Search Results (736)

Microalgae, with their unparalleled capabilities for sunlight-driven growth, CO₂ fixation, and synthesis of diverse high-value compounds, represent sustainable cell factories for a circular bioeconomy. However, industrial deployment has been hindered by biological constraints and the inadequacy of conventional genetic tools. The advent of CRISPR-Cas systems initially provided precise gene editing via targeted DNA cleavage. This review argues that the true transformative potential lies in moving decisively beyond cutting to harness CRISPR as a versatile synthetic biology “Swiss Army Knife”. We synthesize the rapid evolution of CRISPR-derived tools—including transcriptional modulators (CRISPRa/i), epigenome editors, base/prime editors, multiplexed systems, and biosensor-integrated logic gates—and their revolutionary applications in microalgal engineering. These tools enable tunable gene expression, stable epigenetic reprogramming, DSB-free nucleotide-level precision editing, coordinated rewiring of complex metabolic networks, and dynamic, autonomous control in response to environmental cues. We critically evaluate their deployment to enhance photosynthesis, boost lipid/biofuel production, engineer high-value compound pathways (carotenoids, PUFAs, proteins), improve stress resilience, and optimize carbon utilization. Persistent challenges—species-specific tool optimization, delivery efficiency, genetic stability, scalability, and biosafety—are analyzed, alongside emerging solutions and future directions integrating AI, automation, and multi-omics. The strategic integration of this CRISPR toolkit unlocks the potential to engineer robust, high-productivity microalgal cell factories, finally realizing their promise as sustainable platforms for next-generation biomanufacturing. Full article

Background/Objectives: Myelodysplastic syndrome (MDS) is a heterogeneous clonal hematopoietic disorder characterized by ineffective hematopoiesis and leukemic transformation risk. Current therapies show limited efficacy, with ~50% of patients failing hypomethylating agents. This review aims to synthesize recent discoveries through an integrated network model and examine translation into precision therapeutic approaches. Methods: We reviewed breakthrough discoveries from the past three years, analyzing single-cell multi-omics technologies, epitranscriptomics, stem cell architecture analysis, and precision medicine approaches. We examined cell-type-specific splicing aberrations, distinct stem cell architectures, epitranscriptomic modifications, and microenvironmental alterations in MDS pathogenesis. Results: Four interconnected mechanisms drive MDS: genetic alterations (splicing factor mutations), aberrant stem cell architecture (CMP-pattern vs. GMP-pattern), epitranscriptomic dysregulation involving pseudouridine-modified tRNA-derived fragments, and microenvironmental changes. Splicing aberrations show cell-type specificity, with SF3B1 mutations preferentially affecting erythroid lineages. Stem cell architectures predict therapeutic responses, with CMP-pattern MDS achieving superior venetoclax response rates (>70%) versus GMP-pattern MDS (<30%). Epitranscriptomic alterations provide independent prognostic information, while microenvironmental changes mediate treatment resistance. Conclusions: These advances represent a paradigm shift toward personalized MDS medicine, moving from single-biomarker to comprehensive molecular profiling guiding multi-target strategies. While challenges remain in standardizing molecular profiling and developing clinical decision algorithms, this systems-level understanding provides a foundation for precision oncology implementation and overcoming current therapeutic limitations. Full article

Pancreatic cancer is frequently accompanied by cancer-associated cachexia, a debilitating metabolic syndrome marked by progressive skeletal muscle wasting and systemic metabolic dysfunction. This study presents a systems biology framework to simultaneously identify therapeutic targets for both pancreatic ductal adenocarcinoma (PDAC) and its associated cachexia (PDAC-CX), using cell-specific genome-scale metabolic models (GSMMs). The human metabolic network Recon3D was extended to include protein synthesis, degradation, and recycling pathways for key inflammatory and structural proteins. These enhancements enabled the reconstruction of cell-specific GSMMs for PDAC and PDAC-CX, and their respective healthy counterparts, based on transcriptomic datasets. Medium-independent metabolic biomarkers were identified through Parsimonious Metabolite Flow Variability Analysis and differential expression analysis across five nutritional conditions. A fuzzy multi-objective optimization framework was employed within the anticancer target discovery platform to evaluate cell viability and metabolic deviation as dual criteria for assessing therapeutic efficacy and potential side effects. While single-enzyme targets were found to be context-specific and medium-dependent, eight combinatorial targets demonstrated robust, medium-independent effects in both PDAC and PDAC-CX cells. These include the knockout of SLC29A2, SGMS1, CRLS1, and the RNF20–RNF40 complex, alongside upregulation of CERK and PIKFYVE. The proposed integrative strategy offers novel therapeutic avenues that address both tumor progression and cancer-associated cachexia, with improved specificity and reduced off-target effects, thereby contributing to translational oncology. Full article

