Search Results (50)

Background: Growth factor therapy often fails in diabetic foot ulcers (DFUs). The reason remains unclear. Standard differential expression analysis may miss functionally critical genes with modest expression changes. Methods: We performed a secondary computational analysis of a longitudinal DFU transcriptomic dataset (Dryad; 17 patients, 117 serial biopsy samples, 12-week follow-up). Co-expression networks were built separately for healed (n = 37) and non-healed (n = 80) samples. Virtual gene knockout (VGK) was used to rank genes by topological impact on network cohesion. Single-cell analysis (GSE165816) assessed the association between endogenous KIF13A expression and keratinocyte migration-related signatures. A conceptual Hill-equation simulation was used to illustrate the transport-signaling threshold relationship. Drug repurposing used DSigDB enrichment. An independent bulk DFU cohort (GSE134431) was used for external validation. Results: KIF13A showed no differential expression (log₂FC = 0.173, p = 0.263) yet ranked first by VGK topological impact. In keratinocytes, high KIF13A expression correlated with greater migration scores versus zero-detection cells (p = 0.0058). A clear threshold effect emerged: below the 30th expression percentile, EGF, PDGF, and FGF pathway activation scores remained near baseline. In a structural-equation model, transport activity negatively predicted inflammation (standardized β = −0.92, p < 0.001). HIF1A showed the strongest positive correlation with KIF13A in keratinocytes (Spearman ρ = 0.26, p < 0.001), and FOS showed a negative correlation in the single-cell analysis (ρ = −0.16, p < 0.001) and in the bulk longitudinal cohort (ρ = −0.32, p < 0.001, n = 117). Recurrent AKR1B1-related drug signatures nominated the aldose-reductase pathway, and epalrestat was therefore prioritized as a hypothesis-generating candidate compound rather than a direct top-ranked enrichment hit. External validation confirmed consistent upregulation of KIF13A (Fold-Change = 1.58, adj. p = 0.0075), EPN1, and CLIP1 in DFU tissue. Despite population-level upregulation, a subset of cells fell below the functional signaling threshold. Conclusions: These computational findings suggest that KIF13A-associated vesicle transport impairment may represent a candidate systems-level bottleneck for growth-factor responsiveness in DFUs, a network-level pattern not captured by standard differential-expression analysis. Epalrestat, an AKR1B1 inhibitor prioritized through recurrent AKR1B1-related drug signatures, is presented as a candidate compound for further evaluation. As the present analysis is observational and computational, the findings should be interpreted as hypothesis-generating; experimental perturbation studies and prospective clinical validation are required. Full article

The bactericidal mechanisms of fluoroquinolones extend beyond their canonical inhibition of DNA topoisomerases, yet the associated metabolic perturbations remain incompletely understood. In this study, we systematically investigated the metabolic responses of Escherichia coli to three representative FQs—ofloxacin, enrofloxacin, and ciprofloxacin—using untargeted UPLC–Q Exactive Orbitrap–MS-based metabolomics. Bacterial cells were exposed to bactericidal concentrations (2 × MIC) for a single-time point (1 h), followed by comprehensive metabolomic profiling with six biological replicates per group. Our findings demonstrate that FQ-induced metabolic reprogramming serves as a primary driver of oxidative stress and nucleic acid damage, rather than a mere secondary effect. All three FQs induced substantial metabolic reprogramming characterized by disruptions in nucleotide biosynthesis, central carbon metabolism, and redox-related pathways, with notable drug-specific differences. Ciprofloxacin exhibited the most pronounced suppression of energy metabolism and antioxidant systems, whereas ofloxacin and enrofloxacin showed partial compensatory metabolic responses. Consistently, intracellular ROS levels were significantly elevated in all treatment groups, and this effect was attenuated by antioxidant supplementation. Furthermore, increased accumulation of 8-hydroxydeoxyguanosine and 8-hydroxyguanosine confirmed the occurrence of oxidative DNA and RNA damage. Collectively, these findings indicate that FQs induce distinct metabolic perturbations that are closely associated with oxidative stress and nucleic acid damage, providing a metabolic perspective on their bactericidal activity and suggesting potential targets for metabolic adjuvant strategies. Full article

