The increasing sophistication of cyber threats has led to the identification of some major shortcomings associated with honeypots, which include staticness, inflexibility, and vulnerability to fingerprinting. The proposed work aims at overcoming the aforementioned shortcomings by creating an Explainability-Driven Adaptive Cyber Deception Control System capable of engaging in intelligent, interactive interactions with cyber attackers. The key goal of the proposed solution is to improve threat intelligence gathering and deception efficiency by leveraging the benefits of adaptability and explainability. Machine learning, XAI, behavioral profiling, and environment mutation are the four key components that form the backbone of the proposed pipeline system. A Random Forest classifier is used for classification of normal and malicious sessions based on behavioral features at the level of commands. An explainability-driven metric known as the Feature Dominance Deception Index (FDDI) is developed to guide deception approaches, whereas Behavioral Convergence Score (BCS) is considered to assess behavioral convergence of attackers. Intent recognition using kill chain methodology allows generating responses in context-dependent fashion, while the mutation engine creates unique environments in each session to prevent fingerprinting attacks. Furthermore, Reinforcement Learning (RL) layer based on Q-learning is added to the framework to adaptively make decisions by learning the best possible deception tactics over multiple sessions. The Deception Quality Score (DQS) metric is used to measure the quality of deception within each session. Moreover, the UNSW-NB15 network intrusion data set is employed for validating the proposed model. Through benchmarking based on the generated behavioral dataset, the Random Forest-based behavioral profiler yielded a classification accuracy of 90.0%, recall of 85.7%, and an F1-score of 92.3%. Thereafter, the end-to-end deployment of the proposed framework through Cowrie honeypot sessions yielded better deception effectiveness, giving a framework-level attack classification accuracy of 90.0% and a 77.0% improvement in threat intelligence extraction per session than baseline Cowrie deployment. Kill chain stages were identified for the evaluated cases, deception goals were accomplished for all sessions under testing, fingerprinting efforts by the attacker were unsuccessful, and high-quality deception was maintained. The reward per session for the RL agent ranges from + 0 to + 14.0 for different session types, resulting in the formation of a converged Q-table containing values of 21 out of 90 possible states. Additionally, the technique ensures the resistance against honeypot fingerprinting, and demonstrates resistance against evaluated fingerprinting attempts. As far as it is currently known, few previous works can be found which have managed to include explainable scoring, convergence of behavior analysis, adaptive control, environment mutation, and reinforcement learning into one cyber deception framework. The presented framework manages to incorporate all of these features while still preserving transparency and adaptability during the whole process of deception. The research makes advances in the current state-of-the-art research by enabling passive honeypots to become intelligent autonomous systems for detecting cyber threats.
Understanding how sustained warming reshapes ecological boundaries remains a critical challenge in climate-ecosystem research, particularly across climate-sensitive regions of sub-Saharan Africa. Although temperature increases and land-cover change have been widely documented, the causal mechanisms linking climate variability to the redistribution of agro-ecological zones (AEZs) remain limited. Here we integrate multi-decadal land surface temperature (LST) and precipitation (PRE) datasets (1975-2025) with correlation analysis and nonlinear causal inference methods, including Extended Convergent Cross Mapping (ECCM) and its geographical extension (EGCCM), to examine climate-driven ecological restructuring in Ghana. Results reveal a significant warming trend of +2.39°C (≈0.47°C decade-1; p < 0.05), while precipitation shows strong inter-decadal variability without a significant long-term trend. During the same period, savanna systems expanded from 74,945 km2 to 128,355 km2, whereas forest ecosystems declined from 91,895 km2 to 60,070 km2. ECCM and EGCCM analyses identify temperature as the dominant driver of AEZ redistribution, demonstrating that sustained warming is reorganizing ecological boundaries and accelerating savanna-forest transitions across West Africa. These findings provide a causal and spatially explicit basis for prioritizing climate adaptation and land management strategies in regions most vulnerable to warming-driven ecological change.
