A Multi-Parameter Analysis of Cellular Coordination of Major Transcriptome Regulation Mechanisms.

To understand cellular coordination of multiple transcriptome regulation mechanisms, we simultaneously measured transcription rate (TR), mRNA abundance (RA) and translation activity (TA). This revealed multiple insights. First, the three parameters displayed systematic statistical differences. Sequentially more genes exhibited extreme (low or high) expression values from TR to RA, and then to TA; that is, cellular coordination of multiple transcriptome regulatory mechanisms leads to sequentially enhanced gene expression selectivity as the genetic information flow from the genome to the proteome. Second, contribution of the stabilization-by-translation regulatory mechanism to the cellular coordination process was assessed. The data enabled an estimation of mRNA stability, revealing a moderate but significant positive correlation between mRNA stability and translation activity. Third, the proportion of mRNA occupied by un-translated regions (UTR) exhibited a negative relationship with the level of this correlation, and was thus a major determinant of the mode of regulation of the mRNA. High-UTR-proportion mRNAs tend to defy the stabilization-by-translation regulatory mechanism, staying out of the polysome but remaining stable; mRNAs with little UTRs largely followed this regulation. In summary, we quantitatively delineated the relationship among multiple transcriptome regulation parameters, i.e., cellular coordination of corresponding regulatory mechanisms.

Relationship between gene duplicability and diversifiability in the topology of biochemical networks.

BACKGROUND: Selective gene duplicability, the extensive expansion of a small number of gene families, is universal. Quantitatively, the number of genes (P(K)) with K duplicates in a genome decreases precipitously as K increases, and often follows a power law (P(k)∝k-α). Functional diversification, either neo- or sub-functionalization, is a major evolution route for duplicate genes. RESULTS: Using three lines of genomic datasets, we studied the relationship between gene duplicability and diversifiability in the topology of biochemical networks. First, we explored scenario where two pathways in the biochemical networks antagonize each other. Synthetic knockout of respective genes for the two pathways rescues the phenotypic defects of each individual knockout. We identified duplicate gene pairs with sufficient divergences that represent this antagonism relationship in the yeast S. cerevisiae. Such pairs overwhelmingly belong to large gene families, thus tend to have high duplicability. Second, we used distances between proteins of duplicate genes in the protein interaction network as a metric of their diversification. The higher a gene's duplicate count, the further the proteins of this gene and its duplicates drift away from one another in the networks, which is especially true for genetically antagonizing duplicate genes. Third, we computed a sequence-homology-based clustering coefficient to quantify sequence diversifiability among duplicate genes - the lower the coefficient, the more the sequences have diverged. Duplicate count (K) of a gene is negatively correlated to the clustering coefficient of its duplicates, suggesting that gene duplicability is related to the extent of sequence divergence within the duplicate gene family. CONCLUSION: Thus, a positive correlation exists between gene diversifiability and duplicability in the context of biochemical networks - an improvement of our understanding of gene duplicability.

Optimization of time-course experiments for kinetic model discrimination.

Systems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key experiments. Often, it is possible to find several models that describe the data equally well, but provide significantly different quantitative predictions regarding particular variables of the network. In those cases, one is faced with a problem of model discrimination, the procedure of rejecting inappropriate models from a set of candidates in order to elect one as the best model to use for prediction.In this work, a method is proposed to optimize the design of enzyme kinetic assays with the goal of selecting a model among a set of candidates. We focus on models with systems of ordinary differential equations as the underlying mathematical description. The method provides a design where an extension of the Kullback-Leibler distance, computed over the time courses predicted by the models, is maximized. Given the asymmetric nature this measure, a generalized differential evolution algorithm for multi-objective optimization problems was used.The kinetics of yeast glyoxalase I (EC 4.4.1.5) was chosen as a difficult test case to evaluate the method. Although a single-substrate kinetic model is usually considered, a two-substrate mechanism has also been proposed for this enzyme. We designed an experiment capable of discriminating between the two models by optimizing the initial substrate concentrations of glyoxalase I, in the presence of the subsequent pathway enzyme, glyoxalase II (EC 3.1.2.6). This discriminatory experiment was conducted in the laboratory and the results indicate a two-substrate mechanism for the kinetics of yeast glyoxalase I.

Metabolic impact of redox cofactor perturbations in Saccharomyces cerevisiae.

Redox cofactors play a pivotal role in coupling catabolism with anabolism and energy generation during metabolism. There exists a delicate balance in the intracellular level of these cofactors to ascertain an optimal metabolic output. Therefore, cofactors are emerging to be attractive targets to induce widespread changes in metabolism. We present a detailed analysis of the impact of perturbations in redox cofactors in the cytosol or mitochondria on glucose and energy metabolism in Saccharomyces cerevisiae to aid metabolic engineering decisions that involve cofactor engineering. We enhanced NADH oxidation by introducing NADH oxidase or alternative oxidase, its ATP-mediated conversion to NADPH using NADH kinase as well as the interconversion of NADH and NADPH independent of ATP by the soluble, non-proton-translocating bacterial transhydrogenase. Decreasing cytosolic NADH level lowered glycerol production, while decreasing mitochondrial NADH lowered ethanol production. However, when these reactions were coupled with NADPH production, the metabolic changes were more moderated. The direct consequence of these perturbations could be seen in the shift of the intracellular concentrations of the cofactors. The changes in product profile and intracellular metabolite levels were closely linked to the ATP requirement for biomass synthesis and the efficiency of oxidative phosphorylation, as estimated from a simple stoichiometric model. The results presented here will provide valuable insights for a quantitative understanding and prediction of cellular response to redox-based perturbations for metabolic engineering applications.