Search Results (2,172)

Agronomic modulation of secondary metabolism may influence not only crop resilience but also the biological activity of fruit-derived phytochemicals. In this study, we evaluated the impact of exogenous methyl jasmonate (MeJA) application under contrasting water regimes on the selected phenolic compounds and vascular bioactivity of Vaccinium corymbosum L. cv. Legacy. Antioxidant capacity was assessed by FRAP and DPPH assays, phytochemical composition was characterized by HPLC-DAD, and antiplatelet activity was evaluated through inhibition of TRAP-6–induced P-selectin (CD62P) expression in human platelets. Selected phenolic constituents were further examined using molecular docking and molecular dynamics simulations against a platelet receptor model. Although MeJA treatment altered the abundance of selected phenolic compounds identified by HPLC-DAD, total antioxidant capacity remained largely unchanged. Blueberry extracts significantly inhibited platelet activation in a concentration-dependent manner without cytotoxic effects, and antiplatelet potency was not strictly related to global antioxidant indices. Computational analyses revealed stable ligand–receptor interactions and favorable binding free energies for selected phenolics, providing a structural explanation for receptor-level modulation. These findings suggest that elicitor-driven responses in blueberries can influence platelet functional responses and highlight the importance of qualitative phytochemical composition in determining vascular bioactivity. This multiscale approach connects plant stress physiology, natural product chemistry, and human platelet biology, underscoring the translational relevance of agronomic strategies for nutraceutical functionality. Full article

Age-related skeletal muscle atrophy (sarcopenia) poses a major public health challenge, emphasizing the need for safe and effective interventions. Our previous studies demonstrated that C-C chemokine receptor type 5 (CCR5) is a key therapeutic target for skeletal muscle atrophy, as its activation by C-C motif chemokine ligand 11 (CCL11) promotes the dissociation and degradation of the structural protein α-actin, ultimately contributing to muscle loss. To identify potential CCR5 inhibitors, a database of 7860 natural alkaloids was constructed for pharmacophore-based virtual screening using the CCR5–Maraviroc crystal structure. Screening yielded 789 candidates, and subsequent batch molecular docking analysis identified Isoliensinine (ISO), a lotus seed alkaloid, as a potential CCR5 inhibitor with low binding energy (−10 kcal/mol) and stable hydrogen bonding interactions with Glu283 and Tyr251. Molecular dynamics simulations further confirmed the structural stability of the ISO–CCR5 complex. Molecular dynamics simulations further confirmed the structural stability of the ISO-CCR5 complex. In vitro, ISO dose-dependently inhibited CCL11-induced CCR5 activity (IC₅₀ = 1.314 μM) with low cytotoxicity in C2C12 myotubes, and markedly alleviated CCL11-induced myotube atrophy by suppressing CCR5 activation and the upregulation of the muscle atrophy–related markers MAFbx and MuRF1. These findings provide preliminary evidence for ISO as a potential CCR5-targeting candidate for further investigation in sarcopenia. Full article