Acute myeloid leukemia (AML) often coincides with type 2 diabetes mellitus (T2D), yet the impact of metabolic comorbidity on leukaemic biology and prognosis remains underexplored. We integrated two transcriptome-derived stemness indices (mRNAsi, EREG.mRNAsi) with weighted gene-co-expression network analysis (WGCNA) in 137 TCGA-LAML samples. Higher stemness scores clustered with adverse cytogenetics, and a stemness-linked "brown" module yielded 13 candidate genes. We prospectively enrolled 152 newly diagnosed AML patients with confirmed T2D. Peripheral-blood RNA quantification placed patients into INTS7-high and INTS7-low groups by cohort median. INTS7 not only predicted overall survival in TCGA but also retained significance in four independent GEO cohorts (total n = 306). Importantly, GWASdb ranks INTS7 as a significant T2D locus (standardised score ≈ 0.57), suggesting shared pathogenic circuits between dysglycaemia and leukaemia. Baseline demographics, BMI, counts and ELN-2022 risk were balanced, yet complete-remission after induction therapy was 41% versus 96% (p < 0.001). Kaplan-Meier analysis showed that high INTS7 expression halved median overall (18.2 vs 41.3 months) and progression-free survival (10.1 vs 24.8 months); multivariable Cox models confirmed independent adverse impact (HR_OS = 2.05; HR_PFS = 2.33). The effect size exceeded that in unselected AML, indicating that diabetic physiology amplifies INTS7-driven chemoresistance. Our integrative approach-linking stemness metrics, network modelling and comorbidity-focused validation-identifies INTS7 as a biomarker at the nexus of metabolic disorder and leukaemic aggressiveness. Quantifying INTS7 could refine risk stratification and inspire metabolism-tailored therapeutic strategies for the growing AML-T2D population.
γδ T cells represent a promising avenue for cancer immunotherapy. The Vγ9Vδ2 T-cell receptor (TCR), which is expressed by the predominant subset of γδ T cells, responds to phosphoantigen (pAg)-engaged butyrophilins (BTNs) on various cancer cells. However, the molecular mechanism underlying the pAg-mediated activation of Vγ9Vδ2 TCRs remains a subject of debate. Here, we employed an integrative approach to elucidate the mechanism of pAg reactivity in Vγ9Vδ2 T cells. Our results demonstrate that BTNs form higher-order oligomers in the absence of pAg. Upon pAg binding, these higher-order oligomers dissociate into separate tetramers, enabling Vγ9Vδ2 TCR engagement. This pAg-induced dissociation of higher-order BTN oligomers is critical for pAg-mediated activation of γδ T cells. Our findings reveal a mechanism of BTN higher-order oligomer dissociation-driven pAg sensing, providing valuable insight for future immunotherapeutic strategies.
The Three Gorges Dam (TGD), the largest hydraulic project on the Yangtze River mainstream, has altered hydrodynamic regimes and sediment transport. However, how its interplay with basin-scale pollution control policies regulates heavy metal behavior remains poorly understood. The spatiotemporal dynamics of eight priority heavy metals (As, Cd, Cr, Cu, Ni, Pb, Zn, Mn) in surface water and sediments along the Yangtze mainstream from 2016 to 2022 were investigated, with the TGD employed as a critical hydrological boundary. Sediment-water partition coefficients were calculated, geo‑accumulation and health risk indices were applied, and source apportionment was performed using positive matrix factorization. The results showed that most metals peaked in 2018 and subsequently declined, a pattern that coincides with the phased implementation of the Yangtze River Protection Strategy and suggests a measurable response to strengthened pollution controls. Arsenic, however, exhibited increasing upstream and decreasing downstream, highlighting its redox sensitivity to dam-altered hydrodynamics. TGD operation created two distinct geochemical regimes: sedimentary sequestration upstream and enhanced aqueous mobilization downstream. Partition coefficients increased over time, with Cr showing an 80.41% rise upstream (2016 vs 2022), indicating strengthened sedimentary retention. Source apportionment revealed a transition from direct industrial inputs upstream to mixed sources dominated by legacy contamination resuspension downstream. Non‑carcinogenic health risk assessments identified Cr and As as key drivers, with children facing potential risks primarily through dermal contact. These findings establish a dual-control framework wherein policy interventions reduce overall metal loads while dam regulation redistributes metals and associated risks across sediment-water phases, necessitating spatially differentiated management strategies for large regulated rivers.
Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) is a complex chronic inflammatory disease involving mucosal barrier defects, neurogenic inflammation, mast cell activation, and tissue remodeling. Current diagnosis lacks objective biomarkers, relying instead on symptom assessment and exclusion of confusable diseases. Cystoscopy is recommended for identifying Hunner lesions per ESSIC/AUA guidelines, and first-line intravesical GAG replacement therapies only achieve sustained response rates of 20-38% at 12 months. Medical-engineering integration and materials innovations propel IC management toward multi-scale regulation and intelligent repair. This article proposes intelligent materials and systems to advance IC from stepwise diagnosis to an integrated "sensing-feedback-execution" closed loop. At the diagnostic level, it integrates medical phenotyping with engineering multidimensional detection to construct a synergistic system of subjective symptoms, objective molecules, and physical function: attomolar long non-coding RNA(lncRNA) detection via CRISPR-graphene transistors, glycosaminoglycans (GAGs) quantification with 13C-FNDs, and real-time bladder monitoring with flexible electrodes and wireless sensing. At the therapeutic level, centering on targeted delivery, dynamic repair, functional remodeling, it integrates four pathways: inflammation-responsive engineered bacteria; DNA nanocages and dual-responsive hydrogels for precise release; bioprinted patches and digital twin scaffolds for personalized repair; and trimodal combination therapy for mucosa, inflammation, and fibrosis. It further envisions Material Intelligence Agent systems medicine integrating nanosensors, quantum computing, and micro-nanorobots to construct a cross-scale diagnostic and therapeutic closed loop, with dynamic individualized intervention via digital twins. This paper provides a systematic technological pathway for precision IC management and a transferable medical-engineering integration paradigm for chronic inflammatory diseases.
Participatory design approaches to develop global health interventions have gained traction in recent years. However, while broad frameworks for the application of design approaches exist, literature outlining concrete experiences of and guidance for navigating specific design challenges remains limited. This article proposes a design framework based on our own experiences when applying human-centered design principles to bolster sustainable hypertension medication financing in rural Uganda. In our case study, we embedded an mHealth platform within a community-led health intervention aiming to fund blood pressure medication through a shared financial pooling system. Over the course of this project and in close collaboration with intervention end-users, we developed a design framework that outlines challenges, decision-making processes, and design solutions. We iteratively refined the framework between November 2021 and April 2023 over two phases of extensive formative research and one phase of structured qualitative data collection, consisting of 55 in-depth interviews and 4 focus group discussions with clients with hypertension, health care providers, and key intervention stakeholders. The resulting MILEPOST framework consists of 7 domains to consider amid mHealth intervention co-development: medical (e.g., health challenge scope and management); interpersonal (e.g., communication, decision-making, and trust within and across communities); logistical (e.g., current processes and sustainable implementation pathways); ethical (e.g., research and implementation ethics); political (e.g., stakeholder buy-in and long-term support); scientific (e.g., rigorous and feasible approaches for data collection, management, and analysis); and technical (e.g., mHealth component development, refinement, and implementation). We define each domain, provide examples of design challenges and derived solutions as they emerged in our work, and outline starting points for operationalizing each domain in other contexts or studies. We discuss how MILEPOST can provide guidance to researchers and implementors by contributing specific and actionable insights for participatory health intervention design efforts.
