Lu Mingji, Schneider Dominik, Daniel Rolf
Department of Genomic and Applied Microbiology, Institute of Microbiology and Genetics, Georg August University of Göttingen, Göttingen, Germany.
Front Microbiol. 2022 Jun 10;13:851969. doi: 10.3389/fmicb.2022.851969. eCollection 2022.
Lipolytic enzymes are one of the most important enzyme types for application in various industrial processes. Despite the continuously increasing demand, only a small portion of the so far encountered lipolytic enzymes exhibit adequate stability and activities for biotechnological applications. To explore novel and/or extremophilic lipolytic enzymes, microbial consortia in two composts at thermophilic stage were analyzed using function-driven and sequence-based metagenomic approaches. Analysis of community composition by amplicon-based 16S rRNA genes and transcripts, and direct metagenome sequencing revealed that the communities of the compost samples were dominated by members of the phyla , , , , and . Function-driven screening of the metagenomic libraries constructed from the two samples yielded 115 unique lipolytic enzymes. The family assignment of these enzymes was conducted by analyzing the phylogenetic relationship and generation of a protein sequence similarity network according to an integrated classification system. The sequence-based screening was performed by using a newly developed database, containing a set of profile Hidden Markov models, highly sensitive and specific for detection of lipolytic enzymes. By comparing the lipolytic enzymes identified through both approaches, we demonstrated that the activity-directed complements sequence-based detection, and vice versa. The sequence-based comparative analysis of lipolytic genes regarding diversity, function and taxonomic origin derived from 175 metagenomes indicated significant differences between habitats. Analysis of the prevalent and distinct microbial groups providing the lipolytic genes revealed characteristic patterns and groups driven by ecological factors. The here presented data suggests that the diversity and distribution of lipolytic genes in metagenomes of various habitats are largely constrained by ecological factors.
脂肪分解酶是应用于各种工业过程的最重要的酶类型之一。尽管需求持续增长，但到目前为止所发现的脂肪分解酶中，只有一小部分表现出足够的稳定性和活性以用于生物技术应用。为了探索新型和/或嗜极端环境的脂肪分解酶，利用功能驱动和基于序列的宏基因组学方法分析了两个处于嗜热阶段的堆肥中的微生物群落。通过基于扩增子的16S rRNA基因和转录本以及直接宏基因组测序对群落组成进行分析，结果表明堆肥样品中的群落主要由门、、、、和的成员主导。对从这两个样品构建的宏基因组文库进行功能驱动筛选，得到了115种独特的脂肪分解酶。根据综合分类系统，通过分析系统发育关系和生成蛋白质序列相似性网络对这些酶进行家族分类。基于序列的筛选是通过使用一个新开发的数据库进行的，该数据库包含一组对脂肪分解酶检测具有高灵敏度和特异性的轮廓隐马尔可夫模型。通过比较通过这两种方法鉴定出的脂肪分解酶，我们证明了活性导向的方法补充了基于序列的检测，反之亦然。对来自175个宏基因组的脂肪分解基因在多样性、功能和分类学起源方面进行的基于序列的比较分析表明，不同生境之间存在显著差异。对提供脂肪分解基因的常见和独特微生物群体的分析揭示了由生态因素驱动的特征模式和群体。本文所呈现的数据表明，各种生境宏基因组中脂肪分解基因的多样性和分布在很大程度上受生态因素的限制。