Background and objectives: Adducins are cytoskeletal proteins essential for membrane stability, actin–spectrin network organization, and cell signaling. Mutations in the genes ADD1, ADD2, and ADD3 have been linked to hypertension, neurodevelopmental disorders, and cancer. However, no comprehensive in silico saturation mutagenesis study has systematically evaluated the pathogenic potential and structural consequences of all possible missense mutations in adducins. This study aimed to identify high-risk variants and their potential impact on protein stability and function. Methods: We performed computational saturation mutagenesis for all possible single amino acid substitutions across the adducin proteins family. Pathogenicity predictions were conducted using four independent tools: AlphaMissense, Rhapsody, PolyPhen-2, and PMut. Predictions were validated against UniProt-annotated pathogenic variants. Predictive performance was assessed using Cohen’s Kappa, sensitivity, and precision. Mutations with a prediction probability ≥ 0.8 were further analyzed for structural stability using mCSM, DynaMut2, MutPred2, and Missense3D, with particular focus on functionally relevant domains such as phosphorylation and calmodulin-binding sites. Results: PMut identified the highest number of pathogenic mutations, while PolyPhen-2 yielded more conservative predictions. Several high-risk mutations clustered in known regulatory and binding regions. Substitutions involving glycine were consistently among the most destabilizing due to increased backbone flexibility. Validated variants showed strong agreement across multiple tools, supporting the robustness of the analysis. Conclusions: This study highlights the utility of multi-tool bioinformatic strategies for comprehensive mutation profiling. The results provide a prioritized list of high-impact adducin variants for future experimental validation and offer insights into potential therapeutic targets for disorders involving ADD1, ADD2, and ADD3 mutations. Full article

Background/Objectives: Moschus (musk) has long been used in traditional Tibetan medicine to prevent and treat epidemic febrile illnesses. However, its antiviral mechanisms remain poorly understood. Given the urgent need for effective treatments against viral respiratory tract infections (VRTIs), this study aimed to systematically investigate the molecular targets and pharmacological pathways through which Moschus may exert therapeutic effects. Methods: Based on the identification of bioactive compounds with favorable pharmacokinetics, we applied integrated network pharmacology and multi-omics analyses to systematically identify key therapeutic targets involved in VRTIs. Gene Set Enrichment Analysis (GSEA) and immune infiltration further revealed strong associations with multiple immune cell subsets, reflecting their pivotal roles in immunomodulatory mechanisms during viral infections. Molecular docking confirmed the strong binding affinities between Moschus compounds and these key targets. Results: Notably, testosterone exhibited the strongest and most consistent binding across key targets, suggesting its potential as a pivotal bioactive compound. Importantly, the antiviral effects of Moschus may be mediated in part by the downregulation of the key genes MCL1, MAPK3, and CDK2, which are involved in the regulation of viral replication, apoptosis, and host immune responses. Conclusions: This study provides a comprehensive mechanistic framework supporting the multi-target antiviral potential of Moschus, offering a scientific basis for its further development as a therapeutic agent against VRTIs. Full article

Prognostic markers such as overall survival (OS) and tertiary lymphoid structure (TLS) ratios, alongside diagnostic signatures like primary cancer-type classification, provide critical information for treatment selection, risk stratification, and longitudinal care planning across the oncology continuum. However, extracting these signals solely from sparse, high-dimensional multi-omics data remains a major challenge due to heterogeneity and frequent missingness in patient profiles. To address this challenge, we present SeNMo, a self-normalizing deep neural network trained on five heterogeneous omics layers—gene expression, DNA methylation, miRNA abundance, somatic mutations, and protein expression—along with the clinical variables, that learns a unified representation robust to missing modalities. Trained on more than 10,000 patient profiles across 32 tumor types from The Cancer Genome Atlas (TCGA), SeNMo provides a baseline that can be readily fine-tuned for diverse downstream tasks. On a held-out TCGA test set, the model achieved a concordance index of 0.758 for OS prediction, while external evaluation yielded 0.73 on the CPTAC lung squamous cell carcinoma cohort and 0.66 on an independent 108-patient Moffitt Cancer Center cohort. Furthermore, on Moffitt’s cohort, baseline SeNMo fine-tuned for TLS ratio prediction aligned with expert annotations (p < 0.05) and sharply separated high- versus low-TLS groups, reflecting distinct survival outcomes. Without altering the backbone, a single linear head classified primary cancer type with 99.8% accuracy across the 33 classes. By unifying diagnostic and prognostic predictions in a modality-robust architecture, SeNMo demonstrated strong performance across multiple clinically relevant tasks, including survival estimation, cancer classification, and TLS ratio prediction, highlighting its translational potential for multi-omics oncology applications. Full article

Human-Robot Collaboration (HRC) is pivotal for flexible, worker-centric manufacturing in Industry 5.0, yet dynamic task allocation remains difficult because operator states—fatigue and skill—fluctuate abruptly. I address this gap with a hybrid framework that couples real-time perception and double-estimating reinforcement learning. A Convolutional Neural Network (CNN) classifies nine fatigue–skill combinations from synthetic physiological cues (heart-rate, blink rate, posture, wrist acceleration); its outputs feed a Double Deep Q-Network (DDQN) whose state vector also includes task-queue and robot-status features. The DDQN optimises a multi-objective reward balancing throughput, workload and safety and executes at 10 Hz within a closed-loop pipeline implemented in MATLAB R2025a and RoboDK v5.9. Benchmarking on a 1000-episode HRC dataset (2500 allocations·episode⁻¹) shows the hybrid CNN+DDQN controller raises throughput to 60.48 ± 0.08 tasks·min⁻¹ (+21% vs. rule-based, +12% vs. SARSA, +8% vs. Dueling DQN, +5% vs. PPO), trims operator fatigue by 7% and sustains 99.9% collision-free operation (one-way ANOVA, p < 0.05; post-hoc power 1 − β = 0.87). Visual analyses confirm responsive task reallocation as fatigue rises or skill varies. The approach outperforms strong baselines (PPO, A3C, Dueling DQN) by mitigating Q-value over-estimation through double learning, providing robust policies under stochastic human states and offering a reproducible blueprint for multi-robot, Industry 5.0 factories. Future work will validate the controller on a physical Doosan H2017 cell and incorporate fairness constraints to avoid workload bias across multiple operators. Full article

Codonopsis pilosula polysaccharides have demonstrated multiple biological activities including immune regulation, antitumor, and antioxidant properties. The rapid development and integrated application of multi-omics can facilitate the unraveling of the complex network of immune system regulation. In this study, C. pilosula alkali-extracted polysaccharide (CPAP) were prepared, and their effects on gut microbiota compositions, metabolic pathways, and protein expressions in peripheral blood and solid tumors in mice were further evaluated. The 16S rDNA sequencing results showed that CPAP could effectively promote the enrichment of intestinal Lactobacillus in tumor-bearing mice. In addition, it could be inferred from peripheral blood and solid tumor proteomics results that CPAP might activate T cell-mediated antitumor immune functions by regulating purine metabolism and alleviate tumor-caused inflammation by promoting neutrophil degranulation, finally inducing apoptosis in tumor cells by increasing oxidative stress. These results will provide a theoretical foundation and data support for the further development of CPAP as dietary adjuvants targeting immune deficiency-related diseases. Full article

Fumonisin B1, deoxynivalenol (DON), and zearalenone (ZEA) are toxic secondary metabolites produced by Fusarium molds. These mycotoxins are common food and feed pollutants and represent a risk to human and animal health. Although the mycotoxins produced by this genus can cross the blood–brain barrier in many species, their effect on neuronal function remains unclear. We investigated the cell viability effects of these toxins on specified neural cell types, including mouse primary neuronal, astroglial, and mixed-cell cultures 24 or 48 h after mycotoxin administration. DON decreased cell viability in a dose-dependent manner, independent of the culture type. Fumonisin B1 was toxic in pure neuronal cultures only at high doses, but toxicity was attenuated in mixed and pure astroglial cultures. ZEA had significant effects on all culture types in 10 nM by increasing cell viability and network activity, as revealed by multi-electrode array measurements. Since ZEA is a mycoestrogen, we analyzed the effects of ZEA on the expression of estrogen receptor isotypes ERα and ERβ and the mitochondrial voltage-dependent anion channel via qRT-PCR. In neuronal and mixed cultures, ZEA administration decreased ERα expression, while in astroglial cultures, it induced the opposite effect. Thus, our results emphasize that Fusarium mycotoxins act in a cell-specific manner. Full article

