Time series of Methanococcus maripaludis MM901, a shift from a H2-excess and N-limiting condition to a H2-limited and N-excess condition in chemostats (growth rate held constant)

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. Shifts from N-limited to N-excess conditions were achieved by raising NH4Cl from 2.8 mM to the standard 10 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.

产甲烷古菌（Methanogens）可催化有机物厌氧分解过程中关键的产甲烷步骤（即甲烷生成作用，methanogenesis），在碳中和燃料生产中具有重要应用价值。本研究首次构建了氢营养型产甲烷古菌马氏甲烷球菌（Methanococcus maripaludis）甲烷生成作用的全局基因调控预测模型。
研究人员通过整合分析该菌株的转录组结构与新构建的肽谱（Peptide Atlas），得到了其完整的基因列表（包含蛋白编码基因与非编码基因）。该菌株的环境与基因调控影响网络（EGRIN, environment and gene-regulatory influence network）模型，基于100余种不同稳态与时间进程实验的转录组汇编数据集构建而成。这些实验在恒化器（chemostat）或分批培养（batch cultures）体系中开展，通过一系列可调控甲烷生成的环境扰动完成。
研究发现，至少五种调控机制以组合模式协同调控甲烷生成关键步骤与运动、ATP生物合成、碳同化等不同生理过程。通过结合遗传与环境扰动实验，研究人员验证了两个新型转录因子（TF, transcription factor）在磷酸盐依赖型甲酸脱氢酶（formate dehydrogenase，甲烷生成通路中的关键酶）调控中的EGRIN预测作用。
该菌株于37℃下在1升发酵罐（New Brunswick Scientific, Edison, NJ）中进行连续培养（参考FEMS Microbiol Lett 238: 85-91, 2004），培养基与气体组成参考非限制生长条件进行修改（参考BMC Microbiol 9: 149, 2009）。实验设计旨在分离不同环境因子的影响：为确保理化因素恒定，所有细胞培养均采用恒定稀释率的连续培养方式。标准通气方案为：110 mL/min氢气、40 mL/min二氧化碳、35 mL/min氩气，以及15 mL/min H₂S/Ar混合气体（体积比1:99）。当从氢过量条件切换至氢限制条件时，将氢气流量从标准110 mL/min降至21 mL/min，同时将氩气流量从35 mL/min提升至125 mL/min。从氮限制切换至氮过量条件则通过将氯化铵浓度从2.8 mM提升至标准浓度10 mM实现。稀释率恒定维持在0.083 h⁻¹。
针对时间序列芯片数据，扰动前的培养物需先达到稳态。随后快速改变氢气和/或营养物浓度，并在扰动后按以下时间间隔取样：即刻、5分钟、10分钟、20分钟、30分钟、45分钟、60分钟、90分钟、120分钟、180分钟及300分钟。通过注射器从恒化器容器中快速采集1.5 mL培养样本，经微量离心收集细胞沉淀，立即置于乙醇-干冰浴中冷冻，并于-80℃保存。
每个样本的总RNA均与从MM901菌株中期对数生长期培养物批量制备的参考RNA池进行对照实验。样本与参考的总RNA分别直接用Cy3或Cy5标记，并与瓦片阵列芯片（tiling array）进行杂交。按照阵列制造商的说明书完成杂交与洗涤后，使用微阵列扫描仪（Microarray Scanner, Agilent Technologies, Santa Clara, CA）扫描芯片。每个样本均完成了染料互换（dye-flip）实验。