A consensus protocol for the recovery of mercury methylation genes from metagenomes

Eric Capo, Benjamin D. Peterson, Minjae Kim, Daniel S. Jones, Silvia G. Acinas, Marc Amyot, Stefan Bertilsson, Erik Björn, Moritz Buck, Claudia Cosio, Dwayne A. Elias, Cynthia Gilmour, Marisol Goñi-Urriza, Baohua Gu, Heyu Lin, Yu Rong Liu, Katherine McMahon, John W. Moreau, Jarone Pinhassi, Mircea PodarFernando Puente-Sánchez, Pablo Sánchez, Veronika Storck, Yuya Tada, Adrien Vigneron, David A. Walsh, Marine Vandewalle-Capo, Andrea G. Bravo, Caitlin M. Gionfriddo

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

Mercury (Hg) methylation genes (hgcAB) mediate the formation of the toxic methylmercury and have been identified from diverse environments, including freshwater and marine ecosystems, Arctic permafrost, forest and paddy soils, coal-ash amended sediments, chlor-alkali plants discharges and geothermal springs. Here we present the first attempt at a standardized protocol for the detection, identification and quantification of hgc genes from metagenomes. Our Hg-cycling microorganisms in aquatic and terrestrial ecosystems (Hg-MATE) database, a catalogue of hgc genes, provides the most accurate information to date on the taxonomic identity and functional/metabolic attributes of microorganisms responsible for Hg methylation in the environment. Furthermore, we introduce “marky-coco”, a ready-to-use bioinformatic pipeline based on de novo single-metagenome assembly, for easy and accurate characterization of hgc genes from environmental samples. We compared the recovery of hgc genes from environmental metagenomes using the marky-coco pipeline with an approach based on coassembly of multiple metagenomes. Our data show similar efficiency in both approaches for most environments except those with high diversity (i.e., paddy soils) for which a coassembly approach was preferred. Finally, we discuss the definition of true hgc genes and methods to normalize hgc gene counts from metagenomes.

Original languageEnglish
Pages (from-to)190-204
Number of pages15
JournalMolecular Ecology Resources
Volume23
Issue number1
DOIs
StatePublished - Jan 2023

Funding

This work was funded by the Severo Ochoa Excellence Programme postdoctoral fellowship awarded in 2021 to Eric Capo (CEX2019-000928-S), the Swedish Research Council Formas (grant 2018-01031), the EMFF-Blue Economy project MER-CLUB (grant 863584). Caitlin Gionfriddo was a Robert and Arlene Kogod Secretarial Scholar with the Smithsonian Environmental Research Center while conducting work described in this manuscript. Benjamin Peterson was funded as a postdoctoral research associate by the National Science Foundation (award 1935173) during the work in this manuscript. The computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Centre for Advanced Computational Science (UPPMAX) using the compute project SNIC 2021/5-53. Some of the computations for compiling the Hg-MATE database were conducted on the Smithsonian High Performance Cluster (SI/HPC), Smithsonian Institution. https://doi.org/10.25572/SIHPC. Oak Ridge National Laboratory is managed by UT-Battelle, LLC under contract no. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE). The authors are thankful to an anonymous reviewer and the associate editor Lucie Zinger for their insightful comments, the members of the Meta-Hg working group (https://ercapo.wixsite.com/meta-hg) and of the Mersorcium network (https://mersorcium.github.io/). This work was funded by the Severo Ochoa Excellence Programme postdoctoral fellowship awarded in 2021 to Eric Capo (CEX2019‐000928‐S), the Swedish Research Council Formas (grant 2018‐01031), the EMFF‐Blue Economy project MER‐CLUB (grant 863584). Caitlin Gionfriddo was a Robert and Arlene Kogod Secretarial Scholar with the Smithsonian Environmental Research Center while conducting work described in this manuscript. Benjamin Peterson was funded as a postdoctoral research associate by the National Science Foundation (award 1935173) during the work in this manuscript. The computations were performed on resources provided by SNIC through Uppsala Multidisciplinary Centre for Advanced Computational Science (UPPMAX) using the compute project SNIC 2021/5‐53. Some of the computations for compiling the Hg‐MATE database were conducted on the Smithsonian High Performance Cluster (SI/HPC), Smithsonian Institution. https://doi.org/10.25572/SIHPC . Oak Ridge National Laboratory is managed by UT‐Battelle, LLC under contract no. DE‐AC05‐00OR22725 with the U.S. Department of Energy (DOE). The authors are thankful to an anonymous reviewer and the associate editor Lucie Zinger for their insightful comments, the members of the Meta‐Hg working group ( https://ercapo.wixsite.com/meta‐hg ) and of the Mersorcium network ( https://mersorcium.github.io/ ).

Keywords

  • bioinformatics
  • hg methylation
  • hg-MATE
  • hgcAB genes
  • marky-coco
  • mercury
  • metagenomics

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