Tumor drug resistance, a major cause of treatment failure, involves complex multi-gene networks, remodeling of signaling pathways, and interactions with the tumor microenvironment. Yin Yang 1 (YY1) is a critical oncogene overexpressed in many tumors and mediates multiple tumor-related processes, such as cell proliferation, metabolic reprogramming, immune evasion, and drug resistance. Notably, YY1 drives resistance through multiple mechanisms, such as upregulation of drug efflux, maintenance of cancer stemness, enhancement of DNA repair capacity, modulation of the tumor microenvironment, and epithelial–mesenchymal transition, thereby positioning it as a pivotal regulator of drug resistance. This review examines the pivotal role of YY1 in resistance, elucidating its molecular mechanisms and clinical relevance. We demonstrate that YY1 inhibition could effectively reverse drug resistance and restore therapeutic sensitivity across various treatment modalities. Importantly, we highlight the promising potential of YY1-targeted strategies, particularly combined with anti-tumor agents, to overcome resistance barriers. Furthermore, we discuss critical translational considerations for advancing these combinatorial approaches into clinical practice. Full article

Leymus chinensis (Trin.) Tzvel., a vital native forage grass in northern China for ecological restoration and livestock production, faces severe yield losses and grassland degradation due to rust (Puccinia spp.) infection. Current control strategies, reliant on chemical interventions, are limited by evolving resistance risks and environmental concerns, while rust-resistant breeding remains hindered by insufficient molecular insights. To address this, we systematically evaluated rust resistance in 24 L. chinensis germplasms from diverse geographic origins, identifying six highly resistant (HR) and five extremely susceptible (ES) genotypes. Integrating transcriptomics and metabolomics, we dissected molecular responses to Puccinia infection, focusing on contrasting HR (Lc71) and ES (Lc5) germplasms at 48 h post-inoculation. Transcriptomic analysis revealed 1012 differentially expressed genes (DEGs: 247 upregulated, 765 downregulated), with enrichment in cell wall biosynthesis and photosynthesis pathways but suppression of flavonoid synthesis. Metabolomic profiling identified 287 differentially accumulated metabolites (DAMs: 133 upregulated, 188 downregulated), showing significant downregulation of pterocarpans and flavonoids in HR germplasms, alongside upregulated cutin synthesis-related metabolites. Multi-omics integration uncovered 79 co-enriched pathways, pinpointing critical regulatory networks: (1) In the nucleotide metabolism pathway, genes Lc5Ns011910, Lc1Xm057211, and Lc4Xm043884 exhibited negative cor-relations with metabolites Deoxycytidine and Cytosine. (2) In flavonoid biosynthesis, Lc2Xm054924, Lc4Xm044161, novel.8850, Lc2Ns006303, and Lc7Ns021884 were linked to naringenin and naringenin-7-O-glucoside accumulation. These candidate genes likely orchestrate rust resistance mechanisms in L. chinensis. Our findings advance the molecular understanding of rust resistance and provide actionable targets for breeding resilient germplasms. Full article

Background: The prognosis management of thyroid cancer remains a significant challenge. This study highlights the critical role of T cells in the tumor microenvironment and aims to improve prognostic precision by integrating bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data, providing a more comprehensive view of tumor biology at the single-cell level. Method: 15 thyroid cancer scRNA-seq samples were analyzed from GEO and 489 patients from TCGA. A multi-level attention graph neural network (MLA-GNN) model was applied to integrate T-cell-related differentially expressed genes (DEGs) for predicting disease-free survival (DFS). Patients were divided into training and validation cohorts in an 8:2 ratio. Result: We systematically characterized the immune microenvironment of metastatic thyroid cancer by using single-cell transcriptomics and identified the important role of T-cell subtypes in the development of thyroid cancer. T-cell-based DEGS between tumor tissues and normal tissues were also identified. Subsequently, T-cell-based risk signatures were selected for establishing a risk model using MLA-GNN. Finally, our MLA-GNN-based model demonstrated an excellent ability to predict the DFS of thyroid cancer patients (1-year AUC: 0.965, 3-years AUC: 0.979, and 5-years AUC: 0.949 in training groups, and 1-year AUC: 0.879, 3-years AUC: 0.804, and 5-years AUC: 0.804 in validation groups). Conclusions: Risk features based on T-cell genes have demonstrated the effectiveness in predicting the prognosis of thyroid cancer. By conducting a comprehensive characterization of T-cell features, we aim to enhance our understanding of the tumor’s response to immunotherapy and uncover new strategies for the treatment of cancer. Full article

