Search Results (208)

Non-cystic fibrosis bronchiectasis (NCFB) is a heterogeneous chronic airway disease characterized by irreversible bronchial dilatation, impaired mucociliary clearance, and recurrent infection. Historically, research and clinical practice have focused mainly on bacteria, particularly Pseudomonas aeruginosa, as major drivers of exacerbations and disease progression, whereas the contribution of fungi has received far less attention. Over the last decade, evidence from mycobiome studies, large registries, and prospective cohorts has increasingly identified Aspergillus spp. as clinically relevant contributors in a substantial subset of patients with bronchiectasis. Data from the European Bronchiectasis Registry (EMBARC) indicate that approximately one quarter of patients exhibit Aspergillus-related immunological signals, including allergic bronchopulmonary aspergillosis (ABPA), Aspergillus sensitization, and elevated Aspergillus-specific IgG, and that these phenotypes are associated with more severe disease and worse clinical outcomes. Mechanistic studies further suggest that Aspergillus-related disease in bronchiectasis is underpinned by distinct molecular and immunological programs involving epithelial dysfunction, impaired mucociliary clearance, innate fungal sensing, inflammasome-related signaling, and divergent type-2 versus non-type-2 inflammatory responses. In parallel, mycobiome and multi-biome studies indicate that Aspergillus should be interpreted within a broader airway interactome shaped by cross-kingdom relationships with bacterial pathogens and by host immune tone. In this review, we synthesize current evidence on the epidemiology, molecular pathobiology, inflammatory endotypes, biomarker profiles, clinical–radiologic spectrum, and therapeutic implications of Aspergillus in bronchiectasis. Current evidence suggests that Aspergillus-related findings in bronchiectasis should be interpreted within a structured clinical, radiological, microbiological, and immunological framework rather than considered solely as isolated culture results. However, most data remain observational or extrapolated from related airway diseases, and bronchiectasis-specific interventional evidence is limited. A cautious biomarker-informed approach may help standardize phenotyping, identify patients requiring closer follow-up, and define priorities for future prospective trials. Full article

Therapy resistance remains a major obstacle to successful cancer treatment and is driven by complex interactions between tumor-intrinsic adaptive mechanisms and signals originating from the tumor microenvironment. Among the molecular regulators implicated in these processes, the transcription factor FOXM1 has emerged as a key mediator of DNA damage repair, cell cycle progression, and stress adaptation. Although FOXM1 has traditionally been studied as a regulator of intracellular signaling pathways, accumulating evidence suggests that its functions extend beyond canonical transcriptional control. In this review, we analyze current knowledge on the mechanisms regulating FOXM1 expression and activity and discuss how FOXM1 contributes to therapy resistance. We propose that FOXM1 should be viewed not merely as a regulator of individual oncogenic pathways but as a systems-level coordinator that integrates intracellular stress adaptation with microenvironment-driven resistance mechanisms. Particular attention is given to the FOXM1 interactome, complemented by an analysis of protein interaction data from BioGRID. We also discuss emerging evidence implicating FOXM1 in intercellular communication. To identify potential links between FOXM1 signaling and extracellular vesicle cargo, we analyzed the overlap between FOXM1 target genes and proteins identified in extracellular vesicle proteome databases. These emerging regulatory networks may represent previously underappreciated contributors to therapy resistance. Full article

Aesthetic medicine is shifting from symptomatic correction to biological structural restoration. Regenerative aesthetics represents a frontier in dermatology, focusing on the restoration of the skin microenvironment to enhance cellular vitality and tissue resilience. Central to this approach is the concept of “skin bed preparation”, a strategic priming phase designed to optimize the physiological terrain before the delivery of advanced aesthetic interventions. This review explores the molecular and cellular mechanisms by which skin bed preparation modulates the extracellular matrix (ECM) and the dermal niche to maximize the efficacy of subsequent treatments and promote long-term skin longevity. Evidence suggests that biostimulatory priming utilizing senolytics, senomorphics, mitochondrial, and/or epigenetic rejuvenators rehabilitates the fibroblast–collagen interactome. By reducing oxidative stress and chronic low-grade inflammation, these preparatory steps transition the skin from a catabolic to an anabolic state. This metabolic reset ensures that subsequent procedures, such as laser therapy, injectable fillers, encounter a responsive cellular environment, resulting in superior collagen induction and prolonged clinical outcomes. Optimizing the skin microenvironment via regenerative aesthetics is not merely an adjunctive step but a fundamental requirement for therapeutic success. Integrating skin bed preparation into clinical protocols provides a synergistic framework that enhances immediate procedural results while addressing the underlying hallmarks of skin aging, ultimately redefining the trajectory of skin health and longevity. Full article

