Metabolic networks and their evolution

Genomic factors shape carbon and nitrogen metabolic niche breadth across Saccharomycotina yeasts

Organisms exhibit extensive variation in ecological niche breadth, from very narrow (specialists) to very broad (generalists). Two general paradigms have been proposed to explain this variation: (i) trade-offs between performance efficiency and breadth and (ii) the joint influence of extrinsic (environmental) and intrinsic (genomic) factors. We assembled genomic, metabolic, and ecological data from nearly all known species of the ancient fungal subphylum Saccharomycotina (1154 yeast strains from 1051 species), grown in 24 different environmental conditions, to examine niche breadth evolution. We found that large differences in the breadth of carbon utilization traits between yeasts stem from intrinsic differences in genes encoding specific metabolic pathways, but we found limited evidence for trade-offs. These comprehensive data argue that intrinsic factors shape niche breadth variation in microbes.

MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data

Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms. Metabolic network modeling is commonly used to frame metabolomics data in the context of a broader biological system. However, network modeling of poorly characterized nonmodel organisms remains challenging due to gene homology mismatches which lead to network architecture errors. To address this, we developed the Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that uses empirical metabolomics data to refine metabolic networks. MINNO allows users to create, modify, and interact with metabolic pathway visualizations for thousands of organisms, in both individual and multispecies contexts. Herein, we illustrate the use of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme and relapsing fever diseases. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for threeBorrelia species. Using these empirically refined networks, we were able to metabolically differentiate these species via their nucleotide metabolism, which cannot be predicted from genomic networks. Additionally, using MINNO, we identified 18 missing reactions from the KEGG database, of which nine were supported by the primary literature. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study nonmodel organisms.

Are Escherichia coli causing recurrent cystitis just ordinary Uropathogenic E. coli (UPEC) strains?

Specific determinants associated with Uropathogenic Escherichia coli (UPEC) causing recurrent cystitis are still poorly characterized. The aims of this study were (i) to describe genomic and phenotypic traits associated with recurrence using a large collection of recurrent and paired sporadic UPEC isolates, and (ii) to explore within-host genomic adaptation associated with recurrence using series of 2 to 5 sequential UPEC isolates. Whole genome comparative analyses between 24 recurrent cystitis isolates (RCIs) and 24 phylogenetically paired sporadic cystitis isolates (SCIs) suggested a lower prevalence of putative mobile genetic elements (MGE) in RCIs, such as plasmids and prophages. The intra-patient evolution of the 24 RCI series over time was characterized by SNP occurrence in genes involved in metabolism or membrane transport, and by plasmid loss in 5 out of the 24 RCI series. Genomic evolution occurred early in the course of recurrence, suggesting rapid adaptation to strong selection pressure in the urinary tract. However, RCIs did not exhibit specific virulence factor determinants and could not be distinguished from SCIs by their fitness, biofilm formation, or ability to invade HTB-9 bladder epithelial cells. Taken together, these results suggest a rapid but not convergent adaptation of RCIs that involves both strain- and host-specific characteristics.

Metabolic Interactive Nodular Network for Omics (MINNO): Refining and investigating metabolic networks based on empirical metabolomics data

Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms, and metabolic network modeling is commonly used to frame results in the context of a broader homeostatic system. However, network modeling of poorly characterized, non-model organisms remains challenging due to gene homology mismatches. To address this challenge, we developed Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that takes in empirical metabolomics data to refine metabolic networks for both model and unusual organisms. MINNO allows users to create and modify interactive metabolic pathway visualizations for thousands of organisms, in both individual and multi-species contexts. Herein, we demonstrate an important application of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme disease and relapsing fever. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for three Borrelia species. Using these empirically refined networks, we were able to metabolically differentiate these genetically similar species via their nucleotide and nicotinate metabolic pathways that cannot be predicted from genomic networks. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study non-model organisms.

