Ordered structure of the transcription network inherited from the yeast whole-genome duplication

<p>Abstract</p> <p>Background</p> <p>Gene duplication, a major evolutionary path to genomic innovation, can occur at the scale of an entire genome. One such "whole-genome duplication" (WGD) event among the Ascomycota fungi gave rise to genes with distinct biological properties compared to small-scale duplications.</p> <p>Results</p> <p>We studied the evolution of transcriptional interactions of whole-genome duplicates, to understand how they are wired into the yeast regulatory system. Our work combines network analysis and modeling of the large-scale structure of the interactions stemming from the WGD.</p> <p>Conclusions</p> <p>The results uncover the WGD as a major source for the evolution of a complex interconnected block of transcriptional pathways. The inheritance of interactions among WGD duplicates follows elementary "duplication subgraphs", relating ancestral interactions with newly formed ones. Duplication subgraphs are correlated with their neighbours and give rise to higher order circuits with two elementary properties: newly formed transcriptional pathways remain connected (paths are not broken), and are preferentially cross-connected with ancestral ones. The result is a coherent and connected "WGD-network", where duplication subgraphs are arranged in an astonishingly ordered configuration.</p

Complex networks: the key to systems biology

Though introduced recently, complex networks research has grown steadily because of its potential to represent, characterize and model a wide range of intricate natural systems and phenomena. Because of the intrinsic complexity and systemic organization of life, complex networks provide a specially promising framework for systems biology investigation. The current article is an up-to-date review of the major developments related to the application of complex networks in biology, with special attention focused on the more recent literature. The main concepts and models of complex networks are presented and illustrated in an accessible fashion. Three main types of networks are covered: transcriptional regulatory networks, protein-protein interaction networks and metabolic networks. The key role of complex networks for systems biology is extensively illustrated by several of the papers reviewed.FAPESPCNP

Horizontal and vertical growth of S. cerevisiae metabolic network

<p>Abstract</p> <p>Background</p> <p>The growth and development of a biological organism is reflected by its metabolic network, the evolution of which relies on the essential gene duplication mechanism. There are two current views about the evolution of metabolic networks. The retrograde model hypothesizes that a pathway evolves by recruiting novel enzymes in a direction opposite to the metabolic flow. The patchwork model is instead based on the assumption that the evolution is based on the exploitation of broad-specificity enzymes capable of catalysing a variety of metabolic reactions.</p> <p>Results</p> <p>We analysed a well-studied unicellular eukaryotic organism, <it>S. cerevisiae</it>, and studied the effect of the removal of paralogous gene products on its metabolic network. Our results, obtained using different paralog and network definitions, show that, after an initial period when gene duplication was indeed instrumental in expanding the metabolic space, the latter reached an equilibrium and subsequent gene duplications were used as a source of more specialized enzymes rather than as a source of novel reactions. We also show that the switch between the two evolutionary strategies in <it>S. cerevisiae </it>can be dated to about 350 million years ago.</p> <p>Conclusions</p> <p>Our data, obtained through a novel analysis methodology, strongly supports the hypothesis that the patchwork model better explains the more recent evolution of the <it>S. cerevisiae </it>metabolic network. Interestingly, the effects of a patchwork strategy acting before the Euascomycete-Hemiascomycete divergence are still detectable today.</p

Probabilistic methods in the analysis of protein interaction networks

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Protein interactions across and between eukaryotic kingdoms: networks, inference strategies, integration of functional data and evolutionary dynamics

Thesis (Ph.D.)--Boston UniversityHow cellular elements coordinate their function is a fundamental question in biology. A crucial step towards understanding cellular systems is the mapping of physical interactions between protein, DNA, RNA and other macromolecules or metabolites. Genome-scale technologies have yielded protein-protein interaction networks for several eukaryotic species and have provided insight into biological processes and evolution, but many of the currently available networks are biased. Towards a true human protein-protein interaction network, we examined literature-based aggregations of lowthroughput experiments, high-throughput experimental networks validated using different strategies, and predicted interaction networks to infer how the underlying interactome may differ from current maps. Using systematically mapped interactome networks, which appear to be the least biased, we explored the functional organization of Arabidopsis thaliana and characterize the asymmetric divergence of duplicated paralogous proteins through their interaction profiles. To further dissect the relationship between interactions and function enforced by evolution, we investigated a first-of-its-kind systematic crossspecies human-yeast hybrid interactome network. Although the cross-species network is topologically similar to conventional intra-species networks, we found signatures of dynamic changes in interaction propensities due to countervailing evolutionary forces. Collectively, these analyses of human, plant and yeast interactome networks bridge separate experiments to characterize bias, function and evolution across eukaryotic kingdoms

Measuring the Evolutionary Rewiring of Biological Networks

We have accumulated a large amount of biological network data and expect even more to come. Soon, we anticipate being able to compare many different biological networks as we commonly do for molecular sequences. It has long been believed that many of these networks change, or “rewire”, at different rates. It is therefore important to develop a framework to quantify the differences between networks in a unified fashion. We developed such a formalism based on analogy to simple models of sequence evolution, and used it to conduct a systematic study of network rewiring on all the currently available biological networks. We found that, similar to sequences, biological networks show a decreased rate of change at large time divergences, because of saturation in potential substitutions. However, different types of biological networks consistently rewire at different rates. Using comparative genomics and proteomics data, we found a consistent ordering of the rewiring rates: transcription regulatory, phosphorylation regulatory, genetic interaction, miRNA regulatory, protein interaction, and metabolic pathway network, from fast to slow. This ordering was found in all comparisons we did of matched networks between organisms. To gain further intuition on network rewiring, we compared our observed rewirings with those obtained from simulation. We also investigated how readily our formalism could be mapped to other network contexts; in particular, we showed how it could be applied to analyze changes in a range of “commonplace” networks such as family trees, co-authorships and linux-kernel function dependencies

Article Comparative Genomics as a Time Machine: How Relative Gene Dosage and Metabolic Requirements Shaped the Time-dependent Resolution of Yeast Polyploidy

Abstract Using a phylogenetic model of evolution after genome duplication (i.e., polyploidy) and 12 yeast genomes with a shared genome duplication, I show that the loss of duplicate genes after that duplication occurred in three phases. First, losses that occurred immediately after the event were biased toward genes functioning in DNA repair and organellar functions. Then, the main group of duplicate losses appear to have been shaped by a requirement to maintain balance in protein levels: There is a strong statistical association between the number of protein interactions a gene&apos;s product is involved in and its propensity to have remained in duplicate. Moreover, when duplicated genes with interactions were lost, it was more common than expected for both members of an interaction pair to have been lost on the same branch of the phylogeny. Finally, in the third phase of the resolution process, overretention of duplicated enzymes carrying high flux and of duplicated genes involved in transcriptional regulation became dominant. I speculate that initial retention of such genes by a requirement to maintain gene dosage set the stage for the later functional changes that then maintained these duplicates for long periods