Molecular Biology of Microbial Consortia, University of Hamburg, Biocenter Klein Flottbek, Ohnhorststr. 18, 22609, Hamburg, Germany.
ISME J. 2018 May;12(5):1225-1236. doi: 10.1038/s41396-017-0040-6. Epub 2018 Jan 17.
Hydrogen is one of the most common elements on Earth. The enzymes converting molecular hydrogen into protons and electrons are the hydrogenases. Hydrogenases are ubiquitously distributed in all three domains of life where they play a central role in cell metabolism. So far, the recovery of hydrogenases has been restricted to culture-dependent and sequence-based approaches. We have recently developed the only activity-based screen for seeking H-uptake enzymes from metagenomes without having to rely on enrichment and isolation of hydrogen-oxidizing microorganisms or prior metagenomic sequencing. When screening 14,400 fosmid clones from three hydrothermal vent metagenomes using this solely activity-based approach, four clones with H-uptake activity were identified with specific activities of up to 258 ± 19 nmol H/min/mg protein of partially purified membrane fractions. The respective metagenomic fragments exhibited mostly very low or no similarities to sequences in the public databases. A search with hidden Markov models for different hydrogenase groups showed no hits for three of the four metagenomic inserts, indicating that they do not encode for classical hydrogenases. Our activity-based screen serves as a powerful tool for the discovery of (novel) hydrogenases which would not have been identified by the currently available techniques. This screen can be ideally combined with culture- and sequence-based approaches to investigate the tremendous hydrogen-converting potential in the environment.
氢是地球上最常见的元素之一。将氢气转化为质子和电子的酶是氢化酶。氢化酶广泛存在于生命的三个领域,在细胞代谢中起着核心作用。到目前为止,氢化酶的回收一直受到基于培养和基于序列的方法的限制。我们最近开发了唯一一种基于活性的筛选方法,用于从宏基因组中寻找 H 摄取酶,而无需依赖于富集和分离产氢微生物或先前的宏基因组测序。当使用这种仅基于活性的方法筛选来自三个热液喷口宏基因组的 14400 个 fosmid 克隆时,从部分纯化的膜部分中鉴定出具有高达 258±19 nmol H/min/mg 蛋白的特定活性的四个具有 H 摄取活性的克隆。各自的宏基因组片段与公共数据库中的序列大多非常相似或没有相似性。用不同的氢化酶组的隐马尔可夫模型进行搜索,四个宏基因组插入物中的三个没有命中,表明它们不编码经典的氢化酶。我们的基于活性的筛选为(新型)氢化酶的发现提供了有力的工具,而这些氢化酶是目前可用技术无法识别的。该筛选可以与基于培养和基于序列的方法理想地结合,以研究环境中巨大的氢转化潜力。