Role of cellulose family in fibril organization of collagen for forming 3D cancer spheroids: In vitro and in silico approach

Insights into spheroids formation in cellulose nanofibrils and Matrigel hydrogels using AFM-based techniques

The recent FDA decision to eliminate animal testing requirements emphasises the role of cell models, such as spheroids, as regulatory test alternatives for investigations of cellular behaviour, drug responses, and disease modelling. The influence of environment on spheroid formation are incompletely understood, leading to uncertainty in matrix selection for scaffold-based 3D culture. This study uses atomic force microscopy-based techniques to quantify cell adhesion to Matrigel and cellulose nanofibrils (CNF), and cell-cell adhesion forces, and their role in spheroid formation of hepatocellular carcinoma (HepG2) and induced pluripotent stem cells (iPS(IMR90)-4). Results showed different cell behaviour in CNF and Matrigel cultures. Both cell lines formed compact spheroids in CNF but loose cell aggregates in Matrigel. Interestingly, the type of cell adhesion protein, and not the bond strength, appeared to be a key factor in the formation of compact spheroids. The gene expression of E- and N-cadherins, proteins on cell membrane responsible for cell-cell interactions, was increased in CNF culture, leading to formation of compact spheroids while Matrigel culture induced integrin-laminin binding and downregulated E-cadherin expression, resulting in looser cell aggregates. These findings enhance our understanding of cell-biomaterial interactions in 3D cultures and offer insights for improved 3D cell models, culture biomaterials, and applications in drug research.

Assessing the Layer-by-Layer Assembly of Cellulose Nanofibrils and Polyelectrolytes in Pancreatic Tumor Spheroid Formation

Three-dimensional (3D) tumor spheroids are regarded as promising models for utilization as preclinical assessments of chemo-sensitivity. However, the creation of these tumor spheroids presents challenges, given that not all tumor cell lines are able to form consistent and regular spheroids. In this context, we have developed a novel layer-by-layer coating of cellulose nanofibril-polyelectrolyte bilayers for the generation of spheroids. This technique builds bilayers of cellulose nanofibrils and polyelectrolytes and is used here to coat two distinct 96-well plate types: nontreated/non-sterilized and Nunclon Delta. In this work, we optimized the protocol aimed at generating and characterizing spheroids on difficult-to-grow pancreatic tumor cell lines. Here, diverse parameters were explored, encompassing the bilayer count (five and ten) and multiple cell-seeding concentrations (10, 100, 200, 500, and 1000 cells per well), using four pancreatic tumor cell lines-KPCT, PANC-1, MiaPaCa-2, and CFPAC-I. The evaluation includes the quantification (number of spheroids, size, and morphology) and proliferation of the produced spheroids, as well as an assessment of their viability. Notably, our findings reveal a significant influence from both the number of bilayers and the plate type used on the successful formation of spheroids. The novel and simple layer-by-layer-based coating method has the potential to offer the large-scale production of spheroids across a spectrum of tumor cell lines.

Multiple stimulus-response berberine plus baicalin micelles with particle size-charge-release triple variable properties for breast cancer therapy

OBJECTIVE

The aim was to develop a nanoscale drug delivery system with enzyme responsive and acid sensitive particle size and intelligent degradation aiming to research the inhibitory effect on breast cancer.

SIGNIFICANCE

The delivery system addressed the problems of tissue targeting, cellular internalization, and slow drug release at the target site, which could improve the efficiency of drug delivery and provide a feasible therapeutic approach for breast cancer.

METHODS

The acid sensitive functional material DSPE-PEG₂₀₀₀-dyn-PEG-R9 was synthesized by Michael addition reaction. Then, the berberine plus baicalin intelligent micelles were prepared by thin-film hydration. Subsequently, we characterized the physical and chemical properties of berberine plus baicalin intelligent micelles, evaluated its anti-tumor effects in vivo and in vitro.

RESULTS

The target molecule was successfully synthesized, and the intelligent micelles showed excellent chemical and physical properties, delayed drug release and high encapsulation efficiency. In vitro and in vivo experiments also confirmed that the intelligent micelles could effectively target tumor sites, penetrate tumor tissues, enrich in tumor cells, inhibit tumor cell proliferation, inhibit tumor cell invasion and migration, and induce tumor cell apoptosis.