Natural products have historically been integral to pharmacotherapy, attributed to their remarkable structural diversity and evolutionary refinement for biological interactions. Nonetheless, traditional natural product-based drug discovery has faced challenges such as mechanistic ambiguity, scalability limitations, and inadequate translational predictability. Concurrently, reductionist single-target approaches have been insufficient for addressing complex diseases characterized by network-level dysregulations. Recent advancements in analytical chemistry, genomics, and data-driven methodologies have rejuvenated natural product research by facilitating rapid structural elucidation, systematic exploration of biosynthetic diversity, and rational prioritization of bioactive compounds. Notably, many natural products exhibit multitarget effects that necessitate interpretation beyond isolated molecular interactions. Systems pharmacology offers a quantitative framework to analyze such network-level perturbations by integrating omics data, computational modeling, and experimental validation. However, molecular and computational predictions alone do not suffice to establish therapeutic relevance. Experimental pathology, encompassing histopathology, immunohistochemistry, spatial analysis, and ultrastructural evaluation, remains essential for validating efficacy and safety at tissue and organ levels. This review synthesizes technological innovation, systems pharmacology, and pathological validation to reposition natural products as mechanistically grounded and translationally robust resources for contemporary drug discovery. Full article

Background: Dry eye disease (DED) is a multifactorial ocular surface disorder characterized by tear film instability, inflammation, and neurosensory abnormalities. Current therapies remain limited by slow onset and suboptimal efficacy. Teriflunomide, an immunomodulatory agent approved for multiple sclerosis, has shown therapeutic potential in DED, but its multi-target mechanisms remain unclear. Methods: We employed an integrated computational and transcriptomic framework combining ADMET profiling, multi-dataset transcriptomic integration, and single-cell RNA sequencing (scRNA-seq) to identify disease-relevant targets. Candidate genes were further refined through molecular docking and 50 ns molecular dynamics (MD) simulations. The AetherCell virtual cell model was applied to evaluate both the concordance between target perturbation and drug-induced responses and the potential mechanistic roles of candidate targets. Results: Transcriptomic integration identified 16 consensus genes across heterogeneous DED models, which were further localized to disease-relevant epithelial and immune cell populations by scRNA-seq. Molecular simulations prioritized three core targets—CTSS, STAT1, and PTGS1—based on binding stability and affinity. AetherCell simulations demonstrated that perturbation of these targets not only recapitulated teriflunomide-induced transcriptional and pathway changes but also revealed their distinct mechanistic contributions, including epithelial barrier regulation (CTSS), microvascular and lipid homeostasis (PTGS1), and inflammation suppression coupled with tissue repair (STAT1). Conclusions: Teriflunomide exerts therapeutic effects in DED through coordinated multi-target regulation involving inflammation control, barrier restoration, and tissue repair. This study provides a rationale for novel therapeutic targets in dry eye disease, establishes a paradigm for applying virtual cell modeling to elucidate drug mechanisms, and offers a bioinformatics framework for validating drug repositioning outcomes. Full article

Background: CCL20 is a key chemokine involved in tumor-associated inflammation and immune microenvironment remodeling, but its biological and clinical relevance in esophageal cancer (ESCA) and across cancers remains incompletely defined. This study aimed to systematically characterize the expression pattern, prognostic value, immune associations, and potential translational relevance of CCL20 using an integrative multi-omics framework. Methods: The biological and clinical significance of CCL20 was investigated through differential expression analysis, weighted gene co-expression network analysis, survival and clinicopathological association analyses, ROC-based diagnostic evaluation, immune infiltration and tumor microenvironment characterization, immune checkpoint correlation analysis, single-cell transcriptomic analysis, in silico knockout analysis, drug sensitivity prediction, molecular docking, and tissue microarray-based immunohistochemistry. Results: CCL20 was aberrantly upregulated in ESCA and multiple other solid tumors, and its high expression was associated with poor prognosis in several cancer types. Tissue microarray-based immunohistochemistry further confirmed CCL20 overexpression at the protein level in ESCA. In the TCGA cohort, CCL20 showed favorable diagnostic performance and was associated with poorer survival outcomes in ESCA. High CCL20 expression was also closely associated with immunoregulatory infiltration patterns, increased immune checkpoint expression, and enhanced stromal features. Single-cell analysis showed that CCL20 was predominantly expressed in monocytes/macrophages and may mediate immune communication between myeloid and lymphoid/dendritic cells through the CCL20–CCR6 axis. In silico knockout analysis further suggested that CCL20 depletion perturbs inflammation-, chemotaxis-, and immune regulation-related transcriptional programs in monocytes/macrophages. Drug sensitivity prediction and molecular docking provided preliminary clues supporting the therapeutic relevance of the CCL20 axis. Conclusions: CCL20 may represent a biologically relevant candidate biomarker in ESCA, with potential diagnostic, prognostic, and therapeutic relevance. Full article

