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Abstract

Microbes control the decomposition of soil organic matter, a key biogeochemical process significant to global climate. The complex chemistry of soils and the great diversity of microbial strains with flexible metabolic capabilities have impeded the elucidation of degradation pathways from plant tissues to greenhouse gases. A mechanistic understanding of soil processes can improve models used to predict the fate of vast quantities of carbon stored in Arctic soils. Arctic warming is accelerating microbial decomposition but also increasing plant biomass, counteracting carbon loss. Floras with a significant nonvascular component are being replaced by floras dominated by larger and woodier plants. The changing vegetation may mediate the effects of warming on soil microbial activity through interactions with roots and the composition of plant detritus. Metaproteomics is a promising approach for studying soil processes, since proteins catalyze key biogeochemical transformations. I collected soil cores from major floral ecotypes in the area of Toolik Field Station, Alaska and extracted proteins for metaproteomic analysis. To overcome impediments to the routine application of proteomics to complex samples, I developed novel bioinformatic methods to analyze protein mass spectrometry data. The standard database search method of assigning amino acid sequences to peptide mass spectra requires a tailored reference database of sequences that may be present in the proteomic dataset. Environmental metaproteomes may lack appropriate reference databases, especially in the absence of paired metagenomes. As an alternative to database search, sequences can be deduced directly from mass spectra, a computationally challenging approach known as de novo sequencing. To improve the low accuracy of de novo sequences predicted by existing algorithms, I created post-processing software called Postnovo, which rescores and reranks sequences from multiple input algorithms using newly calculated metrics. I demonstrated that Postnovo improves the yield of accurate de novo sequences by about an order of magnitude and predicts the false discovery rate of the results. Postnovo extends the applicability of de novo sequencing, which is currently used with relatively simple samples such as monoclonal antibodies. Furthermore, I characterized the minimum length of environmental de novo sequences necessary for functional annotation from large reference databases. I also employed database search methods to identify peptide sequences in my metaproteomic datasets, using Alaskan soil metagenomes and metatranscriptomes published in other studies as a reference database. To link proteins to taxa, I identified bins of metagenomic sequences representing the major bacterial groups known from 16S rRNA surveys of microbial taxonomic diversity. The challenge of utilizing the full information content of the reference nucleotide datasets – including sequence reads, unbinned contigs, and binned contigs – led me to create software called ProteinExpress. ProteinExpress increases the number of protein identifications and the quality of protein annotations from complex metaproteomes. Additionally, I constructed a classification system relating protein functional annotation terms from the eggNOG database to protein “Functional Groups” of biogeochemical significance. Metaproteomic analyses revealed key processes in the soils, patterns of resource partitioning between major taxa, and changes associated with increasing plant biomass. Microbial activity in the rhizosphere appears to be distinct from activity in the bulk soil, with groups such as Rhizobiales strongly interacting with roots and other groups such as Acidobacteria dominating the degradation of plant cell wall polymers. Rhizospheric groups concentrate on the acquisition of small, soluble compounds, especially simple sugars likely exuded from roots, and most strongly express transporters for nitrogenous compounds, potentially due to severe nutrient limitation in the proximity of roots. Acidobacteria degrade relatively labile polysaccharides, such as hemicelluloses, Actinobacteria depolymerize cellulose, and Burkholderiaceae cleave aromatics including lignin. These ecophysiological findings run counter to the expectation that major groups of heterotrophic soil bacteria are generalists without strong preferences for carbon and nutrient resources. Acidobacteria are the most active group across floral ecotypes, given their high expression of ribosomal proteins and other core functions, yet the activity of rhizospheric bacteria increases from low to high biomass floras. This suggests that further Arctic warming will be accompanied by a shift in soil microbial activity toward groups engaged in both mutualistic and competitive interactions with plants.

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