2015 Annual Science Report

University of Illinois at Urbana-Champaign Reporting  |  JAN 2015 – DEC 2015

Project 7: Mining Archaeal Genomes for Signatures of Early Life: Comparison of Metabolic Genes in Methanogens

Project Summary

Methanogens represent the largest diversity among the archaea and have the unique ability to generate methane from simple compounds such as carbon dioxide, acetate and methylamines which were common in the anaerobic environments of early Earth and perhaps Mars. Methane biosynthesis also requires the presence/uptake of important ions such as sulfates, sulfides, carbonates, phosphates, and various light metal ions. In this project, we are attempting to analyze the evolution of the methanogens’ central cellular functions of translation, transcription, replication, and metabolism. To accomplish this, we are constructing the metabolic and regulatory networks of Methanosarcina acetivorans, the most complex methanogen known, and using these models to establish a framework for studying the evolution of methanogens. Results will be tested through microfluidic studies using varying carbon and ion sources.

4 Institutions
3 Teams
1 Publication
0 Field Sites
Field Sites

Project Progress

In our first methanogen paper [1] published in the journal Archaea, we focused primarily on the methanogenesis pathway. During the past year, we focused on developing a fully functional and complete metabolic and regulatory model for Methanosarcina acetivorans capable of capturing the metabolic fluxes observed under different growth enviornments. To do this, we build upon the work from Benedict, et al. [2] who had previously constructed a genome-scale metabolic model for M. acetivorans that contained 825 reactions associated with 745 genes. Our first step was to create a map of metabolism which allows, for the first time, visualization of metabolic fluxes, gene expression data, and evolutionary conservation of metabolic pathways in an integrative way. Our current map and the associated metabolic model (shown in Figure 1) serve as an invaluable tools to analyze metabolic behavior that results from the various enviornments in which the organism has evolved and will both be released with our currently submitted article.

During the map generation process we identified several key areas where the model could be improved. First, the pathway for pyrrolysine biosynthesis was added to reflect the requirement for this amino acid when growing on methylamine substrates. This amino acid is unique to methanogens, providing an interesting evolutionary case study. Second, we added the alternate cysteine aminoacylation pathway—a relic from more ancient methanogens—wherein O-phosphoserine is converted to cysteine while charged to tRNACys. Third, the methanofuran biosynthesis pathway, a unique cofactor found only in methanogens, was updated based on recent experimental characterization.

The model was refined by including organic osmolytes, salts and metals; factors that would have varied significantly over Earth’s history and have played key roles in defining how the methanogen adapted to its enviornments. By including osmolytes such as α-glutamate, N-acetyl-β-lysine and glycine betaine, which the cell produces in significant quantities to survive in their saline environments, we can probe the energetic requirements of maintaining osmotic pressure. By incorporating metals, we can investigate how changing metal availability, such as nickel, could have affected the prevalence of these oranism, and contributed to events such as the end-Permian extinction, for which methanogens have been implicated in causing. Altogether the new metabolic reconstruction consists of 757 reactions (827 including metabolite exchange) with 752 associated genes and greatly increases the predictive power of the model.

With this updated metabolic network, we used our mRNA half-life and expression data (Figure 3) to probe how this mRNA stability controls metabolic activity. This was done by classifying genes as either controlled by a transcriptional or degradational mechanism when comparing their expression across different growth substrates. The expression data was also integrated with the updated metabolic network for M. acetivorans to predict changes in pathway flux due to changes in carbon source, generating numerous hypothesis to be experimentally validated. We linked these metabolic changes to specific reactions and identified the mechanism of regulation predicted (transcription or degradation). The results suggest that M. acetivorans employs extensive post-transcriptional degradation mechanism along with less prevalent transcriptional regulation to optimize key metabolic steps for growth in different environments, and that degradation could play a much greater role in optimizing an organism’s metabolism than previously thought. These results have been submitted for review [3].

Using M. acetivorans as a model organism and our knowledge of metabolism, regulation, translation, our RNAseq data, and the ITEP database [4] of methanogen genomes, we identified the homologs of differentially expressed genes across the tree of methanogens as shown in Fig. 2. This genome-wide analysis covers all the cellular networks. The identification of these genes serves as the first analysis of regulation across methanogens and a unique perspective into the evolution of regulation in these ancient organisms. Ongoing work focuses on constructing integrated metabolic/regulatory networks for all methanogens which will allow us to identify how these organisms adapted to their varied environmental niches.

[1] Joseph R. Peterson, Piyush Labhsetwar, Jeremy R. Ellermeier, et al., “Towards a Computational Model of a Methane Producing Archaeum,” Archaea, vol. 2014, Article ID 898453, 18 pages, 2014. doi:10.1155/2014/898453.

[2] M. N. Benedict, M. C. Gonnerman, W. W. Metcalf, and N. D. Price. J. Bacteriol. 2012, 194(4):855.

[3] J.R. Peterson, S. Thor, L. Kohler, P.R.A. Kohler, W.W. Metcalf, Z. Luthey-Schulten, “mRNA Degradation Plays a Substantial Role in Regulating Metabolism in Methanosarcina acetivorans”, Submitted, 2016.

[4] M. N. Benedict, J. R. Henriksen, W. W. Metcalf, R. J. Whitaker, and N. D. Price, “ITEP: An integrated toolkit for exploration of microbial pan-genomes”, BMC Genomics, 2014, 15:8.

Figure 1. Metabolic network for M. acetivorans that was generated during this research.
Figure 2. Conservation of the genes that are differentially expressed between MeOH and Acetate growth (in M. acetivorans) across the methanogen tree of life. Genes are organized by COG class (indicated by letter) and indicated by colored lines if present in that organism. C: energy production, J: translation, R/S: general function, E: amino acid biosynthesis, O: post-translational modification, P: inorganic ion transport/metabolism, H: coenzyme biosynthesis, K: transcription, F: nucleotide metabolism, M: cell membrane biosynthesis.

Figure 3. A diagram showing differentially expressed genes as they are located on the genome of M. acetivorans. Metabolic genes are indicated in the outer ring, and magnitude of differentially expressed genes is shown in the inner ring.

  • PROJECT INVESTIGATORS:
    Zaida Luthey-Schulten Zaida Luthey-Schulten
    Project Investigator
  • PROJECT MEMBERS:
    Joseph Peterson
    Co-Investigator

    Shen Shee Thor
    Co-Investigator

    Isaac Cann
    Collaborator

    Bruce Fouke
    Collaborator

    Willam Metcalf
    Collaborator

    Rachel Whitaker
    Collaborator

  • RELATED OBJECTIVES:
    Objective 1.1
    Formation and evolution of habitable planets.

    Objective 2.1
    Mars exploration.

    Objective 3.1
    Sources of prebiotic materials and catalysts

    Objective 3.2
    Origins and evolution of functional biomolecules

    Objective 3.3
    Origins of energy transduction

    Objective 3.4
    Origins of cellularity and protobiological systems

    Objective 4.1
    Earth's early biosphere.

    Objective 4.2
    Production of complex life.

    Objective 5.1
    Environment-dependent, molecular evolution in microorganisms

    Objective 5.2
    Co-evolution of microbial communities

    Objective 5.3
    Biochemical adaptation to extreme environments

    Objective 6.1
    Effects of environmental changes on microbial ecosystems

    Objective 6.2
    Adaptation and evolution of life beyond Earth

    Objective 7.1
    Biosignatures to be sought in Solar System materials