In search of a Drosophila core cellular network with single-cell transcriptome data

Along with specialized functions, cells of multicellular organisms also perform essential functions common to most if not all cells. Whether diverse cells do this by using the same set of genes, interacting in a fixed coordinated fashion to execute essential functions, or a subset of genes specific to certain cells, remains a central question in biology. Here, we focus on gene coexpression to search for a core cellular network across a whole organism. Single-cell RNA-sequencing measures gene expression of individual cells, enabling researchers to discover gene expression patterns that contribute to the diversity of cell functions. Current efforts to study cellular functions focus primarily on identifying differentially expressed genes across cells. However, patterns of coexpression between genes are probably more indicative of biological processes than are the expression of individual genes. We constructed cell-type-specific gene coexpression networks using single-cell transcriptome datasets covering diverse cell types from the fruit fly, Drosophila melanogaster. We detected a set of highly coordinated genes preserved across cell types and present this as the best estimate of a core cellular network. This core is very small compared with cell-type-specific gene coexpression networks and shows dense connectivity. Gene members of this core tend to be ancient genes and are enriched for those encoding ribosomal proteins. Overall, we find evidence for a core cellular network in diverse cell types of the fruit fly. The topological, structural, functional, and evolutionary properties of this core indicate that it accounts for only a minority of essential functions.

Nutrition or nature: using elementary flux modes to disentangle the complex forces shaping prokaryote pan-genomes

BACKGROUND

Microbial pan-genomes are shaped by a complex combination of stochastic and deterministic forces. Even closely related genomes exhibit extensive variation in their gene content. Understanding what drives this variation requires exploring the interactions of gene products with each other and with the organism's external environment. However, to date, conceptual models of pan-genome dynamics often represent genes as independent units and provide limited information about their mechanistic interactions.

RESULTS

We simulated the stochastic process of gene-loss using the pooled genome-scale metabolic reaction networks of 46 taxonomically diverse bacterial and archaeal families as proxies for their pan-genomes. The frequency by which reactions are retained in functional networks when stochastic gene loss is simulated in diverse environments allowed us to disentangle the metabolic reactions whose presence depends on the metabolite composition of the external environment (constrained by "nutrition") from those that are independent of the environment (constrained by "nature"). By comparing the frequency of reactions from the first group with their observed frequencies in bacterial and archaeal families, we predicted the metabolic niches that shaped the genomic composition of these lineages. Moreover, we found that the lineages that were shaped by a more diverse metabolic niche also occur in more diverse biomes as assessed by global environmental sequencing datasets.

CONCLUSION

We introduce a computational framework for analyzing and interpreting pan-reactomes that provides novel insights into the ecological and evolutionary drivers of pan-genome dynamics.

Genome-Scale Metabolic Modeling Enables In-Depth Understanding of Big Data

Genome-scale metabolic models (GEMs) enable the mathematical simulation of the metabolism of archaea, bacteria, and eukaryotic organisms. GEMs quantitatively define a relationship between genotype and phenotype by contextualizing different types of Big Data (e.g., genomics, metabolomics, and transcriptomics). In this review, we analyze the available Big Data useful for metabolic modeling and compile the available GEM reconstruction tools that integrate Big Data. We also discuss recent applications in industry and research that include predicting phenotypes, elucidating metabolic pathways, producing industry-relevant chemicals, identifying drug targets, and generating knowledge to better understand host-associated diseases. In addition to the up-to-date review of GEMs currently available, we assessed a plethora of tools for developing new GEMs that include macromolecular expression and dynamic resolution. Finally, we provide a perspective in emerging areas, such as annotation, data managing, and machine learning, in which GEMs will play a key role in the further utilization of Big Data.