CONCLUSION

Berberine plus baicalin intelligent micelles have excellent anti-tumor effects and no toxicity to normal tissues, which provides a new potential drug delivery strategy for the treatment of breast cancer.

CTLs, NK cells and NK-derived EVs against breast cancer

Patients with advanced stage breast cancer need novel therapies. New potential treatments have been developed, such as adoptive cellular therapies and alternative cell-free immunotherapies. The goal of this study was to assess the cytotoxicity of three of the patient-derived immune components, CTLs, NK cells and NK-derived EVs, and evaluate the potential for the development of novel therapy against breast cancer. CTLs were activated against MUC-1 antigen. The in vitro cytotoxic activity of three components was assessed with flow cytometry and in vivo study revealed the efficacy of adoptive cell therapy. Overall, CTLs exhibited the highest cytotoxicity against spheroids of MCF7 breast adenocarcinoma, reaching in all cases higher than double the percentage of NK cells' cytotoxicity. NK-derived EVs exhibited the lowest effect against MCF7 spheroids comparing to the two cell populations. MUC-1 specific CTLs were evaluated with adoptive cell therapy mice study and appeared to be well tolerable and moderately efficacious. More studies need to be performed with CTLs to evaluate safety and efficacy in order to assess their clinical potential, while NK cells and NK-derived EVs are promising candidates that require more experiments to enhance their cytotoxicity.

Characterization and Evaluation of Commercial Carboxymethyl Cellulose Potential as an Active Ingredient for Cosmetics

Carboxymethyl cellulose is the most used water-soluble cellulose with applications in industries such as food, cosmetics, and tissue engineering. However, due to a perceived lack of biological activity, carboxymethyl cellulose is mostly used as a structural element. As such, this work sought to investigate whether CMC possesses relevant biological properties that could grant it added value as a cosmeceutical ingredient in future skincare formulations. To that end, CMC samples (Mw between 471 and 322 kDa) skin cell cytotoxicity, impact upon pro-collagen I α I production, and inflammatory response were evaluated. Results showed that samples were not cytotoxic towards HaCat and HDFa up to 10 mg/mL while simultaneously promoting intracellular production of pro-collagen I α I up by 228% relative to the basal metabolism, which appeared to be related to the highest DS and Mw. Additionally, CMC samples modulated HaCat immune response as they decreased by ca. 1.4-fold IL-8 production and increased IL-6 levels by ca. five fold. Despite this increase, only two samples presented IL-6 levels similar to those of the inflammation control. Considering these results, CMC showed potential to be a more natural alternative to traditional bioactive cosmetic ingredients and, as it is capable of being a bioactive and structural ingredient, it may play a key role in future skincare formulations.

A novel multi-objective metaheuristic algorithm for protein-peptide docking and benchmarking on the LEADS-PEP dataset

Protein-peptide interactions have attracted the attention of many drug discovery scientists due to their possible druggability features on most key biological activities such as regulating disease-related signaling pathways and enhancing the immune system's responses. Different studies have utilized some protein-peptide-specific docking algorithms/methods to predict protein-peptide interactions. However, the existing algorithms/methods suffer from two serious limitations which make them unsuitable for protein-peptide docking problems. First, it seems that the prevalent approaches require to be modified and remodeled for weighting the unbounded forces between a protein and a peptide. Second, they do not employ state-of-the-art search algorithms for detecting the 3D pose of a peptide relative to a protein. To address these restrictions, the present study aims to introduce a novel multi-objective algorithm, which first generates some potential 3D poses of a peptide, and then, improves them through its operators. The candidate solutions are further evaluated using Multi-Objective Pareto Front (MOPF) optimization concepts. To this end, van der Waals, electrostatic, solvation, and hydrogen bond energies between the atoms of a protein and designated peptide are computed. To evaluate the algorithm, it is first applied to the LEADS-PEP dataset containing 53 protein-peptide complexes with up to 53 rotatable branches/bonds and then compared with three popular/efficient algorithms. The obtained results indicate that the MOPF-based approaches which reduce the backbone RMSD between the original and predicted states, achieve significantly better results in terms of the success rate in predicting the near-native conditions. Besides, a comparison between the different types of search algorithms reveals that efficient ones like the multi-objective Trader/differential evolution algorithm can predict protein-peptide interactions better than the popular algorithms such as the multi-objective genetic/particle swarm optimization algorithms.