Ecosystem-wide metagenomic binning enables prediction of ecological niches from genomes

Abstract

The genome encodes the metabolic and functional capabilities of an organism and should be a major determinant of its ecological niche. Yet, it is unknown if the niche can be predicted directly from the genome. Here, we conduct metagenomic binning on 123 water samples spanning major environmental gradients of the Baltic Sea. The resulting 1961 metagenomeassembled genomes represent 352 species-level clusters that correspond to 1/3 of the metagenome sequences of the prokaryotic size-fraction. By using machine-learning, the placement of a genome cluster along various niche gradients (salinity level, depth, sizefraction) could be predicted based solely on its functional genes. The same approach predicted the genomes' placement in a virtual niche-space that captures the highest variation in distribution patterns. The predictions generally outperformed those inferred from phylogenetic information. Our study demonstrates a strong link between genome and ecological niche and provides a conceptual framework for predictive ecology based on genomic data.

Deltaproteobacteria and Spirochaetes-Like Bacteria Are Abundant Putative Mercury Methylators in Oxygen-Deficient Water and Marine Particles in the Baltic Sea

“…in any Baltic Sea water sample. In support of this, eleven metagenomeassembled genomes (MAGs) of Nitrospinae were extracted from the Baltic Sea metagenomes (Alneberg et al, 2020), and none of these contained hgcAB-like genes (data not shown). Microbial syntrophy, defined as an obligate mutualistic metabolism, is a process known to occur mainly in environments with shortage of favorable electron acceptors, e.g., mostly in anoxic environments (McInerney et al, 2009;Morris et al, 2013).…”

“…in any Baltic Sea water sample. In support of this, eleven metagenome-assembled genomes (MAGs) of Nitrospinae were extracted from the Baltic Sea metagenomes ( Alneberg et al, 2020 ), and none of these contained hgcAB -like genes (data not shown).…”

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

“…Smith et al 2015 [87] demonstrated that microbial community composition as determined by 16S rRNA can be used to predict a diverse set of geochemical factors including pH, manganese and aluminum. Alneberg et al (2020) [97] also highlight the application of SML to predict the ecological niche of microbial groups with a focus on microbial communities from the Baltic Sea. The authors of this study use metagenomic binning to obtain 1962 metagenome assembled genomes (MAGs) representing the majority of prokaryotic diversity in in the Baltic Sea.…”

The Trait Repertoire Enabling Cyanobacteria to Bloom Assessed through Comparative Genomic Complexity and Metatranscriptomics

“…While this approach was here applied to the case of bloom-forming cyanobacteria, there is in principle no reason why this approach may not find application in a variety of other microbial habitats and organismal groups. On a broader scale, our approach can be combined with metagenome-assembled genomes to infer the link between genomic complexity and ecological niche, where together they could provide a comprehensive conceptual framework for predictive ecology from genomic data (52).…”