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High‐throughput sequencing has enabled robust shotgun metagenomic sequencing that informs our understanding of the genetic basis of important biogeochemical processes. Slower to develop, however, are the application of these tools in a controlled experimental framework that pushes the field beyond exploratory analysis toward hypothesis‐driven research. We performed flow‐through reactor experiments to examine how salt marsh sediments from varying depths respond to nitrate addition and linked biogeochemical processes to this underlying genetic foundation. Understanding the mechanistic basis of carbon and nitrogen cycling in salt marsh sediments is critical for predicting how important ecosystem services provided by marshes, including carbon storage and nutrient removal, will respond to global change. Prior to the addition of nitrate, we used metagenomics to examine the functional potential of the sediment microbial community that occurred along a depth gradient, where organic matter reactivity changes due to decomposition. Metagenomic data indicated that genes encoding enzymes involved in respiration, including denitrification, were higher in shallow sediments, and genes indicative of resource limitation were greatest at depth. After 92 d of nitrate enrichment, we measured cumulative increases in dissolved inorganic carbon production, denitrification, and dissimilatory nitrate reduction to ammonium; these rates correlated strongly with genes that encode essential enzymes in these important pathways. Our results highlight the importance of controlled experiments in linking biogeochemical rates to underlying genetic pathways. Furthermore, they indicate the importance of nitrate as an electron acceptor in fueling microbial respiration, which has consequences for carbon and nitrogen cycling and fate in coastal marine systems. [ABSTRACT FROM AUTHOR]

Antarctica polynyas support intense phytoplankton blooms, impacting their environment by a substantial depletion of inorganic carbon and nutrients. These blooms are dominated by the colony-forming haptophyte Phaeocystis antarctica and they are accompanied by a distinct bacterial population. Yet, the ecological role these bacteria may play in P. antarctica blooms awaits elucidation of their functional gene pool and of the geochemical activities they support. Here, we report on a metagenome (~160 million reads) analysis of the microbial community associated with a P. antarctica bloom event in the Amundsen Sea polynya (West Antarctica). Genomes of the most abundant Bacteroidetes and Proteobacteria populations have been reconstructed and a network analysis indicates a strong functional partitioning of these bacterial taxa. Three of them (SAR92, and members of the Oceanospirillaceae and Cryomorphaceae) are found in close association with P. antarctica colonies. Distinct features of their carbohydrate, nitrogen, sulfur and iron metabolisms may serve to support mutualistic relationships with P. antarctica. The SAR92 genome indicates a specialization in the degradation of fatty acids and dimethylsulfoniopropionate (compounds released by P. antarctica) into dimethyl sulfide, an aerosol precursor. The Oceanospirillaceae genome carries genes that may enhance algal physiology (cobalamin synthesis). Finally, the Cryomorphaceae genome is enriched in genes that function in cell or colony invasion. A novel pico-eukaryote, Micromonas related genome (19.6 Mb, ~94% completion) was also recovered. It contains the gene for an anti-freeze protein, which is lacking in Micromonas at lower latitudes. These draft genomes are representative for abundant microbial taxa across the Southern Ocean surface. [ABSTRACT FROM AUTHOR]

Consensus between independent reads improves the accuracy of genome and transcriptome analyses, however lack of consensus between very similar sequences in metagenomic studies can and often does represent natural variation of biological significance. The common use of machine-assigned quality scores on next generation platforms does not necessarily correlate with accuracy. Here, we describe using the overlap of paired-end, short sequence reads to identify error-prone reads in marker gene analyses and their contribution to spurious OTUs following clustering analysis using QIIME. Our approach can also reduce error in shotgun sequencing data generated from libraries with small, tightly constrained insert sizes. The open-source implementation of this algorithm in Python programming language with user instructions can be obtained from https://github.com/meren/illumina-utils. [ABSTRACT FROM AUTHOR]