News and Stories

Project Team: Matthew Bernards, Nathan Schiele, Dharmeshkumar Patel, Stephanie Haag

In the US, over 130,000 surgical procedures each year require a bone graft material, but almost 30% of grafts fail. Recent studies have implicated albumin as important during bone repair and it has been adapted for improving scaffold integration with bone tissue. This is important because albumin is abundant and cost-effective. In contrast, however, are studies that demonstrate albumin blocking cell adhesion to biomaterials. These contradictions indicate that albumin is bioactive only when in specific conformations. The over-arching goal of this research effort is to deliver bioactive albumin via a tissue engineering platform for enhanced bone repair and regeneration. The development of this platform will address the clinical need for a bone graft with reduced failure rates. To achieve this goal, the bioactive conformation of albumin responsible for bone repair must be identified before it can be incorporated into a scaffold, and this is the objective of the proposed investigation. Our central hypothesis is that albumin has a bioactive conformation that promotes bone repair, that is imparted to albumin upon calcium binding.

First, we will use molecular modeling to determine the conformational landscape of calcium-free and calcium-bound albumin. The conformational preferences of albumin for various calcium-bound conditions will be determined using molecular modeling. Determining conformational preferences of a protein is a challenging problem in molecular modeling so we will tackle it using molecular dynamics or Monte Carlo simulations of coarse-grained protein models. These approaches allow for large-scale conformational changes within the timescale of the simulations. If our hypothesis is correct then changing the binding pattern for calcium will make specific conformation(s) more stable – and these conformations will promote cell adhesion. In parallel, we will identify conformation changes in albumin induced by calcium with Fourier transform infrared spectroscopy (FTIR). The conformational states of albumin adsorbed to tissue culture polystyrene in both a cell adhesive and nonadhesive conformation will be probed using FTIR. After identifying the critical calcium concentration that induces a cell adhesive conformation to albumin, we will use FTIR to quantify differences in the secondary structure of the adsorbed species. These will be directly compared to molecular modeling results obtained in Aim 1, to better delineate conformational changes that facilitate cell adhesion. If our hypothesis is correct then there should be significant changes in the secondary structure upon exposure to calcium.

By pairing molecular modeling with empirical evidence of the secondary structure of albumin it will be possible to delineate the bioactive region of albumin more clearly. This represents a significant step towards the therapeutic application of albumin or subdomains of albumin for bone tissue repair from an implanted scaffold.

Project Team: Audrey Fu, Md. Bahadur Badsha, Evan Martin

Complex diseases often involve changes in DNA sequence, and in DNA transcription and methylation, an epigenetic process that can both regulate and be regulated by gene expression. These changes result in a wide range of symptoms or multiple subtypes of the same disease. In breast cancer, for example, different patterns of gene expression and DNA methylation characterize subtypes that vary in terms of tumor progression and treatment. In order to develop more effective treatments for different subtypes, it is necessary to understand the genes and processes (i.e., transcription and methylation) that drive the differences between subtypes. It is therefore of immense interest to understand how genetic variation influences disease through gene regulatory networks. Unfortunately, identification of genes and processes that are key to diseases is often compromised by inference based on correlation, not causation.

Our long-term goal is to develop computational methods to infer gene regulatory networks that are potentially causal for multiple clinical phenotypes using genomic and clinical data of complex diseases. In this project, we will develop new statistical approaches based on the principle of Mendelian randomization to systematically identify regulatory networks involving both transcription and methylation that are potentially causal for disease subtype. We will use breast cancer as the disease model and apply our methods to genomic data. The principle of Mendelian randomization assumes that the alleles of a genetic variant are randomly assigned to individuals in a population, analogous to a natural randomization experiment. This principle has gained increasing attention in genomics, given its power to separate correlation due to causation from correlation not due to causation.

The models and algorithms developed here will allow us to make causal statements about the two processes at the single gene level and account for confounding variables, which similar studies have not examined. These methods will help to identify key genes for specific breast cancer subtypes and elucidate the roles of transcription and methylation when many genes are involved, offering insights into genes and processes that could better inform subtype classification, cancer diagnosis and development of novel drug targets. These methods are not limited to breast cancer but are applicable to complex diseases in general.