clarify their potential clinical utility. Full article

Background/Objectives: Staphylococcus aureus virulence is tightly regulated by the agr (accessory gene regulator) quorum-sensing system. Targeting AgrC, the histidine kinase receptor that serves as a core regulator of agr signaling, represents a promising antivirulence strategy that circumvents conventional bactericidal pressure. Methods: In this study, structure-based virtual screening using AutoDock Vina was performed, followed by molecular dynamics simulations, to identify potent analogs of known AgrC inhibitors. Results: A cyclo[Ala-Phe-OLeu-Phe-D-Leu] exhibiting high binding affinity and stable receptor interaction was selected for further evaluation. Antimicrobial susceptibility testing confirmed that the compound did not inhibit bacterial growth. However, at a concentration of 16 µg/mL, it significantly inhibited hemolytic activity with high reproducibility, and the inhibition rate reached 77.60%. Quantitative reverse transcription PCR (RT-qPCR) demonstrated that the compound decreased some key AgrC-mediated genes, including agrC, agrA, saeS, hla, spa, fnbA, and lukS. Conclusions: These findings identify a promising cyclic pentapeptide inhibitor of AgrC that effectively attenuates S. aureus virulence without exerting bactericidal pressure. This work provides a valuable lead compound and offers novel insights for the development of advanced, safe, and effective antivirulence therapeutics. Full article

Background/Objectives: Hypermobile Ehlers–Danlos syndrome (hEDS) remains genetically unexplained despite decades of clinical investigation, with the molecular basis undefined for the vast majority of cases. This study employs integrated machine learning approaches with rigorous subject-level statistical methods to decode the genetic architecture underlying hEDS. Methods: We analyzed 35,923 rare genetic variants (gnomAD MAF < 0.2) across 116 subjects from 43 families (86 hEDS patients diagnosed per 2017 international criteria; 30 unaffected intrafamilial controls) using whole-exome sequencing. Machine learning analysis employed Random Forest feature selection, deep neural networks, and ensemble methods with subject-stratified cross-validation to prevent data leakage. Statistical association testing used subject-level Fisher’s exact tests with Bonferroni correction (α = 3.77 × 10⁻⁶ for 13,281 genes). Sensitivity analyses assessed robustness to family structure. Results: Subject-level analysis identified statistically significant enrichment in variants associated with three major biological systems: (1) collagen biosynthesis pathway variants (present in 63% of hEDS subjects vs. 17% of controls, Fisher’s p = 1.06 × 10⁻⁵, OR = 8.4), predominantly affecting COL5A1, COL18A1, COL17A1, and post-translational modification enzymes; (2) HLA/adaptive immune axis variants (74% of hEDS vs. 30% of controls, p = 2.23 × 10⁻⁵, OR = 6.8), involving HLA-B, HLA-A, HLA-C, and TAP transporters; (3) mitochondrial respiratory chain variants (34% of hEDS vs. 7% of controls, p = 2.29 × 10⁻³, OR = 7.1), with striking 4.2-fold enrichment in pediatric fracture cases (52% vs. 21%, p = 0.021, 95% CI: 1.2–14.6). These associations require independent validation and functional studies to determine their mechanistic relevance. Genome-wide analysis identified seven genes achieving Bonferroni significance (p < 3.77 × 10⁻⁶), all encoding structural/cytoskeletal proteins. Machine learning models with proper subject-stratified cross-validation achieved 80% accuracy (95% CI: 73–86%, sensitivity = 82%, specificity = 77%). Conclusions: Our findings suggest that hEDS may involve genetic variation across multiple biological systems beyond classical collagen pathways. These hypothesis-generating associations require validation in independent cohorts and functional studies before mechanistic or clinical conclusions can be drawn. Full article