Varying environments can speed up evolution

Kashtan, Nadav; Noor, Elad; Alon, Uri

doi:10.1073/pnas.0611630104

Cited by 268 publications

(302 citation statements)

References 36 publications

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“…The second maintains that, although modularity is not directly selected for, there is nevertheless an evolutionary congruence between modularity and other directly selectable properties. Such properties may include acceleration of gene clustering due to horizontal gene transfer (HGT) (in accordance with the selfish-operon theory) (16), the minimization of pleiotropic effects (17), and adaptation to new environments (18,19). Indeed, a recent study of 117 bacterial metabolic networks (20) has found that the level of variability of the environment in which a bacterial species resides is positively correlated with its modularity, supporting the hypothesis that environmental variability promotes modularity.…”

mentioning

confidence: 88%

The evolution of modularity in bacterial metabolic networks

Kreimer¹,

Borenstein

Gophna³

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

197

173

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Deciphering the modular organization of metabolic networks and understanding how modularity evolves have attracted tremendous interest in recent years. Here, we present a comprehensive large scale characterization of modularity across the bacterial tree of life, systematically quantifying the modularity of the metabolic networks of >300 bacterial species. Three main determinants of metabolic network modularity are identified. First, network size is an important topological determinant of network modularity. Second, several environmental factors influence network modularity, with endosymbionts and mammal-specific pathogens having lower modularity scores than bacterial species that occupy a wider range of niches. Moreover, even among the pathogens, those that alternate between two distinct niches, such as insect and mammal, tend to have relatively high metabolic network modularity. Third, horizontal gene transfer is an important force that contributes significantly to metabolic modularity. We additionally reconstruct the metabolic network of ancestral bacterial species and examine the evolution of modularity across the tree of life. This reveals a trend of modularity decrease from ancestors to descendants that is likely the outcome of niche specialization and the incorporation of peripheral metabolic reactions.horizontal gene transfer ͉ lateral gene transfer ͉ systems biology ͉ bacterial evolution ͉ network modules M odularity is considered to be one of the main organizing principles of biological networks (1, 2). A biological network module consists of a set of elements (e.g., proteins/ reactions) that form a coherent structural subsystem and have a distinct function. Several studies have explored the role of modularity and network organization in various proteininteraction and regulatory cellular networks (3-10). Focusing specifically on modularity in metabolic networks (which is also the subject of this article), the metabolic networks of 43 distinct organisms were shown to be organized in many small, highly connected topologic modules that hierarchically combine into larger units (11). The functional and evolutionary modularity of the human metabolic network was also investigated from a topological perspective by using network decomposition (12). The network was shown to be organized in a highly modular way into basic core metabolism modules and peripheral modules that have specialized functions and evolve at a faster rate.Considering the evolution of modularity, two major hypotheses have been proposed: the ''evolution of evolvability'' and the congruence principle (13). The first posits that there is positive selection favoring modularity because it enhances evolvability by enabling evolutionary changes to take place in confined modules while preserving global cellular functions (14, 15). The second maintains that, although modularity is not directly selected for, there is nevertheless an evolutionary congruence between modularity and other directly selectable properties. Such properties may include accelerat...

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mentioning

confidence: 88%

The evolution of modularity in bacterial metabolic networks

Kreimer¹,

Borenstein

Gophna³

et al. 2008

Proc. Natl. Acad. Sci. U.S.A.

197

173

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“…chromatin), and surrounded by transcriptional changes Corresponding author: Ralser, M. (ralser@molgen.mpg.de) requirements drive the evolution of modular structures [14]. Moreover, bacteria that live in complex environments, such as soil, possess greater network modularity than do bacterial parasites that live in constant environments [15].…”