Background/Objectives: Immune exclusion in head and neck squamous cell carcinoma (HNSCC) limits immunotherapy efficacy, yet the molecular determinants of stromal-immune organization remain incompletely characterized. The desmosomal cadherin DSG2 is highly expressed in squamous epithelium; its role in shaping the tumor microenvironment (TME) is unknown. Methods: We integrated bulk RNA-seq from 836 HNSCC patients (TCGA-HNSC n = 566, GSE65858 n = 270), single-cell RNA-seq (GSE139324, n = 26 patients, 133,308 cells), spatial transcriptomics (GSE208253, n = 12), proteomics (CPTAC-HNSCC, n = 108), and external validation cohorts (GSE41613, n = 97). CellChat ligand-receptor analysis, mediation analysis, Mendelian randomization (MR), LASSO-penalized Cox regression, HPV-stratified sensitivity analysis, and transcription factor (TF) correlation analysis were employed. Results: DSG2 exhibited epithelial-specific expression and showed consistent positive correlation with CXCL8 (IL-8; TCGA ρ = 0.228, p = 4.4 × 10⁻⁸) and myCAF activation across independent cohorts. Single-cell analysis revealed that 99.5% of CXCL8-producing cells have zero DSG2 expression, establishing the bulk correlation as compositional rather than cell-intrinsic. CellChat identified CXCL8-CXCR2 as the strongest tumor-stroma interaction in DSG2-high regions (probability = 0.821, 1.80-fold enrichment). Mediation analysis demonstrated 43.6% (95% CI [34.3–53.6%]) of DSG2’s tissue-level association with myCAF activation is mediated through CXCL8 (compositional mediation). Multi-instrument MR (IVW: Beta = −0.028, p = 0.028; I² = 0.0%) corroborated the compositional model. Protein-level validation in CPTAC-HNSCC confirmed DSG2-CD8A inverse correlation (Spearman ρ = −0.35, p = 2.2 × 10⁻⁴). Pan-squamous meta-analysis confirmed negative DSG2-cytolytic activity correlations (pooled ρ = −0.213, 95% CI [−0.296, −0.128], I² = 58.6%, 4 cohorts). DSG2 correlated with TIDE score (ρ = 0.176) and TGF-β exclusion subscore (ρ = 0.428). DepMap analysis identified CXCR2 inhibitor collateral sensitivity (ρ = −0.408, p < 0.0001). An eight-gene co-expression module was validated in two independent cohorts (GSE41613: HR = 3.09, p = 0.003; GSE65858: HR = 1.57, p = 0.032). Conclusions: DSG2 marks a stromal-immune organizational state characterized by CXCL8-CXCR2 paracrine signaling, myCAF activation, and immune exclusion, conserved across squamous malignancies. DSG2-high/PD-L1-high tumors (30.4% prevalence) exhibit the worst predicted ICI response and represent a candidate population for biomarker-selected CXCR2 inhibitor trials in combination with anti-PD-1 therapy. Full article

Background: The rapid escalation of multidrug-resistant P. aeruginosa necessitates anti-virulence strategies targeting quorum sensing rather than bacterial survival; however, integrating scaffold repurposing with generative AI to inhibit LasR remains underexplored. Here, we address this gap by combining triazole scaffold mining with transformer-based de novo molecular generation to systematically identify putative LasR inhibitors. Methods: An integrated computational pipeline involving Structure-based inhibitor design using Generative Transformer Architecture, deep learning-assisted GNINA rescoring, density functional theory optimization, and molecular dynamics simulations was employed, followed by MM-GBSA binding free energy estimation. Results: Screening of 2666 triazole derivatives and 19,861 DrugGPT-generated compounds yielded top hits with superior binding affinities (−11.59 to −13.81 kcal/mol) compared to the reference ligand (−8.50 kcal/mol). MD simulations yielded stable protein–ligand complexes with RMSD values of 2.24–3.01 Å, while key interactions involving residues Tyr50, Asp67, and Ser123 were consistently maintained. Binding free energy calculations further confirmed strong thermodynamic stability, with MM-GBSA ΔG_bind values significantly favorable, supporting robust ligand–receptor affinity. Conclusions: Collectively, these findings establish a powerful AI-integrated framework for anti-virulence drug discovery and identify structurally diverse, high-affinity triazole-based and de novo compounds as promising lead candidates for disrupting LasR-mediated quorum sensing in P. aeruginosa. Full article

