Evolution of metabolic pathways by chance assembly of enzyme proteins generated from sense and antisense strands of pre-existing genes.

In order to get an insight into the evolutionary aspect of metabolic pathways, especially of the ubiquitous glycolytic pathway, we have carried out an extensive search of sense-sense and sense-antisense similarities for enzyme proteins in the glycolytic pathway, the pentose phosphate cycle, alcohol and lactate fermentation pathways and the TCA cycle. This investigation of amino acid sequences reveals a curious pattern of similarity relations; no similarity can be found between the enzyme proteins in a section of the glycolytic pathway where the glyceraldehyde-3-phosphate or even glycerol-3-phosphate is converted into the pyruvate while many examples of sense-sense and sense-antisense similarities are found even between enzyme proteins in distant blocks, e.g. between the proteins in the TCA cycle and those in the pentose phosphate cycle, as well as between the functionally associated proteins in each of these blocks. Complementary to this characteristic pattern of amino acid sequence similarity, the search for similarities of nucleotide sequences also finds that the similarities of glycolytic enzyme genes, some sense-sense and others sense-antisense similarities, are concentrated on the nucleotide sequences of prokaryotic 16S or eukaryotic 18S ribosomal RNA gene with its flanks, although some of the copy sequences are also found in transfer RNA genes as well as in 23S or 26S ribosomal RNA gene. These results strongly suggest that the metabolic pathways have been developed by the chance assembly of enzyme proteins generated from the sense and antisense strands of pre-existing genes, e.g. the fermentation pathways and pentose phosphate cycle by the proteins from the genes of enzymes in the glycolytic pathway and the TCA cycle from all these successively increased genes, ascribing the origin of metabolic enzyme genes to the close relation between the glycolytic enzyme protein genes and the RNA gene cluster.     

NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.     

Evolution of dominance in metabolic pathways

Dominance is a form of phenotypic robustness to mutations. Understanding how such robustness can evolve provides a window into how the relation between genotype and phenotype can evolve. As such, the issue of dominance evolution is a question about the evolution of inheritance systems. Attempts at explaining the evolution of dominance have run into two problems. One is that selection for dominance is sensitive to the frequency of heterozygotes. Accordingly, dominance cannot evolve unless special conditions lead to the presence of a high frequency of mutant alleles in the population. Second, on the basis of theoretical results in metabolic control analysis, it has been proposed that metabolic systems possess inherent constraints. These hypothetical constraints imply the default manifestation of dominance of the wild type with respect to the effects of mutations at most loci. Hence, some biologists have maintained that an evolutionary explanation is not relevant to dominance. In this article, we put into question the hypothetical assumption of default metabolic constraints. We show that this assumption is based on an exclusion of important nonlinear interactions that can occur between enzymes in a pathway. With an a priori exclusion of such interactions, the possibility of epistasis and hence dominance modification is eliminated. We present a theoretical model that integrates enzyme kinetics and population genetics to address dominance evolution in metabolic pathways. In the case of mutations that decrease enzyme concentrations, and given the mechanistic constraints of Michaelis-Menten-type catalysis, it is shown that dominance of the wild type can be extensively modified in a two-enzyme pathway. Moreover, we discuss analytical results indicating that the conclusions from the two-enzyme case can be generalized to any number of enzymes. Dominance modification is achieved chiefly through changes in enzyme concentrations or kinetic parameters such as k(cat), both of which can alter saturation levels. Low saturation translates into higher levels of dominance with respect to mutations that decrease enzyme concentrations. Furthermore, it is shown that in the two-enzyme example, dominance evolves as a by-product of selection in a manner that is insensitive to the frequency of heterozygotes. Using variation in k(cat) as an example of modifier mutations, it is shown that the latter can have direct fitness effects in addition to dominance modification effects. Dominance evolution can occur in a frequency-insensitive manner as a result of selection for such dual-effects alleles. This type of selection may prove to be a common pattern for the evolution of phenotypic robustness to mutations.     

