E-GEOD-42162 - Time series of Methanococcus maripaludis MM901:del_MMP1100, a shift from a H2-excess and P-limiting condition to a H2-limited and P-excess condition in chemostats (growth rate held constant)
Released on 16 September 2013, last updated on 2 June 2014
Methanococcus maripaludis S2
Methanogens catalyze the critical, methane-producing step (called methanogenesis) in the anaerobic decomposition of organic matter and have applications in carbon-neutral fuel production. 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 non-coding) 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 100 different steady-state and time course experiments that were performed in chemostats, or batch cultures, under a spectrum of environmental perturbations that modulated methanogenesis. We discovered that at least five regulatory mechanisms act in a combinatorial scheme to inter-coordinate 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 TFs in the regulation of phosphate-dependent repression of formate dehydorgenase – a key enzyme in the methanogenesis pathway. The strain was grown by continuous culture in a one-liter fermenter (New Brunswick Scientific, Edison, NJ) at 37°C (FEMS Microbiol Lett 238: 85-91, 2004). Medium and gas compositions were modified from those for non-limiting conditions (BMC Microbiol 9: 149, 2009). Growth conditions were carefully designed to separate the effects of different environmental factors. To ensure physico-chemical factors being constant, all cell cultures were performed using continuous cultivations with a constant dilution rate. The standard gassing regime was 110 mL/min H2, 40 mL/min CO2, 35 mL/min Ar, and 15 mL/min H2S/Ar mixture (1:99). For shifts from a H2 excess to a H2 limited condition, H2 was lowered from standard 110 mL/min to 21 mL/min and Ar was raised from standard 35 mL/min to 125 mL/min. For shifts from P-limited to P-excess conditions, phosphate was raised from 0.18 mM to the standard 0.8 mM. The dilution rate was held constant at 0.083 h-1. For time-series array data, cultures before perturbation were allowed to reach steady state. We rapidly changed concentration(s) of H2 and/or a nutrient, and sampled at intervals after the perturbation; right away, after 5 mins, 10, 20, 30, 45, 60, 90, 120, 180, and 300 mins. Culture samples (1.5 mL) were rapidly removed from the chemostat vessels by syringe and cell pellets collected by microcentrifugation, immediately frozen in an ethanol-dry ice bath, and stored at -80°C. Total RNA from each sample was compared against a reference RNA pool that was generated in bulk from a mid-log phase culture of MM901. Total RNA from samples and reference were directly labeled with Cy3 or Cy5, and were hybridized to the tiling array. After hybridization and washing according to array manufacturer's instructions, the arrays were scanned by Microarray Scanner (Agilent Technologies, Santa Clara, CA). Dye-flip experiments were done for each sample.
transcription profiling by tiling array
Sung Ho Yoon <email@example.com>, John A Leigh, Kyle C Costa, Min Pan, Nitin S Baliga, Sung-Ho Yoon, Thomas J Lie