Modeling Stem Cell Induction Processes

Technology for converting human cells to pluripotent stem cell using induction processes has the potential to revolutionize regenerative medicine. However, the production of these so called iPS cells is still quite inefficient and may be dominated by stochastic effects. In this work we build mass-action models of the core regulatory elements controlling stem cell induction and maintenance. The models include not only the network of transcription factors NANOG, OCT4, SOX2, but also important epigenetic regulatory features of DNA methylation and histone modification. We show that the network topology reported in the literature is consistent with the observed experimental behavior of bistability and inducibility. Based on simulations of stem cell generation protocols, and in particular focusing on changes in epigenetic cellular states, we show that cooperative and independent reaction mechanisms have experimentally identifiable differences in the dynamics of reprogramming, and we analyze such differences and their biological basis. It had been argued that stochastic and elite models of stem cell generation represent distinct fundamental mechanisms. Work presented here suggests an alternative possibility that they represent differences in the amount of information we have about the distribution of cellular states before and during reprogramming protocols. We show further that unpredictability and variation in reprogramming decreases as the cell progresses along the induction process, and that identifiable groups of cells with elite-seeming behavior can come about by a stochastic process. Finally we show how different mechanisms and kinetic properties impact the prospects of improving the efficiency of iPS cell generation protocols.Fundação para a Ciência e a Tecnologia (BD 42942)MIT-Portugal ProgramNational Institutes of Health (U.S.) (CA112967)Singapore–MIT Alliance for Research and TechnologyIntel Corporatio

E-NTPDases in human airways: Regulation and relevance for chronic lung diseases

Chronic obstructive lung diseases are characterized by the inability to prevent bacterial infection and a gradual loss of lung function caused by recurrent inflammatory responses. In the past decade, numerous studies have demonstrated the importance of nucleotide-mediated bacterial clearance. Their interaction with P2 receptors on airway epithelia provides a rapid ‘on-and-off’ signal stimulating mucus secretion, cilia beating activity and surface hydration. On the other hand, abnormally high ATP levels resulting from damaged epithelia and bacterial lysis may cause lung edema and exacerbate inflammatory responses. Airway ATP concentrations are regulated by ecto nucleoside triphosphate diphosphohydrolases (E-NTPDases) which are expressed on the mucosal surface and catalyze the sequential dephosphorylation of nucleoside triphosphates to nucleoside monophosphates (ATP → ADP → AMP). The common bacterial product, Pseudomonas aeruginosa lipopolysaccharide (LPS), induces an acute reduction in azide-sensitive E-NTPDase activities, followed by a sustained increase in activity as well as NTPDase 1 and NTPDase 3 expression. Accordingly, chronic lung diseases, including cystic fibrosis (CF) and primary ciliary dyskinesia, are characterized by higher rates of nucleotide elimination, azide-sensitive E-NTPDase activities and expression. This review integrates the biphasic regulation of airway E-NTPDases with the function of purine signaling in lung diseases. During acute insults, a transient reduction in E-NTPDase activities may be beneficial to stimulate ATP-mediated bacterial clearance. In chronic lung diseases, elevating E-NTPDase activities may represent an attempt to prevent P2 receptor desensitization and nucleotide-mediated lung damage

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell population's growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings