We applied theoretical and simulation-based approaches to characterize how microbial community structure influences the amount of sequencing effort to reconstruct metagenomes that are assembled from short-read sequences. First, a coupon collector equation was proposed as an analytical model for predicting sequencing effort as a function of microbial community structure. Characterization was performed by varying community structure properties such as richness, evenness, and genome size. Simulations demonstrated that while community richness and evenness influenced the sequencing effort required to sequence a community metagenome to exhaustion, the effort necessary to sequence an individual genome to a target fraction of exhaustion depended only on the relative abundance of the genome and its genome size. A second analysis evaluated the quantity, completion, and contamination of metagenome-assembled genomes (MAGs) as a function of sequencing effort on four preexisting sequence read data sets from different environments. These data sets were subsampled to various degrees of completeness to simulate the effect of sequencing effort on MAG retrieval. Modeling suggested that sequencing efforts beyond what is typical in published experiments (1 to 10 Gbp) would generate diminishing returns in terms of MAG binning. A software tool, Genome Relative Abundance to Sequencing Effort (GRASE), was created to assist investigators to further explore this relationship. Reevaluation of the relationship between sequencing effort and binning success in the context of genome relative abundance, as opposed to base pairs, provides a constraint on sequencing experiments based on the relative abundance of microbes in an environment rather than arbitrary levels of sequencing effort.
Widely used microbial taxonomies, such as the NCBI taxonomy, are based on a combination of sequence homology among conserved genes and historically accepted taxonomies, which were developed based on observable traits such as morphology and physiology. A recently proposed alternative taxonomy database, the Genome Taxonomy Database (GTDB), incorporates only sequence homology of conserved genes and attempts to partition taxonomic ranks such that each rank implies the same amount of evolutionary distance, regardless of its position on the phylogenetic tree. This provides the first opportunity to completely separate taxonomy from traits and therefore to quantify how taxonomic rank corresponds to traits across the microbial tree of life. We quantified the relative abundances of clusters of orthologous group functional categories (COG-FCs) as a proxy for traits within the lineages of 13,735 cultured and uncultured microbial lineages from a custom-curated genome database. On average, 41.4% of the variation in COG-FC relative abundance is explained by taxonomic rank, with domain, phylum, class, order, family, and genus explaining, on average, 3.2%, 14.6%, 4.1%, 9.2%, 4.8%, and 5.5% of the variance, respectively (P < 0.001 for all). To our knowledge, this is the first work to quantify the variance in metabolic potential contributed by individual taxonomic ranks. A qualitative comparison between the COG-FC relative abundances and genus-level phylogenies, generated from published concatenated protein sequence alignments, further supports the idea that metabolic potential is taxonomically coherent at higher taxonomic ranks. The quantitative analyses presented here characterize the integral relationship between diversification of microbial lineages and the metabolisms which they host.
Anoxic subsurface sediments contain communities of heterotrophic microorganisms that metabolize organic carbon at extraordinarily slow rates. In order to assess the mechanisms by which subsurface microorganisms access detrital sedimentary organic matter, we measured kinetics of a range of extracellular peptidases in anoxic sediments of the White Oak River estuary, NC. Nine distinct peptidase substrates were enzymatically hydrolyzed at all depths. Potential peptidase activities (Vmax) decreased with increasing sediment depth, although Vmaxexpressed on a per cell basis was approximately the same at all depths. Half-saturation constants (Km) decreased with depth, indicating peptidases that functioned more efficiently at low substrate concentrations. Potential activities of extracellular peptidases acting on molecules that are enriched in degraded organic matter (D-phenylalanine and L-ornithine) increased relative to enzymes that act on L-phenylalanine, further suggesting microbial community adaptation to access degraded organic matter. Nineteen classes of predicted, exported peptidases were identified in genomic data from the same site, of which genes for class C25 (gingipain-like) peptidases represented more than 40% at each depth. Methionine aminopeptidases, zinc carboxypeptidases, and class S24-like peptidases, which are involved in single-stranded DNA repair, were also abundant. These results suggest a subsurface heterotrophic microbial community that primarily accesses low-quality detrital organic matter via a diverse suite of well-adapted extracellular enzymes.
Approximately 150 Pg of organic carbon resides in subsurface sediments (Ciais et al. 2013). Understanding the preservation, transformation, and remineralization of this carbon is a prerequisite to understanding the global carbon cycle. Extracellular enzymes are required by subsurface heterotrophic microbes to access complex molecules from this reservoir (Arnosti 2011) and thus, influence carbon turnover. Identifying which microbes produce which extracellular enzymes and under what conditions has remained a challenge. Here, it is proposed to apply bottom-up metaproteomics, metatranscriptomics, and shotgun-metagenomics on subsurface microbe communities from the White Oak River estuary to help close this knowledge gap.