Expression of a Mutant Retinoic Acid Receptor β Alters Lineage Differentiation in Mouse Embryonic Stem Cells

Toxic stress prioritizes and imbalances stem cell differentiation: implications for new biomarkers and in vitro toxicology tests

This hypothesis and review introduces rules of stem cell stress responses that provide biomarkers and alternative testing that replaces or reduces gestational tests using whole animals. These rules for the stress responses of cultured stem cells validate the organismal strategy of the stress response and show that it emulates what must happen if the conceptus implants during a response to stress in vivo. Specifically there is a profound threshold during a stress dose response where stem cell accumulation is significantly reduced. Below this threshold stress enzymes manage the stress response by converting anabolic to catabolic processes and by suppressing apoptosis, without affecting differentiation. However above this threshold the stem cell survival response converts to an organismal survival response where stress enzymes switch to new substrates and mediate loss of potency factors, gain of early essential differentiated lineages, and suppression of later essential lineages. Stressed stem cells 'compensate' for lower accumulation rates by differentiating a higher fraction of cells, and the organismal survival response further enhances adaptation by prioritizing the differentiation of early essential lineages. Thus compensatory and prioritized differentiation and the sets of markers produced are part of a response of cultured embryos and stem cells that emulate what must happen during implantation of a stressed gestation. Knowledge of these markers and use of stressed stem cell assays in culture should replace or reduce the number of animals needed for developmental toxicity and should produce biomarkers for stressed development in vitro and in vivo.

A scalable approach for discovering conserved active subnetworks across species

Overlaying differential changes in gene expression on protein interaction networks has proven to be a useful approach to interpreting the cell's dynamic response to a changing environment. Despite successes in finding active subnetworks in the context of a single species, the idea of overlaying lists of differentially expressed genes on networks has not yet been extended to support the analysis of multiple species' interaction networks. To address this problem, we designed a scalable, cross-species network search algorithm, neXus (Network - cross(X)-species - Search), that discovers conserved, active subnetworks based on parallel differential expression studies in multiple species. Our approach leverages functional linkage networks, which provide more comprehensive coverage of functional relationships than physical interaction networks by combining heterogeneous types of genomic data. We applied our cross-species approach to identify conserved modules that are differentially active in stem cells relative to differentiated cells based on parallel gene expression studies and functional linkage networks from mouse and human. We find hundreds of conserved active subnetworks enriched for stem cell-associated functions such as cell cycle, DNA repair, and chromatin modification processes. Using a variation of this approach, we also find a number of species-specific networks, which likely reflect mechanisms of stem cell function that have diverged between mouse and human. We assess the statistical significance of the subnetworks by comparing them with subnetworks discovered on random permutations of the differential expression data. We also describe several case examples that illustrate the utility of comparative analysis of active subnetworks.

Microarrays are a powerful tool for discovering genes whose expression is associated with a particular biological process or phenotype. Differential expression analysis can often generate a list of several hundred or even thousands of significant genes. While these genes represent real expression differences, the large number of candidates can make the process of hypothesis generation for further experimental studies challenging. Use of complementary datasets such as protein-protein interactions can help filter such candidate lists to genes involved with the most relevant pathways. This approach has been applied successfully by many groups, but to date, no one has developed an approach for discovering active pathways or subnetworks that are conserved across multiple species. We propose an algorithm, neXus (Network – cross(X)-species – Search), for cross-species active subnetwork discovery given candidate gene lists from two species and weighted protein-protein interaction networks. We validate our approach on expression studies from human and mouse stem cells. We find many active subnetworks that are conserved across species relevant to stem cell biology as well as other subnetworks that show species-specific behavior. We show that these networks are not likely to have been discovered by chance and discuss several specific cases that reveal potentially novel stem cell biology.