The phylogenetic extent of metabolic enzymes and pathways

Assembling bacterial puzzles: piecing together functions into microbial pathways

Functional metagenomics enables the study of unexplored bacterial diversity, gene families, and pathways essential to microbial communities. However, discovering biological insights with these data is impeded by the scarcity of quality annotations. Here, we use a co-occurrence-based analysis of predicted microbial protein functions to uncover pathways in genomic and metagenomic biological systems. Our approach, based on phylogenetic profiles, improves the identification of functional relationships, or participation in the same biochemical pathway, between enzymes over a comparable homology-based approach. We optimized the design of our profiles to identify potential pathways using minimal data, clustered functionally related enzyme pairs into multi-enzymatic pathways, and evaluated our predictions against reference pathways in the KEGG database. We then demonstrated a novel extension of this approach to predict inter-bacterial protein interactions amongst members of a marine microbiome. Most significantly, we show our method predicts emergent biochemical pathways between known and unknown functions. Thus, our work establishes a basis for identifying the potential functional capacities of the entire metagenome, capturing previously unknown and abstract functions into discrete putative pathways.

Architecture, Function, Regulation, and Evolution of α-Glucans Metabolic Enzymes in Prokaryotes

Bacteria have acquired sophisticated mechanisms for assembling and disassembling polysaccharides of different chemistry. α-d-Glucose homopolysaccharides, so-called α-glucans, are the most widespread polymers in nature being key components of microorganisms. Glycogen functions as an intracellular energy storage while some bacteria also produce extracellular assorted α-glucans. The classical bacterial glycogen metabolic pathway comprises the action of ADP-glucose pyrophosphorylase and glycogen synthase, whereas extracellular α-glucans are mostly related to peripheral enzymes dependent on sucrose. An alternative pathway of glycogen biosynthesis, operating via a maltose 1-phosphate polymerizing enzyme, displays an essential wiring with the trehalose metabolism to interconvert disaccharides into polysaccharides. Furthermore, some bacteria show a connection of intracellular glycogen metabolism with the genesis of extracellular capsular α-glucans, revealing a relationship between the storage and structural function of these compounds. Altogether, the current picture shows that bacteria have evolved an intricate α-glucan metabolism that ultimately relies on the evolution of a specific enzymatic machinery. The structural landscape of these enzymes exposes a limited number of core catalytic folds handling many different chemical reactions. In this Review, we present a rationale to explain how the chemical diversity of α-glucans emerged from these systems, highlighting the underlying structural evolution of the enzymes driving α-glucan bacterial metabolism.

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.

Evaluation of fatty acids and carnitine as biomarkers of PFOS exposure in biota (fish and dolphin) from Galveston Bay and the northwestern Gulf of Mexico

Perfluorooctane sulfonate (PFOS) is a ubiquitous pollutant that elicits a wide range of toxic effects in exposed biota. Coastal zones in highly urbanized or industrial areas are particularly vulnerable to PFOS pollution. At present, information is lacking on biomarkers to assess PFOS effects on aquatic wildlife. This study investigated the efficacy of l-carnitine (or carnitine) and fatty acids as biomarkers of PFOS exposure in aquatic biota. The levels of PFOS, total and free carnitine, and 24 fatty acids (measured as fatty acid methyl esters or FAMEs) were measured in the liver, and muscle or blubber, of fish and dolphins sampled from Galveston Bay and the northern Gulf of Mexico (nGoM). Overall, bottlenose dolphins (Tursiops truncatus) had the highest hepatic PFOS levels. Galveston Bay fish, gafftopsail catfish (Bagre marinus), red drum (Sciaenops ocellatus), and spotted seatrout (Cynoscion nebulosus), had hepatic PFOS levels ∼8-13× higher than nGoM pelagic fish species, red snapper (Lutjanus campechanus) and yellowfin tuna (Thunnus albacares). The multivariate analysis of PFOS liver body-burdens and biomarkers found carnitine to be a more modal biomarker of PFOS exposure than FAMEs. Significant positive correlation of hepatic PFOS levels with total carnitine was evident for biota from Galveston Bay (fish only), and a significant correlation between PFOS and total and free carnitine was evident for biota from the nGoM (fish and dolphins). Given the essential role of carnitine in mediating fatty acid β-oxidation, our results suggest carnitine to be a likely candidate biomarker of environmental PFOS exposure and indicative of potential dyslipidemia effects.

A universal metabolite repair enzyme removes a strong inhibitor of the TCA cycle

A prevalent side-reaction of succinate dehydrogenase oxidizes malate to enol-oxaloacetate (OAA), a metabolically inactive form of OAA that is a strong inhibitor of succinate dehydrogenase. We purified from cow heart mitochondria an enzyme (OAT1) with OAA tautomerase (OAT) activity that converts enol-OAA to the physiological keto-OAA form, and determined that it belongs to the highly conserved and previously uncharacterized Fumarylacetoacetate_hydrolase_domain-containing protein family. From all three domains of life, heterologously expressed proteins were shown to have strong OAT activity, and ablating the OAT1 homolog caused significant growth defects. In Escherichia coli, expression of succinate dehydrogenase was necessary for OAT1-associated growth defects to occur, and ablating OAT1 caused a significant increase in acetate and other metabolites associated with anaerobic respiration. OAT1 increased the succinate dehydrogenase reaction rate by 35% in in vitro assays with physiological concentrations of both succinate and malate. Our results suggest that OAT1 is a universal metabolite repair enzyme that is required to maximize aerobic respiration efficiency by preventing succinate dehydrogenase inhibition.

Upper glycolytic components contribute differently in controlling retinal vascular endothelial cellular behavior: Implications for endothelial-related retinal diseases

BACKGROUND

Retinal degenerative diseases such as diabetic retinopathy and diabetic macular edema are characterized by impaired retinal endothelial cells (RECs) functionality. While the role of glycolysis in glucose homeostasis is well-established, its contributions to REC barrier assembly and cell spreading remain poorly understood. This study aimed to investigate the importance of upper glycolytic components in regulating the behavior of human RECs (HRECs).