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a major cause of morbidity and mortality worldwide, particularly due to the emergence of drug-resistant strains. Membrane-active antimicrobial peptides (AMPs) represent attractive therapeutic candidates because they target bacterial envelope integrity and disrupt essential cellular processes. We evaluated two rationally designed LL-37-derived peptides: a truncated C-terminally amidated analog (LL37-1) and a modified variant incorporating N-terminal acetylation and a single D-amino acid substitution (D-LL37). Dose–response analysis demonstrated that D-LL37 exhibited greater antimycobacterial potency, with lower inhibitory concentrations of 90% (IC₉₀) and 50% (IC₅₀) values (18.40 ± 0.39 μM and 10.11 ± 0.60 μM, respectively) compared with LL37-1 (25.44 ± 0.36 μM and 15.45 ± 1.40 μM). Fluorescence-based permeability assays revealed partial membrane disruption (36% and 44% at IC₉₀ for LL37-1 and D-LL37, respectively), which was supported by ultrastructural alterations observed by scanning electron microscopy, including bacillary shortening, rough surface formation, cell clusters, and the presence of cellular debris, all of which are consistent with membrane damage. RT-qPCR analysis demonstrated significant upregulation of the P-type ATPase genes ctpF, ctpA, and ctpH following D-LL37 exposure. Collectively, these findings indicate that optimized LL-37-derived peptides exert antitubercular activity associated with envelope perturbation and coordinated activation of ion transport-related stress responses. Full article

The bone marrow tumor microenvironment (TME) is critical for acute myeloid leukemia (AML) progression, immune evasion, and treatment resistance. SRC, a non-receptor tyrosine kinase involved in multiple oncogenic pathways, has not been systematically characterized in AML in relation to prognosis and immune regulation. We integrated bulk transcriptomic and single-cell RNA-sequencing datasets from TCGA, BeatAML, and GEO. Immune-related targets were identified using xCell-based immune scoring and weighted gene co-expression network analysis (WGCNA), followed by protein–protein interaction analysis and multi-algorithm machine-learning screening. We then evaluated SRC expression patterns, prognostic associations, immune microenvironment features, predicted drug sensitivity, single-cell differentiation dynamics, intercellular communication, and in silico virtual knockout perturbation (scTenifoldKnk). SRC emerged as the most robust hub gene after integration of WGCNA, PPI analysis, machine-learning feature selection, and survival screening. SRC was significantly upregulated in AML compared with normal controls and was independently associated with poor overall survival (HR = 1.231, p = 0.037). High SRC expression was linked to adverse ELN/FAB features, increased immune checkpoint expression, enrichment of inflammatory and immunoregulatory pathways, and a higher proportion of primitive leukemia-associated cell states. Single-cell analyses further suggested that SRC was enriched in CD34⁺ progenitor compartments, associated with altered cell–cell communication, and accompanied by distinct mutation and pathway profiles. Drug-response prediction and in silico network perturbation analysis further supported the potential biological and translational relevance of SRC-centered programs. SRC is a prognostically relevant and immune-associated hub linked to AML microenvironment remodeling, and may serve as a candidate biomarker and potential therapeutic target that warrants further experimental validation. Full article

