Cellulose-derived nanomaterial building blocks, including cellulose nanocrystals (CNCs), have become increasingly important in sustainable materials development. However, the preparation of CNCs requires hazardous chemicals to introduce surface charges that enable liquid crystalline phase behavior, a key parameter for obtaining self-organized, nanostructured materials from CNCs. Lytic polysaccharide monooxygenases (LPMOs), oxidative enzymes that introduce charged carboxyl groups on their cleavage sites in aqueous reaction conditions, offer an environmentally friendly alternative. In this work, two C1-oxidizing LPMOs from fungus Neurospora crassa, one of which contained a carbohydrate-binding module (CBM), were investigated for CNC preparation. The LPMO-oxidized CNCs shared similar features with chemical-derived CNCs, including colloidal stability and a needle-like morphology with typical dimensions of 7 ± 3 nm in width and 142 ± 57 nm in length for CBM-lacking LPMO-oxidized CNCs. The self-organization of the LPMO-oxidized CNCs was characterized in suspensions and solution cast films. Both LPMO-oxidized CNCs showed electrostatically driven self-organization in aqueous colloidal suspension and pseudo-chiral nematic ordering in solid films. The CBM-lacking LPMO generated a higher carboxyl content (0.70 mmol g^–1), leading to a more uniform CNC self-organization, favoring LPMOs without CBMs for CNC production. The obtained results demonstrate production of stable colloidal CNCs with self-assembly by C1-oxidizing LPMOs toward a completely green production of advanced, nanostructured cellulose materials.

Gliomas have displayed significant challenges in oncology due to their high degree of invasiveness, recurrence, and resistance to treatment strategies. In this work, the key hub genes mainly associated with different grades of glioma, which were represented by pilocytic astrocytoma (PA), oligodendroglioma (OG), anaplastic astrocytoma (AA), and glioblastoma multiforme (GBM), were identified through weighted gene co-expression network analysis (WGCNA) of microarray datasets retrieved from the Gene Expression Omnibus (GEO) database. Through this, four highly correlated modules were observed to be present across the PA (GSE50161), OG (GSE4290), AA (GSE43378), and GBM (GSE36245) datasets. The functional annotation and pathway enrichment analysis done through the Database for Annotation, Visualization, and Integrated Discovery (DAVID) showed that the modules and hub genes identified were mainly involved in signal transduction, transcription regulation, and protein binding, which collectively deregulate several signaling pathways, mainly PI3K/Akt and metabolic pathways. The involvement of several hub genes primarily linked to other signaling pathways, including the cAMP, MAPK/ERK, Wnt/β-catenin, and calcium signaling pathways, indicates potential interconnectivity and influence on the PI3K/Akt pathway and, subsequently, glioma severity. The Drug Repurposing Encyclopedia (DRE) was used to screen for potential drugs based on the up- and downregulated hub genes, wherein the synthetic progestin hormones norgestimate and ethisterone were the top drug candidates. This shows the potential neuroprotective effect of progesterone against glioma due to its influence on EGFR expression and other signaling pathways. Aside from these, several experimental and approved drug candidates were also identified, which include an adrenergic receptor antagonist, a PPAR-γ receptor agonist, a CDK inhibitor, a sodium channel blocker, a bradykinin receptor antagonist, and a dopamine receptor agonist, which further highlights the gene network as a potential therapeutic avenue for glioma.

Background/Objectives: The molecular heterogeneity and metabolic flexibility of Hepatocellular Carcinoma (HCC) pose significant challenges to the efficacy of systemic therapy for advanced cases. Early screening difficulties often delay diagnosis, leading to more advanced stages at presentation. Combined with the inconsistent responses to current systemic therapies, HCC continues to have one of the highest mortality rates among cancers. Thus, this paper seeks to contribute to the development of systemic therapy options through the consideration of HCC’s metabolic vulnerabilities and lay the groundwork for future in vitro studies. Methods: Transcriptomic data were used to calculate single and double knockout options for HCC using genetic Minimal Cut Sets. Furthermore, using QSAR modeling, drug repositioning opportunities were assessed to inhibit the selected genes. Results: Two single knockout options that were also annotated as essential pairs were found within the pyrimidine metabolism pathway of HCC, wherein the knockout of either DHODH or TYMS is potentially disruptive to proliferation. The result of the flux balance analysis and gene knockout simulation indicated a significant decrease in biomass production. Three machine learning algorithms were assessed for their performance in predicting the pIC50 of a given compound for the selected genes. SVM-rbf performed the best on unseen data achieving an R² of 0.82 for DHODH and 0.81 for TYMS. For DHODH, the drugs Oteseconazole, Tipranavir, and Lusutrombopag were identified as potential inhibitors. For TYMS, the drugs Tadalafil, Dabigatran, Baloxavir Marboxil, and Candesartan Cilexetil showed promise as inhibitors. Conclusions: Overall, this study suggests in vitro testing of the identified drugs to assess their capabilities in inducing pyrimidine starvation on HCC.