The specific goal of this proposal is to construct a data-driven mathematical model of human embryonic stem cells so that we can make predictions about how best to optimize self-renewal and differentiation. Long-term self-renewal is a property shared only by stem cells. Self-renewal is the ability to grow robustly without differentiating or mutating. Human embryonic stem cell (hES cell) lines differ dramatically in their potential for self-renewal. We will compare federally-approved hES cell lines that have poor self-renewal with newer, non-approved lines that are robust. The data from these comparisons will be entered into our mathematical model of hES cells and used to make predictions about the genetic differences between approved and non-approved lines that explain the discrepancies in their performance, and to predict changes in the growth conditions that improve self-renewal for any given hES cell line. We will compare undifferentiated, self-renewing hES cells to their progeny during and after the process of differentiation. The data from these comparisons will be entered into our mathematical model of hES cells and used to make predictions about genetic differences or conditions of growth that enable the early and terminal stages of differentiation. We will examine in detail the proteins that mediate metabolism in hES cells and predict which proteins and their substrates within the cells or in the growth media determine self-renewal and differentiation. As part of this detailed examination we will isolate the metabolic engines of hES cells, mitochondria and peroxisomes, and compare the changes in proteins and metabolites that occur in them during self-renewal or differentiation. The product of this project will be a computer model that any investigator can download and use to: guide their creation of hES cell lines; develop hES cell growth conditions; or produce differentiated tissues from hES cells for regenerative medicine.
Statement of Benefit to California:
California will benefit from funding this grant because it will accelerate the progress of many, if not all, research projects on hES cells. Our data-driven model of hES cells could be the basis for new biotechnology companies or it could be broadly licensed to enable all companies to accelerate their development of cell-based therapies.
SYNOPSIS: The goal is to construct the metabolic and signaling networks of mitochondria and peroxisomes in hESCs using a data-driven mathematical model of hES cells to facilitate predictions about optimizing self-renewal and differentiation, especially in regard to the anaerobic to aerobic shift. The strategy is to characterize isolated mitochondria and peroxisomes via proteomic profiles, including unmodified and phosphorylated proteins and correlate with measurements of substrate uptake and product secretion. Profiles of mRNAs and proteins of intact hES cells will confirm observations from isolated organelles and extend the analysis to extra-organellar proteins. Changes during differentiation to EBs and committed lineages will be determined. The first goal is to develop a proteomic definition of whole cells, mitochondria and peroxisomes. The second goal is to characterize metabolic flux by measurement of core-metabolism uptake, secretion fluxes and microarray transcript profiles to incorporate into the in-silico model. SIGNIFICANCE AND INNOVATION: The question of metabolism is widely ignored. A comprehensive overview of human stem cell metabolism, the changes that occur upon commitment to separate differentiation programs, and the final differences with mature cells in situ could be exceedingly valuable and have a high impact on the field. The great potential innovation in this application is the marriage of the expertise of the PI, and his recent construction of a comprehensive analysis of human ES cell metabolism. STRENGTHS: The PI's expertise and the state of the art proteomics, genomics and metabolic mapping are critical strengths. The creation of RECON 1, the reconstruction of the global human metabolic network from the human genome and comprehensive literature analysis, is a tour de force. The PI is an expert in genomic and proteomic analysis of plants and has recently applied this expertise to examine the human metabolic network by integrating gene expression data, proteomics, and metabolic analyses. The results of simulations, though not given for review, indicate that the in-silico network accurately reflects many metabolic functions. WEAKNESSES: If sufficient amounts of a homogeneous undifferentiated hESC population can be obtained and confirmed, then this expert group will discover which major energy metabolic paths are operational, but it is not clear how that information will help. Also unclear is how the analysis of embryoid bodies with a broad collection of differentiated and partly differentiated cell types can be a useful comparison with the singular undifferentiated state of a hES cell line. A huge amount of work is proposed, and an experimental plan is generally lacking. For example, as implied in the abstract, the analysis of homogeneous differentiated (committed) cells derived in-vitro would indeed be an ideal solution, but no information is presented concerning which committed lineages will be examined and how they would be generated. The lack of a clear description of applications directly related to data acquisition, methodology and relevance to human ES cell biology suggests that this was viewed as an opportunity to obtain data about an interesting cell without a clear indication of how this information can be applied to important questions of hES cell biology. Several other minor weaknesses are found in this proposal. First, Dr. Karl Willert, who directs the UCSD Human Stem Cell Core Facility, will consult and make available the equipment of the Core, but no preliminary data is presented and no group members have experience with ES cells. Second, this study could be done equally with approved and nonapproved human lines. Third, an independent evaluation of the RECON1 model was unavailable. DISCUSSION: This proposal aims to study metabolism as hESCs differentiate. The goal of the work is to construct metabolism and signaling networks in mitochondria and peroxisomes, and a mathematical model is sought for understanding differentiation as it relates to anaerobic shift. This applicant is from an established National Academy of Sciences member working with plant genomes and networks. He is a real leader in the field of cell metabolism, which makes this group better qualified than any other to undertake the proposed studies. It is desirable to recruit a biophysicist of this stature to work in the field; however, there is substantial naivety reflected in what it takes to work in hESC. mESC might be more tractable. The proposal could benefit from a more limited scope as reviewers don't see how the work could be accomplished in 2 years. The experimental plan is largely missing, and a number of blind alleys exist. There is an excessive use of jargon regarding metabolic networks, and there is nothing in the grant about the nature of the cells, the differentiation of embryoid bodies, or how to manipulate the cells. It is unclear if the applicant will be able to get cell populations of sufficient homogeneity to get meaningful data - EBs are unlikely to be such a homogenous population. Reviewers generally agreed that this is important work in a largely unexplored area that should be done once a better proposal is developed, and the addition of a full time ES cell lab would benefit this proposal.