Colorectal cancer (CRC) remains a leading cause of cancer mortality, yet no systematic effort has linked druggable CRC driver genes to downstream ion channel effectors. We integrated differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein–protein interaction (PPI) network pharmacology to identify CRC hub genes and their ion channel connections, validated by dual single-cell perturbation approaches: variational graph autoencoder-based virtual knockout (VGAE-KO) and experimental HCT116 CRISPRi Perturb-seq (6 genes, 8445 cells). WGCNA identified 100 hub genes spanning three functional programs. Ribosomal proteins link to K⁺ channels (RPS21 → KCNQ2, targetable by EMA-approved ataluren, passed dual validation at 97.8th–98.7th percentile). RNA processing genes connect to Cl⁻ channels (LSM7 → CLIC1, strongest signal at 99.8th–99.4th percentile). Immune checkpoint receptors (LAG3, CD27) connect via PPI intermediates to ${\mathrm{Ca}}^{2+}$ and K⁺ channels, targetable by relatlimab (FDA-approved) and varlilumab (Phase 2). This work maps previously unknown links between CRC driver genes and ion channel regulation, with the ataluren-RPS21-KCNQ2 axis ready for pharmacological testing. Full article

The prediction of drug response would significantly improve the treatment of lung cancer. Tumor heterogeneity and complex signal transduction pathways lead to varied treatment effects among patients, but traditional computational approaches struggle to model the nonlinear, high-dimensional relationship between genes and drug responses. In order to develop a Generative Adversarial Network (GAN)-based model that can predict drug-induced gene expression profiles from lung cancer cell lines, we developed GRIP-Lung (Generative Model of Response to Drug-Induced Perturbation in Lung Cancer). By making use of biologically informed embeddings of cell line identity as well as drug treatment conditions, this model is able to gain a fairly good understanding of cell types and their transcriptional perturbations induced by different drugs. The GRIP-Lung model displayed reasonably good prediction ability in terms of predictive accuracy and showed high concordance between the predicted and experimental expression profiles. We not only predicted transcriptional changes induced by drug therapy but also used single-sample Gene Set Enrichment Analysis (ssGSEA) to classify post-treatment response states based on characteristic molecular biomarkers, offering a means for selecting effective drugs to target specific heterogeneity within lung tumors. The proposed GRIP-Lung framework faithfully reproduces drug-induced transcriptional perturbations in lung cell line models. By integrating biologically informed embeddings and adversarial learning, the model advances drug response prediction. This makes it a flexible computational tool for drug repositioning. Full article

Background: Clotrimazole (CLZ) is an approved antifungal with reported pleiotropic effects. Beyond its antifungal use, CLZ can perturb glycolytic flux and ionic homeostasis, motivating its evaluation as a repurposing candidate in oncology. Objective: We aimed to evaluate CLZ and nitazoxanide (NTZ) as drug repurposing candidates for pancreatic ductal adenocarcinoma (PDAC) in comparison with standard chemotherapeutics gemcitabine (GEM) and 5-fluorouracil (5-FU). Methods: T3M4 PDAC cells were treated (0.1–100 µM; 48–72 h) with 5-FU, GEM, CLZ, and NTZ. Cell viability (MTT) and morphology were assessed, and CLZ-based combinations were analyzed by the Chou–Talalay method. In silico studies provided physicochemical descriptors and ADMET profiles, along with predicted interactions with relevant bioorganic targets (e.g., KCa3.1/KCNN4 ion channels). Results: CLZ produced marked cytotoxicity at 72 h (IC₅₀ ≈ 9 µM) and achieved a greater reduction in cell viability at higher concentrations compared to 5-FU and GEM under identical conditions, whereas NTZ showed modest and inconsistent effects. CLZ combinations with 5-FU or GEM were mainly antagonistic. In silico analyses indicated high membrane permeability and suggested potential interactions with KCa3.1, supporting a hypothesis-generating interpretation of the observed in vitro effects. Conclusions: Within a drug repurposing framework, CLZ exhibited consistent cytotoxic activity as a single agent in a PDAC cell model, whereas NTZ revealed limited effects and CLZ-based combinations were not beneficial under the tested conditions. These findings position CLZ as a monotherapy-oriented repurposing candidate for PDAC and motivate further mechanistic and translational studies to clarify the biological basis of its in vitro activity. Full article

