Search Results (171)

Macrophages are highly plastic innate immune cells that adopt context-dependent phenotypes along a continuum, integrating developmental origin with local microenvironmental cues rather than conforming to discrete M1/M2 states. This review delineates the molecular circuits shaping macrophage identity—TLR/cytokine signaling, microRNA networks, metabolic rewiring, and epigenetic mechanisms including histone lactylation—and traces how circulating monocyte subsets contribute to tissue macrophage diversity. We examine macrophage plasticity across a broad disease spectrum—oncology, autoimmune and rheumatic diseases, inflammatory bowel disease, infectious diseases, metabolic disorders, and neurological conditions—showing that the pathogenic phenotype is strikingly context-dependent: for instance, M2-like tumor-associated macrophages promote immune evasion in solid tumors, whereas M1-skewed programs drive tissue damage in autoimmunity. Soluble markers (sCD163, sCD14, soluble mannose receptor) are emerging biomarkers of disease activity and prognosis. High-dimensional flow cytometry and mass cytometry (CyTOF) bridge molecular biology and clinical phenotyping, enabling integrated readouts of surface phenotype, intracellular signaling, and metabolic state. Therapeutic strategies discussed include selective tumor-associated macrophage (TAM) reprogramming, chimeric antigen receptor (CAR)-M cell therapies, and biomaterial-based platforms. Future priorities encompass spatially resolved multi-omics, epigenetic and metabolic targeting, and macrophage-centered vaccine approaches. Standardized cytometry panels will be essential for biomarker-guided stratification and context-specific interventions. Full article

Background: Dysregulated G protein-coupled receptor (GPCR) signaling is increasingly implicated as an important driver for oncogenesis. Uveal melanoma (UM) represents a highly metastatic intraocular malignancy primarily driven by activating mutations in G protein family members Gαq/11. Although Tebentafusp, the first FDA-approved bi-specific T-cell engager for UM, improves survival, its activity is restricted to specific human leukocyte antigen (HLA) alleles, highlighting the need to identify broadly expressed targetable proteins for immunotherapeutic strategies. Here we aimed to define surfaceome and phospho-signaling signatures associated with oncogenic Gαq-signaling. Methods: Heterologous and UM in vitro systems were used to interrogate Gαq-driven changes. HEK293T cells were transfected with wild-type Gαq or the oncogenic Gαq (R183Q) mutant, with surface marker profiles quantified by flow cytometry. Complementary immunophenotyping was performed in the Gαq-mutant UM cell line MP46 and Gα11-mutant line MP41. Kinase phosphorylation was assessed in control and Gαq mutant conditions followed by effect size estimation (Hedges’ g), Welch’s t-test, principal component analysis, and Spearman correlation-based network analysis of surface and phosphoprotein readouts. Results: Hyperactive Gαq in HEK293T cells induced graded remodeling of surface protein profiles, including reduced CD56 (NCAM) and CD49c (ITGA3) expression. Similarly, in UM models, MP46 versus MP41 had limited expression of CD56 and CD49c. Moreover, phospho kinase profiling and network analysis identified altered surface-phosphoprotein relationships, including a CD56-p70 S6 kinase association. Conclusions: These data provide new insights into Gαq-driven modulators of UM phenotype of relevance for studies of tumor–microenvironment interaction and metastasis. Full article

Radiation-induced biological responses emerge through complex interactions across multiple biological scales, ranging from molecular damage to tissue remodeling and organism-level outcomes. Although traditional radiobiology has primarily focused on DNA damage and linear dose–response relationships, increasing evidence suggests that radiation responses are highly context-dependent and cannot be fully explained by genomic alterations alone. In particular, low-dose and chronic radiation exposures often induce biological effects that involve dynamic regulatory processes beyond direct mutational burden. The narrative review proposes a conceptual multiscale framework for predictive radiobiology that integrates genomic damage, post-transcriptional regulation, network rewiring, and tissue microenvironmental interactions. Within this framework, “predictive radiobiology” refers to the integrative prediction of radiation-induced outcomes, including radiosensitivity, tissue remodeling, fibrosis progression, therapeutic response, and long-term carcinogenic risk. We discuss how radiation-induced signaling extends beyond DNA double-strand breaks to include RNA-binding protein-mediated regulation, adaptive network responses, and extracellular matrix-dependent cellular plasticity. Recent advances in multi-omics, single-cell analysis, spatial biology, and three-dimensional organotypic models have revealed that radiation responses are governed by interconnected molecular and tissue-level processes. Furthermore, artificial intelligence and systems-level computational approaches provide new opportunities for modeling non-linear and context-dependent radiation effects across biological scales. We further discuss current limitations, including data integration challenges, reproducibility issues, and the translational gap between experimental models and clinical applications. Collectively, this conceptual framework highlights the need for integrative and multiscale approaches to improve mechanistic understanding and predictive modeling in modern radiobiology. Full article

