A systems level predictive model for global gene regulation of methanogenesis in a hydrogenotrophic methanogen
Abstract
Methanogens catalyze the critical methane-producing step (called methanogenesis) in the anaerobic decomposition of organic matter. Here, we present the first predictive model of global gene regulation of methanogenesis in a hydrogenotrophic methanogen, Methanococcus maripaludis. We generated a comprehensive list of genes (protein-coding and noncoding) for M. maripaludis through integrated analysis of the transcriptome structure and a newly constructed Peptide Atlas. The environment and gene-regulatory influence network (EGRIN) model of the strain was constructed from a compendium of transcriptome data that was collected over 58 different steady-state and time-course experiments that were performed in chemostats or batch cultures under a spectrum of environmental perturbations that modulated methanogenesis. Analyses of the EGRIN model have revealed novel components of methanogenesis that included at least three additional protein-coding genes of previously unknown function as well as one noncoding RNA. We discovered that at least five regulatory mechanisms act in a combinatorial scheme to intercoordinate key steps of methanogenesis with different processes such as motility, ATP biosynthesis, and carbon assimilation. Through a combination of genetic and environmental perturbation experiments we have validated the EGRIN-predicted role of two novel transcription factors in the regulation of phosphate-dependent repression of formate dehydrogenase—a key enzyme in the methanogenesis pathway.more »
- Authors:
-
- Inst. for Systems Biology, Seattle, WA (United States); Korea Research Inst. of Bioscience and Biotechnology, Daejeon (Korea, Republic of)
- Inst. for Systems Biology, Seattle, WA (United States)
- Univ. of Washington, Seattle, WA (United States). Dept. of Microbiology
- Univ. of Washington, Seattle, WA (United States). Dept. of Chemical Engineering
- Publication Date:
- Research Org.:
- Univ. of California, Oakland, CA (United States); Institute for Systems Biology, Seattle, WA (United States); Univ. of Washington, Seattle, WA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division
- OSTI Identifier:
- 1625625
- Grant/Contract Number:
- AC02-05CH11231; FG02-07ER64327; FG02-08ER64685
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Genome Research
- Additional Journal Information:
- Journal Volume: 23; Journal Issue: 11; Journal ID: ISSN 1088-9051
- Publisher:
- Cold Spring Harbor Laboratory Press
- Country of Publication:
- United States
- Language:
- English
- Subject:
- Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Genetics & Heredity
Citation Formats
Yoon, Sung Ho, Turkarslan, Serdar, Reiss, David J., Pan, Min, Burn, June A., Costa, Kyle C., Lie, Thomas J., Slagel, Joseph, Moritz, Robert L., Hackett, Murray, Leigh, John A., and Baliga, Nitin S. A systems level predictive model for global gene regulation of methanogenesis in a hydrogenotrophic methanogen. United States: N. p., 2013.
Web. doi:10.1101/gr.153916.112.
Yoon, Sung Ho, Turkarslan, Serdar, Reiss, David J., Pan, Min, Burn, June A., Costa, Kyle C., Lie, Thomas J., Slagel, Joseph, Moritz, Robert L., Hackett, Murray, Leigh, John A., & Baliga, Nitin S. A systems level predictive model for global gene regulation of methanogenesis in a hydrogenotrophic methanogen. United States. https://doi.org/10.1101/gr.153916.112
Yoon, Sung Ho, Turkarslan, Serdar, Reiss, David J., Pan, Min, Burn, June A., Costa, Kyle C., Lie, Thomas J., Slagel, Joseph, Moritz, Robert L., Hackett, Murray, Leigh, John A., and Baliga, Nitin S. Wed .
"A systems level predictive model for global gene regulation of methanogenesis in a hydrogenotrophic methanogen". United States. https://doi.org/10.1101/gr.153916.112. https://www.osti.gov/servlets/purl/1625625.
@article{osti_1625625,
title = {A systems level predictive model for global gene regulation of methanogenesis in a hydrogenotrophic methanogen},
author = {Yoon, Sung Ho and Turkarslan, Serdar and Reiss, David J. and Pan, Min and Burn, June A. and Costa, Kyle C. and Lie, Thomas J. and Slagel, Joseph and Moritz, Robert L. and Hackett, Murray and Leigh, John A. and Baliga, Nitin S.},
abstractNote = {Methanogens catalyze the critical methane-producing step (called methanogenesis) in the anaerobic decomposition of organic matter. Here, we present the first predictive model of global gene regulation of methanogenesis in a hydrogenotrophic methanogen, Methanococcus maripaludis. We generated a comprehensive list of genes (protein-coding and noncoding) for M. maripaludis through integrated analysis of the transcriptome structure and a newly constructed Peptide Atlas. The environment and gene-regulatory influence network (EGRIN) model of the strain was constructed from a compendium of transcriptome data that was collected over 58 different steady-state and time-course experiments that were performed in chemostats or batch cultures under a spectrum of environmental perturbations that modulated methanogenesis. Analyses of the EGRIN model have revealed novel components of methanogenesis that included at least three additional protein-coding genes of previously unknown function as well as one noncoding RNA. We discovered that at least five regulatory mechanisms act in a combinatorial scheme to intercoordinate key steps of methanogenesis with different processes such as motility, ATP biosynthesis, and carbon assimilation. Through a combination of genetic and environmental perturbation experiments we have validated the EGRIN-predicted role of two novel transcription factors in the regulation of phosphate-dependent repression of formate dehydrogenase—a key enzyme in the methanogenesis pathway. The EGRIN model demonstrates regulatory affiliations within methanogenesis as well as between methanogenesis and other cellular functions.},
doi = {10.1101/gr.153916.112},
journal = {Genome Research},
number = 11,
volume = 23,
place = {United States},
year = {Wed Oct 02 00:00:00 EDT 2013},
month = {Wed Oct 02 00:00:00 EDT 2013}
}
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