METHODS

Electric cell-substrate impedance sensing (ECIS) technology was employed to analyze the real-time impact of various upper glycolytic components on maintaining barrier functionality and cell spreading of HRECs by measuring cell resistance and capacitance, respectively. Specific inhibitors were used: WZB117 to inhibit Glut1/3, lonidamine to inhibit hexokinases, PFK158 to inhibit the PFKFB3-PFK axis, and TDZD-8 to inhibit aldolases. Additionally, the viability of HRECs was evaluated using the lactate dehydrogenase (LDH) cytotoxicity assay.

RESULTS

The most significant reduction in electrical resistance and increase in capacitance of HRECs resulted from the dose-dependent inhibition of PFKFB3/PFK using PFK158, followed by aldolase inhibition using TDZD-8. LDH level analysis at 24- and 48-hours post-treatment with PFK158 (1 μM) or TDZD-8 (1 and 10 μM) showed no significant difference compared to the control, indicating that the disruption of HRECs functionality was not attributed to cell death. Conversely, inhibiting Glut1/3 with WZB117 had minimal impact on HREC behavior, except at higher concentrations (10 μM) and prolonged exposure. Lastly, inhibiting hexokinase with lonidamine did not noticeably alter HREC cell behavior.

CONCLUSION

This study illustrates the unique impacts of components within upper glycolysis on HREC functionality, emphasizing the crucial role of the PFKFB3/PFK axis in regulating HREC behavior. Understanding the specific contributions of each glycolytic component in preserving normal REC functionality will facilitate the development of targeted interventions for treating endothelial cell dysfunction in retinal disorders while minimizing effects on healthy cells.

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.

The origin of genetic and metabolic systems: Evolutionary structuralinsights

DNA is derived from reverse transcription and its origin is related to reverse transcriptase, DNA polymerase and integrase. The gene structure originated from the evolution of the first RNA polymerase. Thus, an explanation of the origin of the genetic system must also explain the evolution of these enzymes. This paper proposes a polymer structure model, termed the stable complex evolution model, which explains the evolution of enzymes and functional molecules. Enzymes evolved their functions by forming locally tightly packed complexes with specific substrates. A metabolic reaction can therefore be considered to be the result of adaptive evolution in this way when a certain essential molecule is lacking in a cell. The evolution of the primitive genetic and metabolic systems was thus coordinated and synchronized. According to the stable complex model, almost all functional molecules establish binding affinity and specific recognition through complementary interactions, and functional molecules therefore have the nature of being auto-reactive. This is thermodynamically favorable and leads to functional duplication and self-organization. Therefore, it can be speculated that biological systems have a certain tendency to maintain functional stability or are influenced by an inherent selective power. The evolution of dormant bacteria may support this hypothesis, and inherent selectivity can be unified with natural selection at the molecular level.

Identification of 121 variants of honey bee Vitellogenin protein sequences with structural differences at functional sites

Proteins are under selection to maintain central functions and to accommodate needs that arise in ever-changing environments. The positive selection and neutral drift that preserve functions result in a diversity of protein variants. The amount of diversity differs between proteins: multifunctional or disease-related proteins tend to have fewer variants than proteins involved in some aspects of immunity. Our work focuses on the extensively studied protein Vitellogenin (Vg), which in honey bees (Apis mellifera) is multifunctional and highly expressed and plays roles in immunity. Yet, almost nothing is known about the natural variation in the coding sequences of this protein or how amino acid-altering variants might impact structure-function relationships. Here, we map out allelic variation in honey bee Vg using biological samples from 15 countries. The successful barcoded amplicon Nanopore sequencing of 543 bees revealed 121 protein variants, indicating a high level of diversity in Vg. We find that the distribution of non-synonymous single nucleotide polymorphisms (nsSNPs) differs between protein regions with different functions; domains involved in DNA and protein-protein interactions contain fewer nsSNPs than the protein's lipid binding cavities. We outline how the central functions of the protein can be maintained in different variants and how the variation pattern may inform about selection from pathogens and nutrition.

Predicting metabolic pathways of plant enzymes without using sequence similarity: Models from machine learning

Most of the bioinformatics tools for enzyme annotation focus on enzymatic function assignments. Sequence similarity to well-characterized enzymes is often used for functional annotation and to assign metabolic pathways. However, these approaches are not feasible for all sequences leading to inaccurate annotations or lack of metabolic pathway information. Here we present the mApLe (metabolic pathway predictor of plant enzymes), a high-performance machine learning-based tool with models to label the metabolic pathway of enzymes rather than specifying enzymes' reactions. The mApLe uses molecular descriptors of the enzyme sequences to perform predictions without considering sequence similarities with reference sequences. Hence, mApLe can classify a diversity of enzymes, even the ones without any homolog or with incomplete EC numbers. This tool can be used to improve the quality of genomic annotation of plants or to narrow down the number of candidate genes for metabolic engineering researches. The mApLe tool is available online, and the GUI can be locally installed.

Synthetic Methylotrophy in Yeasts: Towards a Circular Bioeconomy

Mitigating climate change is a key driver for the development of sustainable and CO₂-neutral production processes. In this regard, connecting carbon capture and utilization processes to derive microbial C₁ fermentation substrates from CO₂ is highly promising. This strategy uses methylotrophic microbes to unlock next-generation processes, converting CO₂-derived methanol. Synthetic biology approaches in particular can empower synthetic methylotrophs to produce a variety of commodity chemicals. We believe that yeasts have outstanding potential for this purpose, because they are able to separate toxic intermediates and metabolic reactions in organelles. This compartmentalization can be harnessed to design superior synthetic methylotrophs, capable of utilizing methanol and other hitherto largely disregarded C₁ compounds, thus supporting the establishment of a future circular economy.

Gene Dispensability in Escherichia coli Grown in Thirty Different Carbon Environments

While there has been much study of bacterial gene dispensability, there is a lack of comprehensive genome-scale examinations of the impact of gene deletion on growth in different carbon sources. In this context, a lot can be learned from such experiments in the model microbe Escherichia coli where much is already understood and there are existing tools for the investigation of carbon metabolism and physiology (1). Gene deletion studies have practical potential in the field of antibiotic drug discovery where there is emerging interest in bacterial central metabolism as a target for new antibiotics (2). Furthermore, some carbon utilization pathways have been shown to be critical for initiating and maintaining infection for certain pathogens and sites of infection (3–5). Here, with the use of high-throughput solid medium phenotyping methods, we have generated kinetic growth measurements for 3,796 genes under 30 different carbon source conditions. This data set provides a foundation for research that will improve our understanding of genes with unknown function, aid in predicting potential antibiotic targets, validate and advance metabolic models, and help to develop our understanding of E. coli metabolism.

