Modular community structure suggests metabolic plasticity during the transition to polar night in ice-covered Antarctic lakes

Abstract

High-latitude environments, such as the Antarctic McMurdo Dry Valley lakes, are subject to seasonally segregated light-dark cycles, which have important consequences for microbial diversity and function on an annual basis. Owing largely to the logistical difficulties of sampling polar environments during the darkness of winter, little is known about planktonic microbial community responses to the cessation of photosynthetic primary production during the austral sunset, which lingers from approximately February to April. Here, we hypothesized that changes in bacterial, archaeal and eukaryotic community structure, particularly shifts in favor of chemolithotrophs and mixotrophs, would manifest during the transition to polar night. Our work represents the first concurrent molecular characterization, using 454 pyrosequencing of hypervariable regions of the small-subunit ribosomal RNA gene, of bacterial, archaeal and eukaryotic communities in permanently ice-covered lakes Fryxell and Bonney, before and during the polar night transition. We found vertically stratified populations that varied at the community and/or operational taxonomic unit-level between lakes and seasons. Network analysis based on operational taxonomic unit level interactions revealed nonrandomly structured microbial communities organized into modules (groups of taxa) containing key metabolic potential capacities, including photoheterotrophy, mixotrophy and chemolithotrophy, which are likely to be differentially favored during the transition to polar night.

Antarctic microbial mats: A modern analog for Archean lacustrine oxygen oases

“…During the summer, 0.5% to 3% of incident light penetrates the ice cover, and illumination declines with depth with an extinction coefficient of 0.5-0.6 m -1 (Howard-Williams et al, 1998). There is a sharp chlorophyll maximum in the planktonic community at ~8 m, just above the depth at which O 2 concentration and planktonic net photosynthesis fall to zero (e.g., Vincent, 1981;McKnight et al, 2000;Burnett et al, 2006;Vick-Majors et al, 2014). There are extensive benthic mat communities extending from the moat around the margin of the lake to ~11 m depth that are dominated by cyanobacteria, diatoms, and heterotrophic bacteria (e.g., Wharton et al, 1983;Taton et al, 2003).…”

Microbial Mat Communities along an Oxygen Gradient in a Perennially Ice-Covered Antarctic Lake

“…These properties make the MDV lake systems among the most physically stable lacustrine habitats on Earth, and research over the past decades has shown that microbial communities and processes are consistently embedded at specific positions in environmental gradients within their stable water columns (e.g., 3). There is a strong connection between dominant microbial physiologies and redox-related biogeochemistry through the water column (4)(5)(6)(7)(8)(9), and microbial distributions along stable redox gradients have provided useful test cases for understanding links between environment and metabolism. To date, such observations have largely been confined to planktonic communities (3,6,10), despite the fact that in all MDV lakes studied, thick microbial mats cover the floors of the lakes to the base of the photic zone.…”

Network topology reveals high connectance levels and few key microbial genera within soils

“…BC counts the fraction of shortest paths going through a given bacterial taxon to another. The BC of a taxon in a network reflects the importance of control that the taxon exerts over the interactions of other taxons in the network (Martín González et al, 2010;Vick-Majors et al, 2014). CC denotes the proximity of a node to all other nodes in the network quantifying how many steps away genus i is from all others in the web (Freeman, 1979).…”

“…This approach is based on using a fixed number of links to connect randomly chosen nodes and serves as point of reference against which our real biological networks might be compared (Vick-Majors et al, 2014). To measure the relative importance (how influential a taxon is within a network) of each taxon within the network we calculated two measures of centrality: Betweenness Centrality (BC) (Martín González et al, 2010;Vick-Majors et al, 2014) and Closeness Centrality (CC) (Freeman, 1979). BC counts the fraction of shortest paths going through a given bacterial taxon to another.…”