Background: Glioblastoma (GBM) is the most aggressive primary brain tumor with extremely poor prognosis. Conventional diagnostic and prognostic approaches remain inadequate, highlighting the need for integrative strategies to improve patient outcomes. Methods: We analyzed ligand–receptor (L–R) interactions in TCGA-GBM transcriptomes using BulkSignaL-R, and validated their spatial expression patterns with single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. Prognostic histopathological features were extracted from hematoxylin and eosin (H&E)-stained sections through omics-guided feature identification, followed by classification using machine learning algorithms. Results: We identified four pivotal L–R pairs (LTB–CD40, VEGFA–ITGB1, FN1–COL13A1, and TGM2–ITGB1) to construct a risk model, which served as an independent prognostic factor for overall survival. The multivariate Cox regression analyses revealed that the risk score was significantly associated with Overall Survival (OS) (HR = 1.67, 95% CI: 1.25–2.25, p < 0.001). High-risk patients exhibited distinct molecular signatures, including CALN1 mutations, specific CNV patterns, and enriched Notch/interferon-γ signalings. scRNA-seq and spatial transcriptomics revealed that these L–R pairs were predominantly expressed in gMES-like glioma cells, OPC-like cells, and pericytes. Finally, our deep learning model successfully stratified risk groups based on histological features, identifying specific tumor regions (Clusters 0, 2, 4, and 5) as critical determinants of prognosis (AUC = 0.750 by Logistic Regression). Conclusions: We developed a novel multi-modal framework integrating L–R interactomics and deep learning-based pathomics. This approach not only elucidates the molecular and spatial landscape of glioma intercellular communication but also provides a methodological framework for risk stratification. Full article

Colorectal cancer (CRC) pathogenesis is driven by complex protein–protein interactions (PPIs) between the host and the gut microbiome, yet these molecular dialogs remain largely unmapped. This study utilizes a Deep Learning framework, enhanced by protein structure embeddings, to predict approximately 8.9 billion interspecies PPIs from clinical metagenomic data. The model achieved high accuracy with an AUROC of 0.9960, identifying a high-confidence interactome representing roughly 16% of evaluated protein pairs. Phenotype-specific analysis revealed that while microbial hubs shift—transitioning from metabolic enzymes in healthy states to transport and regulatory proteins in CRC—the primary human targets remain remarkably consistent across both cohorts. These core human interactors are predominantly metalloproteins and regulators of ubiquitination, apoptosis, and zinc transport, suggesting these pathways are primary focal points for microbial manipulation regardless of disease state. Furthermore, co-occurring bacterial genera exhibit over 99% overlap in host target profiles, indicating significant functional redundancy in microbial engagement with the host. These findings suggest that CRC probably arises from network-level perturbations of stable host signaling hubs, offering a blueprint for identifying novel therapeutic targets and biomarkers. Full article