Escherichia coli Causing Recurrent Urinary Tract Infections: Comparison to Non-Recurrent Isolates and Genomic Adaptation in Recurrent Infections

Recurrent urinary tract infection (rUTI) remains a major problem for many women and therefore the pursuit for genomic and phenotypic traits which could define rUTI has been ongoing. The present study applied a genomic approach to investigate recurrent urinary tract infections by comparative analyses of recurrent and non-recurrent Escherichia coli isolates from general practice. From whole-genome sequencing data, phylogenetic clustering and genomic traits were studied on a collection of isolates which caused recurrent infection compared to non-recurrent isolates. In addition, genomic variation between the 1st and following infection was studied on a subset of the isolates. Evidence of limited adaptation between the recurrent infections based on single nucleotide polymorphism analyses with a range of 0-13 non-synonymous single nucleotide polymorphisms (SNPs) between the paired isolates. This included an overrepresentation of SNPs in metabolism genes. We identified several genes which were more common in rUTI isolates, including nine fimbrial genes, however, not significantly after false-discovery rate. Finally, the results show that recurrent isolates of the present dataset are not distinctive by variation in the core genome, and thus, did not cluster distinct from non-rUTI isolates in a SNP phylogeny.

The evolutionary ecology of fatty-acid variation: Implications for consumer adaptation and diversification

The nutritional diversity of resources can affect the adaptive evolution of consumer metabolism and consumer diversification. The omega-3 long-chain polyunsaturated fatty acids eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3) have a high potential to affect consumer fitness, through their widespread effects on reproduction, growth and survival. However, few studies consider the evolution of fatty acid metabolism within an ecological context. In this review, we first document the extensive diversity in both primary producer and consumer fatty acid distributions amongst major ecosystems, between habitats and amongst species within habitats. We highlight some of the key nutritional contrasts that can shape behavioural and/or metabolic adaptation in consumers, discussing how consumers can evolve in response to the spatial, seasonal and community-level variation of resource quality. We propose a hierarchical trait-based approach for studying the evolution of consumers' metabolic networks and review the evolutionary genetic mechanisms underpinning consumer adaptation to EPA and DHA distributions. In doing so, we consider how the metabolic traits of consumers are hierarchically structured, from cell membrane function to maternal investment, and have strongly environment-dependent expression. Finally, we conclude with an outlook on how studying the metabolic adaptation of consumers within the context of nutritional landscapes can open up new opportunities for understanding evolutionary diversification.

Physiological and genomic evidence that selection on the transcription factor Epas1 has altered cardiovascular function in high-altitude deer mice

Evolutionary adaptation to extreme environments often requires coordinated changes in multiple intersecting physiological pathways, but how such multi-trait adaptation occurs remains unresolved. Transcription factors, which regulate the expression of many genes and can simultaneously alter multiple phenotypes, may be common targets of selection if the benefits of induced changes outweigh the costs of negative pleiotropic effects. We combined complimentary population genetic analyses and physiological experiments in North American deer mice (Peromyscus maniculatus) to examine links between genetic variation in transcription factors that coordinate physiological responses to hypoxia (hypoxia-inducible factors, HIFs) and multiple physiological traits that potentially contribute to high-altitude adaptation. First, we sequenced the exomes of 100 mice sampled from different elevations and discovered that several SNPs in the gene Epas1, which encodes the oxygen sensitive subunit of HIF-2α, exhibited extreme allele frequency differences between highland and lowland populations. Broader geographic sampling confirmed that Epas1 genotype varied predictably with altitude throughout the western US. We then discovered that Epas1 genotype influences heart rate in hypoxia, and the transcriptomic responses to hypoxia (including HIF targets and genes involved in catecholamine signaling) in the heart and adrenal gland. Finally, we used a demographically-informed selection scan to show that Epas1 variants have experienced a history of spatially varying selection, suggesting that differences in cardiovascular function and gene regulation contribute to high-altitude adaptation. Our results suggest a mechanism by which Epas1 may aid long-term survival of high-altitude deer mice and provide general insights into the role that highly pleiotropic transcription factors may play in the process of environmental adaptation.

UNVEILing connections between genotype, phenotype, and fitness in natural populations

Understanding the links between genetic variation and fitness in natural populations is a central goal of evolutionary genetics. This monumental task spans the fields of classical and molecular genetics, population genetics, biochemistry, physiology, developmental biology, and ecology. Advances to our molecular and developmental toolkits are facilitating integrative approaches across these traditionally separate fields, providing a more complete picture of the genotype-phenotype map in natural and non-model systems. Here, we summarize research presented at the first annual symposium of the UNVEIL Network, an NSF-funded collaboration between the University of Montana and the University of Nebraska, Lincoln, which took place from the 1st to the 3rd of June, 2018. We discuss how this body of work advances basic evolutionary science, what it implies for our ability to predict evolutionary change, and how it might inform novel conservation strategies.

