Gene divergence and pathway duplication in the metabolic network of yeast and digital organisms

A systems level approach to study metabolic networks in prokaryotes with the aromatic amino acid biosynthesis pathway

Metabolism of an organism underlies its phenotype, which depends on many factors, such as the genetic makeup, habitat, and stresses to which it is exposed. This is particularly important for the prokaryotes, which undergo significant vertical and horizontal gene transfers. In this study we have used the energy-intensive Aromatic Amino Acid (Tryptophan, Tyrosine and Phenylalanine, TTP) biosynthesis pathway, in a large number of prokaryotes, as a model system to query the different levels of organization of metabolism in the whole intracellular biochemical network, and to understand how perturbations, such as mutations, affects the metabolic flux through the pathway - in isolation and in the context of other pathways connected to it. Using an agglomerative approach involving complex network analysis and Flux Balance Analyses (FBA), of the Tryptophan, Tyrosine and Phenylalanine and other pathways connected to it, we identify several novel results. Using the reaction network analysis and Flux Balance Analyses of the Tryptophan, Tyrosine and Phenylalanine and the genome-scale reconstructed metabolic pathways, many common hubs between the connected networks and the whole genome network are identified. The results show that the connected pathway network can act as a proxy for the whole genome network in Prokaryotes. This systems level analysis also points towards designing functional smaller synthetic pathways based on the reaction network and Flux Balance Analyses analysis.

Coevolution trumps pleiotropy: carbon assimilation traits are independent of metabolic network structure in budding yeast

Phenotypic traits may be gained and lost together because of pleiotropy, the involvement of common genes and networks, or because of simultaneous selection for multiple traits across environments (multiple-trait coevolution). However, the extent to which network pleiotropy versus environmental coevolution shapes shared responses has not been addressed. To test these alternatives, we took advantage of the fact that the genus Saccharomyces has variation in habitat usage and diversity in the carbon sources that a given strain can metabolize. We examined patterns of gain and loss in carbon utilization traits across 488 strains of Saccharomyces to investigate whether the structure of metabolic pathways or selection pressure from common environments may have caused carbon utilization traits to be gained and lost together. While most carbon sources were gained and lost independently of each other, we found four clusters that exhibit non-random patterns of gain and loss across strains. Contrary to the network pleiotropy hypothesis, we did not find that these patterns are explained by the structure of metabolic pathways or shared enzymes. Consistent with the hypothesis that common environments shape suites of phenotypes, we found that the environment a strain was isolated from partially predicts the carbon sources it can assimilate.

Random phenotypic variation of yeast (Saccharomyces cerevisiae) single-gene knockouts fits a double pareto-lognormal distribution

BACKGROUND

Distributed robustness is thought to influence the buffering of random phenotypic variation through the scale-free topology of gene regulatory, metabolic, and protein-protein interaction networks. If this hypothesis is true, then the phenotypic response to the perturbation of particular nodes in such a network should be proportional to the number of links those nodes make with neighboring nodes. This suggests a probability distribution approximating an inverse power-law of random phenotypic variation. Zero phenotypic variation, however, is impossible, because random molecular and cellular processes are essential to normal development. Consequently, a more realistic distribution should have a y-intercept close to zero in the lower tail, a mode greater than zero, and a long (fat) upper tail. The double Pareto-lognormal (DPLN) distribution is an ideal candidate distribution. It consists of a mixture of a lognormal body and upper and lower power-law tails.

OBJECTIVE AND METHODS

If our assumptions are true, the DPLN distribution should provide a better fit to random phenotypic variation in a large series of single-gene knockout lines than other skewed or symmetrical distributions. We fit a large published data set of single-gene knockout lines in Saccharomyces cerevisiae to seven different probability distributions: DPLN, right Pareto-lognormal (RPLN), left Pareto-lognormal (LPLN), normal, lognormal, exponential, and Pareto. The best model was judged by the Akaike Information Criterion (AIC).

RESULTS

Phenotypic variation among gene knockouts in S. cerevisiae fits a double Pareto-lognormal (DPLN) distribution better than any of the alternative distributions, including the right Pareto-lognormal and lognormal distributions.

CONCLUSIONS AND SIGNIFICANCE

A DPLN distribution is consistent with the hypothesis that developmental stability is mediated, in part, by distributed robustness, the resilience of gene regulatory, metabolic, and protein-protein interaction networks. Alternatively, multiplicative cell growth, and the mixing of lognormal distributions having different variances, may generate a DPLN distribution.