Search Results (102)

Background: Bacterial adaptation to environmental and chemical stress involves coordinated, system-level responses collectively described as perturbome. Understanding conserved elements within core perturbomes may reveal strategic vulnerabilities for antimicrobial development. Methods: In this study, we implemented an integrative framework combining functional and comparative genomics, drug–target interactions and molecular docking to prioritize conserved stress-response targets in Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus. Results: A total of 147 genes from previously defined core perturbomes were analyzed through interactome reconstruction and functional enrichment. Interactome and functional analyses revealed significant connectivity and functional clustering, primarily associated with molecule biosynthesis, translation, transcriptional regulation, and energy metabolism. Orthology-based comparative genomics identified six conserved orthogroups shared across at least two species, representing key stress-adaptive nodes including fatty acid synthesis initiation, metabolic stress buffering, transcription termination (Rho), ATP synthesis, peptidoglycan remodeling, and UDP-glucose-mediated envelope biosynthesis. Drug–target interaction analyses suggested that these conserved proteins are modulated by enzymatic inhibitors, metabolite analogs, or active-site competitors. Structural and docking analyses focused on a selected protein, FabF (β-ketoacyl-ACP synthase II) and confirmed catalytically coherent binding of cerulenin within the active site, with high concordance between experimentally resolved and AlphaFold-predicted structures, supporting the reliability of structure-based prioritization. Conclusions: Overall, the results demonstrate that bacterial stress responses converge on evolutionarily conserved metabolic and regulatory elements essential for homeostasis and tolerance to perturbations, being the first work integrating core perturbome data from different microorganisms. The proposed perturbome-informed framework provides a rational strategy to identify robust, broad-spectrum antimicrobial targets and highlights opportunities for drug repurposing and future experimental validation. Full article

Tomatoes are globally significant crops worldwide. Understanding the molecular mechanisms underlying their growth, development, and stress responses is crucial to enhance crop productivity and resilience. The WUSCHEL-related homeobox (WOX) gene family is implicated in developmental processes and stress responses, yet its regulatory complexity in tomato remains underexplored. This study presents an integrative genome-wide analysis approach to characterize the WOX family in tomato. Ten SlWOX genes were identified and phylogenetically classified into three clades, WUS, intermediate and ancient, underscoring their evolutionary relationships. Structural analysis revealed significant variability in gene structure even within the same clade, indicating potential diversity in functional roles. Conserved domains’ screening enables the detection of conserved motifs, including the homeodomain and WUS box. Cis-element analysis showed diverse regulatory elements across the SlWOXs, with a strong emphasis on elements involved in growth and development and stress response. Expression profiling across different organs and growth conditions including abiotic and biotic stresses revealed variability in SlWOXs’ expression patterns. Furthermore, several miRNAs were predicted to target the SlWOXs, emphasizing the existence of post-transcriptional regulation. Functional annotation and interactome analysis further revealed the key role of some SlWOXs, mainly SlWUS, SlWOX4 and SlWOX13, as central regulatory hubs. Collectively, these findings uncover the structural diversity, regulatory mechanisms and functional flexibility of the SlWOX gene family. It also highlights potential targets for improving tomato crop resilience and productivity, making it a significant contribution to plant biology and agriculture. 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

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

Avian influenza, a disease caused by avian influenza virus (AIV), mainly infects birds but can also infect mammals, which poses a serious threat to public health. Therefore, thorough understanding of its pathogenic mechanism and the identification of antiviral targets are essential for the prevention, control, and treatment of AIV. The polymerase acidic protein (PA) is a core component of the viral RNA-dependent RNA polymerase complex and plays a central role in viral transcription through its cap-snatching activity during early infection. We employed a multi-omics approach combining transcriptome analysis with PA interaction proteomics to characterize host responses during AIV infection and explore the PA–host interaction network. Transcriptomics revealed a polarized host response marked by activated translation-related processes, mitochondrial energy metabolism, and innate immune signaling, alongside broad suppression of nuclear transcriptional regulation and cell cycle pathways. Immunoprecipitation–mass spectrometry identified host proteins associated with PA that were enriched in RNA metabolism, ribosome biogenesis, and protein homeostasis. Integrative analysis of transcriptomic and interactome data, along with protein–protein interaction network analysis, prioritized a subset of high-confidence PA-interacting host factors. Among these, ribosomal protein RPS27A was validated to interact with PA and to support viral replication during early infection in this study. Full article

