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Singleton, C. M.

Publications and source records attributed to Singleton, C. M..

2 recordsLinked to original sources

Diverse Arctic lake sediment microbiota shape methane emission temperature sensitivity

Northern post-glacial lakes are a significant and increasing source of atmospheric carbon (C), largely through ebullition (bubbling) of microbially-produced methane (CH4) from the sediments1. Ebullitive CH4 flux correlates strongly with temperature, suggesting that solar radiation is the primary driver of these CH4 emissions2. However, here we show that the slope of the temperature-CH4 flux relationship differs spatially, both within and among lakes. Hypothesizing that differences in microbiota could explain this heterogeneity, we compared site-specific CH4 emissions with underlying sediment microbial (metagenomic and amplicon), isotopic, and geochemical data across two post-glacial lakes in Northern Sweden. The temperature-associated increase in CH4 emissions was greater in lake middles--where methanogens were more abundant--than edges, and sediment microbial communities were distinct between lake edges and middles. Although CH4 emissions projections are typically driven by abiotic factors1, regression modeling revealed that microbial abundances, including those of CH4-cycling microorganisms and syntrophs that generate H2 for methanogenesis, can be useful predictors of porewater CH4 concentrations. Our results suggest that deeper lake regions, which currently emit less CH4 than shallower edges, could add substantially to overall CH4 emissions in a warmer Arctic with longer ice-free seasons and that future CH4 emission predictions from northern lakes may be improved by accounting for spatial variations in sediment microbiota.

microbiology

The signal and the noise - characteristics of antisense RNA in complex microbial communities

High-throughput sequencing has allowed unprecedented insight into the composition and function of complex microbial communities. With the onset of metatranscriptomics, it is now possible to interrogate the transcriptome of multiple organisms simultaneously to get an overview of the gene expression of the entire community. Studies have successfully used metatranscriptomics to identify and describe relationships between gene expression levels and community characteristics. However, metatranscriptomic datasets contain a rich suite of additional information which is just beginning to be explored. In this minireview we discuss the different computational strategies for handling antisense expression in metatranscriptomic samples and highlight their potentially detrimental effects on downstream analysis and interpretation. We also surveyed the antisense transcriptome of multiple genomes and metagenome-assembled genomes (MAGs) from five different datasets and found high variability in the level of antisense transcription for individual species which were consistent across samples. Importantly, we tested the hypothesis that antisense transcription is primarily the product of transcriptional noise and found mixed support, suggesting that the total observed antisense RNA in complex communities arises from a compounded effect of both random, biological and technical factors. Antisense transcription can provide a rich set of information, from technical details about data quality to novel insight into the biology of complex microbial communities.\n\nKey pointsO_LISeveral fundamentally different approaches are used to handle antisense RNA\nC_LIO_LIPrevalence of antisense RNA is highly variable between communities, genomes, and genes.\nC_LIO_LIAntisense RNA is likely an opaque mixture of technical, biological and random effects\nC_LI

microbiology