ALBERT

Description: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in The ISME Journal 12 (2018): 237–252, doi:10.1038/ismej.2017.165.

Description: Temperate coastal marine environments are replete with complex biotic and abiotic interactions that are amplified during spring and summer phytoplankton blooms. During these events, heterotrophic bacterioplankton respond to successional releases of dissolved organic matter as algal cells are lysed. Annual seasonal shifts in the community composition of free-living bacterioplankton follow broadly predictable patterns, but whether similar communities respond each year to bloom disturbance events remains unknown owing to a lack of data sets, employing high-frequency sampling over multiple years. We capture the fine-scale microdiversity of these events with weekly sampling using a high-resolution method to discriminate 16S ribosomal RNA gene amplicons that are 〉99% identical. Furthermore, we used 2 complete years of data to facilitate identification of recurrent sub-networks of co-varying microbes. We demonstrate that despite inter-annual variation in phytoplankton blooms and despite the dynamism of a coastal–oceanic transition zone, patterns of microdiversity are recurrent during both bloom and non-bloom conditions. Sub-networks of co-occurring microbes identified reveal that correlation structures between community members appear quite stable in a seasonally driven response to oligotrophic and eutrophic conditions.

Description: PLB is supported by the European Research Council Advanced Investigator grant ABYSS 294757 to Antje Boetius. AF-G is supported by the European Union’s Horizon 2020 research and innovation program (Blue Growth: Unlocking the potential of Seas and Oceans) under grant agreement no. (634486) (project acronym INMARE). This study was funded by the Max Planck Society. Further support by the Department of Energy Joint Genome Institute (CSP COGITO) and DFG (FOR2406) is acknowledged by HT (TE 813/2-1) and RA (Am 73/9-1).

Description: 〈jats:p〉 Microorganisms comprise an immense phylogenetic and metabolic diversity, inhabit every conceivable niche on earth, and play a fundamental role in global biogeochemical processes. Among others, their study is highly relevant to develop biotechnological applications, understand ecosystem processes and monitor environmental systems. Functional traits (FTs) (i.e., measurable properties of an organism that influence its fitness (McGill et al. 2006)) provide complementary information to the taxonomic composition to improve the characterization of microbial communities and study their ecology (Martiny et al. 2012). The application of FT-based approaches can be particularly enhanced when coupled with metagenomics, which as a culture-independent method, allows us to obtain the genetic material of microorganisms from the environment: Metagenomic data can be used to compute functional traits at the genome level from a random sample of individuals in a microbial community, irrespective of their taxonomic affiliation (Fierer et al. 2014). Previous works using FT-based approaches in metagenomics include the study of community assembly processes (Burke et al. 2011) and responses to environmental change (Leff et al. 2015), and ecosystem functioning (Babilonia et al. 2018). 〈/jats:p〉 〈jats:p〉 In this work, we present the Metagenomic Traits pipeline: Mg-Traits. Mg-Traits is dedicated to the computation of 25 (and counting) functional traits in short-read metagenomic data, ranging from GC content and amino acid composition to functional diversity and average genome size (see Fig. 1). As an example application, we used the Mg-Traits pipeline to process the 139 prokaryotic metagenomes of the TARA Oceans data set (Sunagawa et al. 2015). In this analysis, we observed that the computed metagenomic traits track community changes along the water column, which denote microorganisms’ environmental adaptations. 〈/jats:p〉 〈jats:p〉 Mg-Traits allows the systematic computation of a comprehensive set of metagenomic functional traits, which can be used to generate a functional and taxonomic fingerprint and reveal the predominant life-history strategies and ecological processes in a microbial community. Mg-Traits contributes to improving the exploitation of metagenomic data and facilitates comparative and quantitative studies. Considering the high genomic plasticity of microorganisms and their capacity to rapidly adapt to changing environmental conditions, Mg-Traits constitutes a valuable tool to monitor environmental systems. 〈/jats:p〉 〈jats:p〉 〈/jats:p〉 〈jats:p〉The Mg-Traits pipeline is available at https://github.com/pereiramemo/metagenomic_pipelines. It is programmed in AWK, BASH, and R, and it was devised using a modular design to facilitate the integration of new metagenomic traits.〈/jats:p〉