Microflora Danica: Bacterial 16S rRNA genes (UMIs)

In 1752, King Frederik V of Denmark, in line with the historical context of the Age of Enlightenment, commissioned the Flora Danica project to Georg C. Oeder, initiating an “Opus Incomparibile” that took 122 years and produced one of the world’s most unique works in natural history. The resulting atlas included 3000 botanic engravings of flowers and plants over 17 volumes. In 2019, we initiated the Microflora Danica (MFD) project with the aim of cataloguing the microbiomes of Denmark. Microflora Danica is the Atlas of the environmental microbiomes of Denmark. It relies upon a large scale dataset encompassing 10,683 shotgun metagenomes, 420 near-full length UMI 16S rRNA and 450 bacterial and eukaryotic operon datasets. The samples are linked to a detailed 5-level habitat ontology. The manuscript determines that while human-disturbed habitats have high alpha diversity, the same species reoccur, revealing hidden homogeneity and underlining the importance of natural systems for total species (gamma) diversity. In-depth studies of nitrifiers, a functional group closely linked to climate change, challenge existing perceptions regarding habitat preference and discover several novel nitrifiers as more abundant than canonical nitrifiers. Together, the Microflora Danica dataset provides an unprecedented resource and the foundation for answering fundamental questions underlying microbial ecology: what drives microbial diversity, distribution and function.

Funding was provided by the Poul Due Jensen Foundation, PDJF (grant MicroFlora Danica to MA and PHN), Villum Foundation (grant 15510 and 50093 to MA, grant 13351 to PHN), the European Union (ERC grant 101078234 to MA), the Velux Foundation (Grant 21517 to PFT), the Carlsberg Foundation (Grant CF18-0949 to PFT). CMS was supported by a Novo Nordisk Foundation Postdoctoral Fellowship grant (NNF20OC0065005). DS and MW were supported by the Cluster of Excellence grant “Microbiomes drive Planetary Health” of the Austrian Science Fund FWF (10.55776/COE7). SA was supported by the EMERGE National Science Foundation (NSF) Biology Integration Institute (2022070). BW was supported by an Australian Research Council Future Fellowship (FT210100521). This study is funded by the European Union Horizon Europe research and innovation program under Grant agreement No. 101086179 (AI4SoilHealth). Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or the Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.