Lung organogenesis is orchestrated by dynamic epithelial–mesenchymal interactions during embryogenesis, yet the gene regulatory programs and signaling dynamics governing these processes in the pseudoglandular stage remain incompletely understood. In this study, we integrated spatial and single-cell transcriptomic data across embryonic developmental stages to systematically characterize epithelial and mesenchymal dynamics during lung development. To achieve more refined cell types at single-cell resolution in spatial transcriptomic data, we developed a bin-based deconvolution strategy that enabled high-precision cell-type assignment. We subsequently constructed a 3D spatiotemporal landscape of lung development and elucidated the molecular regulatory mechanisms underlying epithelial–mesenchymal maturation during lung morphogenesis. In addition, we analyzed transcription factor module activity, intercellular communication signaling, and predicted downstream target genes, while integrating public GWAS metadata to link developmental programs with lung cancer-related features. We observed pronounced stage-specific functional heterogeneity between the pseudoglandular and late embryonic stages. Notably, E13.5 emerged as a critical transition window, during which progenitor states shifted toward more mature cellular phenotypes. We reconstructed epithelial–mesenchymal interactions and uncovered coordinated rewiring of ligand–receptor signaling and transcriptional networks across developmental stages. Regulatory network analysis further identified temporally coordinated transcription factor modules centered on Tbx3, Tbx5, Gli1, Gata4/5, Foxa1/2, and Cebpa, which collectively orchestrated branching morphogenesis, epithelial patterning, and tissue stabilization. Integration with lung cancer genome-wide association data demonstrated that embryonic lung progenitor states exhibit strong associations with lung cancer-related transcriptional programs, particularly involving epithelial–mesenchymal plasticity and RNA-splicing pathways. Furthermore, TP53/HNRNP-mutant lung adenocarcinomas displayed embryonic-like molecular features associated with cytoskeletal remodeling and progenitor-state reactivation. Together, our study provided a spatiotemporally resolved framework of embryonic lung development and identifies a critical transition window linking lung morphogenesis, regulatory network remodeling, and cancer-associated epithelial plasticity. Full article

Single-cell RNA sequencing (scRNAseq) captures unique profiles of individual cells and uncovers cell-to-cell communication (CCC) through ligand–receptor (LR) interactions. Moreover, it reveals signalling mechanisms underlying cellular heterogeneity and complexity in downstream responses in healthy and disease states. In this work, we developed a composite computational pipeline to track CCC patterns in the tumour microenvironment (TME) during Multiple Myeloma (MM) progression as a case study. Three publicly available scRNAseq datasets were analysed using basic single-cell analytics and stage-specific CCC networks were reconstructed with CellChat, in a microenvironment-specific approach. Basic network analytics (CytoHubba) were performed to identify key cell nodes based on network topology metrics; differential network rewiring (DyNet) was performed to calculate rewired nodes. Follow-up analyses were conducted with NicheNet to investigate downstream responses and target genes influenced by CCC. Our network analyses highlighted dendritic cells (DCs), plasmacytoid DCs (pDCs), hematopoietic stem cells (HSCs), red pulp macrophages (RPMs), natural killer (NK) cells, and T and B cells as important cell nodes. Moreover, in neutrophils, the HLA-DRA–JUN–FOS was shown to play a key role in the progression of monoclonal gammopathies of uncertain significance (MGUS) to active MM by supporting cancer hallmarks and MM pathophysiology. To conclude, our work suggests an explanatory–computational pipeline that incorporates well-known frameworks in a hypothesis-driven scope, which leads to results relevant to the pathophysiology of MM. Full article