Evolutionary design principles in metabolism

Microorganisms are ubiquitous and adapt to various dynamic environments to sustain growth. These adaptations accumulate, generating new traits forming the basis of evolution. Organisms adapt at various levels, such as gene regulation, signalling, protein-protein interactions and metabolism. Of these, metabolism forms the integral core of an organism for maintaining the growth and function of a cell. Therefore, studying adaptations in metabolic networks is crucial to understand the emergence of novel metabolic capabilities. Metabolic networks, composed of enzyme-catalysed reactions, exhibit certain repeating paradigms or design principles that arise out of different selection pressures. In this review, we discuss the design principles that are known to exist in metabolic networks, such as functional redundancy, modularity, flux coupling and exaptations. We elaborate on the studies that have helped gain insights highlighting the interplay of these design principles and adaptation. Further, we discuss how evolution plays a role in exploiting such paradigms to enhance the robustness of organisms. Looking forward, we predict that with the availability of ever-increasing numbers of bacterial, archaeal and eukaryotic genomic sequences, novel design principles will be identified, expanding our understanding of these paradigms shaped by varied evolutionary processes.

Natural variation in sugar tolerance associates with changes in signaling and mitochondrial ribosome biogenesis

How dietary selection affects genome evolution to define the optimal range of nutrient intake is a poorly understood question with medical relevance. We have addressed this question by analyzing Drosophila simulans and sechellia, recently diverged species with differential diet choice. D. sechellia larvae, specialized to a nutrient scarce diet, did not survive on sugar-rich conditions, while the generalist species D. simulans was sugar tolerant. Sugar tolerance in D. simulans was a tradeoff for performance on low-energy diet and was associated with global reprogramming of metabolic gene expression. Hybridization and phenotype-based introgression revealed the genomic regions of D. simulans that were sufficient for sugar tolerance. These regions included genes that are involved in mitochondrial ribosome biogenesis and intracellular signaling, such as PPP1R15/Gadd34 and SERCA, which contributed to sugar tolerance. In conclusion, genomic variation affecting genes involved in global metabolic control defines the optimal range for dietary macronutrient composition.

Animals meet their nutritional needs in a variety of ways. Some animals are specialists feeding only on one type of food; others are generalists that can choose many different kinds of food depending on the situation. Despite these differences in diet, animals have similar needs for basic cellular metabolism. This suggests that generalist and specialist species likely process the foods they eat in different ways in order to meet their basic needs. For example, the metabolism of generalist species may be more flexible to adapt to changing food sources.

To learn more about how metabolism evolves to respond to diet, scientists can study closely related species that eat different foods. For example, a species of fruit fly called Drosophila simulans is a generalist and its larvae can grow and develop by feeding on different kinds of decaying fruits and vegetables. Larvae of a closely related fruit fly called Drosophila sechellia are specialized to eat only the nutrient-poor Morinda fruit. Looking at how genetic differences between these species affect metabolism may provide scientists with clues about how these feeding strategies evolved.

Melvin et al. grew larvae of D. sechellia and D. simulans in different conditions. D. sechellia larvae thrived in low nutrient conditions, but died when exposed to high sugar foods. By contrast, D. simulans larvae tolerated high sugar levels, but did poorly in low-nutrient conditions.

Melvin et al. then bred the two species with each other, selecting flies that are genetically similar to D. sechellia but have the genes necessary for larvae to tolerate sugar. Analyzing the selected hybrid flies revealed genetic changes that explain the different survival abilities of each species. These changes suggest that D. sechellia rapidly evolved to thrive in low nutrient conditions, but the trade-off was losing their ability to tolerate high sugar levels.

