Major proliferation of transposable elements shaped the genome of the soybean rust pathogen Phakopsora pachyrhizi.

With >7000 species the order of rust fungi has a disproportionately large impact on agriculture, horticulture, forestry and foreign ecosystems. The infectious spores are typically dikaryotic, a feature unique to fungi in which two haploid nuclei reside in the same cell. A key example is Phakopsora pachyrhizi, the causal agent of Asian soybean rust disease, one of the world's most economically damaging agricultural diseases. Despite P. pachyrhizi's impact, the exceptional size and complexity of its genome prevented generation of an accurate genome assembly. Here, we sequence three independent P. pachyrhizi genomes and uncover a genome up to 1.25 Gb comprising two haplotypes with a transposable element (TE) content of ~93%. We study the incursion and dominant impact of these TEs on the genome and show how they have a key impact on various processes such as host range adaptation, stress responses and genetic plasticity.

Redox balance is key to explaining full vs. partial switching to low-yield metabolism.

BACKGROUND: Low-yield metabolism is a puzzling phenomenon in many unicellular and multicellular organisms. In abundance of glucose, many cells use a highly wasteful fermentation pathway despite the availability of a high-yield pathway, producing many ATP molecules per glucose, e.g., oxidative phosphorylation. Some of these organisms, including the lactic acid bacterium Lactococcus lactis, downregulate their high-yield pathway in favor of the low-yield pathway. Other organisms, including Escherichia coli do not reduce the flux through the high-yield pathway, employing the low-yield pathway in parallel with a fully active high-yield pathway. For what reasons do some species use the high-yield and low-yield pathways concurrently and what makes others downregulate the high-yield pathway? A classic rationale for metabolic fermentation is overflow metabolism. Because the throughput of metabolic pathways is limited, influx of glucose exceeding the pathway's throughput capacity is thought to be redirected into an alternative, low-yield pathway. This overflow metabolism rationale suggests that cells would only use fermentation once the high-yield pathway runs at maximum rate, but it cannot explain why cells would decrease the flux through the high-yield pathway. RESULTS: Using flux balance analysis with molecular crowding (FBAwMC), a recent extension to flux balance analysis (FBA) that assumes that the total flux through the metabolic network is limited, we investigate the differences between Saccharomyces cerevisiae and L. lactis that downregulate the high-yield pathway at increasing glucose concentrations, and E. coli, which keeps the high-yield pathway functioning at maximal rate. FBAwMC correctly predicts the metabolic switching mode in these three organisms, suggesting that metabolic network architecture is responsible for differences in metabolic switching mode. Based on our analysis, we expect gradual, "overflow-like" switching behavior in organisms that have an additional energy-yielding pathway that does not consume NADH (e.g., acetate production in E. coli). Flux decrease through the high-yield pathway is expected in organisms in which the high-yield and low-yield pathways compete for NADH. In support of this analysis, a simplified model of metabolic switching suggests that the extra energy generated during acetate production produces an additional optimal growth mode that smoothens the metabolic switch in E. coli. CONCLUSIONS: Maintaining redox balance is key to explaining why some microbes decrease the flux through the high-yield pathway, while other microbes use "overflow-like" low-yield metabolism.

Metabolic adaptation after whole genome duplication.

Whole genome duplications (WGDs) have been hypothesized to be responsible for major transitions in evolution. However, the effects of WGD and subsequent gene loss on cellular behavior and metabolism are still poorly understood. Here we develop a genome scale evolutionary model to study the dynamics of gene loss and metabolic adaptation after WGD. Using the metabolic network of Saccharomyces cerevisiae as an example, we primarily study the outcome of WGD on yeast as it currently is. However, similar results were obtained using a recontructed hypothetical metabolic network of the pre-WGD ancestor. We show that the retention of genes in duplicate in the model, corresponds nicely with those retained in duplicate after the ancestral WGD in S. cerevisiae. Also, we observe that transporter and glycolytic genes have a higher probability to be retained in duplicate after WGD and subsequent gene loss, both in the model as in S. cerevisiae, which leads to an increase in glycolytic flux after WGD. Furthermore, the model shows that WGD leads to better adaptation than small-scale duplications, in environments for which duplication of a whole pathway instead of single reactions is needed to increase fitness. This is indeed the case for adaptation to high glucose levels. Thus, our model confirms the hypothesis that WGD has been important in the adaptation of yeast to the new, glucose-rich environment that arose after the appearance of angiosperms. Moreover, the model shows that WGD is almost always detrimental on the short term in environments to which the lineage is preadapted, but can have immediate fitness benefits in "new" environments. This explains why WGD, while pivotal in the evolution of many lineages and an apparent "easy" genetic operator, occurs relatively rarely.

The role of mutational dynamics in genome shrinkage.

Genome shrinkage occurs after whole genome duplications (WGDs) and in the evolution of parasitic or symbiotic species. The dynamics of this process, whether it occurs by single gene deletions or also by larger deletions are however unknown. In yeast, genome shrinkage has occurred after a WGD. Using a computational model of genome evolution, we show that in a random genome single gene deletions cannot explain the observed pattern of gene loss in yeast. The distribution of genes deleted per event can be very well described by a geometric distribution, with a mean of 1.1 genes per event. In terms of deletions of a stretch of base pairs, we find that a geometric distribution with an average of 500-600 base pairs per event describes the data very well. Moreover, in the model, as in the data, gene pairs that have a small intergenic distance are more likely to be both deleted. This proves that simultaneous deletion of multiple genes causes the observed pattern of gene deletions, rather than deletion of functionally clustered genes by selection. Furthermore, we found that in the bacterium Buchnera aphidicola larger deletions than in yeast are necessary to explain the clustering of deleted genes. We show that the excess clustering of deleted genes in B. aphidicola can be explained by the clustering of genes in operons. Therefore, we show that selection has little effect on the clustering of deleted genes after the WGD in yeast, while it has during genome shrinkage in B. aphidicola.

The effect of stochasticity on the lac operon: an evolutionary perspective.

The role of stochasticity on gene expression is widely discussed. Both potential advantages and disadvantages have been revealed. In some systems, noise in gene expression has been quantified, in among others the lac operon of Escherichia coli. Whether stochastic gene expression in this system is detrimental or beneficial for the cells is, however, still unclear. We are interested in the effects of stochasticity from an evolutionary point of view. We study this question in the lac operon, taking a computational approach: using a detailed, quantitative, spatial model, we evolve through a mutation-selection process the shape of the promoter function and therewith the effective amount of stochasticity. We find that noise values for lactose, the natural inducer, are much lower than for artificial, nonmetabolizable inducers, because these artificial inducers experience a stronger positive feedback. In the evolved promoter functions, noise due to stochasticity in gene expression, when induced by lactose, only plays a very minor role in short-term physiological adaptation, because other sources of population heterogeneity dominate. Finally, promoter functions evolved in the stochastic model evolve to higher repressed transcription rates than those evolved in a deterministic version of the model. This causes these promoter functions to experience less stochasticity in gene expression. We show that a high repression rate and hence high stochasticity increases the delay in lactose uptake in a variable environment. We conclude that the lac operon evolved such that the impact of stochastic gene expression is minor in its natural environment, but happens to respond with much stronger stochasticity when confronted with artificial inducers. In this particular system, we have shown that stochasticity is detrimental. Moreover, we demonstrate that in silico evolution in a quantitative model, by mutating the parameters of interest, is a promising way to unravel the functional properties of biological systems.