Demonstration of a multi-technique approach to assess glacial microbial populations in the field

Megan J Barnett,Jemma L Wadham,David C Cullen,Mark Pawlett,Miriam Jackson

doi:10.1017/jog.2016.23

Abstract

ABSTRACTThe ability to perform microbial detection and characterization in-field at extreme environments, rather than on returned samples, has the potential to improve the efficiency, relevance and quantity of data from field campaigns. To date, few examples of this approach have been reported. Therefore, we demonstrate that the approach is feasible in subglacial environments by deploying four techniques for microbial detection: real-time polymerase chain reaction; microscopic fluorescence cell counts, adenosine triphosphate bioluminescence assay and recombinant Factor C assay (to detect lipopolysaccharide). Each technique was applied to 12 subglacial ice samples, 12 meltwater samples and two snow samples from Engabreen, Northern Norway. Using this multi-technique approach, the detected biomarker levels were as expected, being highest in debris-rich subglacial ice, moderate in glacial meltwater and low in clean ice (debris-poor) and snow. Principal component analysis was applied to the resulting dataset and could be performed in-field to rapidly aid the allocation of resources for further sample analysis. We anticipate that in-field data collection will allow for multiple rounds of sampling, analysis, interpretation and refinement within a single field campaign, resulting in the collection of larger and more appropriate datasets, ultimately with more efficient science return.

Highlights

The discovery of active microbial communities at glacier beds (Sharp and others, 1999; Skidmore and others, 2000) led to an increased interest in the influence that microbial communities may have on the chemistry of glacial environments and biogeochemical cycling in aligned systems such as the oceans (Anesio and Laybourn-Parry, 2012; Wadham and others, 2013)
No nirK was detected in the clean ice samples, no nifH was detected in the snow samples and no mxaF was detected in the mixed water samples (Table 4)
Foght and others (2004) found fluorescence cell counts of 2.3 × 106 (±0.1) cells g−1 sediment in recently exposed, glacially abraded bedrock and viable cell counts were typically 3–4 orders of magnitude higher in these sediments than glacial ice, which is consistent with the difference we found here of 3 × 104 to 1 × 106 cells mL−1 in debrisrich ice and 2 × 102 to 1 × 103 cells mL−1 in clean ice

Summary

Introduction

The discovery of active microbial communities at glacier beds (Sharp and others, 1999; Skidmore and others, 2000) led to an increased interest in the influence that microbial communities may have on the chemistry of glacial environments and biogeochemical cycling in aligned systems such as the oceans (Anesio and Laybourn-Parry, 2012; Wadham and others, 2013). Recent efforts have been devoted to characterize the microbial communities, estimating rates of metabolism and identifying potential metabolic pathways underneath different glaciers, and in different types of glacial environments (Christner and others, 2001; Skidmore and others, 2005; Stibal and others, 2012) Together, these studies have revealed distinct sub- and supra-glacial microbial communities that influence geochemical processes (Gaidos and others, 2004; Kaštovská and others, 2007), including carbon, nitrogen, sulphur and iron cycles (Christner and others, 2008; Hodson and others, 2008; Boyd and others, 2011). The microbial activity of glacial ice has been studied (1) by measuring the uptake of labelled substrates such as acetate and amino acids (Foght and others, 2004; Sala and others, 2005; Hodson and others, 2007), (2) through inferences from imbalances in carbon and sulphur ions (Sharp and others, 1999; Wadham and others, 2004) and (3) by quantifying methane production (Stibal and others, 2012) Together, these techniques can be used to provide insights into the presence, abundance, diversity or activity of microorganisms. A few studies have transported equipment to the field and successfully analysed samples near the sampling site (Cowan and others, 2002; Hodson and others, 2007; Nadeau and others, 2008)

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