OOS 34-7
Determining drivers of carbon cycling through co-occurrence of soil microorganisms and their traits

Tuesday, August 11, 2015: 3:40 PM
342, Baltimore Convention Center
Ryan J. Williams, Agricultural and Biosystems Engineering, Iowa State University, Ames, IA
Kirsten S. Hofmockel, Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA
Adina C. Howe, Agricultural and Biosystems Engineering, Iowa State University, Ames, IA

The variety of carbon (C) substrates and extreme diversity of microbial communities in soil impedes our ability to determine linkages between microbes, carbon (C) cycling, and climate system feedbacks.  Traditionally, microbial C-cycling has been summarized by extracellular enzymes, which are proximal indicators of decomposition.  However, next-generation sequencing technologies have provided opportunities to produce massive amounts of data describing which microorganisms are present (16S rRNA amplicons) and what traits they harbor (metagenomes).  Using these three data types we asked: Which microorganisms coexist and potentially interact to drive C-cycles, what C-cycling traits are involved, and are these related to our biogeochemical indicators of decomposition?  Using a Bayesian co-occurrence framework, we determined which microorganisms (16S rRNA) coexist and potentially interact through their traits (metagenome assemblies annotated via the Carbohydrate-Active Enzyme (CAZy) database) among different soil aggregate fractions at the Comparison of Biofuel Systems (COBS) experiment in Boone County, Iowa.  When considering microbial communities, we focused specifically on those genera that co-occur with elevated extracellular enzyme activity, and among metagenomes, we identified co-occurrence between enzyme families (i.e. traits) that may also be related to our biogeochemical measurements.  


Co-occurrence relationships between microorganisms and enzyme activity were largely consistent across aggregates, suggesting that specific genera may be important drivers of C-cycling processes.  However, these indicator taxa were not consistent across data types.  For example, Nocardia, an actinomycete, co-occurred with elevated β-xylosidase, and its representative genomes contain the genetic potential to produce this enzyme.  This particular β-xylosidase was only harbored by ϒ-proteobacteria in our metagenomes and was not correlated with enzyme activity.  Nevertheless, the enzyme family containing β-xylosidase co-occurred with other xylan-degrading enzymes not captured by extracellular enzyme assays.  Though our results do not agree on key microorganisms decomposing a particular substrate, we may gain a more holistic view of C-cycling by combining different data types.  Analysis of microbial communities distinguished microbial indicators of C-substrate decomposition based on a single enzyme, while metagenomes illuminated additional C-cycling traits currently ignored from a biogeochemical perspective.  Future work will focus on microbe-enzyme relationships that are specific to aggregate fractions and cropping systems at the COBS experiment.  Using co-occurrence analyses to address coexistence and potential interactions among microorganisms and their C-cycling traits may prove to be a useful step towards linking our rapidly changing climate to the microbes that control biogeochemical cycling.