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SIMS21, Poland 2017 - Christopher Anderton abstract

Christopher Anderton oral presentation (OA2-Mon3-3-2)

High-lateral resolution SIMS reveals how nitrogen state alters carbon metabolism in phototrophic biofilms

Christopher Anderton1, Jamie Nuñez1, Jennifer Mobberley1, Jessica K Cole1, Ryan S Renslow1, Stephen R Lindemann2

1 Pacific Northwest National Laboratory, MSIN K8-98, PO Box 999, WA 99354 Richland, United States
2 Purdue University, 610 Purdue Mall, IN 47907 West Lafayette, United States

Because phototrophic microbial communities are ubiquitous in nature, predicting how changing environmental conditions alter processes governing energy and nutrient flow between phototrophs and their associated heterotrophs has significant global implications. For example, alterations in carbon and nitrogen cycling within these microbial communities can influence Earth’s climate. Using a model unicyanobacterial consortium (UCC-O) derived from a microbial mat that resides in Hot Lake, USA, we tested linkages between C and N cycles and found that this community’s physical structure and function changed considerably by varying the available nitrogen source (i.e., ammonium and/or nitrate) as biofilms assembled. These impacts were mediated by alterations in the abundances of some heterotrophic species and in global protein expression. Bulk stable isotope probing (SIP) experiments revealed community uptake of carbon was unaffected by nitrogen source, although, as expected, ammonium was more rapidly taken up by the community than nitrate. NanoSIMS analysis of these samples provided us key insights into the actual flow of C and N through this community under the different nitrogen conditions, revealing that the nitrogen form governed the partitioning of carbon among the members of the consortia (Figure 1). These results were confirmed using the semi-automated pipeline we previously described, with which we quantitated the relative consumption of C and N across the entire population of autotropic and heterotrophic members imaged and compared. These results further offered insight into the unexpected protein labeling patterns in the SIP-proteomics experiments.