Background: Personalized anti-tumor therapy that activates the immune response has demonstrated clinical benefits in various cancers. However, its efficacy against testicular cancer (TC) remains uncertain. This study aims to identify suitable patients for anti-tumor immunotherapy and to uncover potential therapeutic targets in TC for the development of tailored anti-tumor immunotherapy. Methods: Consensus clustering analysis was conducted to delineate immune subtypes, while weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) regression, and support vector machine (SVM) algorithms were employed to evaluate the potential efficacy of anti-tumor immunotherapy. Candidate immunotherapy targets were systematically identified through multi-gene panel analyses and subsequently validated using molecular biology assays. A prioritized target emerging from cellular screening was further evaluated for its capacity to potentiate anti-tumor immunity. The therapeutic efficacy of this candidate was rigorously confirmed through a comprehensive suite of immunological experiments. Results: Following systematic screening of five candidate genes (WNT11, FAM181B, GRK5, FSCN1, and ECHS1), GRK5 emerged as a promising therapeutic target for immunotherapy based on its distinct functional and molecular associations with immune evasion mechanisms. Cellular functional assays revealed that GRK5 knockdown significantly attenuated the malignant phenotype of testicular cancer cells, as evidenced by reduced proliferative capacity and invasive potential. Complementary immunological validation established that specific targeting of GRK5 with the selective antagonist GRK5-IN-2 disrupts immune evasion pathways in testicular cancer, as quantified by T-cell-mediated cytotoxicity. Conclusions: These findings position GRK5 as a critical modulator of tumor-immune escape, warranting further preclinical exploration of GRK5-IN-2 as a candidate immunotherapeutic agent. Full article

Malignant cutaneous melanoma is among the most aggressive forms of skin cancer, characterized by high metastatic potential and frequent resistance to standard therapies. Hydroxytyrosol, a phenolic compound derived from extra virgin olive oil, has shown promising anticancer properties in various models, yet its effects in 3D melanoma systems remain poorly understood. In this study, we used paired 3D spheroid models of non-tumorigenic (HEMa) and melanoma (C32) to assess the therapeutic potential of hydroxytyrosol. To evaluate the anti-tumoral effect of hydroxytyrosol, we performed cytotoxicity, metastasis, invasiveness, cell cycle arrest, apoptotic, and proteomic assays. Hydroxytyrosol treatment significantly impaired spheroid growth, reduced cell viability, and induced cell cycle arrest and apoptosis in C32 spheroids, with minimal cytotoxicity observed in HEMa models. Proteomic profiling further demonstrated that hydroxytyrosol selectively downregulated a network of oncogenic proteins, including ERBB2, ERBB3, ERBB4, VEGFR-2, and WIF-1, along with suppression of downstream PI3K-Akt and MAPK/ERK signaling pathways. In conclusion, compared to dabrafenib, hydroxytyrosol exerted a broader range of molecular effects and was more selective toward tumor cells. These findings support the use of hydroxytyrosol as a multi-targeted agent capable of attenuating melanoma progression through suppression of kinase signaling and tumor-stromal interactions. Full article

This study presents a multi-objective optimization framework for designing cylindrical triply periodic minimal surface (TPMS) lattices tailored for bone implant applications. Using an artificial neural network (ANN) as a surrogate model trained on simulated data, four key properties—ultimate stress (U), energy absorption (EA), surface area-to-volume ratio (SA/VR), and relative density (RD)—were predicted from seven lattice design parameters. To address anatomical variability, a novel implant size-based categorization (small, medium, and large) was introduced, and separate optimization runs were conducted for each group. The optimization was performed via the NSGA-II algorithm to maximize mechanical performance (U and EA) and surface efficiency (SA/VR), while filtering for biologically relevant RD values (20–40%). Separate optimization runs were conducted for small, medium, and large implant size groups. A total of 105 Pareto-optimal designs were identified, with 75 designs retained after RD filtering. SHapley Additive exPlanations (SHAP) analysis revealed the dominant influence of thickness and unit cell size on target properties. Kernel density and boxplot comparisons confirmed distinct performance trends across size groups. The framework effectively balances competing design goals and enables the selection of size-specific lattices. The proposed approach provides a reproducible pathway for optimizing bioarchitectures, with the potential to accelerate the development of lattice-based implants in personalized medicine. Full article