Central metabolism is a topic that has been studied for decades, and yet, this process is still not fully understood in Escherichia coli, perhaps the most amenable and well-studied model organism in biology. To further our understanding, we used a high-throughput method to measure the growth kinetics of each of 3,796 E. coli single-gene deletion mutants in 30 different carbon sources. In total, there were 342 genes (9.01%) encompassing a breadth of biological functions that showed a growth phenotype on at least 1 carbon source, demonstrating that carbon metabolism is closely linked to a large number of processes in the cell. We identified 74 genes that showed low growth in 90% of conditions, defining a set of genes which are essential in nutrient-limited media, regardless of the carbon source. The data are compiled into a Web application, Carbon Phenotype Explorer (CarPE), to facilitate easy visualization of growth curves for each mutant strain in each carbon source. Our experimental data matched closely with the predictions from the EcoCyc metabolic model which uses flux balance analysis to predict growth phenotypes. From our comparisons to the model, we found that, unexpectedly, phosphoenolpyruvate carboxylase (ppc) was required for robust growth in most carbon sources other than most trichloroacetic acid (TCA) cycle intermediates. We also identified 51 poorly annotated genes that showed a low growth phenotype in at least 1 carbon source, which allowed us to form hypotheses about the functions of these genes. From this list, we further characterized the ydhC gene and demonstrated its role in adenosine efflux.

A network of RNA-binding proteins controls translation efficiency to activate anaerobic metabolism

Protein expression evolves under greater evolutionary constraint than mRNA levels, and translation efficiency represents a primary determinant of protein levels during stimuli adaptation. This raises the question as to the translatome remodelers that titrate protein output from mRNA populations. Here, we uncover a network of RNA-binding proteins (RBPs) that enhances the translation efficiency of glycolytic proteins in cells responding to oxygen deprivation. A system-wide proteomic survey of translational engagement identifies a family of oxygen-regulated RBPs that functions as a switch of glycolytic intensity. Tandem mass tag-pulse SILAC (TMT-pSILAC) and RNA sequencing reveals that each RBP controls a unique but overlapping portfolio of hypoxic responsive proteins. These RBPs collaborate with the hypoxic protein synthesis apparatus, operating as a translation efficiency checkpoint that integrates upstream mRNA signals to activate anaerobic metabolism. This system allows anoxia-resistant animals and mammalian cells to initiate anaerobic glycolysis and survive hypoxia. We suggest that an oxygen-sensitive RBP cluster controls anaerobic metabolism to confer hypoxia tolerance.

mRNA translation efficiency is regulated in response to stimuli. Here the authors employ mass spectrometry analysis of ribosome fractions and show that under hypoxia, oxygen-sensitive RNA binding proteins enhance the translation efficiency of glycolysis pathway transcripts.

Phylogenetic profiling, an untapped resource for the prediction of secreted proteins and its complementation with sequence-based classifiers in bacterial type III, IV and VI secretion systems

In the establishment and maintenance of the interaction between pathogenic or symbiotic bacteria with a eukaryotic organism, protein substrates of specialized bacterial secretion systems called effectors play a critical role once translocated into the host cell. Proteins are also secreted to the extracellular medium by free-living bacteria or directly injected into other competing organisms to hinder or kill. In this work, we explore an approach based on the evolutionary dependence that most of the effectors maintain with their specific secretion system that analyzes the co-occurrence of any orthologous protein group and their corresponding secretion system across multiple genomes. We compared and complemented our methodology with sequence-based machine learning prediction tools for the type III, IV and VI secretion systems. Finally, we provide the predictive results for the three secretion systems in 1606 complete genomes at http://www.iib.unsam.edu.ar/orgsissec/.

Influence of pathway topology and functional class on the molecular evolution of human metabolic genes

Metabolic networks comprise thousands of enzymatic reactions functioning in a controlled manner and have been shaped by natural selection. Thanks to the genome data, the footprints of adaptive (positive) selection are detectable, and the strength of purifying selection can be measured. This has made possible to know where, in the metabolic network, adaptive selection has acted and where purifying selection is more or less strong and efficient. We have carried out a comprehensive molecular evolutionary study of all the genes involved in the human metabolism. We investigated the type and strength of the selective pressures that acted on the enzyme-coding genes belonging to metabolic pathways during the divergence of primates and rodents. Then, we related those selective pressures to the functional and topological characteristics of the pathways. We have used DNA sequences of all enzymes (956) of the metabolic pathways comprised in the HumanCyc database, using genome data for humans and five other mammalian species. We have found that the evolution of metabolic genes is primarily constrained by the layer of the metabolism in which the genes participate: while genes encoding enzymes of the inner core of metabolism are much conserved, those encoding enzymes participating in the outer layer, mediating the interaction with the environment, are evolutionarily less constrained and more plastic, having experienced faster functional evolution. Genes that have been targeted by adaptive selection are endowed by higher out-degree centralities than non-adaptive genes, while genes with high in-degree centralities are under stronger purifying selection. When the position along the pathway is considered, a funnel-like distribution of the strength of the purifying selection is found. Genes at bottom positions are highly preserved by purifying selection, whereas genes at top positions, catalyzing the first steps, are open to evolutionary changes. These results show how functional and topological characteristics of metabolic pathways contribute to shape the patterns of evolutionary pressures driven by natural selection and how pathway network structure matters in the evolutionary process that shapes the evolution of the system.

Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (k_cats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved k_cats. Diminishing returns epistasis prevents enzymes from developing higher k_cats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows k_cat evolution to be convergent. Predicted k_cat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern k_cats and the whole of metabolism.