Among the transcriptional dependencies that sustain leukemic identity in acute myeloid leukemia (AML), the menin–KMT2A chromatin complex has emerged as a central regulatory node. The scaffold protein menin, encoded by MEN1, facilitates transcriptional activation of HOX and MEIS family genes during normal hematopoietic development. In AML, this physiologic and developmentally regulated role is co-opted to sustain constitutive HOX/MEIS-driven programs that block differentiation and maintain leukemic potential. Although dependency on menin is most clearly established in KMT2A-rearranged and NPM1-mutated AML, this vulnerability appears to arise from a shared transcriptional state characterized by persistent HOX activation rather than from any single genetic alteration. Pharmacologic disruption of the menin-KMT2A interaction collapses stemness-associated transcriptional networks, promotes myeloid differentiation, and attenuates leukemic self-renewal. Clinical activity observed with menin inhibitors provides translational validation of this dependency and establishes menin inhibition as a differentiation-based therapeutic strategy. In this review, we examine the molecular basis of menin-dependent transcriptional regulation in AML and its implications for therapeutic targeting with menin inhibitors and resistance to therapy. Full article

Ensuring global food security under accelerating climate change requires transformative approaches to crop improvement that extend beyond the limits of traditional breeding and gene editing. While domestication and modern agriculture have delivered substantial gains in productivity, these advances often came at the cost of genetic diversity, stress resilience, and developmental plasticity. Plants, however, inherently exhibit remarkable flexibility in their morphology and development, as evidenced by the vast diversity of organ shapes, cell types, and adaptive responses that have evolved across lineages. This natural design space provides a foundation for reimagining plant architecture using synthetic biology. Recent advances in plant synthetic biology, including programmable transcription factors, CRISPR-based regulatory systems, synthetic gene circuits, orthogonal signalling pathways, and plant artificial chromosomes, now enable precise, modular, and environmentally responsive manipulation of developmental processes. These tools allow researchers to rewire hormone pathways, tune quantitative gene expression, integrate multiple environmental signals, and create novel regulatory modules that operate independently of endogenous networks. Beyond understanding plant development, these capabilities open avenues for engineering crops with dynamic architectures, enhanced plasticity, and improved resilience to complex and fluctuating stresses. In this review, we synthesise insights from natural diversity, developmental biology, and synthetic regulatory engineering to outline how plant architecture can be rationally redesigned. We argue that integrating synthetic biology with modern breeding and modelling frameworks will be essential for generating the next generation of programmable crops; i.e., varieties capable of sustaining productivity and stability in an era of unprecedented environmental and geopolitical changes. Full article

Alzheimer’s disease (AD) is a multifactorial neurodegenerative disorder characterized by the simultaneous disruption of interconnected molecular pathways, yet the structural mechanisms underlying this transcriptional disintegration remain poorly characterized. To address this, we constructed condition-specific gene co-expression networks from DLPFC bulk RNA-seq data, using a mutual-information (MI) framework with infomap community partitioning. Functional enrichment of network communities via Ingenuity Pathway Analysis (IPA) identified GABAergic signaling, SNARE complex assembly, Synaptogenesis, and neurexin and neuroligin interactions as significantly overrepresented pathways. Integration of node degree with condition-specific average expression revealed coordinated topological centralization of key synaptic genes—including NRXN2, LRRTM1, DLGAP3, and SHANK1—alongside a widespread transcriptional downregulation in GABAergic and Synaptogenesis modules. A shortest-path analysis revealed a consistent expansion of intra-pathway distances across all evaluated canonical pathways in AD, a pattern statistically consistent with reduced local co-expression cohesion. These findings reframe Late-Onset Alzheimer’s Disease (LOAD) as an active structural-rewiring process, in which the observed topological centralization pattern seems to be consistent with a consolidation of co-expression around synaptic components, though we cannot exclude that shifts in cellular composition contribute to this signal in bulk RNA-seq data. Full article

