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Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection.

ABSTRACT: Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.

SUBMITTER: Lu H

PROVIDER: S-EPMC8532513 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection.

Lu Hongzhong H Li Feiran F Yuan Le L Domenzain Iván I Yu Rosemary R Wang Hao H Li Gang G Chen Yu Y Ji Boyang B Kerkhoven Eduard J EJ Nielsen Jens J

Molecular systems biology 20211001 10

Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards ...[more]

PMID: 34676984

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Project description:BackgroundEfforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature.ResultsYeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model's predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3.ConclusionsYeast 5 expands and refines the computational reconstruction of yeast metabolism and improves the predictive accuracy of a stoichiometrically constrained yeast metabolic model. It differs from previous reconstructions and models by emphasizing the distinction between the yeast metabolic reconstruction and the stoichiometrically constrained model, and makes both available as Additional file 4 and Additional file 5 and at http://yeast.sf.net/ as separate systems biology markup language (SBML) files. Through this separation, we intend to make the modeling process more accessible, explicit, transparent, and reproducible.

Project description:Hosts and viruses are locked in an evolutionary arms race. Hosts are constantly evolving to suppress virulence and replication, while viruses, which are reliant on host machinery for survival and reproduction, develop counterstrategies to escape this immune defense. Viruses must also adapt to novel conditions while establishing themselves in a host species. Both processes provide strong selection for viral adaptation. Understanding adaptive evolution in insect viruses can help us to better understand adaptive evolution in general and is important due to the use of these viruses as biocontrol agents and for protecting ecologically or economically important species from outbreaks. Here we examine the molecular evolution of baculoviruses and nudiviruses, a group of insect-infecting viruses with key roles in biocontrol. We looked for signatures of selection between genomes of baculoviruses infecting a range of species and within a population of baculoviruses. Both analyses found only a few strong signatures of positive selection, primarily in replication- and transcription-associated genes and several structural protein genes. In both analyses, we detected a conserved complex of genes, including the helicase gene, showing consistently high levels of adaptive evolution, suggesting that they may be key in antagonistic coevolution to escape host suppression. These genes are integral to the baculovirus life cycle and may be good focal genes for developing baculoviruses as effective biocontrol agents or for targeting baculoviruses infecting ecologically relevant species. Recombination and complex genomes make evolution in these double-stranded DNA viruses more efficient than that in smaller RNA viruses with error-prone replication, as seen via signatures of selection in specific genes within a population of baculoviruses.IMPORTANCE Most viral evolutionary studies focus on RNA viruses. While these viruses cause many human and animal diseases, such studies leave us with a lesser understanding of how DNA viruses adapt to hosts and how the host responds to these pathogens. In this paper, we focus on the evolution of baculoviruses, a group of insect-infecting DNA viruses, many of which have been used in biocontrol. We find that most of the genome is under purifying selection, with only a few key genes evolving adaptively. Our results provide a glimpse into how DNA viruses differ from RNA viruses in their evolutionary dynamics and identify genes that are key to DNA virus adaptation, improving our understanding of how this group of pathogens evolves.

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Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection.

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Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection.

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