Systematic analysis reveals long noncoding RNAs regulating neighboring transcription factors in human cancers

Long noncoding RNAs (lncRNAs) are proposed to play essential roles in regulating gene transcription. Moreover, a subset has been implicated in modulating the expression of the nearby loci. Here we systematically evaluated the relationship between lncRNAs and their neighboring genes based on transcriptome expression profiles from 4900 samples across 12 cancer types. Our findings reveal that lncRNAs, especially those of high syntenic conservation across species, are spatially correlated with transcription factors across the genome. Combining the methods of conservation, co-expression, and causal inference test, we identified a list of 28 lncRNA/TF regulatory pairs across 12 TCGA cancer types, and 19 of which were further confirmed in additional cancer cell lines. Several of these pairs, including PTV1/MYC and GATA6-AS1/GATA6, show prior evidence of regulatory relationships. Other candidates such as LINC00261/FOXA2 and PITRM1-AS1/KLF6 were novel. Our study highlights the significant roles of lncRNAs in tumorigenesis and provides a comprehensive overview of lncRNA regulation on its neighboring TF genes in human cancers.

Genome-Scale Architecture of Small Molecule Regulatory Networks and the Fundamental Trade-Off between Regulation and Enzymatic Activity

Metabolic flux is in part regulated by endogenous small molecules that modulate the catalytic activity of an enzyme, e.g., allosteric inhibition. In contrast to transcriptional regulation of enzymes, technical limitations have hindered the production of a genome-scale atlas of small molecule-enzyme regulatory interactions. Here, we develop a framework leveraging the vast, but fragmented, biochemical literature to reconstruct and analyze the small molecule regulatory network (SMRN) of the model organism Escherichia coli, including the primary metabolite regulators and enzyme targets. Using metabolic control analysis, we prove a fundamental trade-off between regulation and enzymatic activity, and we combine it with metabolomic measurements and the SMRN to make inferences on the sensitivity of enzymes to their regulators. Generalizing the analysis to other organisms, we identify highly conserved regulatory interactions across evolutionarily divergent species, further emphasizing a critical role for small molecule interactions in the maintenance of metabolic homeostasis.

Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks

Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components.

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.

Systematic bacterialization of yeast genes identifies a near-universally swappable pathway

Eukaryotes and prokaryotes last shared a common ancestor ~2 billion years ago, and while many present-day genes in these lineages predate this divergence, the extent to which these genes still perform their ancestral functions is largely unknown. To test principles governing retention of ancient function, we asked if prokaryotic genes could replace their essential eukaryotic orthologs. We systematically replaced essential genes in yeast by their 1:1 orthologs from Escherichia coli. After accounting for mitochondrial localization and alternative start codons, 31 out of 51 bacterial genes tested (61%) could complement a lethal growth defect and replace their yeast orthologs with minimal effects on growth rate. Replaceability was determined on a pathway-by-pathway basis; codon usage, abundance, and sequence similarity contributed predictive power. The heme biosynthesis pathway was particularly amenable to inter-kingdom exchange, with each yeast enzyme replaceable by its bacterial, human, or plant ortholog, suggesting it as a near-universally swappable pathway.

Functional Analogy in Human Metabolism: Enzymes with Different Biological Roles or Functional Redundancy?

Since enzymes catalyze almost all chemical reactions that occur in living organisms, it is crucial that genes encoding such activities are correctly identified and functionally characterized. Several studies suggest that the fraction of enzymatic activities in which multiple events of independent origin have taken place during evolution is substantial. However, this topic is still poorly explored, and a comprehensive investigation of the occurrence, distribution, and implications of these events has not been done so far. Fundamental questions, such as how analogous enzymes originate, why so many events of independent origin have apparently occurred during evolution, and what are the reasons for the coexistence in the same organism of distinct enzymatic forms catalyzing the same reaction, remain unanswered. Also, several isofunctional enzymes are still not recognized as nonhomologous, even with substantial evidence indicating different evolutionary histories. In this work, we begin to investigate the biological significance of the cooccurrence of nonhomologous isofunctional enzymes in human metabolism, characterizing functional analogous enzymes identified in metabolic pathways annotated in the human genome. Our hypothesis is that the coexistence of multiple enzymatic forms might not be interpreted as functional redundancy. Instead, these enzymatic forms may be implicated in distinct (and probably relevant) biological roles.

Enzymatic Antioxidant Systems in Early Anaerobes: Theoretical Considerations

It is widely accepted that cyanobacteria-dependent oxygen that was released into Earth's atmosphere ca. 2.5 billion years ago sparked the evolution of the aerobic metabolism and the antioxidant system. In modern aerobes, enzymes such as superoxide dismutases (SODs), peroxiredoxins (PXs), and catalases (CATs) constitute the core of the enzymatic antioxidant system (EAS) directed against reactive oxygen species (ROS). In many anaerobic prokaryotes, the superoxide reductases (SORs) have been identified as the main force in counteracting ROS toxicity. We found that 93% of the analyzed strict anaerobes possess at least one antioxidant enzyme, and 50% have a functional EAS, that is, consisting of at least two antioxidant enzymes: one for superoxide anion radical detoxification and another for hydrogen peroxide decomposition. The results presented here suggest that the last universal common ancestor (LUCA) was not a strict anaerobe. O2 could have been available for the first microorganisms before oxygenic photosynthesis evolved, however, from the intrinsic activity of EAS, not solely from abiotic sources.

KEY WORDS

Archaea-Atmospheric gases-Evolution-H2O2 resistance-Oxygenic photosynthesis. Astrobiology 16, 348-358.

Of monkeys and men: a metabolomic analysis of static and dynamic urinary metabolic phenotypes in two species

Background

Metabolomics has attracted the interest of the medical community for its potential in predicting early derangements from a healthy to a diseased metabolic phenotype. One key issue is the diversity observed in metabolic profiles of different healthy individuals, commonly attributed to the variation of intrinsic (such as (epi)genetic variation, gut microbiota, etc.) and extrinsic factors (such as dietary habits, life-style and environmental conditions). Understanding the relative contributions of these factors is essential to establish the robustness of the healthy individual metabolic phenotype.

Methods

To assess the relative contribution of intrinsic and extrinsic factors we compared multilevel analysis results obtained from subjects of Homo sapiens and Macaca mulatta, the latter kept in a controlled environment with a standardized diet by making use of previously published data and results.

