Vidana Epa

Discovery of a Novel Polymer for Human Pluripotent Stem Cell Expansion and Multilineage Differentiation

A scalable and cost‐effective synthetic polymer substrate that supports robust expansion and subsequent multilineage differentiation of human pluripotent stem cells (hPSCs) with defined commercial media is presented. This substrate can be applied to common cultureware and used off‐the‐shelf after long‐term storage. Expansion and differentiation of hPSCs are performed entirely on the polymeric surface, enabling the clinical potential of hPSC‐derived cells to be realized.

Naturally occurring polyphenolic inhibitors of amyloid beta aggregation.

Alzheimers disease is the most common neurodegenerative disease and is one of the main causes of death in developed countries. Consumption of foods rich in polyphenolics is strongly correlated with reduced incidence of Alzheimers disease. Our study has investigated the biological activity of previously untested polyphenolic compounds in preventing amyloid β aggregation. The anti-aggregatory potential of these compounds was assessed using the Thioflavin-T assay, transmission electron microscopy, dynamic light scattering and size exclusion chromatography. Two structurally related compounds, luteolin and transilitin were identified as potent inhibitors of Aβ fibril formation. Computational docking studies with an X-ray derived oligomeric structure offer a rationale for the inhibitory activity observed and may facilitate development of improved inhibitors of Aβ aggregation and toxicity.

Modelling and predicting the biological effects of nanomaterials.

The commercial applications of nanoparticles are growing rapidly, but we know relatively little about how nanoparticles interact with biological systems. Their value – but also their risk – is related to their nanophase properties being markedly different to those of the same material in bulk. Experiments to determine how nanoparticles are taken up, distributed, modified, and elicit any adverse effects are essential. However, cost and time considerations mean that predictive models would also be extremely valuable, particularly assisting regulators to minimize health and environmental risks. We used novel sparse machine learning methods that employ Bayesian neural networks to model three nanoparticle data sets using both linear and nonlinear machine learning methods. The first data comprised iron oxide nanoparticles decorated with 108 different molecules tested against five cell lines, HUVEC, pancreatic cancer, and three macrophage or macrophage-like lines. The second data set comprised 52 nanoparticles with various core compositions, coatings, and surface attachments. The nanoparticles were characterized using four descriptors (size, relaxivities, and zeta potential), and their biological effects on four cells lines assessed using four biological assays per cell line and four concentrations per assay. The third data set involved the biological responses to gold nanoparticles functionalized by 80 different small molecules. Nonspecific binding and binding to AChE were the biological endpoints modelled. The biological effects of nanoparticles were modelled using molecular descriptors for the molecules that decorated the nanoparticle surface. Models with good statistical quality were constructed for most biological endpoints. These proof-of-concept models show that modelling biological effects of nanomaterials is possible using modern modelling methods.

Chapter 4 – Computational Approaches

While experimental assessment of the biological effects of nanomaterials is essential to properly assign risk to these materials, computational methods provide considerable promise in supplementing experimental approaches. Indeed, although the biological effects of nanomaterials will be more difficult to model than those of small molecules, drugs and chemicals, recent reports have shown that quantitative structure–activity relationships and quantum chemical methods can provide very useful mechanistic and predictive information for nanomaterials. In the present chapter, we explain why in silico computational methods are an essential addition to the nanomaterial research toolkit and why nanomaterials may be more difficult to model than single molecules, and provide examples of recent successful models of the biological effects of nanomaterials.

Computational studies on the stability of C-terminal beta-amyloid dimer and mutants

Background: Insoluble amyloid plaque deposits characterize the primary pathology of Alzheimer’s disease. The amyloid-beta (Ab) peptide, cleaved from the membrane-bound amyloid precursor protein (APP) by the action of b-secretase and g-secretase into 39-43 amino acid residue long fragments, is the major constituent of this plaque. It is now believed that a major source of the neurotoxicity in AD is due to the action of intermediate soluble Ab oligomers [1]. Recently, a deletion mutant at the Glu 22 position (D E22) of Ab was discovered in some Familial Alzheimer’s Disease (FAD) patients in Japan. This mutation, named the Osaka mutant, appears to cause the A b peptide to oligomerize and fibrillize much more rapidly compared to the wild type [2]. Methods: Streltsov et al. [3] have determined the x-ray crystal structure of Ab (18-41) within the framework of shark IgNAR (Ig New Antigen Receptor) single variable domain antibody CDR3 loop. The Ab portion of the crystal structure is observed to be a tetramer (or a dimer of dimers), and can provide valuable insight into the structure-function relationships of A b. In this work we perform in silico or computational studies in order to understand the stability and conformational dynamics of the Ab peptide and the Osaka mutant oligomers. Results: These simulations, employing molecular dynamics methodology in an explicit solvent environment, commence from the dimer present in the crystal structure. We observe the relative stability of the various secondary structure elements, inter-residue interactions and conformational transitions. Conclusions: The deletion mutation is located in an important turn region with formation and breaking of transient hydrogen bonds and ionic interactions contributing to the dynamical behaviour of the peptide. References: 1. Crouch, P.J., Harding, S.M., White, A.R., Camakaris, J., Bush, A.I., & Masters, C.L., (2008). International Journal of Biochemistry & Cell Biology, 40, 181198. 2. Inayathullah, M. & Teplow, D.P., (2011). Amyloid, 18(3), 98-107. 3. Streltsov, V.A., Varghese, J.N., Masters, C.L., & Nuttall, S.N. (2011). Journal of Neuroscience, 31, 1419 -1426.