Coastal ecosystems are vital for biodiversity but are increasingly threatened by urbanisation and pollution, which significantly alter local microbial communities. This study assessed bacterial diversity and functional profiles in urban and island beaches in Belém, Brazil. Urban beaches showed significantly higher microbial diversity and evenness, alongside functional plasticity due to pollutant input, while island beaches hosted more specialised and stable communities. Taxonomic analysis revealed the significant enrichment of opportunistic genera such as Comamonas, Clostridium and Paenibacillus in urban areas, and the massive dominance of Prochlorococcus and Candidatus Pelagibacter in island sites. Furthermore, shotgun metagenomics identified a robust genomic potential for xenobiotic degradation and antibiotic resistance in urban microbiomes, whereas island microbiomes were significantly enriched in genes for energy production and biosynthesis. These results underscore the ecological divergence between anthropogenically impacted and natural coastal environments, highlighting the importance of microbiome monitoring for sustainable coastal management.
BYSL gene encodes the bystin-like (BYSL) protein, a nucleolar protein involved in eukaryotic ribosome biogenesis and essential for 40S ribosomal subunit synthesis. Although BYSL upregulation has been implicated in hepatocellular carcinoma, its mechanistic contribution to tumor progression remains undefined. We observed that BYSL is consistently upregulated across multiple cancer types and is associated with adverse clinicopathological features and poor prognosis, with the strongest clinical relevance observed in hepatocellular carcinoma through the integrative transcriptomic and proteomic analyses. BYSL-knockout suppresses malignant phenotypes, including proliferation, migration, and invasion, and induced G1/S arrest and apoptosis. Mechanistically, loss of BYSL disrupts nucleolar homeostasis and reduces global protein synthesis, thereby activating the RPL5/RPL11-MDM2-p53 axis, leading to p53 stabilization and tumor suppression. Importantly, MYC directly bound to the BYSL promoter and transcriptionally activated its expression, whereas co-targeting BYSL and MYC produced more synergistic antitumor effects than either intervention alone. Collectively, our study reveals that BYSL acts as a pivotal downstream mediator of MYC-regulated ribosome biogenesis and promotes hepatocellular carcinoma progression. Our findings suggest that BYSL may represent a potential therapeutic target for hepatocellular carcinoma; nevertheless, additional in vivo preclinical studies are warranted to validate its translational prospects.
Current research on the consensus problem in hybrid game-based multi-agent systems (HGBMASs) faces limitations such as insufficient engineering modularity, idealized topological assumptions, and passive game mechanisms. To overcome these issues, this paper introduces an enhanced framework that integrates topology with game theory and incorporates hierarchical macro-regulation. The main contributions are threefold: (i) Achieving modular decoupling between the game decision layer and the system dynamics layer to meet the heterogeneous modular requirements of practical engineering; (ii) Moving beyond merely verifying consensus existence to actively designing systems for it. This involves adapting the game mechanism to topological structures via connectivity-based partitioning (exclusive vs. common parts); (iii) An enhanced consensus mechanism is established, wherein agents in the common part converge to the convex combinations of the exclusive parts, while the game mechanism drives shrinkage via Nash equilibrium; the entire scheme applies to networks with non-negative edge weights. The hierarchical structure enables game strategies to regulate the system via the novel perspective in this paper, which constitutes an extra mechanism-based way to achieve game regulation and enriches the implementation paths of game theory as another regulatory measure for the system. Although employing standard quadratic cost functions and Nash equilibrium solutions, this work redefines their design logic (linking them to topology partitioning) and elevates their objective from individual optimization to global coordination. This establishes a closed-loop where "topology guides game design, and the game optimizes topological functionality." Theoretical analysis and simulations confirm the framework's effectiveness in solving multi-agent consensus problems, highlighting its potential for applications like air traffic control and human-machine collaborative systems.
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The expansion of artificial intelligence (AI) in healthcare is often framed as a response to clinical needs and system inefficiencies; however, its alignment with professional values and everyday practice remains less examined. This study examines the role of AI in dentistry through qualitative semi-structured interviews with 16 dentists in active clinical practice, conducted in Chile in 2025, focusing on how practitioners evaluate AI in relation to their professional ethos. The findings show that dentistry is understood as a moral practice centered on relational care and situated judgment. While technology has historically supported this ethos, the processes of marketization, bureaucratization, and acceleration increasingly undermine its conditions. AI is not simply experienced as being beneficial or harmful. Applications that reduce administrative burden or support diagnostic tasks are often welcomed by practitioners, and in isolation, this assessment is reasonable. However, when examined at the level of the sociotechnical system in which they are embedded, even tools that appear to return time to the clinical encounter operate within institutional arrangements that simultaneously compress time and intensify productivity demands. The study further argues that AI development does not consistently follow articulated clinical needs but is shaped by data availability and technical feasibility. By foregrounding professional ethos as an evaluative lens, this article offers a practice-centered approach to assess when AI may support, distort, or fail to address the aims of healthcare.
