Adamo Martino, Voyron Samuele, Chialva Matteo, Marmeisse Roland, Girlanda Mariangela
Univ Lyon, Université Claude Bernard Lyon 1, CNRS, INRAE, VetAgro Sup, UMR Ecologie Microbienne, Villeurbanne, France.
Dipartimento di Scienze della Vita e Biologia dei Sistemi, Università degli Studi di Torino, Torino, Italy.
PLoS One. 2020 Dec 30;15(12):e0244682. doi: 10.1371/journal.pone.0244682. eCollection 2020.
In recent years, metabarcoding has become a key tool to describe microbial communities from natural and artificial environments. Thanks to its high throughput nature, metabarcoding efficiently explores microbial biodiversity under different conditions. It can be performed on environmental (e)DNA to describe so-called total microbial community, or from environmental (e)RNA to describe active microbial community. As opposed to total microbial communities, active ones exclude dead or dormant organisms. For what concerns Fungi, which are mostly filamentous microorganisms, the relationship between DNA-based (total) and RNA-based (active) communities is unclear. In the present study, we evaluated the consequences of performing metabarcoding on both soil and wood-extracted eDNA and eRNA to delineate molecular operational taxonomic units (MOTUs) and differentiate fungal communities according to the environment they originate from. DNA and RNA-based communities differed not only in their taxonomic composition, but also in the relative abundances of several functional guilds. From a taxonomic perspective, we showed that several higher taxa are globally more represented in either "active" or "total" microbial communities. We also observed that delineation of MOTUs based on their co-occurrence among DNA and RNA sequences highlighted differences between the studied habitats that were overlooked when all MOTUs were considered, including those identified exclusively by eDNA sequences. We conclude that metabarcoding on eRNA provides original functional information on the specific roles of several taxonomic or functional groups that would not have been revealed using eDNA alone.
近年来,宏条形码技术已成为描述自然和人工环境中微生物群落的关键工具。由于其高通量特性,宏条形码技术能够有效地探索不同条件下的微生物多样性。它可以对环境(e)DNA进行分析,以描述所谓的总微生物群落,也可以对环境(e)RNA进行分析,以描述活跃微生物群落。与总微生物群落不同,活跃微生物群落不包括死亡或休眠的生物体。对于大多为丝状微生物的真菌而言,基于DNA的(总)群落与基于RNA的(活跃)群落之间的关系尚不清楚。在本研究中,我们评估了对土壤和木材中提取的eDNA和eRNA进行宏条形码分析的结果,以划定分子操作分类单元(MOTUs),并根据真菌群落的来源环境对其进行区分。基于DNA和RNA的群落不仅在分类组成上有所不同,而且在几个功能类群的相对丰度上也存在差异。从分类学角度来看,我们发现几个较高的分类单元在“活跃”或“总”微生物群落中总体上更具代表性。我们还观察到,基于DNA和RNA序列中共现情况划定MOTUs,突出了所研究栖息地之间的差异,而当考虑所有MOTUs时这些差异被忽视了,包括那些仅由eDNA序列鉴定出的MOTUs。我们得出结论,对eRNA进行宏条形码分析可提供关于几个分类或功能组特定作用的原始功能信息,而仅使用eDNA则无法揭示这些信息。