Parkinson’s disease (PD) is a multisystem disorder in which gastrointestinal dysfunction often precedes motor symptoms, yet the molecular links between peripheral stress and central neurodegeneration remain unclear. We investigated whether genes commonly dysregulated in PD and a classic model of intestinal inflammation (IBD) might reveal conserved stress-responsive molecules relevant to brain pathology. Shared gene signatures between PD and inflammatory bowel disease (IBD) were identified from peripheral blood transcriptomes using weighted gene co-expression network analysis (WGCNA). Hub genes were prioritized via protein–protein interaction (PPI) analysis and evaluated for expression consistency in independent brain tissue transcriptomic datasets. Single-cell RNA sequencing (scRNA-seq) of the PD substantia nigra was used to define the cellular context of the key hub gene, and CellChat analysis assessed intercellular communication changes. Immunofluorescence validation was performed in an MPTP-induced PD mouse model. We identified 79 shared genes and 6 hub genes, among which only HSP90AA1 showed consistent upregulation across independent PD transcriptomic validation datasets. Functional enrichment highlighted inflammation-related pathways. Because peripheral immune infiltration showed only minor changes, we further investigated the cellular context of HSP90AA1 within the PD brain. ScRNA-seq analysis of the PD substantia nigra demonstrated that HSP90AA1 was expressed across multiple cell populations. Integration with transcriptional regulatory analysis identified TP53 as a potential upstream regulator, and the strongest TP53–HSP90AA1 co-expression and cellular colocalization signals were observed in astrocytes, prompting further astrocyte-focused investigation. CellChat analysis revealed altered intercellular communication patterns in PD substantia nigra, including changes in synapse-associated ligand–receptor interaction signatures, particularly involving NCAM-related pathways. In the MPTP-induced PD mouse model, immunofluorescence identified astrocytic HSP90α upregulation, and increased nuclear p53 signal in astrocytes, accompanied by dopaminergic neuron loss. Conclusion: Astrocytic upregulation of HSP90AA1 is associated with altered synapse-related intercellular communication patterns in the PD substantia nigra, potentially involving a predicted TP53 associated regulatory component. These findings, validated in an MPTP mouse model, identify HSP90AA1 as a candidate stress-responsive hub linking peripheral inflammatory states with astrocyte-associated molecular alterations in PD, providing a framework for further experimental investigation. Full article

Drug resistance arising during cancer development and progression remains a major challenge in the treatment of epidermal growth factor receptor (EGFR)-driven tumors, particularly those harboring the clinically relevant T790M/L858R double mutation. In this study, we developed an integrated computational workflow combining explainable machine learning, virtual screening, molecular dynamics simulations, and binding free-energy calculations to identify novel inhibitors of this drug-resistant EGFR variant. An XGBoost regression model was trained using scaffold-aware cross-validation, Bayesian hyperparameter optimization, and sequential feature selection, resulting in a compact model based on 16 molecular descriptors. The model demonstrated robust predictive performance on external validation data, while SHAP analysis identified descriptors related to the local electronic environment, fragment distribution, and molecular topology as the primary contributors to activity prediction. The optimized model was subsequently applied to screen compounds from the Enamine REAL database. Top-ranked candidates were evaluated using explicit-solvent molecular dynamics simulations and MM/GBSA binding free-energy calculations. Several compounds formed stable protein–ligand complexes and maintained key interactions with residues known to be important for EGFR inhibition, including Lys745, Met790, and Leu718. These results demonstrate that the proposed workflow can efficiently prioritize computational candidates of drug-resistant EGFR mutants and may support the development of new therapeutic strategies for overcoming resistance in EGFR-driven cancers. Full article

