Characterizing Chemoautotrophy and Heterotrophy in Marine Archaea and Bacteria With Single-Cell Multi-isotope NanoSIP

Abstract

Characterizing and quantifying in situ metabolisms remains both a central goal and challenge for environmental microbiology. Here, we used a single-cell, multi-isotope approach to investigate the anabolic activity of marine microorganisms, with an emphasis on natural populations of Thaumarchaeota. After incubating coastal Pacific Ocean water with 13 C-bicarbonate and 15 N-amino acids, we used nanoscale secondary ion mass spectrometry (nanoSIMS) to isotopically screen 1,501 individual cells, and 16S rRNA amplicon sequencing to assess community composition. We established isotopic enrichment thresholds for activity and metabolic classification, and with these determined the percentage of anabolically active cells, the distribution of activity across the whole community, and the metabolic lifestyle-chemoautotrophic or heterotrophic-of each cell. Most cells (>90%) were anabolically active during the incubation, and 4-17% were chemoautotrophic. When we inhibited bacteria with antibiotics, the fraction of chemoautotrophic cells detected via nanoSIMS increased, suggesting archaea dominated chemoautotrophy. With fluorescence in situ hybridization coupled to nanoSIMS (FISH-nanoSIMS), we confirmed that most Thaumarchaeota were living chemoautotrophically, while bacteria were not. FISH-nanoSIMS analysis of cells incubated with dual-labeled (13 C, 15 N-) amino acids revealed that most Thaumarchaeota cells assimilated amino-acidderived nitrogen but not carbon, while bacteria assimilated both. This indicates that some Thaumarchaeota do not assimilate intact amino acids, suggesting intra-phylum heterogeneity in organic carbon utilization, and potentially their use of amino acids for nitrification. Together, our results demonstrate the utility of multi-isotope nanoSIMS analysis for high-throughput metabolic screening, and shed light on the activity and metabolism of uncultured marine archaea and bacteria.

Genomic Insights Into the Lifestyles of Thaumarchaeota Inside Sponges

“…They can also be found in the genomes of bacterial sponge symbionts ( Gauthier et al, 2016 ). The expression of thaumarchaeal branched-chain amino acid transporters in the sponge host could indicate a mixotrophic lifestyle of its thaumarchaeal symbionts as previously suggested ( Moeller et al, 2019 ), but experiments are necessary to determine if also the carbon is taken up and not only the amino group as shown for free-living Thaumarchaeota ( Dekas et al, 2019 ).…”

“…Marine planktonic Thaumarchaeota are mixotrophs that can take up organic nitrogen and carbon ( Ouverney and Fuhrman, 2000 ; Herndl et al, 2005 ; Kirchman et al, 2007 ). However, recent experimental data suggest that most assimilate only the nitrogen from amino acids rather than assimilating the complete amino acid ( Dekas et al, 2019 ). As expected, we found ABC-type transporters for di- and oligopeptides (based on COG annotations).…”

Chemical Analysis of Single Cells and Organelles

“…Dekas et al used FISH-NanoSIMS to describe the metabolic lifestyle (chemoautotrophic or heterotrophic) of some archaea and bacteria marine populations by screening C and N isotopic changes in a vast number of individual cells. 150 …”

NanoSIMS sample preparation decreases isotope enrichment: magnitude, variability and implications for single‐cell rates of microbial activity

“…Isotope‐specific analysis by nano‐scale secondary ion mass spectrometry (nanoSIMS) can be used to detect, characterize and quantify the metabolism of individual microbial cells within environmental samples (reviewed in Herrmann et al ., 2007; Wagner and Whiteley, 2007; Boxer et al ., 2009; Orphan and House, 2009; Musat et al ., 2012; Pett‐Ridge and Weber, 2012; Mayali, 2020). NanoSIMS analysis has been extensively used in environmental microbiology, for example, to demonstrate microbial activity in samples from extreme environments (e.g., Morono et al ., 2011), identify metabolic capabilities of uncultured microbes (e.g., Dekas et al ., 2009; Woebken et al ., 2015; Trembath‐Reichert et al ., 2017; Kitzinger et al ., 2019), map the spatial distribution of activity in structured microbial systems (e.g., Fike et al ., 2008; McGlynn et al ., 2015; Wangpraseurt et al ., 2016; Chadwick et al ., 2019), metabolically screen microbial communities in a high‐throughput format (e.g., Arandia‐Gorostidi et al ., 2017; Dekas et al ., 2019), measure metabolite exchange between cells (e.g. Popa et al ., 2007; Moisander et al ., 2010; Ploug et al ., 2010; Foster et al ., 2011; Berry et al ., 2013), examine phenotypic heterogeneity (e.g., Kopf et al ., 2015; Zimmermann et al ., 2015; Calabrese et al ., 2019) and determine the relative contribution of particular taxa to total community productivity (e.g., Musat et al ., 2008).…”