The first baseline assessment of organophosphate flame retardants (OPFRs) in an Indian riverine micro-watershed revealed Σ12OPFR concentrations ranging from 366.0-107,958.6 ng/L (mean: 9,434.0 ng/L) in surface water and from 11.2-4,002.5 ng/g (mean: 869.7 ng/g) in surface sediments. Surface water exhibited significantly higher Σ12OPFRs (p < 0.05) in the urban upstream of Nag and Pili rivers. Alkyl OPFRs were the dominant class in both surface water and sediments. However, a few outlier TPhP concentrations at the upstream of the Nag river suggested localized inputs associated with recreational activities. Principal component analysis, compositional profiles and field observations identified possible source signatures in surface water and sediments, and identified TPhP, TDCIPP, TCIEP, TiPrP, TPrP, TCIPP, TEP and TnBP as potential indicators of municipal wastewater, industrial discharges, polymer-processing activities, urban runoff, and plastic waste inputs. Sediment-water partitioning revealed higher aqueous mobility for TMP and TEP, suggesting potential urban activities and wastewater-derived inputs. Field-derived partitioning indicators (logKOC vs. logKOC') and fugacity fraction (ff) suggested that relatively hydrophilic TMP and TEP preferentially remained in the aqueous phase. In contrast, relatively hydrophobic TDCIPP, TnBP, and TPhP exhibited potential for sediment accumulation. High ecological risk (RQ > 1) for TPhP to fish, invertebrates, and algae at specific sites, while moderate risk for TDCIPP, ToCP, TpCP, and TnBP suggested potential bioaccumulation and trophic transfer. The estimated annual riverine input of Σ12OPFRs was 1.2 t/y, suggesting that urban micro-watersheds are important contributors of emerging contaminants, such as OPFRs, to larger tropical river basins.
Colorectal cancer (CRC) progression involves complex gene regulatory interactions, yet conventional analyses often overlook genes that, while not canonical drivers, may contribute to network-level instability. In this study, we propose a dynamic computational framework that integrates time-series co-expression networks with an autoregressive neural network and local network entropy (ARNN-LNE) to quantify perturbation-induced changes in gene regulatory stability. Rather than relying on static correlation patterns, this framework evaluates how in silico perturbations influence network entropy to identify network-sensitive genes.Applying this approach to CRC datasets, we identify a set of candidate sensitive genes, including MATCAP1, FAM107B, SNX24, and SLC26A2 in human, and mt-Co1 in mouse, which exhibit pronounced entropy responses under perturbation conditions. These genes are not readily captured by conventional differential expression analysis, suggesting complementary information from a network dynamics perspective. Cross-species analysis further indicates partial consistency in identified sensitive genes across human and mouse datasets, supporting the robustness of the proposed framework.Furthermore, expression-based classification analysis suggests that these genes exhibit moderate discriminative ability for distinguishing disease states (ROC-AUC ≈ 0.86; PR-AUC ≈ 0.76), indicating potential utility for further investigation. Overall, this study presents perturbation-entropy profiling as a computational framework for identifying network-sensitive genes, providing a hypothesis-generating approach for exploring gene regulatory dynamics in cancer systems biology.