Eco‐evolutionary feedbacks between prey densities and linkage disequilibrium in the predator maintain diversity

Diversity occurs at multiple scales. Within a single population, there is diversity in genotypes and phenotypes. At a larger scale, within ecological communities, there is diversity in species. A number of studies have investigated how diversity at these two scales influence each other through what has been termed eco‐evolutionary feedbacks. Here we study a three‐species ecological module called apparent competition, in which the predator is evolving in a trait that determines its interaction with two prey species. Unlike previous studies on apparent competition, which employed evolutionary frameworks with very simple genetics, we study an eco‐evolutionary model in which the predator's trait is determined by two recombining diallelic loci, so that its mean and variance can evolve, as well as associations (linkage disequilibrium) between the loci. We ask how eco‐evolutionary feedbacks with these two loci affect the coexistence of the prey species and the maintenance of polymorphisms within the predator species. We uncover a novel eco‐evolutionary feedback between the prey densities and the linkage disequilibrium between the predator's loci. Through a stability analysis, we demonstrate how these feedbacks affect polymorphisms at both loci and, among others, may generate stable cycling.     

Epistatic interaction maps relative to multiple metabolic phenotypes

An epistatic interaction between two genes occurs when the phenotypic impact of one gene depends on another gene, often exposing a functional association between them. Due to experimental scalability and to evolutionary significance, abundant work has been focused on studying how epistasis affects cellular growth rate, most notably in yeast. However, epistasis likely influences many different phenotypes, affecting our capacity to understand cellular functions, biochemical networks adaptation, and genetic diseases. Despite its broad significance, the extent and nature of epistasis relative to different phenotypes remain fundamentally unexplored. Here we use genome-scale metabolic network modeling to investigate the extent and properties of epistatic interactions relative to multiple phenotypes. Specifically, using an experimentally refined stoichiometric model for Saccharomyces cerevisiae, we computed a three-dimensional matrix of epistatic interactions between any two enzyme gene deletions, with respect to all metabolic flux phenotypes. We found that the total number of epistatic interactions between enzymes increases rapidly as phenotypes are added, plateauing at approximately 80 phenotypes, to an overall connectivity that is roughly 8-fold larger than the one observed relative to growth alone. Looking at interactions across all phenotypes, we found that gene pairs interact incoherently relative to different phenotypes, i.e. antagonistically relative to some phenotypes and synergistically relative to others. Specific deletion-deletion-phenotype triplets can be explained metabolically, suggesting a highly informative role of multi-phenotype epistasis in mapping cellular functions. Finally, we found that genes involved in many interactions across multiple phenotypes are more highly expressed, evolve slower, and tend to be associated with diseases, indicating that the importance of genes is hidden in their total phenotypic impact. Our predictions indicate a pervasiveness of nonlinear effects in how genetic perturbations affect multiple metabolic phenotypes. The approaches and results reported could influence future efforts in understanding metabolic diseases and the role of biochemical regulation in the cell.     

Adaptive evolution of metabolic pathways in Drosophila

The adaptive significance of enzyme variation has been of central interest in population genetics. Yet, how natural selection operates on enzymes in the larger context of biochemical pathways has not been broadly explored. A basic expectation is that natural selection on metabolic phenotypes will target enzymes that control metabolic flux, but how adaptive variation is distributed among enzymes in metabolic networks is poorly understood. Here, we use population genetic methods to identify enzymes responding to adaptive selection in the pathways of central metabolism in Drosophila melanogaster and Drosophila simulans. We report polymorphism and divergence data for 17 genes that encode enzymes of 5 metabolic pathways that converge at glucose-6-phosphate (G6P). Deviations from neutral expectations were observed at five loci. Of the 10 genes that encode the enzymes of glycolysis, only aldolase (Ald) deviated from neutrality. The other 4 genes that were inconsistent with neutral evolution (glucose-6-phosphate dehydrogenase [G6pd]), phosphoglucomutase [Pgm], trehalose-6-phosphate synthetase [Tps1], and glucose-6phosphatase [G6pase] encode G6P branch point enzymes that catalyze reactions at the entry point to the pentose-phosphate, glycogenic, trehalose synthesis, and gluconeogenic pathways. We reconcile these results with population genetics theory and existing arguments on metabolic regulation and propose that the incidence of adaptive selection in this system is related to the distribution of flux control. The data suggest that adaptive evolution of G6P branch point enzymes may have special significance in metabolic adaptation.     