Ubiquitin-specific protease 7 (USP7/HAUSP) is one of the most studied deubiquitinating enzymes and plays a crucial role in regulating numerous cellular processes, making it a promising therapeutic target. In the nucleus, USP7 partially colocalizes with PML nuclear bodies (PML-NB)—multifunctional membraneless organelles involved in post-translational modifications and protein complexes assembly. The molecular basis and functional significance of this association remain uncharacterized. In this study, comparison of USP7 and PML interactomes revealed a significant overlap of 166 shared proteins. Functional enrichment analysis showed that USP7 and PML may operate within a common molecular context related to transcriptional regulation, chromatin remodeling, and DNA damage responses. Furthermore, these processes are also linked to cellular senescence and human aging (CellAge and GenAge databases). Focused analysis of overlaps between the USP7 interactome and core PML-NB proteins identified 61 proteins forming a dense “small-world” network. Most are prone to liquid–liquid phase separation, are intrinsically disordered, and serve as substrates for SUMOylation or ubiquitination. These findings not only expand our understanding of the molecular functions of USP7 but also highlight PML-NB as an important cellular context for investigating mechanisms associated with USP7 activity. Full article

Background/Objectives: The clinical management of bladder cancer is severely impeded by high recurrence rates and the rapid emergence of chemoresistance, necessitating the discovery of novel therapeutic agents with distinct mechanisms of action. Dehydrodiisoeugenol (DHE), a bioactive neolignan, exhibits potent anti-tumor efficacy, yet its direct molecular targets and mode of action remain elusive. Methods: To deconvolute the mechanism of DHE, we integrated a phenotypic screening approach using 2D cell lines and 3D patient-derived organoids with a chemoproteomics-based activity-based protein profiling (ABPP) strategy. We synthesized a functionalized photoaffinity probe to capture the specific interactome of DHE under physiological conditions and validated targets via cellular thermal shift assays (CETSA), quantitative mass spectrometry, and 100 ns molecular dynamics (MD) simulations. Results: DHE exhibited potent dose-dependent cytotoxicity in bladder cancer cells, with IC50 values of 39.23 μM in T24 and 34.58 μM in 5637 cells. In 3D patient-derived organoids, DHE significantly reduced viability (p < 0.0001). Using a dual-filtering ABPP strategy, we identified 65 high-confidence candidate targets, prioritizing PTPN1 (PTP1B) as the primary functional interactor. Comparative molecular docking and 100 ns MD analyses showed that multiple stereoisomers of DHE could adopt plausible PTPN1-binding modes. Mechanistically, organoid proteomics indicated that DHE engagement with PTPN1 disrupts ER membrane homeostasis, thereby modulating the PI3K-Akt signaling axes. Conclusions: These findings establish PTPN1 as a critical druggable vulnerability in bladder cancer and define the molecular basis for the therapeutic potential of DHE. This study highlights the power of combining chemoproteomics with physiological 3D models to accelerate the translation of natural products into precision cancer therapies. Full article

Precision medicine is facing a critical transition driven by the growing complexity of biological data and the insufficient ability of current models to translate such data into clinically meaningful information. Linear, single-gene approaches are no longer adequate to explain the multifactorial nature of most modern diseases, whose phenotypes emerge from combinations of genetic, molecular, and environmental factors. Network-based precision medicine addresses this by providing a systemic framework capable of integrating heterogeneous omics data, interactomes, and clinical information to identify disease modules and novel therapeutic opportunities. The distinct novelty of this review is its focus on the potential of “network language” as the primary driver for realizing precision medicine through professional collaboration. We argue that networks are not merely tools that achieve precision “per se”; rather, their transformative power lies in their ability to serve as a shared and interpretable interface grounded in network theory. By offering this common conceptual ground, the paradigm bridges the deep cultural and methodological gaps between clinicians and data analysts, enabling effective cooperation between figures with fundamentally different, and often divergent, backgrounds. Practical tools—such as biological network analysis and Molecular Tumor Boards—demonstrate how computational modeling and clinical expertise can be successfully combined to generate actionable insights. Ultimately, network-based precision medicine represents a decisive step toward reconstructing the patient’s complexity and promoting a genuinely personalized clinical approach in which quantitative analysis and medical reasoning act synergistically through multidisciplinary integration. Full article

