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Title: Decoupling Environment-Dependent and Independent Genetic Robustness across Bacterial Species}
Article:
Abstract:
The evolutionary origins of genetic robustness are still under debate: it may arise as a consequence of requirements imposed by varying environmental conditions, due to intrinsic factors such as metabolic requirements, or directly due to an adaptive selection in favor of genes that allow a species to endure genetic perturbations. Stratifying the individual effects of each origin requires one to study the pertaining evolutionary forces across many species under diverse conditions. Here we conduct the first large-scale computational study charting the level of robustness of metabolic networks of hundreds of bacterial species across many simulated growth environments. We provide evidence that variations among species in their level of robustness reflect ecological adaptations. We decouple metabolic robustness into two components and quantify the extents of each: the first, environmental-dependent, is responsible for at least 20% of the non-essential reactions and its extent is associated with the species' lifestyle( specialized/generalist); the second, environmental-independent, is associated( correlation = ∼0. 6) with the intrinsic metabolic capacities of a species—higher robustness is observed in fast growers or in organisms with an extensive production of secondary metabolites. Finally, we identify reactions that are uniquely susceptible to perturbations in human pathogens, potentially serving as novel drug-targets.
Introduction:
Systematic deletion studies have shown that under laboratory conditions the large majority of genes in the genome are dispensable, and that in many cases dispensability depends on the experimental setting[1]. These studies have reinforced the notion of robustness of biological systems, which denotes the invariance of phenotypes in the face of perturbations[2]. Two types of perturbations are encountered by biological systems: genetic alterations and environmental variations. Genetic robustness of a biological system is viewed as its ability to continue functioning following mutations[3],[4], while environmental robustness refers to buffering against external changes( e. g., changes in temperature and salinity, or in the availability of nutrients). Genetic robustness can be studied at various levels of biological organization, from the molecular level to the organism level, where a state is considered to be robust if a mutation has an insignificant effect on the trait examined[2]. Environmental robustness differs greatly across bacterial species: whereas some species exhibit an impressive ability to proliferate in a wide spectrum of habitats, others demonstrate highly specialized nutritional requirements. In free-living organisms, new pathways have evolved by acquiring reactions that put external nutrients into metabolic use allowing species a greater choice in their metabolic requests[5]. The genetic implications of the selective pressure to increase the nutritional repertoire make environmental robustness and genetic robustness intertwined. The contribution of environmental robustness to genetic robustness is demonstrated by the following example: the evolution of a new metabolic pathway allows a species to put a new external metabolite( metabolite A) into metabolic use as an alternative to an existing pathway making use of metabolite B, hence promoting environmental robustness. Under such conditions where both A and B are available( nutrient-rich conditions), the species will also gain genetic robustness in front of mutations in one of the corresponding pathways( in case of a mutation preventing the use of metabolite B the species can use the alternative pathway utilizing A and vice versa). The effect of environmental robustness on genetic robustness in metabolic networks was studied in detail in yeast[6],[7]: more than half of the genes that were nonessential for growth under nutrient-rich conditions were found to be active under eight restricting growth conditions[7]. However, as environmental robustness cannot fully explain the dispensability of genes, two other hypotheses have been proposed for the evolution of genetic robustness[2]: First, an adaptive origin – i. e., a direct, natural selection in favor of genes which allow a species to endure genetic perturbations( initially suggested by Fisher to explain the observed dominance of wild-type alleles to the overwhelming majority of deleterious mutations[8]). Second, an intrinsic origin– i. e., genetic robustness has evolved as a byproduct of natural selection in favor of other, adaptive, traits. In the context of dominance, selection for increased metabolic flux is a widely accepted explanation for the recessive nature of most mutations[9]–[11]. Since metabolic enzymes act as part of large, multi-enzymes, networks, single-loci mutations( resulting in 50% activity of the corresponding enzyme in the heterozygote) are not expected to affect the overall behavior of the system. Hence such mutations are not detectable at the phenotypic level and mutants are considered to be recessive[12]. For genetic robustness, one can similarly argue that robustness is intrinsic to the optimization of some phenotypes, and has evolved as a byproduct of a selective pressure for increasing steady-state metabolic fluxes via the incorporation of alternative metabolic pathways[2],[13],[14]. Notably, there is an essential difference between man-made systems, where robustness is built in on purpose, and biological systems that are shaped by natural selection at the population level, in which non robust systems can survive if reproduction rate compensates failure rate. Few recently developed methods allow one to systematically address the influence of the environment on metabolic network structure. The network expansion method – a method for generating the set of all possible metabolites that can be produced from a set of compounds- permits the reconstruction of networks in different metabolic environments[15]. Using the network expansion approach, generic sub-networks( describing a collection of metabolic reactions across known genomes rather than the reactions tenable within any specific organism), expanded under different collection of metabolites, were shown to be highly robust against the elimination of mutations[16],[17]. The network expansion method has been helpful in uncovering the role played by some specific metabolites on the evolution of metabolic networks. The introduction of oxygen to the atmosphere, for example, was demonstrated to be coupled with the appearance of many new pathways and reactions, considerably increasing the complexity of the metabolic networks of aerobic species beyond that reachable by any anoxic network[18]. Studying the species-specific expansion of networks given various metabolite combinations has permitted the grouping together of organisms with similar metabolic capabilities[19]. Following the introduction of the expansion method, other related topological approaches have been developed to provide predictions for the network-specific set of externally consumed metabolites, as inferred by the structure of the corresponding metabolic network[20],[21]. These methods aim to define the minimal set of externally acquired compounds – i. e., these metabolites that cannot be synthesized from other compounds and permit the production of all other compounds in the network. Here, we combined the expansion algorithm and an algorithm for predicting species-specific metabolic environments( The seed algorithm[20]). By integrating these two algorithms, we provide a new model that not only predicts robustness across species( as was previously done using the expansion method[17]), but also examines robustness across environments and hence provides predictions for condition-dependent and independent reactions. Taking this integrative approach, we aim to characterize the level of species-specific robustness and, for each species individually, to decouple between robustness which is condition-dependent( due to reactions which are essential in some environments but not in other) and robustness which is condition-independent. We start by describing our model and testing its biological plausibility. We then use it to systematically characterize the level of robustness across bacterial species( condition dependent and independent). Finally, we apply the model for studying whether dispensability is a directly selected feature.