This study presents a systematic comparative evaluation of ten regression-based machine-learning models for day-ahead photovoltaic (PV) energy forecasting under semi-arid climatic conditions. The analysis is conducted using a limited six month dataset (May-October 2024) of real operational production data obtained from a 22 MW grid-connected PV plant in Nakhchivan, Azerbaijan, integrated with key meteorological variables including solar irradiance, air temperature, relative humidity, and wind speed. Linear Regression, Ridge, Lasso, ElasticNet, Support Vector Regression (SVR), Decision Tree, Random Forest, Gradient Boosting, XGBoost, and a Multi-Layer Perceptron (MLP) were benchmarked using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and five-fold cross-validated R2. The results indicate that regularised linear models-particularly Lasso Regression-provide the most consistent balance between predictive accuracy and generalisation stability under moderate data availability, while Random Forest demonstrates strong cross-validated robustness and the MLP exhibits overfitting behaviour, highlighting sensitivity to limited training data. These findings demonstrate that increased model complexity does not necessarily translate into improved forecasting reliability in data-constrained semi-arid environments. The novelty of this work lies in its unified benchmarking framework that simultaneously evaluates predictive accuracy, interpretability, and generalisation performance using real-world utility-scale operational data. By explicitly linking forecasting reliability with sustainability-oriented planning, the study contributes to more reliable life-cycle cost and emission assessment of large-scale PV systems in emerging renewable-energy markets. The findings offer practical guidance for energy planners and policy-makers seeking transparent, computationally efficient forecasting strategies in semi-arid climates.
Polycystic ovary syndrome (PCOS) is a hormonal disorder marked by irregular menstrual cycles, elevated androgen levels, ovarian cysts, hirsutism, acne and other symptoms. While conventional medications such as Metformin and Spironolactone are commonly prescribed, they are often associated with undesirable side effects. As a result, there is growing interest in alternative treatments, particularly those involving medicinal plants. Vitex negundo L., a member of the Lamiaceae family, has demonstrated promising therapeutic effects against PCOS. However, its precise molecular mechanism of action remains unclear. To explore this, we conducted an untargeted metabolomics analysis using UPLC-MS/MS to identify bioactive compounds, followed by network pharmacology to elucidate potential mechanisms. Metabolite fingerprinting revealed 186 metabolites, among which 122 were identified as secondary metabolites. Network pharmacology analysis uncovered 910 potential targets associated with the identified compounds and 297 known PCOS-related disease targets, with 50 overlapping targets between the two datasets. Key hub targets identified included P53, ESR1, AKT1, STAT3, CTNNB1, ERBB2, BCL2, EGFR, MTOR and IL6. Furthermore, molecular docking highlighted several bioactive constituents-syringin, 4-(3,4-dihydroxyphenyl)-6,7-dihydroxynaphthalene-2-carboxylic acid, and Isovitexin-as potential lead compounds for PCOS treatment. Further evaluation of lead compounds can be conducted through in vitro and in vivo studies.
Hypertrophic cardiomyopathy (HCM), the most prevalent inherited cardiovascular disease, is strongly linked to progressive heart failure and sudden cardiac death (SCD). However, its underlying pathogenic mechanisms remain incompletely understood, and effective therapeutic strategies are still lacking. Here, we established two murine HCM models harboring high SCD risk-associated mutations. Single-cell RNA sequencing revealed immune activation and enhanced fibrotic remodeling in the myocardium of these models. Therefore, we hypothesized that colchicine, a widely used anti-inflammatory drug known to reduce cardiovascular events in multiple cardiac disorders, may also represent a promising therapeutic candidate for HCM. As we expected, colchicine treatment attenuated pathological remodeling in our study, as evidenced by reduced cardiomyocyte hypertrophy, decreased fibrosis, and downregulation of cardiac stress markers (Anp, Bnp) and fibrotic mediators (Ctgf, Col1a1, Col3a1). In addition, colchicine attenuated pro-inflammatory macrophage populations and suppressed IL-6 expression, thereby contributing to the preservation of cardiac function. These findings provide the first preclinical evidence that colchicine alleviates myocardial inflammation and fibrosis in HCM, underscoring its potential as a novel therapeutic strategy to reduce fibrosis, lower SCD risk, and improve patient outcomes.