Results

We observed similarities for the two species and found the diversity of urinary metabolic phenotypes as identified by nuclear magnetic resonance (NMR) spectroscopy could be ascribed to the complex interplay of intrinsic factors and, to a lesser extent, of extrinsic factors in particular minimizing the role played by diet in shaping the metabolic phenotype. Moreover, we show that despite the standardization of diet as the most relevant extrinsic factor, a clear individual and discriminative metabolic fingerprint also exists for monkeys. We investigate the metabolic phenotype both at the static (i.e., at the level of the average metabolite concentration) and at the dynamic level (i.e., concerning their variation over time), and we show that these two components sum up to the overall phenotype with different relative contributions of about 1/4 and 3/4, respectively, for both species. Finally, we show that the great degree diversity observed in the urinary metabolic phenotype of both species can be attributed to differences in both the static and dynamic part of their phenotype.

Evolution of the first genetic cells and the universal genetic code: a hypothesis based on macromolecular coevolution of RNA and proteins

A qualitative hypothesis based on coevolution of protein and nucleic acid macromolecules was developed to explain the evolution of the first genetic cells, from the likely organic chemical-rich environment of early earth, through to the Last Universal Common Ancestor (LUCA). The evolution of the first genetic cell was divided into three phases, proto-genetic cells I, II and III, and the transition to each milestone is described, based on development of chemical cross-catalysis, bio-cross-catalysis, and the universal genetic code, respectively. Selection of macromolecular properties of both peptides and nucleic acids, in response to environmental factors, was likely to be a key aspect of early evolution. The development of hereditable nucleic acids with various key functions; translation, transcription and replication, is described. These functions are envisaged to have coevolved with protein enzymes, from simple organic precursors. Genetically heritable nucleotides may have developed after the local earth environment had cooled below 63 °C. Around this temperature G-C bases would have been preferentially utilized for nucleotide synthesis. Under these conditions RNA type nucleotides were then likely selected from a range of different types of nucleotide backbones through template-based synthesis. Initial development of the genetic coding system was simplified by the availability of proto-messenger RNA sequences that contained only G and C bases, and the need to encode only four amino acids. The step-wise addition of further amino acids to the code was predicted to parallel the growing metabolic complexity of the proto-genetic cell. On completion of this evolutionary process the proto-genetic cell is envisaged to have become the LUCA, the last common ancestor of bacteria, eukaryote and archaea domains. Key issues addressed by the model include: (a) the transition from non-hereditable random sequences of peptides and nucleic acids to specific proteins coded by hereditable nucleotide sequences, (b) the origin of homochiral amino acids and sugars, and (c) the mutation limits on the sizes of early nucleic acid genomes. The first genome was limited to a size of about 200 base pairs.

The use of weighted graphs for large-scale genome analysis

There is an acute need for better tools to extract knowledge from the growing flood of sequence data. For example, thousands of complete genomes have been sequenced, and their metabolic networks inferred. Such data should enable a better understanding of evolution. However, most existing network analysis methods are based on pair-wise comparisons, and these do not scale to thousands of genomes. Here we propose the use of weighted graphs as a data structure to enable large-scale phylogenetic analysis of networks. We have developed three types of weighted graph for enzymes: taxonomic (these summarize phylogenetic importance), isoenzymatic (these summarize enzymatic variety/redundancy), and sequence-similarity (these summarize sequence conservation); and we applied these types of weighted graph to survey prokaryotic metabolism. To demonstrate the utility of this approach we have compared and contrasted the large-scale evolution of metabolism in Archaea and Eubacteria. Our results provide evidence for limits to the contingency of evolution.

Detection of genomic idiosyncrasies using fuzzy phylogenetic profiles

Phylogenetic profiles express the presence or absence of genes and their homologs across a number of reference genomes. They have emerged as an elegant representation framework for comparative genomics and have been used for the genome-wide inference and discovery of functionally linked genes or metabolic pathways. As the number of reference genomes grows, there is an acute need for faster and more accurate methods for phylogenetic profile analysis with increased performance in speed and quality. We propose a novel, efficient method for the detection of genomic idiosyncrasies, i.e. sets of genes found in a specific genome with peculiar phylogenetic properties, such as intra-genome correlations or inter-genome relationships. Our algorithm is a four-step process where genome profiles are first defined as fuzzy vectors, then discretized to binary vectors, followed by a de-noising step, and finally a comparison step to generate intra- and inter-genome distances for each gene profile. The method is validated with a carefully selected benchmark set of five reference genomes, using a range of approaches regarding similarity metrics and pre-processing stages for noise reduction. We demonstrate that the fuzzy profile method consistently identifies the actual phylogenetic relationship and origin of the genes under consideration for the majority of the cases, while the detected outliers are found to be particular genes with peculiar phylogenetic patterns. The proposed method provides a time-efficient and highly scalable approach for phylogenetic stratification, with the detected groups of genes being either similar to their own genome profile or different from it, thus revealing atypical evolutionary histories.

Evolution of domain architectures and catalytic functions of enzymes in metabolic systems

Domain architectures and catalytic functions of enzymes constitute the centerpieces of a metabolic network. These types of information are formulated as a two-layered network consisting of domains, proteins, and reactions-a domain-protein-reaction (DPR) network. We propose an algorithm to reconstruct the evolutionary history of DPR networks across multiple species and categorize the mechanisms of metabolic systems evolution in terms of network changes. The reconstructed history reveals distinct patterns of evolutionary mechanisms between prokaryotic and eukaryotic networks. Although the evolutionary mechanisms in early ancestors of prokaryotes and eukaryotes are quite similar, more novel and duplicated domain compositions with identical catalytic functions arise along the eukaryotic lineage. In contrast, prokaryotic enzymes become more versatile by catalyzing multiple reactions with similar chemical operations. Moreover, different metabolic pathways are enriched with distinct network evolution mechanisms. For instance, although the pathways of steroid biosynthesis, protein kinases, and glycosaminoglycan biosynthesis all constitute prominent features of animal-specific physiology, their evolution of domain architectures and catalytic functions follows distinct patterns. Steroid biosynthesis is enriched with reaction creations but retains a relatively conserved repertoire of domain compositions and proteins. Protein kinases retain conserved reactions but possess many novel domains and proteins. In contrast, glycosaminoglycan biosynthesis has high rates of reaction/protein creations and domain recruitments. Finally, we elicit and validate two general principles underlying the evolution of DPR networks: 1) duplicated enzyme proteins possess similar catalytic functions and 2) the majority of novel domains arise to catalyze novel reactions. These results shed new lights on the evolution of metabolic systems.