African swine fever virus (ASFV) causes a fatal disease in domestic pigs and wild boars (Sus scrofa), leading to nearly 100% mortality during acute infection and significant economic losses in swine production. Unlike other eukaryotic viruses, ASFV encodes a histone-like nucleic acid-binding protein, pA104R, which is highly conserved and present in all described ASFV isolates of different genotypes. Moreover, A104R-like sequences have been identified in the genomes of soft ticks, which can replicate and transmit ASFV. Using a virulent genotype IX field isolate from Kenya, we analyzed the importance of A104R for viral replication in a permissive wild boar cell line (WSL). In this study, we confirmed that A104R is not essential for in vitro replication of ASFV. Loss of A104R did not detectably affect viral DNA replication or RNA transcription but led to a moderate reduction in virus titers and plaque sizes. Substitution of A104R with a similar ASFV-like element derived from the genome of an Ornithodoros moubata soft tick was not capable of rescuing the deletion mutant phenotype. In contrast, reintroduction of the authentic A104R open reading frame (ORF) into the deletion mutant fully restored wild-type virus growth properties. In accompanying studies, we verified the DNA-binding activities of the ASFV- and tick-derived A104R proteins and performed mass spectrometric analyses of the pA104R interactome. These experiments revealed, besides DNA-dependent co-precipitated proteins, specific DNA-independent protein–protein interactions of pA104R with other viral and cellular proteins. Full article

Protein–protein interactions (PPIs) are fundamental regulators of cellular function and disease. Systematic mapping of the interactome is essential for identifying therapeutic targets and advancing drug design, a pursuit that has driven significant innovation to capture the spatiotemporal regulation of PPIs in vivo. This review summarizes this methodological revolution. We outline foundational, first-generation techniques—yeast two-hybrid and co-immunoprecipitation—which established frameworks for binary interaction mapping and static network generation, especially when integrated with mass spectrometry. The discussion then pivots to second-generation methods, including proximity-dependent labeling and advanced imaging, which enable the capture of PPIs within their native, dynamic cellular contexts. We provide a comparative analysis of these techniques, detailing their principles, strengths, and limitations. The review concludes with a practical framework for method selection and a perspective on emerging frontiers—such as spatial proteomics and single-cell interactomics—that are poised to further decode the evolving interactome. This concise overview serves as a strategic guide for specialists adopting new techniques and a broader audience integrating network-level data into their research. Full article

Understanding the intimate interactions between plants and their microbiota at the cellular level is essential for unlocking the full potential of plant holobionts in agricultural systems. Traditional bulk and microbial community-level sequencing approaches reveal broad community patterns but fail to resolve how distinct plant cell types interact with or regulate microbial colonization, as well as the diverse antagonistic and synergistic interactions and responses existing between various microbial populations. Recent advances in single-cell and spatial multi-omics have transformed our understanding of plant cell identities as well as gene regulatory programs and their dynamic regulation in response to environmental stresses and plant development. In this review, we highlight the single-cell discoveries that uncover the plant cell-type-specific microbial perception, immune activation, and symbiotic differentiation, particularly in roots, nodules, and leaves. We further discuss how integrating transcriptomic, epigenomic, and spatial data can reconstruct multilayered interaction networks that connect plant cell-type-specific regulatory states with microbial spatial niches and inter-kingdom signaling (e.g., ligand–receptor and metabolite exchange), providing a foundation for developing new strategies to engineer crop–microbiome interactions to support sustainable agriculture. We conclude by outlining key methodological challenges and future research priorities that point toward building a fully integrated cellular interactome of the plant holobiont. 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

Reversible protein phosphorylation is an important regulatory mechanism in cellular signalling and disease, regulated by the opposing actions of kinases and phosphatases. Modern computer methods predict kinase–substrate or phosphatase–substrate interactions in isolation and lack specificity for biological conditions, neglecting triadic regulation. We present SPINET-KSP, a multi-modal LLM–Graph foundation model engineered for the prediction of kinase–substrate–phosphatase (KSP) triads with contextual awareness. SPINET-KSP integrates high-confidence interactomes (SIGNOR, BioGRID, STRING), structural contacts obtained from AlphaFold3, ESM-3 sequence embeddings, and a 512-dimensional cell-state manifold with 1612 quantitative phosphoproteomic conditions. A heterogeneous KSP graph is examined utilising a cross-attention Graphormer with Reversible Triad Attention to mimic kinase–phosphatase antagonism. SPINET-KSP, pre-trained on 3.41 million validated phospho-sites utilising masked phosphorylation modelling and contrastive cell-state learning, achieves an AUROC of 0.852 for kinase-family classification (sensitivity 0.821, specificity 0.834, MCC 0.655) and a Pearson correlation coefficient of 0.712 for phospho-occupancy prediction. In distinct 2025 mass spectrometry datasets, it identifies 72% of acknowledged cancer-resistance triads within the top 10 rankings and uncovers 247 supplementary triads validated using orthogonal proteomics. SPINET-KSP is the first foundational model for simulating context-dependent reversible phosphorylation, enabling the targeting of dysregulated kinase-phosphatase pathways in diseases. 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