Systemic lupus erythematosus (SLE) is a prototypic systemic autoimmune disorder arising from the convergence of genetic susceptibility, epigenetic remodeling, environmental exposures, and dysregulated immune networks. Although traditionally characterized by autoantibody production and immune complex mediated tissue injury, advances in genomics, systems immunology, and multi-omics profiling have revealed that lupus represents a multilayered failure of immune homeostasis driven by interconnected molecular circuits. Genetic variants enriched in regulatory immune enhancers establish a permissive transcriptional landscape that sensitizes innate nucleic acid sensing pathways and interferon signaling. Epigenetic remodeling further amplifies inflammatory transcriptional programs, while environmental triggers such as ultraviolet radiation and viral infection initiate bursts of nucleic acid release and immune activation. Defective apoptotic cell clearance, mediated in part by scavenger receptor dysfunction and complement abnormalities, increases the availability of immunogenic nucleic acids that engage pattern recognition receptors and drive chronic type I interferon production. This interferon-dominated environment rewires immune cell metabolism, alters differentiation trajectories of T and B lymphocytes, and sustains autoreactive immune circuits. Emerging multi-omics studies reveal distinct molecular endotypes defined by interferon signatures, metabolic states, and immune cell composition, highlighting the heterogeneity of disease mechanisms across patients. In this review, we integrate genetic, epigenetic, metabolic, and immunological insights to propose a systems-level model of lupus pathogenesis in which defective debris clearance, nucleic acid sensing, interferon amplification, and metabolic reprograming form a self-reinforcing pathogenic network. Understanding this integrated molecular architecture provides a foundation for biomarker-guided therapeutic strategies and precision medicine approaches aimed at disrupting the key nodes that sustain chronic autoimmunity in SLE. Full article

Designing informative descriptors for time-varying complex networks is important for characterizing structural reconfiguration in evolving graph data. This paper introduces a thermodynamics-inspired framework for dynamic graph analysis, centered on Spectral Core Entropy (SCE), node energy, internal energy, and a temperature-like reconfiguration index. These quantities provide a compact representation of how graph organization changes over time. We apply this framework to resting-state fMRI data from autism spectrum disorder (ASD) and control subjects. At the event level, the temperature index shows a statistically significant but modest association with low-SSIM reconfiguration events, indicating that it serves as a weak yet reproducible marker of rapid network change. On controlled synthetic dynamic graphs, the framework exhibits regime-dependent sensitivity: spectral-core change is more informative under rewiring, whereas the temperature index is more informative under gain modulation. At the node level, node energy highlights regional differences between ASD and control groups, providing interpretable neuroscientific context for dynamic brain connectivity. Overall, the proposed framework provides a promising and computationally tractable approach for characterizing reconfiguration patterns in dynamic brain networks and other evolving complex systems. Full article

Glycolysis is a defining feature of cancer metabolism, originally described by the Warburg effect. Increasing evidence indicates that cancer-associated glycolysis is not uniformly upregulated but dynamically rewired in response to oncogenic signaling, cellular demands, and microenvironmental cues. However, a framework integrating its temporal evolution and functional roles across tumorigenesis remains limited. In particular, how glycolytic rewiring drives malignant transformation, adapts during tumor progression, and generates context-dependent vulnerabilities has not been systematically synthesized. In this review, we examine glycolysis as a dynamic metabolic network evolving throughout tumor development. We discuss how early glycolytic rewiring, driven by oncogenic signaling and metabolic–epigenetic coupling, supports cell fate transitions and establishes redox and biosynthetic capacity during tumorigenesis. We then outline how glycolysis is remodeled during tumor progression through coordinated transcriptional, epigenetic, and post-translational regulation, as well as microenvironmental interactions and metabolic heterogeneity. Furthermore, we highlight glycolysis as an integrative hub linking immune evasion, cell death regulation, and metabolic plasticity, and discuss how glycolytic rewiring creates context-dependent metabolic dependencies that may be therapeutically exploited, along with emerging technologies that enable high-resolution characterization of tumor metabolism. Together, this review provides a conceptual framework for understanding glycolytic rewiring in cancer and outlines potential avenues for targeting metabolic vulnerabilities. Full article