Oxygen and hydrogen peroxide in the early evolution of life on earth: in silico comparative analysis of biochemical pathways

In the Universe, oxygen is the third most widespread element, while on Earth it is the most abundant one. Moreover, oxygen is a major constituent of all biopolymers fundamental to living organisms. Besides O(2), reactive oxygen species (ROS), among them hydrogen peroxide (H(2)O(2)), are also important reactants in the present aerobic metabolism. According to a widely accepted hypothesis, aerobic metabolism and many other reactions/pathways involving O(2) appeared after the evolution of oxygenic photosynthesis. In this study, the hypothesis was formulated that the Last Universal Common Ancestor (LUCA) was at least able to tolerate O(2) and detoxify ROS in a primordial environment. A comparative analysis was carried out of a number of the O(2)-and H(2)O(2)-involving metabolic reactions that occur in strict anaerobes, facultative anaerobes, and aerobes. The results indicate that the most likely LUCA possessed O(2)-and H(2)O(2)-involving pathways, mainly reactions to remove ROS, and had, at least in part, the components of aerobic respiration. Based on this, the presence of a low, but significant, quantity of H(2)O(2) and O(2) should be taken into account in theoretical models of the early Archean atmosphere and oceans and the evolution of life. It is suggested that the early metabolism involving O(2)/H(2)O(2) was a key adaptation of LUCA to already existing weakly oxic zones in Earth's primordial environment.

Origins of specificity and promiscuity in metabolic networks

How enzymes have evolved to their present form is linked to the question of how pathways emerged and evolved into extant metabolic networks. To investigate this mechanism, we have explored the chemical diversity present in a largely unbiased data set of catalytic reactions processed by modern enzymes across the tree of life. In order to get a quantitative estimate of enzyme chemical diversity, we measure enzyme multispecificity or promiscuity using the reaction molecular signatures. Our main finding is that reactions that are catalyzed by a highly specific enzyme are shared by poorly divergent species, suggesting a later emergence of this function during evolution. In contrast, reactions that are catalyzed by highly promiscuous enzymes are more likely to appear uniformly distributed across species in the tree of life. From a functional point of view, promiscuous enzymes are mainly involved in amino acid and lipid metabolisms, which might be associated with the earliest form of biochemical reactions. In this way, results presented in this paper might assist us with the identification of primeval promiscuous catalytic functions contributing to life's minimal metabolism.

Protein coalitions in a core mammalian biochemical network linked by rapidly evolving proteins

BACKGROUND

Cellular ATP levels are generated by glucose-stimulated mitochondrial metabolism and determine metabolic responses, such as glucose-stimulated insulin secretion (GSIS) from the β-cells of pancreatic islets. We describe an analysis of the evolutionary processes affecting the core enzymes involved in glucose-stimulated insulin secretion in mammals. The proteins involved in this system belong to ancient enzymatic pathways: glycolysis, the TCA cycle and oxidative phosphorylation.

RESULTS

We identify two sets of proteins, or protein coalitions, in this group of 77 enzymes with distinct evolutionary patterns. Members of the glycolysis, TCA cycle, metabolite transport, pyruvate and NADH shuttles have low rates of protein sequence evolution, as inferred from a human-mouse comparison, and relatively high rates of evolutionary gene duplication. Respiratory chain and glutathione pathway proteins evolve faster, exhibiting lower rates of gene duplication. A small number of proteins in the system evolve significantly faster than co-pathway members and may serve as rapidly evolving adapters, linking groups of co-evolving genes.

CONCLUSIONS

Our results provide insights into the evolution of the involved proteins. We find evidence for two coalitions of proteins and the role of co-adaptation in protein evolution is identified and could be used in future research within a functional context.

Single-molecule paleoenzymology probes the chemistry of resurrected enzymes

A journey back in time is possible at the molecular level by reconstructing proteins from extinct organisms. Here we report the reconstruction, based on sequence predicted by phylogenetic analysis, of seven Precambrian thioredoxin enzymes (Trx), dating back between ~1.4 and ~4 billion years (Gyr). The reconstructed enzymes are up to 32° C more stable than modern enzymes and the oldest show significantly higher activity than extant ones at pH 5. We probed their mechanisms of reduction using single-molecule force spectroscopy. From the force-dependency of the rate of reduction of an engineered substrate, we conclude that ancient Trxs utilize chemical mechanisms of reduction similar to those of modern enzymes. While Trx enzymes have maintained their reductase chemistry unchanged, they have adapted over a 4 Gyr time span to the changes in temperature and ocean acidity that characterize the evolution of the global environment from ancient to modern Earth.

ModEnzA: Accurate Identification of Metabolic Enzymes Using Function Specific Profile HMMs with Optimised Discrimination Threshold and Modified Emission Probabilities

Various enzyme identification protocols involving homology transfer by sequence-sequence or profile-sequence comparisons have been devised which utilise Swiss-Prot sequences associated with EC numbers as the training set. A profile HMM constructed for a particular EC number might select sequences which perform a different enzymatic function due to the presence of certain fold-specific residues which are conserved in enzymes sharing a common fold. We describe a protocol, ModEnzA (HMM-ModE Enzyme Annotation), which generates profile HMMs highly specific at a functional level as defined by the EC numbers by incorporating information from negative training sequences. We enrich the training dataset by mining sequences from the NCBI Non-Redundant database for increased sensitivity. We compare our method with other enzyme identification methods, both for assigning EC numbers to a genome as well as identifying protein sequences associated with an enzymatic activity. We report a sensitivity of 88% and specificity of 95% in identifying EC numbers and annotating enzymatic sequences from the E. coli genome which is higher than any other method. With the next-generation sequencing methods producing a huge amount of sequence data, the development and use of fully automated yet accurate protocols such as ModEnzA is warranted for rapid annotation of newly sequenced genomes and metagenomic sequences.