Ovarian cancer (OC) remains the most lethal gynecological malignancy, mostly due to its frequent diagnosis at advanced stages, early peritoneal dissemination, ascites formation, and limited sensitivity of currently available approaches for early detection. Extracellular vesicles (EVs), particularly exosomes, mediate intercellular communication through the transfer of proteins, lipids, metabolites, and nucleic acids. In OC, EV-associated protein profiles reflect both tumor-cell-intrinsic programs and the complex interactions between malignant cells and the peritoneal microenvironment. This review summarizes current evidence regarding the involvement of exosomal proteins in OC progression, with particular emphasis on epithelial–mesenchymal transition, mesothelial reprogramming, extracellular matrix remodeling, angiogenesis, immune suppression, peritoneal dissemination, and platinum resistance. Mechanistic studies indicate that exosomal proteins, including CD44, the integrin α5β1/asparaginyl endopeptidase complex, annexin A2, low-density lipoprotein receptor-related protein 1, and programmed death-ligand 1, can directly contribute to metastatic niche formation and tumor progression. In parallel, proteomic studies of plasma-, serum-, ascites-, peritoneal-fluid-, and uterine-lavage-derived EVs have identified candidate liquid-biopsy biomarkers, including MUC1, EpCAM, FOLR1, integrins, complement- and coagulation-related proteins, and proteins associated with treatment resistance. To integrate the biological significance of proteins reported in OC-associated exosomes, we additionally performed protein–protein interaction and functional enrichment analyses. These analyses revealed interconnected protein groups associated with cell adhesion, oxidative stress adaptation, secretory remodeling, lipid metabolism, extracellular matrix organization, and inflammatory signaling. Taken together, the available evidence supports exosomal proteome profiling as a promising approach for investigating OC dissemination and developing minimally invasive diagnostic and prognostic tools. However, standardized EV isolation, quantitative proteomics, functional validation, and independent clinical cohorts remain essential for translation into clinical practice. Full article

T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitory motif (ITIM) domains (TIGIT) emerged as one of the most promising next-generation immune checkpoint targets following the success of programmed cell death protein 1 (PD-1), programmed death-ligand 1 (PD-L1), and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) blockade. TIGIT suppresses antitumor immunity through interaction with cluster of differentiation 155 (CD155), inhibition of CD226-mediated co-stimulation, and promotion of immunosuppressive regulatory T-cell (Treg) activity within the tumor microenvironment (TME). Strong preclinical evidence demonstrated that TIGIT blockade, particularly in combination with PD-1/PD-L1 inhibition, restored T-cell and natural killer (NK) cell function and produced durable antitumor responses in multiple tumor models, leading to rapid clinical development. Despite this compelling biological rationale, most late-stage clinical programs failed to reproduce early success. Although the phase II CITYSCAPE trial showed encouraging activity in PD-L1-high non-small cell lung cancer (NSCLC), subsequent phase III trials, including SKYSCRAPER-01, SKYSCRAPER-02, SKYSCRAPER-03, SKYSCRAPER-14, AdvanTIG-302, KEYVIBE, and STAR-221, failed to improve survival outcomes or meet primary endpoints. The notable exception was SKYSCRAPER-08 in esophageal squamous cell carcinoma, suggesting that TIGIT blockade may be effective only in selected biological contexts. This review critically examines the molecular biology of the TIGIT–CD155–CD226 axis, its role in immune regulation and tumor immune evasion, and the preclinical and clinical evidence supporting TIGIT-targeted therapy. Particular emphasis is placed on understanding the causes of clinical failure, including CD226 loss during T-cell exhaustion, checkpoint network redundancy, Fc-engineering uncertainty, immunosuppressive TMEs, inadequate biomarker-guided patient selection, and tumor-type-specific dependence on the TIGIT pathway. We also present original bioinformatics analyses demonstrating that broader checkpoint network signatures outperform TIGIT expression alone for patient stratification. Finally, we evaluate emerging solutions including biomarker-guided precision immunotherapy, Fc-optimized antibodies, bispecific checkpoint inhibitors, TIGIT-engineered chimeric antigen receptor T-cell (CAR-T) cells, radiotherapy combinations, and multi-checkpoint blockade. Collectively, current evidence suggests that the future of TIGIT-directed therapy lies not in universal checkpoint inhibition but in biologically informed, precision-guided immunotherapy strategies. Full article

