Mechanical characterisation of smart DNA hydrogels

Hydrogels form to a large extent owing to the ability of a polymeric or particulate constituent to absorb large amounts of water and then interact with other constituents, thereby creating under certain conditions a percolating and porous three-dimensional soft network. Using DNA to build such networks has many advantages as it offers not only high biocompatibility, but also an unparalleled degree of interaction programmability and mechanical tunability. DNA hydrogels constitute transient self-healing networks, which can be reversibly associated and dissociated on demand via an external trigger (typically temperature), and can be thus classified as physical hydrogels that hold great potential for applications in biosensing, artificial tissue engineering and drug delivery, where target specificity is key.

Together with former colleagues at the University of Cambridge, we have already begun to address the relation between DNA oligonucleotide composition and large-scale mechanical properties of the resulting DNA-hydrogel networks. For instance, our articles in PNAS [1] and Soft Matter [2] display clearly the effect of changing the composition of the ‘sticky ends’ making the connection between DNA nanostars or altering the degree of flexibility. Reducing the number of bases participating in the linking of DNA components lowers the ‘melting point’ of the gel network. Increasing the number of non-binding bases or creating mismatches (point mutations) leads to weakening of the overall structure. Now in collaboration with colleagues from KIT, University of Edinburgh and University of Genoa it will be our aim to more systematically address this outstanding problem relating microscopic structure and macroscopic properties by building a database of DNA-hydrogel networks of tunable mechanical characteristics. This will be achieved through the synergistic use of simulations and experiments, where oxDNA/LAMMPS approaches will be combined with experimental microrheology data. As an outlook, this empirically informed database will be fed into machine-learning algorithms that can predict the structures forming from individual oligos. 

[1] Z. Xing, A. Caciagli, T. Cao, I. Stoev, M.  Zupkauskas, T. O’Neill, T. Wenzel, R. Lamboll, D. Liu and E. Eiser. “Microrheology of DNA hydrogels.” PNAS 115 (32), 8137-8142 (2018).
[2] I. D. Stoev, T. Cao, A. Caciagli, J. Yu, C. Ness, R. Liu, R. Ghosh, T. O’Neill, D. Liu and E. Eiser. ”On the role of flexibility in linker-mediated DNA hydrogels.” Soft Matter 16 (4), 990-1001 (2020).