The rising global incidence of hepatocellular carcinoma demands innovative therapeutic strategies. This study explores the enhanced anticancer potential of magnesium-doped copper oxide (Mg-doped CuO) nanoparticles, which were synthesized to improve upon the properties of undoped CuO nanoparticles. Mg-doped CuO nanoparticles with doping concentrations ranging from 1% to 5% were prepared using the co-precipitation method and thoroughly characterized by SEM, EDS, and FTIR. Their biological activity was evaluated against HepG2 liver cancer cells and normal human fibroblast cells. The MTT assay demonstrated a significant, concentration-dependent increase in cytotoxicity for Mg-doped CuO nanoparticles compared to undoped CuO, with the 3% Mg-doped CuO formulation showing the greatest potency (IC₅₀ = 21.99 µg/mL at 48 h). Cell cycle analysis revealed that treatment with Mg-doped CuO nanoparticles, particularly at 3% and 5% doping concentrations, induced a substantial G2/M phase arrest, indicating a mechanism of action involving the disruption of cell division. Furthermore, all Mg-doped CuO nanoparticles exhibited markedly higher IC₅₀ values in normal fibroblasts, confirming a favorable selective toxicity towards cancer cells. Apoptosis was identified as a key cell death pathway through acridine orange/propidium iodide staining. These results conclusively show that magnesium doping significantly augments the selective anticancer efficacy of CuO nanoparticles via cell cycle arrest and apoptosis induction, presenting a highly promising nanomaterial targeted liver cancer therapy.

The development of anticancer peptides (ACPs) has emerged as a promising strategy in targeted cancer therapy due to their high specificity and therapeutic potential. Cathelicidin-derived antimicrobial peptides represent a particularly attractive class of ACPs, yet systematic evaluation of their anticancer activity remains limited. In this study, we conducted virtual screening of eight cathelicidin-derived peptides (AL-38, LL-37, RK-31, KS-30, KR-20, FK-16, FK-13, and KR-12) to assess their potential against colon cancer. Among these, LL-37 and FK-16 were identified as the most promising candidates, with LL-37 exhibiting the strongest inhibitory effects on both non-metastatic (HT-29) and metastatic (SW-620) colon cancer cell lines in vitro. To overcome challenges associated with peptide stability and delivery, we employed the probiotic lactic acid bacterium Limosilactobacillus fermentum KUB-D18 as both a biosynthetic platform and delivery vehicle. A genome-scale metabolic model (GEM), iTM505, was reconstructed to predict the strain’s biosynthetic capacity for ACP production. Model simulations identified trehalose, sucrose, maltose, and cellobiose as optimal carbon sources supporting both high peptide yield and biomass accumulation, which was subsequently confirmed experimentally. Notably, L. fermentum expressing LL-37 achieved a growth rate of 2.16 gDW/L, closely matching the model prediction of 1.93 gDW/L (accuracy 89.69%), while the measured LL-37 concentration (26.96 ± 0.08 µM) aligned with predictions at 90.65% accuracy. The strong concordance between in silico predictions and experimental outcomes underscore the utility of GEM-guided metabolic engineering for optimizing peptide biosynthesis. This integrative approach—combining virtual screening, genome-scale modeling, and experimental validation—provides a robust framework for accelerating ACP discovery. Moreover, our findings highlight the potential of probiotic-based systems as effective delivery platforms for anticancer peptides, offering new avenues for the rational design and production of peptide therapeutics.

The green microalga Chlamydomonas reinhardtii holds substantial promise as a photosynthetic cell factory for sustainable bioproduction, yet strain-specific metabolic responses to environmental stress remain underexplored. Here, we reconstructed a genome-scale metabolic network (GSMN) of the light-tolerant strain C. reinhardtii CC-4414 by combining de novo genome assembly with integrative proteomics. The resulting model, designated iYH2021, comprises 2021 genes, 2601 reactions, and 2089 metabolites, representing the first genome-scale metabolic reconstruction reported for this strain. To elucidate light-driven metabolic remodeling, we integrated quantitative proteomic data from high-light (HL) and low-light (LL) conditions into the model using the iMAT algorithm and performed metabolic flux analysis (MFA). Because these context-specific networks integrate only proteomically supported reactions, flux predictions were evaluated using an objective function that captures photon-handling capacity, providing a direct readout of how the photosynthetic apparatus reallocates flux under each condition. Our predictions revealed that HL conditions significantly enhanced fluxes through photosynthetic electron transport, the Calvin–Benson cycle, glycolysis, and the tricarboxylic acid (TCA) cycle, indicating a reallocation of carbon and energy metabolism toward light-driven pathways, while amino acid metabolism showed a mixed pattern of change. In contrast, LL conditions induced a marked upregulation of purine metabolism, suggesting accelerated nucleotide turnover as an adaptive response to light limitation. A complementary growth-rate analysis using the unconstrained network further indicated a higher maximum growth rate under HL than LL, consistent with the expected benefit of greater light availability. These condition-specific flux redistributions highlight the metabolic plasticity of C. reinhardtii CC-4414 and provide a systems-level understanding of how light intensity shapes carbon and energy allocation. The iYH2021 model thus offers a robust predictive platform for guiding metabolic engineering strategies aimed at optimizing the production of biofuels and high-value bioproducts in this industrially relevant microalga under stress conditions. These findings represent model-derived predictions and have not yet been experimentally validated. We emphasize that this predictive platform requires experimental validation before its use in guiding metabolic engineering decisions.