Triple-negative breast cancer (TNBC) is the most clinically aggressive subtype of breast cancer, characterized by the absence of targetable hormone receptors and HER2 amplification, significantly constraining treatment choices. Estrogen Receptor Beta (ERβ) has emerged as a biologically relevant yet underutilized target in TNBC, with its re-expression linked to tumor suppression and improved prognosis, prompting the development of selective ERβ modulators as a precision therapeutic approach. We introduce ERβ-Score, an interpretable machine learning scoring system developed using a curated dataset of 1699 ERβ bioactive chemicals obtained from ChEMBL, characterized by 39 physicochemical and three-dimensional molecular descriptors. After implementing scaffold-disjoint train/test partitioning to avert structural data leakage, a Gradient Boosting Classifier, fine-tuned through Bayesian hyperparameter optimization, attained in five-fold cross-validation a Precision–Recall AUC (Area Under the Curve) of 0.891, a ROC-AUC (Receiver Operating Characteristic) of 0.888, a Matthews Correlation Coefficient of 0.664, an F1-score of 0.838, and a balanced accuracy of 0.831; on the scaffold-disjoint hold-out test set it attained a Precision–Recall AUC of 0.905, a ROC-AUC of 0.864, and a Matthews Correlation Coefficient of 0.578, indicating strong and balanced discrimination between active and inactive ERβ modulators. We note explicitly that this scaffold-disjoint hold-out constitutes internal validation, since it derives from the same curated ChEMBL workflow used for model development, and it is therefore reported throughout as scaffold-disjoint internal validation rather than as independent external validation. The applicability domain boundaries were established using a k-nearest-neighbor Tanimoto-similarity method with ECFP4 (Extended-Connectivity Fingerprint with a Diameter of 4) fingerprints, offering a quantitative confidence metric that identifies structurally new molecules beyond the model’s reliable prediction range. External validation against independent Tox21 ERβ bioassay data confirmed genuine, statistically significant predictive signal (ROC-AUC = 0.71) while revealing reduced sensitivity for structurally novel active compounds. The model was subsequently used for extensive virtual screening of natural product and drug-like compound libraries, with prioritized candidates undergoing structure-based molecular docking against the ERβ co-crystal structure (PDB: 7XWQ) using Smina, facilitating a comprehensive evaluation of hits based on both ligand and structural properties. To enhance accessibility, the complete pipeline was implemented as an open-access interactive web application utilizing Streamlit, enabling researchers to input any SMILES string and obtain, in real time, an activity prediction with a probability score, applicability domain classification, Lipinski drug-likeness assessment, interactive three-dimensional visualization of protein–ligand interactions, and on-demand docking within the ERβ active site. Full article

Acute myeloid leukemia (AML) remains a therapeutic challenge due to complex oncogenic networks, including the often-undruggable MYC pathway. Here, we present an integrated in silico framework combining transcriptomic analysis, machine learning, and molecular dynamics (MD) simulations to explore potential therapeutic approaches targeting vault RNA1-1 (VTRNA1-1) in AML. RNA-seq profiling revealed that VTRNA1-1 depletion is associated with a profound disruption of the MYC and FOXM1 regulatory axes. To highlight compounds capable of recapitulating this transcriptomic signature, we developed a machine learning pipeline utilizing a Random Forest classifier trained on a fully compiled L1000FWD database subset. Virtual screening of approved drugs predicted the anthelmintic niclosamide as a top candidate (98.17% mimic probability). Explainable AI further rationalized this prediction by highlighting specific fragments within niclosamide’s salicylanilide core. Furthermore, a 200 ns MD simulation indicated favorable computational stability of niclosamide bound to the p62 (SQSTM1) ZZ domain. The complex showed rapid structural convergence (ligand RMSD plateauing at 1.65 nm) without dissociation, while maintaining strict receptor compactness (steady Radius of Gyration and solvent-accessible surface area) and a persistent interaction network of ~73 close atomic contacts. These findings suggest that niclosamide may function as a stable physical “lid” over the p62 ZZ domain, occluding its N-degron-binding cleft. Taken together, our computational framework highlights niclosamide as a promising candidate for AML drug repurposing, providing a hypothesis-generating foundation that warrants rigorous experimental validation. Full article

