Microbial communities play a vital role in most processes in the biosphere and are essential for solving present and future environmental challenges. Examples include the impact of the human microbiome on health and disease, discovery of new antibiotics, turning waste products into valuables. In essence, life would rapidly cease to exist in a world without microbes. However, given the large difficulties in isolating and culturing bacteria from natural systems, the majority of microbes remain undescribed; also known as the “microbial dark matter”. In the past 10 years, new methods have revolutionized our access to the genomes of the microbial dark matter and have sparked an explosion of new fundamental discoveries based on genomic evidence.
Despite the land-mark fundamental discoveries enabled by new methods the past decade, we are still far from having a meaningful genomic representation of the tree of life - and we are even further away from understanding how microbes realize their genomic potential in complex environments. This can be underlined by the fact that the microbial species diversity in nature is vast, with estimates of millions to billions or even trillions of species, in our project “Microflora Danica” we are currently investigating this fundamental question across 10.000 natural samples in Denmark. The vast estimated diversity is in stark contrast to the 47,894 prokaryotic species that have any genome representation in the databases (GTDB v. 202). Furthermore, for the vast majority of these species, there are no studies on how large a part of the genomic potential they realize and even the most basic information on which substrates they consume in Nature is missing.
In this project we want to explore the fact that the Oxford Nanopore sequencing machines, in principle, can detect any type of modification to DNA or RNA. As most DNA in nature is modified in a multitude of different ways21, this opens a new and unexplored world of opportunities for the next generation of high-resolution microbial ecosystem analysis.
