The systems biology of network stress based on data generated from in vitro differentiated hepatocytes from individual-specific human iPS cells

Our liver is the major chemical factory of our body. It removes unwelcome chemical compounds from our body, often after changing them chemically. The catch 22 situation here is that this function of the liver is good for the rest of the body, but potentially bad for the liver itself: during the chemical removal processes, toxic compounds tend to arise that put the liver under even more stress than already produced by the increased work load of the removal of the unwelcome compounds. A major hurdle in understanding how the liver deals with such stresses, has been the difference between human individuals. Due to this difference the response measured in one individual could not be used to predict the response to the stress in a different individual. Individuals differ in a limited but substantial number of small mutations in their DNA, some of which give rise alterations in molecular function of a number of molecules in the body. It has been impossible to relate differences in the response to stress, back to differences in any one of the mutated molecules. As in human societies, processes in living cells are carried out not by individuals but by networks of individuals. This project will therefore try to relate the response to stress of individuals not to their molecules but to their molecular networks. This will be done by a combination of experimentation with ultra modern experimental methods such as deep sequencing and mathematical modelling using fast and well-programmed computers. One result will be a mathematical model that describes part of the function of the liver and hence of the human.

The human liver cab be in a variety of stress states ranging from manageable stress as in simple steatosis (S) to excessive stress as in steatohepatitis (SH). A major unsolved problem is the marked difference in the individuals' ability to deal with the stress, e.g. as observed in terms of risk to develop steatohepatitis and to progress to cirrhosis. These differences in susceptibility to SH and its progression to cirrhosis have been attributed to a complex interplay of genetic and environmental factors. The concept underpinning this project is that the various environmental exposures and genetic factors affect many different molecules in many different ways, but have very similar effects on network function. Accordingly, the proposed project will adopt a systems biology approach to develop a computational model for S and SH based on gene expression data generated from individualized in vitro differentiated hepatocytes exposed to various environmental stimuli. To achieve this goal we will employ the recently developed technique to reprogram fibroblasts from skin biopsies into iPS cells (induced Pluripotent Stem cells) employing virus-mediated transduction of the embryonic stem cell specific transcription factors-OCT4, SOX2, KlF4 and c-MYC. The systems biology modelling will address the differentiation of the fibroblasts-derived iPS cells into hepatocyte-like cells, the network stress, the network response to the stress, and to a limited extent the failure of the network to respond to the stress.

iPS cells derived from S and SH patients with various genetic or acquired disorders could be used to develop new disease models and increase our meagre knowledge of the underlying mechanisms. They could be eventually used as a source for autologous cell therapies in stem cell-based regenerative medicine. The projected in silico model of steatosis and steatohepatitis comprising its underlying metabolic pathways and regulatory processes can help to understand the disease progression in more detail. The model can foster the identification of potential intervention points for new therapies and thus contribute to the development of new drugs. Supported by the increasing knowledge on genetic variation in the human genome the model can also contribute to a personalized approach to liver stress and personalized therapies medicine of steatohepatitis progression and appropriate therapies for its prevention. The projected in silico model can directly be exploited by the company Alacris Pharmaceutical.