Overall, the results presented by Melvin et al. suggest that genetic adaptions to food sources can occur quickly and drastically change metabolism. Further research will be needed to confirm if similar metabolic trade-offs developed as part of human evolution. If so, human populations that survived with limited nutrition for many generations may have a harder time adapting to high-sugar modern diets.

Metabolic Determinants of Enzyme Evolution in a Genome-Scale Bacterial Metabolic Network

Different genes and proteins evolve at very different rates. To identify the factors that explain these differences is an important aspect of research in molecular evolution. One such factor is the role a protein plays in a large molecular network. Here, we analyze the evolutionary rates of enzyme-coding genes in the genome-scale metabolic network of Escherichia coli to find the evolutionary constraints imposed by the structure and function of this complex metabolic system. Central and highly connected enzymes appear to evolve more slowly than less connected enzymes, but we find that they do so as a by-product of their high abundance, and not because of their position in the metabolic network. In contrast, enzymes catalyzing reactions with high metabolic flux-high substrate to product conversion rates-evolve slowly even after we account for their abundance. Moreover, enzymes catalyzing reactions that are difficult to by-pass through alternative pathways, such that they are essential in many different genetic backgrounds, also evolve more slowly. Our analyses show that an enzyme's role in the function of a metabolic network affects its evolution more than its place in the network's structure. They highlight the value of a system-level perspective for studies of molecular evolution.

Beyond topology: coevolution of structure and flux in metabolic networks

Interactions between the structure of a metabolic network and its functional properties underlie its evolutionary diversification, but the mechanism by which such interactions arise remains elusive. Particularly unclear is whether metabolic fluxes that determine the concentrations of compounds produced by a metabolic network, are causally linked to a network's structure or emerge independently of it. A direct empirical study of populations where both structural and functional properties vary among individuals' metabolic networks is required to establish whether changes in structure affect the distribution of metabolic flux. In a population of house finches (Haemorhous mexicanus), we reconstructed full carotenoid metabolic networks for 442 individuals and uncovered 11 structural variants of this network with different compounds and reactions. We examined the consequences of this structural diversity for the concentrations of plumage-bound carotenoids produced by flux in these networks. We found that concentrations of metabolically derived, but not dietary carotenoids, depended on network structure. Flux was partitioned similarly among compounds in individuals of the same network structure: within each network, compound concentrations were closely correlated. The highest among-individual variation in flux occurred in networks with the strongest among-compound correlations, suggesting that changes in the magnitude, but not the distribution of flux, underlie individual differences in compound concentrations on a static network structure. These findings indicate that the distribution of flux in carotenoid metabolism closely follows network structure. Thus, evolutionary diversification and local adaptations in carotenoid metabolism may depend more on the gain or loss of enzymatic reactions than on changes in flux within a network structure.

Tracing the Repertoire of Promiscuous Enzymes along the Metabolic Pathways in Archaeal Organisms

The metabolic pathways that carry out the biochemical transformations sustaining life depend on the efficiency of their associated enzymes. In recent years, it has become clear that promiscuous enzymes have played an important role in the function and evolution of metabolism. In this work we analyze the repertoire of promiscuous enzymes in 89 non-redundant genomes of the Archaea cellular domain. Promiscuous enzymes are defined as those proteins with two or more different Enzyme Commission (E.C.) numbers, according the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. From this analysis, it was found that the fraction of promiscuous enzymes is lower in Archaea than in Bacteria. A greater diversity of superfamily domains is associated with promiscuous enzymes compared to specialized enzymes, both in Archaea and Bacteria, and there is an enrichment of substrate promiscuity rather than catalytic promiscuity in the archaeal enzymes. Finally, the presence of promiscuous enzymes in the metabolic pathways was found to be heterogeneously distributed at the domain level and in the phyla that make up the Archaea. These analyses increase our understanding of promiscuous enzymes and provide additional clues to the evolution of metabolism in Archaea.