Enzyme sequence similarity improves the reaction alignment method for cross-species pathway comparison

Pathway-based information has become an important source of information for both establishing evolutionary relationships and understanding the mode of action of a chemical or pharmaceutical among species. Cross-species comparison of pathways can address two broad questions: comparison in order to inform evolutionary relationships and to extrapolate species differences used in a number of different applications including drug and toxicity testing. Cross-species comparison of metabolic pathways is complex as there are multiple features of a pathway that can be modeled and compared. Among the various methods that have been proposed, reaction alignment has emerged as the most successful at predicting phylogenetic relationships based on NCBI taxonomy. We propose an improvement of the reaction alignment method by accounting for sequence similarity in addition to reaction alignment method. Using nine species, including human and some model organisms and test species, we evaluate the standard and improved comparison methods by analyzing glycolysis and citrate cycle pathways conservation. In addition, we demonstrate how organism comparison can be conducted by accounting for the cumulative information retrieved from nine pathways in central metabolism as well as a more complete study involving 36 pathways common in all nine species. Our results indicate that reaction alignment with enzyme sequence similarity results in a more accurate representation of pathway specific cross-species similarities and differences based on NCBI taxonomy.

A Bayesian approach to the evolution of metabolic networks on a phylogeny

The availability of genomes of many closely related bacteria with diverse metabolic capabilities offers the possibility of tracing metabolic evolution on a phylogeny relating the genomes to understand the evolutionary processes and constraints that affect the evolution of metabolic networks. Using simple (independent loss/gain of reactions) or complex (incorporating dependencies among reactions) stochastic models of metabolic evolution, it is possible to study how metabolic networks evolve over time. Here, we describe a model that takes the reaction neighborhood into account when modeling metabolic evolution. The model also allows estimation of the strength of the neighborhood effect during the course of evolution. We present Gibbs samplers for sampling networks at the internal node of a phylogeny and for estimating the parameters of evolution over a phylogeny without exploring the whole search space by iteratively sampling from the conditional distributions of the internal networks and parameters. The samplers are used to estimate the parameters of evolution of metabolic networks of bacteria in the genus Pseudomonas and to infer the metabolic networks of the ancestral pseudomonads. The results suggest that pathway maps that are conserved across the Pseudomonas phylogeny have a stronger neighborhood structure than those which have a variable distribution of reactions across the phylogeny, and that some Pseudomonas lineages are going through genome reduction resulting in the loss of a number of reactions from their metabolic networks.

Metabolic networks correspond to one of the most complex cellular processes. Most organisms have a common set of reactions as a part of their metabolic networks that relate to essential processes such as generation of energy and the synthesis of important biological molecules, which are required for their survival. However, a large proportion of the reactions present in different organisms are specific to the needs of individual organisms. The regions of metabolic networks corresponding to these non-essential reactions are under continuous evolution. Using different models of evolution, we can ask important biological questions about the ways in which the metabolic networks of different organisms enable them to be well-adapted to the environments in which they live, and how these metabolic adaptations have evolved. We use a stochastic approach to study the evolution of metabolic networks and show that evolutionary inferences can be made using the structure of these networks. Our results indicate that plant pathogenic Pseudomonas are going through genome reduction resulting in the loss of metabolic functionalities. We also show the potential of stochastic approaches to infer the networks present at ancestral levels of a given phylogeny compared to deterministic methods such as parsimony.

Evolutionary constraints permeate large metabolic networks

BACKGROUND

Metabolic networks show great evolutionary plasticity, because they can differ substantially even among closely related prokaryotes. Any one metabolic network can also effectively compensate for the blockage of individual reactions by rerouting metabolic flux through other pathways. These observations, together with the continual discovery of new microbial metabolic pathways and enzymes, raise the possibility that metabolic networks are only weakly constrained in changing their complement of enzymatic reactions.

RESULTS

To ask whether this is the case, I characterized pairwise and higher-order associations in the co-occurrence of genes encoding metabolic enzymes in more than 200 completely sequenced representatives of prokaryotic genera. The majority of reactions show constrained evolution. Specifically, genes encoding most reactions tend to co-occur with genes encoding other reaction(s). Constrained reaction pairs occur in small sets whose number is substantially greater than expected by chance alone. Most such sets are associated with single biochemical pathways. The respective genes are not always tightly linked, which renders horizontal co-transfer of constrained reaction sets an unlikely sole cause for these patterns of association.

CONCLUSION

Even a limited number of available genomes suffices to show that metabolic network evolution is highly constrained by reaction combinations that are favored by natural selection. With increasing numbers of completely sequenced genomes, an evolutionary constraint-based approach may enable a detailed characterization of co-evolving metabolic modules.

The conservation and evolutionary modularity of metabolism

A novel evolutionary analysis of metabolic networks across 26 taxa reveals a highly-conserved but flexible core of metabolic enzymes.

Background

Cellular metabolism is a fundamental biological system consisting of myriads of enzymatic reactions that together fulfill the basic requirements of life. The recent availability of vast amounts of sequence data from diverse sets of organisms provides an opportunity to systematically examine metabolism from a comparative perspective. Here we supplement existing genome and protein resources with partial genome datasets derived from 193 eukaryotes to present a comprehensive survey of the conservation of metabolism across 26 taxa representing the three domains of life.

Results

In general, metabolic enzymes are highly conserved. However, organizing these enzymes within the context of functional pathways revealed a spectrum of conservation from those that are highly conserved (for example, carbohydrate, energy, amino acid and nucleotide metabolism enzymes) to those specific to individual taxa (for example, those involved in glycan metabolism and secondary metabolite pathways). Applying a novel co-conservation analysis, KEGG defined pathways did not generally display evolutionary coherence. Instead, such modularity appears restricted to smaller subsets of enzymes. Expanding analyses to a global metabolic network revealed a highly conserved, but nonetheless flexible, 'core' of enzymes largely involved in multiple reactions across different pathways. Enzymes and pathways associated with the periphery of this network were less well conserved and associated with taxon-specific innovations.

Conclusions

These findings point to an emerging picture in which a core of enzyme activities involving amino acid, energy, carbohydrate and lipid metabolism have evolved to provide the basic functions required for life. However, the precise complement of enzymes associated within this core for each species is flexible.