Why metabolic enzymes are essential or nonessential for growth of Escherichia coli K12 on glucose

The genes encoding metabolic enzymes involved in glucose metabolism, the TCA cycle, and biosynthesis of amino acids, purines, pyrimidines, and cofactors would be expected to be essential for growth of Escherichia coli on glucose because the cells must synthesize all of the building blocks for cellular macromolecules. Surprisingly, 80 of 227 of these genes are not essential. Analysis of why these genes are not essential provides insights into the metabolic sophistication of E. coli and into the evolutionary pressures that have shaped its physiology. Alternative routes enabled by interconnecting pathways can allow a defective step to be bypassed. Isozymes, alternative enzymes, broad-specificity enzymes, and multifunctional enzymes can often substitute for a missing enzyme. We expect that the apparent redundancy in these metabolic pathways has arisen due to the need for E. coli to survive in a variety of habitats and therefore to have a metabolism that allows optimal exploitation of varying environmental resources and synthesis of small molecules when they cannot be obtained from the environment.     

The strength of selection on ultraconserved elements in the human genome

Ultraconserved elements are stretches of consecutive nucleotides that are perfectly conserved in multiple mammalian genomes. Although these sequences are identical in the reference human, mouse, and rat genomes, we identified numerous polymorphisms within these regions in the human population. To determine whether polymorphisms in ultraconserved elements affect fitness, we genotyped unrelated human DNA samples at loci within these sequences. For all single-nucleotide polymorphisms tested in ultraconserved regions, individuals homozygous for derived alleles (alleles that differ from the rodent reference genomes) were present, viable, and healthy. The distribution of allele frequencies in these samples argues against strong, ongoing selection as the force maintaining the conservation of these sequences. We then used two methods to determine the minimum level of selection required to generate these sequences. Despite the lack of fixed differences in these sequences between humans and rodents, the average level of selection on ultraconserved elements is less than that on essential genes. The strength of selection associated with ultraconserved elements suggests that mutations in these regions may have subtle phenotypic consequences that are not easily detected in the laboratory.     

The guardians of inherited oncogenic vulnerabilities

Similar to seemingly maladaptive genes in general, the persistence of inherited cancer‐causing mutant alleles in populations remains a challenging question for evolutionary biologists. In addition to traditional explanations such as senescence or antagonistic pleiotropy, here we put forward a new hypothesis to explain the retention of oncogenic mutations. We propose that although natural defenses evolve to prevent neoplasm formation and progression thus increasing organismal fitness, they also conceal the effects of cancer‐causing mutant alleles on fitness and concomitantly protect inherited ones from purging by purifying selection. We also argue for the importance of the ecological contexts experienced by individuals and/or species. These contexts determine the locally predominant fitness‐reducing risks, and hence can aid the prediction of how natural selection will influence cancer outcomes.     

Molecular insights into the evolution of an enzyme; esterase6 in Drosophila

It is still a suspicion among some evolutionary biologists that the incursion of molecular biology into their field will do little more than determine the DNA sequence differences underlying evolutionary changes already evident at the organismal level. Work on an esterase enzyme involved in the reproductive biology of Drosophila belies this view. Although it is already one of the most intensively studied gene - enzyme systems at an organismal level, recent molecular invetigations reveal several unexpected, and, in some cases, still inexplicable phenomena in its evolutionary history.     

Alleles in metabolic and oxygen‐sensing genes are associated with antagonistic pleiotropic effects on life history traits and population fitness in an ecological model insect*

Genes with opposing effects on fitness at different life stages are the mechanistic basis for evolutionary theories of aging and life history. Examples come from studies of mutations in model organisms, but there is little knowledge of genetic bases of life history tradeoffs in natural populations. Here, we test the hypothesis that alleles affecting oxygen sensing in Glanville fritillary butterflies have opposing effects on larval versus adult fitness‐related traits. Intermediate‐frequency alleles in Succinate dehydrogenase d, and to a lesser extent Hypoxia inducible factor 1α, are associated in larvae with variation in metabolic rate and activation of the hypoxia inducible factor (HIF) pathway, which affects tracheal development and delivery of oxygen to adult flight muscles. A dominant Sdhd allele is likely to cause antagonistic pleiotropy for fitness through its opposing effects on larval metabolic and growth rate versus adult flight and dispersal, and may have additional effects arising from sensitivity to low‐iron host plants. Prior results in Glanville fritillaries indicate that fitness of alleles in Sdhd and another antagonistically pleiotropic metabolic gene, Phosphoglucose isomerase, depend strongly on the size and distribution of host plant patches. Hence, these intermediate‐frequency alleles are involved in ecoevolutionary dynamics involving life history tradeoffs.     