The potential for non-adaptive origins of evolutionary innovations in central carbon metabolism

BACKGROUND

Biological systems are rife with examples of pre-adaptations or exaptations. They range from the molecular scale - lens crystallins, which originated from metabolic enzymes - to the macroscopic scale, such as feathers used in flying, which originally served thermal insulation or waterproofing. An important class of exaptations are novel and useful traits with non-adaptive origins. Whether such origins could be frequent cannot be answered with individual examples, because it is a question about a biological system's potential for exaptation. We here take a step towards answering this question by analyzing central carbon metabolism, and novel traits that allow an organism to survive on novel sources of carbon and energy. We have previously applied flux balance analysis to this system and predicted the viability of 10¹⁵ metabolic genotypes on each of ten different carbon sources.

RESULTS

We here use this exhaustive genotype-phenotype map to ask whether a central carbon metabolism that is viable on a given, focal carbon source C - the equivalent of an adaptation in our framework - is usually or rarely viable on one or more other carbon sources C _new - a potential exaptation. We show that most metabolic genotypes harbor potential exaptations, that is, they are viable on one or more carbon sources C _new . The nature and number of these carbon sources depends on the focal carbon source C itself, and on the biochemical similarity between C and C _new . Moreover, metabolisms that show a higher biomass yield on C, and that are more complex, i.e., they harbor more metabolic reactions, are viable on a greater number of carbon sources C _new .

CONCLUSIONS

A high potential for exaptation results from correlations between the phenotypes of different genotypes, and such correlations are frequent in central carbon metabolism. If they are similarly abundant in other metabolic or biological systems, innovations may frequently have non-adaptive ("exaptive") origins.

Exhaustive Analysis of a Genotype Space Comprising 10(15 )Central Carbon Metabolisms Reveals an Organization Conducive to Metabolic Innovation

All biological evolution takes place in a space of possible genotypes and their phenotypes. The structure of this space defines the evolutionary potential and limitations of an evolving system. Metabolism is one of the most ancient and fundamental evolving systems, sustaining life by extracting energy from extracellular nutrients. Here we study metabolism’s potential for innovation by analyzing an exhaustive genotype-phenotype map for a space of 10¹⁵ metabolisms that encodes all possible subsets of 51 reactions in central carbon metabolism. Using flux balance analysis, we predict the viability of these metabolisms on 10 different carbon sources which give rise to 1024 potential metabolic phenotypes. Although viable metabolisms with any one phenotype comprise a tiny fraction of genotype space, their absolute numbers exceed 10⁹ for some phenotypes. Metabolisms with any one phenotype typically form a single network of genotypes that extends far or all the way through metabolic genotype space, where any two genotypes can be reached from each other through a series of single reaction changes. The minimal distance of genotype networks associated with different phenotypes is small, such that one can reach metabolisms with novel phenotypes – viable on new carbon sources – through one or few genotypic changes. Exceptions to these principles exist for those metabolisms whose complexity (number of reactions) is close to the minimum needed for viability. Increasing metabolic complexity enhances the potential for both evolutionary conservation and evolutionary innovation.

Genotype-phenotype mapping is one of the ultimate goals of computational systems biology, and can provide new insights into the function and evolution of biological systems. We present a comprehensive genotype-phenotype map for a space of metabolic genotypes that comprises more than 10¹⁵ central carbon metabolisms. Only one in a million of these metabolisms can sustain life on any one of 10 carbon sources we consider, but these viable metabolisms form connected genotype networks that extend far through genotype space. In addition, they render multiple novel metabolic phenotypes in their immediate neighborhood accessible through small evolutionary changes that require only the alteration of single metabolic reactions. The map we construct reveals an organization of core metabolism that simultaneously facilitates evolutionary conservation of existing metabolic phenotypes, and the origination of novel metabolic traits that allow viability on novel carbon sources. Such metabolic innovation is essential, particularly for organisms that experience unexpected environmental changes, and that explore or invade new habitats.