Phylogenomic analysis of EST datasets

To date the genomes of over 600 organisms have been generated of which 100 are from eukaryotes. Together with partial genome data for an additional 700 eukaryotic organisms, these exceptional sequence resources offer new opportunities to explore phylogenetic relationships and species diversity. The identification of highly diverse sequences specific to an EST-based sequence dataset offers insights into the extent of genetic novelty within that dataset. Sequences that are only shared with other related species from the same taxon might represent genes associated with taxon-specific innovations. On the other hand, sequences that are highly conserved across many other species offer valuable resources for performing more in-depth phylogenetic analyses. In the following chapter, we guide the reader through the process of examining their sequence datasets in the context of phylogenetic relationships. Performed across large-scale datasets, such analyses are termed Phylogenomics. Two complementary approaches are described, both based on the use of BLAST similarity metrics. The first uses an established Java tool - SimiTri - to visualize sequence similarity relationships between the EST dataset and three user-defined datasets. The second focuses on the use of phylogenetic profiles to identify groups of taxonomically related sequences.

Phylogenetic profiling

Phylogenetic profiles describe the presence or absence of a protein in a set of reference genomes. Similarity between profiles is an indicator of functional coupling between gene products: the greater the similarity, the greater the likelihood of proteins sharing membership in the same pathway or cellular system. By virtue of this property, uncharacterized proteins can be assigned putative functions, based on the similarity of their profiles with those of known proteins. Profile comparisons, when extended to the entire genome, have the power to reveal functional linkages on a genome-wide scale (the functional "interactome"), elucidating both known and novel pathways and cellular systems.

Patterns of evolutionary constraints on genes in humans

Background

Different regions in a genome evolve at different rates depending on structural and functional constraints. Some genomic regions are highly conserved during metazoan evolution, while other regions may evolve rapidly, either in all species or in a lineage-specific manner. A strong or even moderate change in constraints in functional regions, for example in coding regions, can have significant evolutionary consequences.

Results

Here we discuss a novel framework, 'BaseDiver', to classify groups of genes in humans based on the patterns of evolutionary constraints on polymorphic positions in their coding regions. Comparing the nucleotide-level divergence among mammals with the extent of deviation from the ancestral base in the human lineage, we identify patterns of evolutionary pressure on nonsynonymous base-positions in groups of genes belonging to the same functional category. Focussing on groups of genes in functional categories, we find that transcription factors contain a significant excess of nonsynonymous base-positions that are conserved in other mammals but changed in human, while immunity related genes harbour mutations at base-positions that evolve rapidly in all mammals including humans due to strong preference for advantageous alleles. Genes involved in olfaction also evolve rapidly in all mammals, and in humans this appears to be due to weak negative selection.

Conclusion

While recent studies have identified genes under positive selection in humans, our approach identifies evolutionary constraints on Gene Ontology groups identifying changes in humans relative to some of the other mammals.

Metabolic innovations towards the human lineage

BACKGROUND

We describe a function-driven approach to the analysis of metabolism which takes into account the phylogenetic origin of biochemical reactions to reveal subtle lineage-specific metabolic innovations, undetectable by more traditional methods based on sequence comparison. The origins of reactions and thus entire pathways are inferred using a simple taxonomic classification scheme that describes the evolutionary course of events towards the lineage of interest. We investigate the evolutionary history of the human metabolic network extracted from a metabolic database, construct a network of interconnected pathways and classify this network according to the taxonomic categories representing eukaryotes, metazoa and vertebrates.

RESULTS

It is demonstrated that lineage-specific innovations correspond to reactions and pathways associated with key phenotypic changes during evolution, such as the emergence of cellular organelles in eukaryotes, cell adhesion cascades in metazoa and the biosynthesis of complex cell-specific biomolecules in vertebrates.

CONCLUSION

This phylogenetic view of metabolic networks puts gene innovations within an evolutionary context, demonstrating how the emergence of a phenotype in a lineage provides a platform for the development of specialized traits.

The origin and evolution of modern metabolism

One fundamental goal of current research is to understand how complex biomolecular networks took the form that we observe today. Cellular metabolism is probably one of the most ancient biological networks and constitutes a good model system for the study of network evolution. While many evolutionary models have been proposed, a substantial body of work suggests metabolic pathways evolve fundamentally by recruitment, in which enzymes are drawn from close or distant regions of the network to perform novel chemistries or use different substrates. Here we review how structural and functional genomics has impacted our knowledge of evolution of modern metabolism and describe some approaches that merge evolutionary and structural genomics with advances in bioinformatics. These include mining the data on structure and function of enzymes for salient patterns of enzyme recruitment. Initial studies suggest modern metabolism originated in enzymes of nucleotide metabolism harboring the P-loop hydrolase fold, probably in pathways linked to the purine metabolic subnetwork. This gateway of recruitment gave rise to pathways related to the synthesis of nucleotides and cofactors for an ancient RNA world. Once the TIM beta/alpha-barrel fold architecture was discovered, it appears metabolic activities were recruited explosively giving rise to subnetworks related to carbohydrate and then amino acid metabolism. Remarkably, recruitment occurred in a layered system reminiscent of Morowitz's prebiotic shells, supporting the notion that modern metabolism represents a palimpsest of ancient metabolic chemistries.

Functional protein divergence in the evolution of Homo sapiens

BACKGROUND

Protein-coding regions in a genome evolve by sequence divergence and gene gain and loss, altering the gene content of the organism. However, it is not well understood how this has given rise to the enormous diversity of metazoa present today.

RESULTS

To obtain a global view of human genomic evolution, we quantify the divergence of proteins by functional category at different evolutionary distances from human.

CONCLUSION

This analysis highlights some general systems-level characteristics of human evolution: regulatory processes, such as signal transducers, transcription factors and receptors, have a high degree of plasticity, while core processes, such as metabolism, transport and protein synthesis, are largely conserved. Additionally, this study reveals a dynamic picture of selective forces at short, medium and long evolutionary timescales. Certain functional categories, such as 'development' and 'organogenesis', exhibit temporal patterns of sequence divergence in eukaryotes relative to human. This framework for a grammar of human evolution supports previously postulated theories of robustness and evolvability.