Section: Glossarymentioning

confidence: 99%

Regulatory crosstalk of the metabolic network

Grüning

Lehrach

Ralser

2010

Trends in Biochemical Sciences

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The metabolic network has a modular architecture, is robust to perturbations, and responds to biological stimuli and environmental conditions. Through monitoring by metabolite responsive macromolecules, metabolic pathways interact with the transcriptome and proteome. Whereas pathway interconnecting cofactors and substrates report on the overall state of the network, specialised intermediates measure the activity of individual functional units. Transitions in the network affect many of these regulatory metabolites, facilitating the parallel regulation of the timing and control of diverse biological processes. The metabolic network controls its own balance, chromatin structure and the biosynthesis of molecular cofactors; moreover, metabolic shifts are crucial in the response to oxidative stress and play a regulatory role in cancer.Evolution of the metabolic network and its structure Life probably began with the formation of compartmentalised autocatalytic chemical cycles. With the appearance of complex catalysts (ribonucleic acid-or protein-based enzymes), these cycles gained complexity, and evolved in their effectiveness and robustness [1]. These metabolic pathways now form the basis for life.As was the case for the first primitive life forms, the survival of today's complex organisms depends on the robustness and functionality of the metabolic framework [2]. To prevent and counteract perturbations in the flux, many biological control and sensor systems have evolved around the metabolic network. Metabolic pathways provide the cell with energy and supply macromolecular synthesis pathways with their required intermediates. They also balance metabolic systems under a variety of physiological conditions, and act as transducers and sensor systems for environmental conditions and stimuli. In addition, metabolic pathways are essential for crosstalk between metabolic regulatory components and the cellular transcriptome and proteome.As early as the 1960s, the principle that cells alter their gene expression in response to metabolic requirements has been known. The seminal work of Jacob and Monod, investigating the lac operon, uncovered one of the first examples of chemical-genetic regulation, by which bacterial cells adjust their enzyme production to the nutrient supply [3]. However, only recently have the broader implications of this principle emerged. Indeed, changes in the metabolic network not only control the metabolome, but they also have wide-ranging regulatory functions throughout biology.Most of the biochemical mechanisms that link the metabolic network to the transcriptome and proteome are incompletely understood. However, one type of regulation appears to be common: representative network intermediates bind to and modulate sensor function and activity of transcription factors, translational regulators, chromatin, enzymes, RNA molecules, and ion channels. Significant transcriptional changes around these intermediates identified them as reporter metabolites [4,5]. The use of intermediates as activity reporters ne...

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“…Controlled experiments 28 , rather than natural experiments as here, will be needed to progress from asking whether Lévy flights (walks) occur in animals 8,9 to exploring why they occur and whether animals evolved such that they exploit Lévy flights as an optimal search strategy for life in complex, highly changeable landscapes. Simulations of biological evolution indicate that varying environments posing complex goals can speed up natural selection 29 , which also raises the question of when, if animals have evolved Lévy flight behaviour, did such a strategy first appear among organisms.…”

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confidence: 99%

Environmental context explains Lévy and Brownian movement patterns of marine predators

Humphries

Queiroz

Dyer

et al. 2010

Nature

812

816

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An optimal search theory, the so-called Lévy-flight foraging hypothesis 1 , predicts that predators should adopt search strategies known as Lévy flights where prey is sparse and distributed unpredictably, but that Brownian movement is sufficiently efficient for locating abundant prey [2][3][4] . Empirical studies have generated controversy because the accuracy of statistical methods that have been used to identify Lévy behaviour has recently been questioned 5,6 . Consequently, whether foragers exhibit Lévy flights in the wild remains unclear. Crucially, moreover, it has not been tested whether observed movement patterns across natural landscapes having different expected resource distributions conform to the theory's central predictions. Here we use maximum-likelihood methods to test for Lévy patterns in relation to environmental gradients in the largest animal movement data set assembled for this purpose. Strong support was found for Lévy search patterns across 14 species of open-ocean predatory fish (sharks, tuna, billfish and ocean sunfish), with some individuals switching between Lévy and Brownian movement as they traversed different habitat types. We tested the spatial occurrence of these two principal patterns and found Lévy behaviour to be associated with less productive waters (sparser prey) and Brownian movements to be associated with productive shelf or convergence-front habitats (abundant prey). These results are consistent with the Lévy-flight foraging hypothesis 1,7 , supporting the contention 8,9 that organism search strategies naturally evolved in such a way that they exploit optimal Lévy patterns.Lévy flights are a special class of random walk with movement displacements (steps) drawn from a probability distribution with a power-law tail (the so-called Pareto-Lévy distribution) 1,10 , and give rise to stochastic processes closely linked to fractal geometry and anomalous diffusion phenomena 7,11 . Lévy flights describe a movement pattern characterized by many small steps connected by longer relocations, with this pattern having scale invariance under projection, such that the probability density function, P(l j ), has a power-law tail in the long-distance regime: P(l j ) < l j 2m , where l j is the flight length (step length of move j), and m, 1 , m # 3, is the power-law exponent. Lévy flights comprise instantaneous steps and hence involve infinite velocities, whereas a Lévy walk 10 refers to a finitevelocity walk such that displacement is determined after a time t, reflecting a dynamical process such as movement 1,10,11 . Lévy flights and walks are theorized to be the most efficient movement pattern for locating patchy prey in low concentrations on spatial scales beyond a searcher's sensory range, with an optimal search having a power-law exponent of m < 2 (refs 4, 13). It is proposed that organisms have therefore naturally evolved search patterns that can be modelled as optimal Lévy flights 1,7,13 .However, burgeoning empirical support for this hypothesis recently foundered following studies sug...

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Varying environments can speed up evolution

Cited by 268 publications

References 36 publications

The evolution of modularity in bacterial metabolic networks

The evolution of modularity in bacterial metabolic networks

Regulatory crosstalk of the metabolic network

Environmental context explains Lévy and Brownian movement patterns of marine predators

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