Recent positive selection has acted on genes encoding proteins with more interactions within the whole human interactome

Genes vary in their likelihood to undergo adaptive evolution. The genomic factors that determine adaptability, however, remain poorly understood. Genes function in the context of molecular networks, with some occupying more important positions than others and thus being likely to be under stronger selective pressures. However, how positive selection distributes across the different parts of molecular networks is still not fully understood. Here, we inferred positive selection using comparative genomics and population genetics approaches through the comparison of 10 mammalian and 270 human genomes, respectively. In agreement with previous results, we found that genes with lower network centralities are more likely to evolve under positive selection (as inferred from divergence data). Surprisingly, polymorphism data yield results in the opposite direction than divergence data: Genes with higher centralities are more likely to have been targeted by recent positive selection during recent human evolution. Our results indicate that the relationship between centrality and the impact of adaptive evolution highly depends on the mode of positive selection and/or the evolutionary time-scale.

Sequence divergence and diversity suggests ongoing functional diversification of vertebrate NAD metabolism

NAD is not only an important cofactor in redox reactions but has also received attention in recent years because of its physiological importance in metabolic regulation, DNA repair and signaling. In contrast to the redox reactions, these regulatory processes involve degradation of NAD and therefore necessitate a constant replenishment of its cellular pool. NAD biosynthetic enzymes are common to almost all species in all clades, but the number of NAD degrading enzymes varies substantially across taxa. In particular, vertebrates, including humans, have a manifold of NAD degrading enzymes which require a high turnover of NAD. As there is currently a lack of a systematic study of how natural selection has shaped enzymes involved in NAD metabolism we conducted a comprehensive evolutionary analysis based on intraspecific variation and interspecific divergence. We compare NAD biosynthetic and degrading enzymes in four eukaryotic model species and subsequently focus on human NAD metabolic enzymes and their orthologs in other vertebrates. We find that the majority of enzymes involved in NAD metabolism are subject to varying levels of purifying selection. While NAD biosynthetic enzymes appear to experience a rather high level of evolutionary constraint, there is evidence for positive selection among enzymes mediating NAD-dependent signaling. This is particularly evident for members of the PARP family, a diverse protein family involved in DNA damage repair and programmed cell death. Based on haplotype information and substitution rate analysis we pinpoint sites that are potential targets of positive selection. We also link our findings to a three dimensional structure, which suggests that positive selection occurs in domains responsible for DNA binding and polymerization rather than the NAD catalytic domain. Taken together, our results indicate that vertebrate NAD metabolism is still undergoing functional diversification.

Relationship between gene duplicability and diversifiability in the topology of biochemical networks

Background

Selective gene duplicability, the extensive expansion of a small number of gene families, is universal. Quantitatively, the number of genes (P_(K)) with K duplicates in a genome decreases precipitously as K increases, and often follows a power law (P_(k)∝k^-α). Functional diversification, either neo- or sub-functionalization, is a major evolution route for duplicate genes.

Results

Using three lines of genomic datasets, we studied the relationship between gene duplicability and diversifiability in the topology of biochemical networks. First, we explored scenario where two pathways in the biochemical networks antagonize each other. Synthetic knockout of respective genes for the two pathways rescues the phenotypic defects of each individual knockout. We identified duplicate gene pairs with sufficient divergences that represent this antagonism relationship in the yeast S. cerevisiae. Such pairs overwhelmingly belong to large gene families, thus tend to have high duplicability. Second, we used distances between proteins of duplicate genes in the protein interaction network as a metric of their diversification. The higher a gene’s duplicate count, the further the proteins of this gene and its duplicates drift away from one another in the networks, which is especially true for genetically antagonizing duplicate genes. Third, we computed a sequence-homology-based clustering coefficient to quantify sequence diversifiability among duplicate genes – the lower the coefficient, the more the sequences have diverged. Duplicate count (K) of a gene is negatively correlated to the clustering coefficient of its duplicates, suggesting that gene duplicability is related to the extent of sequence divergence within the duplicate gene family.

Conclusion

Thus, a positive correlation exists between gene diversifiability and duplicability in the context of biochemical networks – an improvement of our understanding of gene duplicability.