Recurrent patterns of DNA copy number alterations in tumors reflect metabolic selection pressures

Copy number alteration (CNA) profiling of human tumors has revealed recurrent patterns of DNA amplifications and deletions across diverse cancer types. These patterns are suggestive of conserved selection pressures during tumor evolution but cannot be fully explained by known oncogenes and tumor suppressor genes. Using a pan‐cancer analysis of CNA data from patient tumors and experimental systems, here we show that principal component analysis‐defined CNA signatures are predictive of glycolytic phenotypes, including ¹⁸F‐fluorodeoxy‐glucose (FDG) avidity of patient tumors, and increased proliferation. The primary CNA signature is enriched for p53 mutations and is associated with glycolysis through coordinate amplification of glycolytic genes and other cancer‐linked metabolic enzymes. A pan‐cancer and cross‐species comparison of CNAs highlighted 26 consistently altered DNA regions, containing 11 enzymes in the glycolysis pathway in addition to known cancer‐driving genes. Furthermore, exogenous expression of hexokinase and enolase enzymes in an experimental immortalization system altered the subsequent copy number status of the corresponding endogenous loci, supporting the hypothesis that these metabolic genes act as drivers within the conserved CNA amplification regions. Taken together, these results demonstrate that metabolic stress acts as a selective pressure underlying the recurrent CNAs observed in human tumors, and further cast genomic instability as an enabling event in tumorigenesis and metabolic evolution.     

Adaptive noise

In biology, noise implies error and disorder and is therefore something which organisms may seek to minimize and mitigate against. We argue that such noise can be adaptive. Recent studies have shown that gene expression can be noisy, noise can be genetically controlled, genes and gene networks vary in how noisy they are and noise generates phenotypic differences among genetically identical cells. Such phenotypic differences can have fitness benefits, suggesting that evolution can shape noise and that noise may be adaptive. For example, gene networks can generate bistable states resulting in phenotypic diversity and switching among individual cells of a genotype, which may be a bet hedging strategy. Here, we review the sources of noise in gene expression, the extent to which noise in biological systems may be adaptive and suggest that applying evolutionary rigour to the study of noise is necessary to fully understand organismal phenotypes.     

Carbohydrate metabolic pathway genes associated with quantitative trait loci (QTL) for obesity and type 2 diabetes: Identification by data mining

Increasing consumption of refined carbohydrates is now being recognized as a primary contributor to the development of nutritionally related chronic diseases such as obesity and type 2 diabetes mellitus (T2DM). A data mining approach was used to evaluate the role of carbohydrate metabolic pathway genes in the development of obesity and T2DM. Data from public databases were used to map the position of the carbohydrate metabolic pathway genes to known quantitative trait loci (QTL) for obesity and T2DM and for examining the pathway genes for the presence of sequence and structural genetic variants such as single nucleotide polymorphisms (SNPs) and copy number variants (CNS), respectively. The results demonstrated that a majority of the genes of the carbohydrate metabolic pathways are associated with QTL for obesity and many for T2DM. In addition, some key genes of the pathways also encode non-synonymous SNPs that exhibit significant differences in population frequencies. This study emphasizes the significance of the metabolic pathways genes in the development of disease phenotypes, its differential occurrence across populations and between individuals, and a strategy for interpreting an individuals' risk for disease.     

Widespread compensatory evolution conserves DNA-encoded nucleosome organization in yeast

Evolution maintains organismal fitness by preserving genomic information. This is widely assumed to involve conservation of specific genomic loci among species. Many genomic encodings are now recognized to integrate small contributions from multiple genomic positions into quantitative dispersed codes, but the evolutionary dynamics of such codes are still poorly understood. Here we show that in yeast, sequences that quantitatively affect nucleosome occupancy evolve under compensatory dynamics that maintain heterogeneous levels of A+T content through spatially coupled A/T-losing and A/T-gaining substitutions. Evolutionary modeling combined with data on yeast polymorphisms supports the idea that these substitution dynamics are a consequence of weak selection. This shows that compensatory evolution, so far believed to affect specific groups of epistatically linked loci like paired RNA bases, is a widespread phenomenon in the yeast genome, affecting the majority of intergenic sequences in it. The model thus derived suggests that compensation is inevitable when evolution conserves quantitative and dispersed genomic functions.