NANOG overexpression has been reported to reverse aging-associated decline in mesenchymal stem/stromal cell (MSC) function, but the molecular machinery engaged by NANOG in MSCs remains incompletely defined. Here, we applied APEX proximity labeling coupled with quantitative mass spectrometry to define the NANOG proximity interactome (proxeome) in human MSCs. Of 1040 quantified proteins, 828 were significantly enriched in the APEX-NANOG (H₂O₂ labeling) samples, consistent with a broad NANOG-centered neighborhood rather than a single stoichiometric complex. Enriched proteins encompass RNA-processing pathways (including splicing/RNP factors and selected m6A-related proteins), transcriptional coactivation and elongation control (Mediator and 7SK/P-TEFb regulators), chromatin repression/poising modules (Polycomb and HDAC/NuRD/CoREST/SIN3), ATP-dependent chromatin remodeling (BAF/SWI-SNF), three-dimensional genome organization and replication-coupled chromatin maintenance (CTCF/cohesin, CHAF1A, RIF1, UHRF1), and regulators of MSC identity and signal integration (Hippo/mechanotransduction and TGFβ-linked transcriptional circuits). Together, these data provide a spatial proteomic map of NANOG-associated nuclear neighborhoods in MSCs and a foundation for mechanistic hypotheses for how NANOG may stabilize stem-like programs. Full article

Plant immunity research is being reshaped by integrative multi-omics approaches that connect transcriptomic, proteomic, and interactomic data to build systems-level views of plant–pathogen interactions. This review outlines the scope and methodological landscape of these approaches, with particular emphasis on how transcriptomic and proteomic insights converge through network-based analyses to elucidate defense regulation. Transcriptomics captures infection-induced transcriptional reprogramming, while proteomics reveals protein abundance changes, post-translational modifications, and signaling dynamics essential for immune activation. Network-driven computational frameworks including iOmicsPASS, WGCNA, and DIABLO enable the identification of regulatory modules, hub genes, and concordant or discordant molecular patterns that structure plant defense responses. Interactomic techniques such as yeast two-hybrid screening and affinity purification–mass spectrometry further map host–pathogen protein–protein interactions, highlighting key immune nodes such as receptor-like kinases, R proteins, and effector-targeted complexes. Recent advances in machine learning and gene regulatory network modeling enhance the predictive interpretation of transcription–translation relationships, especially under combined or fluctuating stress conditions. By synthesizing these developments, this review clarifies how integrative multi-omics and network-based frameworks deepen understanding of the architecture and coordination of plant immune networks and support the identification of molecular targets for engineering durable pathogen resistance. Full article

This review aims to provide a broad, multidisciplinary perspective on how dynamic genomics and systems biology are transforming modern healthcare, with a focus on cancer especially liver cancer (HCC). It explains how integrating multi-omics technologies such as genomics, transcriptomics, proteomics, interactomics, metabolomics, and spatial transcriptomics deepens our understanding of the complex tumor environment. These innovations enable precise patient stratification based on molecular, spatial, and functional tumor characteristics, allowing for personalized treatment plans. Emphasizing the role of regulatory networks and cell-specific pathways, the review shows how mapping these networks using multi-omics data can predict resistance, identify therapeutic targets, and aid in the development of targeted therapies. The approach shifts from standard, uniform treatments to flexible, real-time strategies guided by technologies such as liquid biopsies and wearable biosensors. A case study showcases the benefits of personalized therapy, which integrates epigenetic modifications, checkpoint inhibitors, and ongoing multi-omics monitoring in a patient with HCC. Future innovations, such as cloud-based genomic ecosystems, federated learning for privacy, and AI-driven data analysis, are also discussed to enhance decision-making and outcomes. The review underscores a move toward predictive and preventive healthcare by integrating layered data into clinical workflows. It reviews ongoing clinical trials using advanced molecular and immunological techniques for HCC. Overall, it promotes a systemic, technological, and spatial approach to cancer treatment, emphasizing the importance of experimental, biochemical–functional, and biophysical data-driven insights in personalizing medicine. Full article