Background: Anaplastic Lymphoma Kinase (ALK) is an oncogenic receptor tyrosine kinase implicated in several cancers. Despite the clinical success of ALK inhibitors, acquired resistance continues to drive the search for novel chemotypes. We developed a multiclass machine learning framework to classify ALK inhibitory activity using a curated ChEMBL dataset. Methods: Models were built using 2D molecular descriptors together with MACCS and ECFP4 fingerprints. Three widely used algorithms, Support Vector Machine (SVM), Random Forest (RF), and XGBoost, were applied for model development. Results: RF and XGBoost models demonstrated the best performance, achieving accuracies of ~0.75–0.79 with consistently high ROC–AUC values, particularly for fingerprint-based features. Bemis–Murcko scaffold analysis identified enriched chemotypes and underexplored scaffolds for further prioritization. The validated models were subsequently used to screen the Maybridge library, and compounds predicted to possess potential ALK inhibitory activity were prioritized for further computational evaluation. Applicability-domain filtering confirmed that the selected compounds occupied the predicted ALK inhibitor chemical space across multiple activity classes. The shortlisted compounds were subsequently evaluated by molecular docking to characterize their binding modes and interactions. Three candidate hits (SCR00078, SCR00073, and AW01085) were selected for further evaluation using 500 ns molecular dynamics simulations alongside the reference inhibitor Brigatinib. Simulation analyses revealed stable protein–ligand complexes and reduced conformational fluctuations relative to apo ALK, while MM/PBSA calculations identified SCR00078 and AW01085 as the most favorable binders. Conclusions: This integrated ML-to-simulation workflow prioritizes structurally novel candidate hits with predicted ALK inhibitory activity and provides an effective strategy for scaffold discovery and hit prioritization. Full article

Peroxisome proliferator-activated receptors (PPARα, PPARβ/δ, and PPARγ) are ligand-regulated nuclear receptors that coordinate lipid metabolism, glucose homeostasis, inflammation, adipogenesis, differentiation, and disease-associated transcriptional programs. Although early structural models emphasized ligand-dependent stabilization of helix 12 (H12) and the activation function-2 surface (AF-2), evidence from molecular dynamics (MD) simulations, NMR, HDX-MS, crystallography, mutagenesis, and biochemical assays supports a more complex conformational-ensemble mechanism. Apo and ligand-bound PPARs populate multiple functional substates whose distributions are shifted by ligands, RXR heterodimerization, DNA binding, co-regulators, post-translational modifications, and disease-associated mutations. This review summarizes MD and integrative structural studies of PPAR conformational dynamics, isoform-specific behavior, PPAR-RXR and co-regulator interactions, ligand entry, graded activation, inverse agonism, disease-associated mutations, and phosphorylation-dependent regulation. PPARγ is the most extensively characterized isoform, whereas PPARα and particularly PPARβ/δ remain comparatively underexplored by atomistic and enhanced-sampling approaches. MD is most informative when it extends beyond post-docking pose stability and is integrated with long-timescale sampling, free-energy methods, dynamic-network analysis, and experimental validation. Future simulations should increasingly model biologically realistic assemblies containing RXR, DNA, coactivators or corepressors, disease mutations, and post-translational modifications to connect ligand chemistry with receptor allostery, transcriptional output, and disease biology. Full article

