Functional repertoire convergence of distantly related eukaryotic plankton lineages abundant in the sunlit ocean

Delmont, Tom O.; Gaïa, Morgan; Hinsinger, Damien Daniel; Frémont, Paul; Vanni, Chiara; Fernàndez-Guerra, Antonio; Eren, A. Murat; Kourlaiev, Artem; d'Agata, Léo; Clayssen, Quentin; Villar, Emilie; Labadie, Karine; Cruaud, Corinne; Poulain, Julie; Silva, Corinne Da; Wessner, Marc; Noël, Benjamin; Aury, Jean‐Marc; Pelletier, Éric; Sunagawa, Shinichi; Acinas, Silvia G.; Bork, Peer; Karsenti, Eric; Bowler, Chris; Sardet, Christian; Stemmann, Lars; Vargas, Colomban de; Wincker, Patrick; Lescot, Magali; Babin, Marcel; Gorsky, Gabriel; Grimsley, Nigel; Guidi, Lionel; Hingamp, Pascal; Jaillon, Olivier; Kandels, Stefanie; Iudicone, Daniele; Ogata, Hiroyuki; Pesant, Stéphane; Sullivan, Matthew B.; Not, Fabrice; Karp‐Boss, Lee; Boss, Emmanuel; Cochrane, Guy; Follows, Michael J.; Poulton, Nicole; Raes, Jeroen; Sieracki, Mike; Speich, Sabrina

doi:10.1016/j.xgen.2022.100123

Cited by 108 publications

(167 citation statements)

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“…We also covered 50% of the Pacific Ocean Virome proteins, and 77% of the TARA Ocean Virome proteins, for overall coverage of 70% of the selected viral proteins. The majority of genes from both the FMT MAGs of Lee et al, 2017 and the TARA MAGs of Delmont et al, 2022 , were found homologous to genes in our dataset (91% and 77% respectively). With the only exception of the FUnkFams, and the mutant genes, for which we did not find any homology to EU GCs, the other datasets reported homologies to clusters from all four functional categories.…”

Section: Appendix 1—figurementioning

confidence: 60%

“…The results presented here prove that the integration and the analysis of the unknown fraction are possible. We are unveiling a brighter future, not only for microbiome analyses but also for boosting eukaryotic-related studies, thanks to the increasing number of projects, including metatranscriptomic data ( Delmont et al, 2022 ; Vorobev et al, 2020 ). Furthermore, our work lays the foundations for further developments of clear guidelines and protocols to define the different levels of unknown ( Thomas and Segata, 2019 ) and should encourage the scientific community for a collaborative effort to tackle this challenge.…”

Section: Discussionmentioning

confidence: 99%

“…The last two subsets contain 52,231 (26%) EU found in GTDB metagenome-assembled genomes (MAGs). To test whether we could also place the subset of metagenomic EU in the context of MAGs, we searched the GCs of this set against the manually curated TARA Ocean MAG collection from Delmont et al, 2022 . In addition, we deepened the investigation of the metagenomic EU subset, focusing on the GCCs found broadly distributed in metagenomes according to the results of Levin’s niche breadth analysis ( Figure 4 ).…”

Section: Appendix 1—figurementioning

confidence: 99%

Section: Appendix 1—figurementioning

confidence: 99%

“…Lastly, sequences that do not meet either of these criteria are classified as "Genomic Unknown" (GU) if they can be found in genomes from reference databases or contain domains of unknown function (DUF); and ‘Environmental Unknown’ (EU) when only have been observed in environmental samples. By contextualizing the different categories with information from several sources ( Figure 1C ), we hope this will prove an invaluable resource for including genes of unknown function in evolutionary and ecological studies ( Delmont et al, 2022 ; Gaïa et al, 2021 ; Holland-Moritz et al, 2021 ) as well as enhancing the current methods for its experimental characterization.…”

Section: Introductionmentioning

confidence: 99%

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Unifying the known and unknown microbial coding sequence space

Vanni

Schechter

Acinas

et al. 2022

eLife

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Genes of unknown function are among the biggest challenges in molecular biology, especially in microbial systems, where 40%-60% of the predicted genes are unknown. Despite previous attempts, systematic approaches to include the unknown fraction into analytical workflows are still lacking. Here, we present a conceptual framework, its translation into the computational workflow AGNOSTOS and a demonstration on how we can bridge the known-unknown gap in genomes and metagenomes. By analyzing 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes, we quantify the extent of the unknown fraction, its diversity, and its relevance across multiple organisms and environments. The unknown sequence space is exceptionally diverse, phylogenetically more conserved than the known fraction and predominantly taxonomically restricted at the species level. From the 71M genes identified to be of unknown function, we compiled a collection of 283,874 lineage-specific genes of unknown function for Cand. Patescibacteria (also known as Candidate Phyla Radiation, CPR), which provides a significant resource to expand our understanding of their unusual biology. Finally, by identifying a target gene of unknown function for antibiotic resistance, we demonstrate how we can enable the generation of hypotheses that can be used to augment experimental data.

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Section: Appendix 1—figurementioning

confidence: 60%

Section: Discussionmentioning

confidence: 99%

Section: Appendix 1—figurementioning

confidence: 99%

Section: Appendix 1—figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unifying the known and unknown microbial coding sequence space

Vanni

Schechter

Acinas

et al. 2022

eLife

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DNA extraction bias is more pronounced for microbial eukaryotes than for prokaryotes

Bräuer

Bengtsson

2022

MicrobiologyOpen

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DNA extraction and preservation bias is a recurring topic in DNA sequencing‐based microbial ecology. The different methodologies can lead to distinct outcomes, which has been demonstrated especially in studies investigating prokaryotic community composition. Eukaryotic microbes are ubiquitous, diverse, and increasingly a subject of investigation in addition to bacteria and archaea. However, little is known about how the choice of DNA preservation and extraction methodology impacts perceived eukaryotic community composition. In this study, we compared the effect of two DNA preservation methods and six DNA extraction methods on the community profiles of both eukaryotes and prokaryotes in phototrophic biofilms on seagrass ( Zostera marina ) leaves from the Baltic Sea. We found that, whereas both DNA preservation and extraction method caused significant bias in perceived community composition for both eukaryotes and prokaryotes, extraction bias was more pronounced for eukaryotes than for prokaryotes. In particular, soft‐bodied and hard‐shelled eukaryotes like nematodes and diatoms, respectively, were differentially abundant depending on the extraction method. We conclude that careful consideration of DNA preservation and extraction methodology is crucial to achieving representative community profiles of eukaryotes in marine biofilms and likely all other habitats containing diverse eukaryotic microbial communities.

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