Discussion:
Overall, our analysis charts out the robustness of the metabolic system across a wide variety of bacterial species and growth media. Several limitation of this analysis should yet be acknowledged. The predictions for the essentiality of reactions, as well as the set of growth environments, are based on a topological network-based computation. Hence, this analysis ignores many other properties of metabolic reactions such as stochiometry, rate, and dynamics. Incorporating these properties into the metabolic network model can potentially yield more accurate results[51]. Nevertheless, metabolic network topologies can readily be obtained for hundreds of species, allowing a phylogenetic, large-scale analysis[37] and may thus delineate emerging patterns in the metabolic data. This broad perspective enables the elucidation of general principles underlying the structure and evolution of metabolic networks[16],[17],[52]. Finally, our predictions of reactions' essentiality are shown to be in general agreement with experimental observations, in the few cases where the latter exist. Several recent studies have used topological-based approaches for systematically revealing the structural properties of metabolic networks. The expansion algorithm was applied for estimating the level of robustness in reference sub-networks, comprising all reactions present in the KEGG database( irrespective of the organism in which they have been found)[16], as well as for conducting a comparative study of the level of robustness in species-specific sub-networks produced by the expansion algorithm under a given combination of external resources[17]. These studies clearly testify for the high robustness of metabolic sub-networks, where the sensitivity following deletion of reactions decreases as the size of the sub-network increases[17]. Here, we introduce a computational approach that integrates together algorithms which were previously used individually for studying the structure and evolution of metabolic network: The expansion algorithm was used for studying robustness under different environments and in different species( though in a given environment); The seed algorithms were used to predict the nutritional environment of species[20],[21]. By integrating these two algorithms, we provide a new model that provides predictions for condition-dependent and independent robustness across many species and environments. By conducting a large-scale comparative study we provide evidence that variations among species in their level of network genetic robustness( condition-dependent and independent) reflect adaptations to different ecological niches and lifestyles. Notably, beyond robustness, other features of metabolic networks such as the collection of enzymatic functions[53] and the ability to utilize external nutrients[19] also reflect environmental adaptations, where in many cases the metabolic capacities of species are better associated with their lifestyle than with their phylogeny. Alternative lifestyle characteristics are associated with the two types of robustness: the extent of conditional-dependent robustness is strongly associated with the environmental diversity of species( specialized or generalist), and the extent of condition-independent robustness is associated with the corresponding metabolic capacities. Importantly, our model only considers qualitative conditions, i. e., the actual availability of metabolites, whereas the choice between alternative pathways can reflect adaptations to quantitative conditions, i. e., the concentration of metabolites[14],[47]. Considering this qualitative description of conditional-dependence of reactions, the association between the level of condition-dependent robustness and species' environmental diversity suggests that the former evolve as a result of a selection for alternating between nutritional sources. For most species examined the range of nutrients that can be consumed by a species can only partially explain the corresponding level of robustness( only about 20 percent of the reactions), where the complementary non condition-dependent robustness has arisen mainly to meet the metabolic requirements of a species. The association between the level of robustness and the corresponding metabolic capacity of a species can be explained either by a selection to increase flux – similarly to the effect of gene dosage[7] – or by selection to optimize the metabolic efficiency under given conditions, for example by alternating between routes in accordance with the corresponding substrate concentrations[47],[48]. Thus, the design of metabolic networks( as viewed by the presence of alternative pathways) represents a species-specific adaptation to both its needs and its environment. Beyond the evolutionary implications of this study, additional applications can be withdrawn from the association between growth rate and topological properties( i. e., network robustness), as reported here. The observed association suggests that topological models can be used for predicting growth rate( for a broader spectrum of species than is currently possible with the existing range of stochiometeric models). An intriguing challenge is to develop supervised learning techniques to learn a predictive function for condition-dependent growth rate, given the topological characteristics of that environment-specific network and the species-specific growth potential under optimal conditions. Currently, there is a lack of systematic data of growth rate across media for a wide collection of species[54], needed for building the growth rate predictor suggested above. Hopefully, the future accumulation of such data will allow conducting such study.
When a species is grown under optimal conditions the single-knockout of most of its genes is not likely to affect its viability . The resilience of biological systems to mutations is termed genetic robustness and its extent across different species has not yet been systematically described . Since the deletion of a gene can have varying consequences depending on the environmental conditions , the extent of species' genetic robustness reflects both the range of conditions ( or environments ) in which it can survive as well as the availability of alternative cellular routes ( compensating for a gene's loss of function ) . Here , we developed a computational model for estimating the essentiality of metabolic reactions across natural-like environments and applied it to chart species' level of genetic robustness , providing the first systematic description of genetic robustness across species . Studying robustness across a wide collection of natural-like environments enables one to stratify , for each species individually , the extent of environmental-dependant and independent robustness and hence advances our understanding of its evolutionary origins . Our main finding is that the level of environmental dependent robustness is associated with the lifestyle of a species ( i . e . , specialists versus generalist ) , whereas the level of environmental-independent robustness is associated with its metabolic production capacities .