Heterochromatin Protein 1γ, encoded by the Cbx3 gene, is a crucial epigenetic regulator that plays an essential role in mammalian meiotic progression. However, the functional divergence and conservation of this protein in teleosts—organisms possessing duplicated Cbx3 paralogs due to whole-genome duplication—remain to be elucidated. Building on previous research, we focused on cbx3a in Nile tilapia (Oreochromis niloticus), a significant aquaculture species and an excellent model for teleost reproductive studies, emphasizing its role in spermatogenesis. Expression analysis revealed that Cbx3a is localized to primordial germ cells and is sustained in spermatogonia, spermatocytes, and spermatids during spermatogenesis. CRISPR/Cas9-mediated knockout of cbx3a demonstrated that Cbx3a deficiency induces germ cell apoptosis, meiotic arrest, and sperm defects, including shortened tails and impaired motility, resulting in profound defects in sperm quantity and quality, strongly implying compromised male fertility. Transcriptomic analysis further identified dysregulated molecular pathways, including cytokine signaling and neuroactive ligand–receptor interactions. This provides novel mechanistic insights into HP1γ-mediated epigenetic regulation of meiosis. Notably, cbx3a mutants exhibited phenotypic bifurcation: a subset showed meiotic defects accompanied by sporadic germ cell apoptosis, whereas others underwent full meiotic arrest with pervasive germ cell apoptosis in adult gonads. Collectively, these findings clarify the essential and conserved role of Cbx3a/HP1γ in Nile tilapia spermatogenesis, thereby advancing the field of vertebrate reproductive epigenetics and providing a valuable theoretical basis for potential applications in reproductive management, such as improving sperm quality. Full article

Most patients with solid tumors do not respond to immune checkpoint blockade, and inadequate T cell infiltration of the tumor parenchyma is the dominant mechanism of primary resistance. Oncolytic viruses address this problem by a distinct route: they replicate selectively within tumor cells, produce immunogenic cell death, and convert infected cells into local sources of any encoded transgene. Most armed designs to date have carried cytokine or checkpoint-antibody payloads, and chemokines have attracted comparatively little attention despite bearing directly on the trafficking bottleneck. This review synthesizes the preclinical literature on chemokine-armed oncolytic viruses across three receptor axes: CXCR3 (CXCL9, CXCL10, CXCL11), CCR5 (CCL5/RANTES), and CCR7 (CCL19). The accumulated evidence indicates that therapeutic outcome depends less on the chemokine payload itself than on the interaction between payload and viral backbone. CXCL11 outperforms its sister CXCR3 ligands not through intrinsic potency but because it is non-redundant with the endogenous chemokines induced by vesicular stomatitis virus and vaccinia, and because it largely escapes proteolytic cleavage by dipeptidyl peptidase 4 (DPP4). CCL5 has shown the most consistent activity in dual-payload designs that pair chemotaxis with a T cell survival cytokine such as IL-15. CCL19, which addresses lymphoid organization rather than effector recruitment, rests on a single published construct. One evidence gap is central: no head-to-head comparison of chemokine payloads within a single viral platform has been published. We therefore propose a translational decision framework that aligns chemokine selection with the immune contexture of the target tumor. Full article

Influenza A(H3N2) remains a significant public health threat due to its rapid antigenic drift, which often compromises vaccine effectiveness. This study characterized the molecular and epidemiological profile of hemagglutinin (HA) variants circulating in Western Mexico throughout 2022. Among 476 positive cases, A(H3N2) was the predominant subtype (88%), with infection peaks during epidemiological weeks (EW) 1, 45, and 46. Sanger sequencing of the HA gene identified 64 amino acid substitutions, with 85.9% of the substitutions located in the HA1 subunit, primarily within the receptor-binding domain (RBD). Homology modeling and molecular docking were performed on five representative variants: C156S, D158N, Y159N, C136S, and L227P. All variants exhibited a slight decrease in binding affinity for sialic acid compared to the 1HGE reference. Notably, while mutations such as D158N and Y159N remodeled the interaction network, Glu190 and His183 remained critical for stabilizing the HA-ligand complex through hydrogen bonds and non-covalent interactions in the structural models. These structural findings suggest that contemporary mutations in the RBD may contribute to changes in receptor-binding interactions while preserving key structural features associated with host cell attachment. Full article