The pathological progression of ischemic stroke is driven by a dynamic signaling network mediated by protein-protein interactions (PPI) that involves excitotoxicity, oxidative stress, neuroinflammation, and regulated cell death (RCD), ultimately leading to neurological dysfunctions. Instead of functioning independently, these PPI-driven pathways engage in extensive cross-talk, creating cycles that exacerbate the injury over both space and time. Therapeutic strategies designed to disrupt key nodal PPIs, comprising small molecule inhibitors, peptide mimetics, and chimeras targeting proteolysis (PROTACs), have shown promise. However, clinical translation faces several major challenges, including the structural complexity of PPIs, the efficiency of blood-brain barrier (BBB) penetration, and the adaptive, multifactorial nature of the ischemic cascade. Emerging approaches are now shifting from single-target inhibition to network-level intervention, utilizing artificial intelligence (AI)-guided drug development, multi-target PPI regulators, and context-responsive delivery systems to achieve spatiotemporal precision. Through the integration of multidisciplinary technologies and mechanism-driven innovative designs, PPI-targeted strategies provide a promising approach to reprogramming the ischemic brain for repair, moving beyond traditional neuroprotection to dynamic network medicine.
Noncanonical inflammasomes have recently emerged as critical regulators of macrophage-driven inflammatory responses, however, their molecular functions remain incompletely defined. Calmodulin (CaM)-lysine N-methyltransferase (CAMKMT) has been identified as a novel nonhistone protein methyltransferase responsible for CaM methylation, yet the contribution of CAMKMT-dependent CaM methylation to inflammatory responses has not been explored. In this study, we investigated the anti-inflammatory role of CAMKMT-mediated CaM methylation in caspase-11 noncanonical inflammasome-activated inflammatory responses in macrophages. Activation of the caspase-11 noncanonical inflammasome in J774A.1 macrophages markedly reduced CAMKMT expression, CaM trimethylation at lysine 116 (Tme-K), and the activation of the CaM-dependent kinases CaMKK2 and CaMKIV. Enforced expression of CAMKMT significantly attenuated caspase-11 noncanonical inflammasome-driven inflammatory responses and restored CaM Tme-K level and CaMKK2 and CaMKIV activation suppressed by inflammasome signaling. Similarly, CaM inhibited caspase-11 noncanonical inflammasome-mediated inflammatory responses, whereas CaM lysine 116 mutant (CaM K116A) abrogated CaMKK2 and CaMKIV activation and exacerbated inflammasome-driven inflammation in J774A.1 cells. In vivo, CaM conferred protection against LPS-induced acute lethal sepsis, as evidenced by improved survival, reduced murine sepsis scores, lower serum pro-inflammatory cytokine levels, and diminished pyroptotic cell death, in contrast, CaM K116A failed to provide such protection. Collectively, these findings identify the CAMKMT-CaM Tme-K axis as a critical anti-inflammatory regulator of caspase-11 noncanonical inflammasome-driven inflammatory responses in macrophages.
Hepatocellular carcinoma (HCC) remains a major global malignancy with high incidence and poor survival rates. Etiology-driven metabolic-immune interactions have emerged as a unifying framework for deciphering hepatocarcinogenesis. Hepatitis B virus (HBV) and metabolic dysfunction-associated steatotic liver disease (MASLD) are the two predominant etiological drivers for HCC. Although immunotherapy serves as a cornerstone of HCC treatment, therapeutic response varies considerably across distinct etiologies. Accumulating evidence suggests that etiology-specific dysregulation of lipid metabolism shapes immune imbalance in the tumor microenvironment, thereby accounting for the heterogeneous responses to immunotherapy. Therefore, deciphering etiology-driven metabolic-immune crosstalk may provide new opportunities to improve therapeutic responses. In this review, we propose the framework of etiology-driven metabolic-immune interactions by comparing two major etiologies, HBV and MASLD. Moreover, taking into account the crucial role of lipid metabolism in hepatocarcinogenesis, dietary interventions and lipid metabolism-targeted therapies have also been included so as to offer potential therapeutic strategies.