Identification of Microbial Dark Matter in Antarctic Environments

Abstract

Numerous studies have applied molecular techniques to understand the diversity, evolution, and ecological function of Antarctic bacteria and archaea. One common technique is sequencing of the 16S rRNA gene, which produces a nearly quantitative profile of community membership. However, the utility of this and similar approaches is limited by what is known about the evolution, physiology, and ecology of surveyed taxa. When representative genomes are available in public databases some of this information can be gleaned from genomic studies, and automated pipelines exist to carry out this task. Here the paprica metabolic inference pipeline was used to assess how well Antarctic microbial communities are represented by the available completed genomes. The NCBI's Sequence Read Archive (SRA) was searched for Antarctic datasets that used one of the Illumina platforms to sequence the 16S rRNA gene. These data were quality controlled and denoised to identify unique reads, then analyzed with paprica to determine the degree of overlap with the closest phylogenetic neighbor with a completely sequenced genome. While some unique reads had perfect mapping to 16S rRNA genes from completed genomes, the mean percent overlap for all mapped reads was 86.6%. When samples were grouped by environment, some environments appeared more or less well represented by the available genomes. For the domain Bacteria, seawater was particularly poorly represented with a mean overlap of 80.2%, while for the domain Archaea glacial ice was particularly poorly represented with an overlap of only 48.0% for a single sample. These findings suggest that a considerable effort is needed to improve the representation of Antarctic microbes in genome sequence databases.

Keywords: 16S rRNA; Antarctica; cryoconite; glacier; permafrost; sea ice; sediment; snow.

FIGURE 1

Sample location by environment. Sample locations (where available in the metadata) are given according to the final consensus environment.

FIGURE 2

Sample diversity for the domain Bacteria and Archaea. (A) Rarefaction curves for all consensus environments for bacteria given on a log-log scale. (B) The number of unique reads identified in each consensus environment as a function of the number of samples, the line of best fit reflects a linear relationship (R² = 0.78, p = 2 × 10^-4). (C) Rarefaction curves for all consensus environments for archaea given on a log–log scale. Note that no archaea were identified in lake ice or sea ice samples. (D) The number of unique reads identified in each consensus environment as a function of the number of samples, the line of best fit reflects a linear relationship (R² = 0.69, p = 9 × 10^-4).

FIGURE 3

Sample mean map ratios for the domain Bacteria. For each consensus environment the distribution of mean map ratios is given. Only samples with greater than 1,000 reads assigned to the domain Bacteria are shown in the distribution.

FIGURE 4

Sample mean map ratios for the domain Archaea. For each consensus environment the distribution of mean map ratios is given. Only samples with greater than 1,000 reads assigned to the domain Archaea are shown in the distribution. Due to the small number of samples with sufficient archaeal reads for glacier ice (n = 1), lake ice (n = 0), snow (n = 1), sea ice (n = 0), and seawater (n = 3), these environments are not shown.

FIGURE 5

The abundance of unique reads as a function of map ratio for (A) bacteria and (B) archaea. The abundance of unique reads was determined within each consensus environment (i.e., each unique read may be tallied more than once in different consensus environments). The distribution of data is displayed via a hexagonal density plot, with the color of the hexagons representing the density of the data.