Zinc is an essential micronutrient involved in structural, catalytic, and regulatory functions across all levels of biological organization. Despite substantial advances over the past two decades, the zinc literature remains highly fragmented, with mechanistic, nutritional, and clinical findings often reported in isolation. Additionally, the synergistic interactions between zinc and other micronutrients—particularly minerals and vitamins—are dispersed across multiple research domains, complicating efforts to understand their integrated roles in maintaining homeostasis. Recent developments in artificial intelligence (AI) present new opportunities to consolidate these data, enabling multi-scale analyses of zinc-dependent processes and the broader zinc interactome. Although a complete map of the zinc interactome is not yet feasible, an integrative perspective is needed to contextualize zinc’s contributions within the framework of the hallmarks of health. This narrative review highlights zinc’s involvement in cellular maintenance, metabolic regulation, stress response, and systemic physiological function. It further examines how disruptions in zinc status, alone or in combination with other nutrient imbalances, contribute to clinically relevant disorders. By combining current knowledge across molecular, cellular, and systems biology levels, this review illustrates zinc’s pleiotropic effects on physiological resilience and healthspan, with particular emphasis on its role in nutritional status, homeostatic regulation, and overall human health. Full article

The huge number of possible permutations of genes, proteins and small molecules make the random emergence of cellular networks problematic. How, therefore, do interactomes come into existence? What selects for their stability and functionality? I hypothesize that interactomes originate from molecularly complementary modules (MCMs) that are selected for stability and retain their interactivity when mixed and matched with other such modules to create novel molecules and complexes displaying emergent properties not present in the individual components of the network. Because evolution can only proceed by working upon existing variants, and these variants emerge from selection of MCMs, the resulting systems must exhibit the characteristics of pleiofunctional, epistatic interactomes (PEIs). The resulting systems should display “molecular paleontology”, providing clues as to the historical process by which these MCMs were incorporated into the system. The MCM mechanism of PEI evolution is illustrated here by two case studies. The first concerns the prebiotic emergence of the glutathione–ascorbate anti-oxidant system and its later incorporation into regulation of glucose transport and catecholamine receptor activity. The second concerns the MCM evolution of the ribosome as, perhaps, the first PEI, and its role as a module for the later construction of the first cellular genomes. Full article

Protein complexes, assembled by scaffold proteins, act as molecular machines driving development. The mechanosensitive adapter protein Zyxin is a key example, integrating actin cytoskeleton dynamics with gene expression. However, the developmental regulation of its interactions and post-translational modifications remains poorly understood. Here, we characterize the dynamic Zyxin interactome across three early developmental stages of Xenopus laevis (from gastrulation to neurulation) using co-immunoprecipitation coupled with quantitative mass spectrometry (DDA and DIA). We identify stage-specific changes in Zyxin’s association with core focal adhesion components, transcriptional regulators and kinases. Furthermore, we uncover developmentally regulated phosphorylation events on isoforms, suggesting dynamic post-translational control of its interactions. Our work provides a comprehensive resource that positions Zyxin as a central orchestrator of cell adhesion, survival, and gene regulatory programs during morphogenesis. These findings underscore the role of Zyxin as a multifaceted regulatory hub, with important implications for understanding tissue homeostasis and related pathologies. Full article

Over the past decade, research on urolithins has expanded significantly due to their role as mediators between polyphenol-rich diets and human health. Understanding the relationships between ellagitannin intake, gut microbiota composition, and urolithin production is essential for evaluating their biological effects and nutraceutical potential. The primary objective of this review is to critically summarise current knowledge on urolithins, bioactive metabolites derived from ellagitannins in plant-based foods, with a focus on their biosynthesis, bioavailability, protein interactions, and potential therapeutic applications. A comprehensive literature search was conducted using PubMed, Scopus, and Google Scholar to identify studies on urolithin biosynthesis, absorption, transport mechanisms, protein binding, and incorporation into extracellular vesicles. Relevant articles were critically analysed to synthesise current evidence and highlight emerging concepts. Key findings indicate that after absorption, urolithins bind to serum albumin, which facilitates their transport to target tissues, exerting anti-inflammatory and antioxidant actions. Recent evidence also shows that urolithins can be packaged into extracellular vesicles, suggesting novel mechanisms for intracellular transport and potential therapeutic applications. This review highlights gaps in current knowledge and proposes directions for future research to optimise their therapeutic potential. Full article