A mathematical approach to emergent properties of metabolic networks: partial coupling relations, hyperarcs and flux ratios

Emergent properties in systems biology are those which arise only when the biological system passes a certain level of complexity. In this study, we introduce some of the emergent properties which appear in the constraint-based analysis of metabolic networks. These properties generally appear as a result of existence of hfdeyperarcs and irreversible reactions in networks. Here, we present examples of metabolic networks in which there exist at least two reactions whose fluxes cannot be written as products and/or ratios of the stoichiometric coefficients of the network. We show that any such network contains at least one hyperarc. Additionally, we prove that partial coupling cannot appear in consistent metabolic networks with less than four reactions, or with less than three irreversible reactions, or without hyperarc(s).

Unravelling the complete genome sequence of Advenella mimigardefordensis strain DPN7T and novel insights in the catabolism of the xenobiotic polythioester precursor 3,3'-dithiodipropionate

Advenella mimigardefordensis strain DPN7(T) is a remarkable betaproteobacterium because of its extraordinary ability to use the synthetic disulfide 3,3'-dithiodipropionic acid (DTDP) as the sole carbon source and electron donor for aerobic growth. One application of DTDP is as a precursor substrate for biotechnically synthesized polythioesters (PTEs), which are interesting non-degradable biopolymers applicable for plastics materials. Metabolic engineering for optimization of PTE production requires an understanding of DTDP conversion. The genome of A. mimigardefordensis strain DPN7(T) was sequenced and annotated. The circular chromosome was found to be composed of 4,740,516 bp and 4112 predicted ORFs, whereas the circular plasmid consisted of 23,610 bp and 24 predicted ORFs. The genes participating in DTDP catabolism had been characterized in detail previously, but knowing the complete genome sequence and with support of Tn5: :mob-induced mutants, putatively involved transporter proteins and a transcriptional regulator were also identified. Most probably, DTDP is transported into the cell by a specific tripartite tricarboxylate transport system and is then cleaved by the disulfide reductase LpdA, sulfoxygenated by the 3-mercaptopropionate dioxygenase Mdo, activated by the CoA ligase SucCD and desulfinated by the acyl-CoA dehydrogenase-like desulfinase AcdA. Regulation of this pathway is presumably performed by a transcriptional regulator of the xenobiotic response element family. The excessive sulfate that is inevitably produced is secreted by the cells by a unique sulfate exporter of the CPA (cation : proton antiporter) superfamily.

Evolutionary rate and duplicability in the Arabidopsis thaliana protein-protein interaction network

Genes show a bewildering variation in their patterns of molecular evolution, as a result of the action of different levels and types of selective forces. The factors underlying this variation are, however, still poorly understood. In the last decade, the position of proteins in the protein-protein interaction network has been put forward as a determinant factor of the evolutionary rate and duplicability of their encoding genes. This conclusion, however, has been based on the analysis of the limited number of microbes and animals for which interactome-level data are available (essentially, Escherichia coli, yeast, worm, fly, and humans). Here, we study, for the first time, the relationship between the position of proteins in the high-density interactome of a plant (Arabidopsis thaliana) and the patterns of molecular evolution of their encoding genes. We found that genes whose encoded products act at the center of the network are more evolutionarily constrained than those acting at the network periphery. This trend remains significant when potential confounding factors (gene expression level and breadth, duplicability, function, and length of the encoded products) are controlled for. Even though the correlation between centrality measures and rates of evolution is generally weak, for some functional categories, it is comparable in strength to (or even stronger than) the correlation between evolutionary rates and expression levels or breadths. In addition, genes encoding interacting proteins in the network evolve at relatively similar rates. Finally, Arabidopsis proteins encoded by duplicated genes are more highly connected than those encoded by singleton genes. This observation is in agreement with the patterns observed in humans, but in contrast with those observed in E. coli, yeast, worm, and fly (whose duplicated genes tend to act at the periphery of the network), implying that the relationship between duplicability and centrality inverted at least twice during eukaryote evolution. Taken together, these results indicate that the structure of the A. thaliana network constrains the evolution of its components at multiple levels.