Red blood cell (RBC) membrane flickering arises from the interplay between thermal fluctuations, cytoskeletal elasticity, and metabolically driven non-equilibrium processes, making it a sensitive reporter of membrane mechanical state. Here, we introduce a computational microscopy framework that integrates bright-field morphometry with high-speed flickering spectroscopy to phenotype single-cell RBC mechanics under flavonoid exposure. As a proof of concept, human erythrocytes from a single donor were incubated with structurally distinct flavonoids (quercetin, apigenin, and rutin) prepared at sub-hemolytic concentrations, ensuring preservation of membrane integrity. Static shape descriptors and dynamic fluctuation spectra were extracted from segmented cell contours and analyzed through Fourier-mode decomposition to obtain compound-specific mechanical signatures. While gross morphology remained largely discocytic across conditions, flavonoid treatment induced reproducible alterations in flickering spectra and effective mechanical parameters, revealing distinct dynamical phenotypes that depend on flavonoid structure. In particular, aglycone flavonoids exhibited modulation patterns that differed from the glycosylated compound, consistent with differential membrane interactions. The combined analysis of geometry and dynamics provided enhanced discriminative power compared to either modality alone. These results establish computational microscopy as a sensitive, label-free approach to map compound-specific perturbations of RBC membrane mechanics and flickering, with potential applications in membrane biophysics, drug–membrane interaction screening, and single-cell mechanical phenotyping. Full article

The Varicella-zoster virus (VZV) open reading frame 54 (ORF54) gene encodes an 87 kDa monomer that oligomerizes to form the pORF54 portal dodecamer. Located at a single viral capsid vertex, the portal facilitates the translocation of the newly synthesized viral genome into the preformed empty capsid. Previously described α-methylbenzyl thiourea compounds were shown to inhibit VZV DNA encapsidation, likely by targeting pORF54. In this study, drug resistant isolates were obtained via passage of VZV in increasing concentrations of one analog, Compound I (Comp I). Mutations identified in four compound resistant isolates (amino acids 48, 304, 324 and 407) all localized to a region of the portal that was predicted to interface with capsid proteins. The portal is known to undergo significant conformational changes at the portal–capsid interface during DNA encapsidation. A set of recombinant viruses was designed to reveal the chemical and physical importance of each of the resistance mutations at the portal–capsid interface, the proposed binding site of the compound series. In addition, we employed a novel complementing cell line to show that despite the presence of the portal in the virion, DNA encapsidation did not occur. We propose that a-methylbenzyl thiourea compounds perturb interactions at or near the portal–capsid interface and prevent conformational changes needed to support DNA encapsidation. Full article

Background: Antibody-drug conjugates (ADCs) have ushered in a new era of precision oncology by combining the targeting specificity of monoclonal antibodies with the potent cytotoxicity of chemotherapeutic drugs. However, the cellular and molecular mechanisms underlying their dose-limiting ocular toxicity remain unclear. Elahere™, the first FDA-approved ADC targeting folate receptor α (FRα), demonstrates remarkable efficacy in platinum-resistant ovarian cancer but causes keratitis and other ocular toxicities in some patients. Notably, FRα is not expressed in the corneal epithelium—the primary site of damage—highlighting the urgent need to elucidate its underlying mechanisms. The aim of this study was to identify the cell-type-specific molecular mechanisms underlying Elahere-induced ocular toxicity. Methods: Sprague-Dawley rats were treated with intravenous Elahere (20 mg/kg) or vehicle weekly for five weeks. Ocular toxicity was determined by clinical examination and histopathology. Corneal single-cell suspensions were analyzed using the BD Rhapsody single-cell RNA sequencing (scRNA-seq) platform. Bioinformatic analyses to characterize changes in corneal cell populations, gene expression, and signaling pathways included cell clustering, differential gene expression, pseudotime trajectory inference, and cell-cell interaction modeling. Results: scRNA-seq profiling of 47,606 corneal cells revealed significant damage to the ocular surface and corneal epithelia in the Elahere group. Twenty distinct cell types were identified. Elahere depleted myeloid immune cells; in particular, homeostatic gene expression was suppressed in phagocytic macrophages. Progenitor populations (limbal stem cells and basal cells) accumulated (e.g., a ~2.6-fold expansion of limbal stem cells), while terminally differentiated cells decreased in corneal epithelium, indicating differentiation blockade. Endothelial cells exhibited signs of injury and inflammation, including reduced angiogenic subtypes and heightened stress responses. Folate receptor alpha, the target of Elahere, was expressed in endothelial and stromal cells, potentially driving stromal cells toward a pro-fibrotic phenotype. Fc receptor genes were predominantly expressed in myeloid cells, suggesting a potential mechanism underlying their depletion. Conclusions: Elahere induces complex, multi-compartmental ocular toxicity characterized by initial perturbations in vascular endothelial and immune cell populations followed by the arrest of epithelial differentiation and stromal remodeling. These findings reveal a cascade of cellular disruptions and provide mechanistic insights into mitigating Elahere-associated ocular side effects. Full article

