Designing nanostructured peptide hydrogels containing graphene oxide and its derivatives for tissue engineering and biomedical applications

Abstract

Progress in biomedicine requires the design of functional biomaterials, in particular, 3-dimensional (3D) scaffolds. Shear thinning, β-sheet based peptide hydrogels have attracted wide interest due to their potential use in tissue engineering and biomedical applications as 3D functional scaffolds. The emergence of carbon nanomaterials has also opened the door for the construction of increasingly functional hybrid hydrogels built from nanofibres and graphene-based materials using non-covalent physical interactions. The relationship between peptide molecular structure and the formed hydrogel is important for understanding the material response to shear. In particular, the physicochemical properties of peptide based biomaterials will affect the feasibility of injecting them during medical procedures. In the first part of this work, four peptides: FEFKFEFK (F8), FKFEFKFK (FK), KFEFKFEFK (KF8) and KFEFKFEFKK (KF8K) (F - phenylalanine, E - glutamic acid, K - lysine) were designed and used at identical charge to explore the effect of lysine rich β-sheet self-assembling sequences on the shear thinning behaviour and final properties of bulk hydrogels. By varying the peptide sequence design and concentration of the peptide, the tendency of the nanofibres formed to aggregate and the balance of nanofibre junction strength versus fibre cohesive strength could be explored. This allowed the existing theory of the shear thinning behaviour of this class of materials to be extended. The relationship between molecular structures of nanofibres forming the 3D network and the nano-filler is critical to understand in order to design tuneable and functional materials. In the next part of the work, three rationally designed β-sheet peptides, which form hydrogels: VEVKVEVK (V8), FEFKFEFK (F8) and FEFEFKFE (FE) (V - valine) and five graphene-based materials: graphene oxide (GO), reduced graphene oxide (rGO), three graphene-polymer hybrid flakes: GO with polydiallyldimethylammonium chloride (GO/PDADMAC), rGO with PDADMAC (rGO/PDADMAC) and rGO with polyvinylpyrrolidone (rGO/PVP) were used to form a selection of hybrid hydrogels. Graphene derivatives of the lateral flake sizes of 16.8 ± 10.1 µm were used. Various interactions between the graphene flakes and the peptides were observed that affected the overall mechanical properties of the hydrogels. Electrostatic interactions and pie-pie stacking, when phenylalanine residues are present, were shown to play a key role in determining the dispersion of graphene materials in the peptide hydrogels and stiffness of the hybrid materials. In particular, FE with reduced graphene oxide (rGO) and FE with rGO covered with polydiallyldimethylammonium chloride (PDADMAC) thin film formed double network-like hybrid hydrogels due to strong formation of peptide nanofibrillar bridges between adjacent rGO flakes. This corresponded to the 3- and 4-fold increase in the storage modulus (Gꞌ) of these hydrogels in comparison to controls. FE hydrogels with homogeneus dispersions of graphene oxide (GO) and reduced graphene oxide (rGO) are further shown to be suitable for 3D culture of human mesenchymal stem cells (hMSCs) with no cytotoxicity. These results focus attention on the importance of understanding interactions between the nano-filler and the nanofibrillar network in forming hybrid hydrogels with tuneable mechanical and biological properties, and demonstrates the possibility of using these materials as 3D cell culture scaffolds for biomedical purposes. Furthermore, graphene oxide (GO) itself is currently used in a number of processes of technological relevance such as wet spinning, injection moulding or inkjet printing to form graphene fibres, composites and printed conductors. Typically, such processes utilise well-aligned layered GO liquid crystal (LC) structures in aqueous dispersions. Flow and confinement encountered during processing affects the alignment and stability of this phase. In the final part of this work, the a

Date of Award	1 Aug 2018
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Alberto Saiani (Supervisor) & Aravind Vijayaraghavan (Supervisor)