Intensive ornamental planting is increasingly prevalent in urban green spaces, yet its effects on soil microbial community assembly and interaction networks remain poorly understood. Here, we examined shifts in soil properties, microbial diversity, community composition, and interaction networks across successive planting cycles. Bacterial alpha-diversity remained relatively stable, whereas fungal communities showed pronounced sensitivity to early planting stages. Beta-diversity analyses revealed that bacterial community composition was jointly influenced by planting stage and site type, while fungal communities were primarily structured by site characteristics. Co-occurrence network analysis revealed contrasting reassembly trajectories between microbial groups. Bacterial networks exhibited increasing complexity and modularity, indicating enhanced interaction intensity and competitive structuring under intensive management. In contrast, fungal networks displayed reduced connectivity but maintained or recovered modular organization, suggesting structural buffering. Notably, keystone taxa remained taxonomically conserved, indicating that network reorganization was driven by interaction rewiring rather than species turnover. We propose a dual-speed reassembly framework in which bacteria function as fast-responding components with dynamic interaction networks, whereas fungi act as slow-buffering, structurally persistent elements. This decoupling of short-term functional responsiveness and long-term stability provides new insights into how intensive management reshapes soil microbiomes in botanical garden ecosystems. Full article

Lung cancer remains the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer accounting for the majority of cases and exhibiting persistent challenges related to therapy resistance and metastatic progression. Increasing evidence indicates that dysregulated G protein-coupled receptor signaling and ion channel activity function cooperatively as master regulators of tumor cell proliferation, migration, survival, and therapeutic response. Cannabinoids, including phytocannabinoids such as delta-9-tetrahydrocannabinol and cannabidiol, as well as endogenous endocannabinoids, are uniquely positioned to modulate both G protein-coupled receptors and ion channels, thereby influencing key oncogenic signaling networks. This review synthesizes current knowledge on the role of major ion channel families, including transient receptor potential channels, potassium channels, and sodium channels, and principal G protein-coupled receptor pathways involved in lung cancer progression. We further discuss how cannabinoids reprogram these interconnected signaling systems through canonical cannabinoid receptors, non-classical targets such as G protein-coupled receptor 55 and adenosine receptors, and direct modulation of ion channel activity. Special attention is given to G protein-coupled receptor–ion channel coupling within membrane microdomains and to the capacity of cannabinoids to act as biased ligands, redirecting downstream pathways, such as the phosphoinositide 3-kinase–protein kinase B–mechanistic target of rapamycin and epidermal growth factor receptor signaling, toward apoptosis and reduced metastatic potential. Emerging strategies, including cannabinoid-based combination therapies, selective receptor biasing, and targeted delivery systems, are also highlighted. Altogether, cannabinoid-driven rewiring of G protein-coupled receptor and ion channel signaling represents a promising mechanistic framework for developing innovative therapeutic approaches against lung cancer. Full article

Soil salinization is a major threat to global crop production. Tartary buckwheat (Fagopyrum tataricum), valued for its hardiness in marginal environments, provides an excellent system for studying plant salt tolerance. Using an integrated multi-omics approach, we deciphered the physiological, metabolic, and transcriptional responses of Tartary buckwheat to prolonged NaCl stress. Physiological profiling confirmed membrane damage alongside osmotic adjustment via proline accumulation and a phased antioxidant response. Metabolomics revealed extensive reprogramming, with dynamic enrichment in pathways of flavonoid biosynthesis, lipid metabolism, and the TCA cycle. Transcriptomics delineated a time-specific cascade from early signaling to late defense activation. Critical regulators within ABA and MAPK signaling pathways showed fine-tuned, divergent expression; for instance, SnRK2.3 was suppressed while specific PP2Cs were induced, and FtMAPK10 was dramatically up-regulated. Integrated analysis demonstrated coordinated induction of osmoprotectant synthesis (e.g., galactinol and betaine pathways) and a rewiring of central carbon metabolism. Our findings reveal a sophisticated, multi-layered adaptation strategy in Tartary buckwheat, integrating enhanced osmolyte production, antioxidant defense, membrane remodeling, and metabolic reprogramming, orchestrated by key signaling networks. This study provides a comprehensive molecular framework for salt tolerance and identifies valuable genetic targets for improving crop resilience. Full article

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