Background/Objective: Sacubitril/Valsartan are a combination drug approved for heart failure treatment, known to enhance natriuretic peptide activity and inhibit the renin–angiotensin–aldosterone system (RAAS). While its clinical efficacy is well-established, its broader impact on human metabolism remains insufficiently characterized. This study aimed to explore the time-resolved metabolic changes induced by Sacubitril/Valsartan in healthy individuals using an untargeted metabolomics approach. Methods: Fourteen healthy male volunteers received a single oral dose of Sacubitril/Valsartan (200 mg; 97.2 mg Sacubitril and 102.8 mg Valsartan) across two phases separated by a two-week washout period. Plasma samples were collected at eight individualized time points based on pharmacokinetic profiles. Metabolites were extracted and analyzed using high-resolution liquid chromatography–mass spectrometry (LC-QToF HRMS). Data processing included peak alignment, annotation via HMDB and METLIN, and statistical modeling through multivariate (PLS-DA, OPLS-DA) and univariate (ANOVA with FDR correction) analyses. Results: Out of 20,472 detected features, 13,840 were retained after quality filtering. A total of 315 metabolites were significantly dysregulated (FDR p < 0.05), of which 31 were confidently annotated as endogenous human metabolites. Among these, key changes were observed in the pyrimidine metabolism pathway, particularly elevated levels of uridine triphosphate (UTP) associated with cellular proliferation and metabolic remodeling. OPLS-DA models demonstrated clear separation between pre-dose and Cmax samples (R²Y = 0.993, Q² = 0.768), supporting the robustness of the time-dependent effects. Conclusions: This is the first study to characterize the dynamic metabolomic signature of Sacubitril/Valsartan in healthy humans. The findings reveal a distinctive perturbation in pyrimidine metabolism, suggesting possible links to drug mechanisms relevant to cardiac cell cycle regulation. These results underscore the utility of untargeted pharmacometabolomics in uncovering systemic drug effects and highlight potential biomarkers for monitoring therapeutic response or guiding precision treatment strategies in heart failure. Full article

Modeling drug-induced transcriptional responses at the single-cell level is essential for advancing human healthcare, particularly in understanding disease mechanisms, assessing therapeutic efficacy, and anticipating adverse effects. However, existing approaches often impose a rigid constraint by enforcing pointwise alignment of latent representations to a standard normal prior, which limits expressiveness and results in biologically uninformative embeddings, especially in complex biological systems. Additionally, many methods inadequately address the challenges of unpaired data, typically relying on naive averaging strategies that ignore cell-type specificity and intercellular heterogeneity. To overcome these limitations, we propose scOTM, a deep learning framework designed to predict single-cell perturbation responses from unpaired data, focusing on generalization to unseen cell types. scOTM integrates prior biological knowledge of perturbations and cellular states, derived from large language models specialized for molecular and single-cell corpora. These informative representations are incorporated into a variational autoencoder with maximum mean discrepancy regularization, allowing flexible modeling of transcriptional shifts without imposing a strict constraint of alignment to a standard normal prior. scOTM further employs optimal transport to establish an efficient and interpretable mapping between control and perturbed distributions, effectively capturing the transcriptional shifts underlying response variation. Extensive experiments demonstrate that scOTM outperforms existing methods in predicting whole-transcriptome responses and identifying top differentially expressed genes. Furthermore, scOTM exhibits superior robustness in data-limited settings and strong generalization capabilities across cell types. Full article