Potential for primary productivity in a globally-distributed bacterial phototroph

Graham, Elaina D.; Heidelberg, John F.; Tully, Benjamin J.

doi:10.1038/s41396-018-0091-3

Cited by 208 publications

(188 citation statements)

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“…The predicted proteins sequences for each genome were searched (HMMER 43 v.3.1b2; hmmsearch -E 1E-5) using HMM models 380 representing the 16 predominantly syntenic ribosomal proteins identified in Hug et al 44 (2016) (Supplemental Data 1). All proteins with a match to a ribosomal protein model were aligned using MUSCLE 45 (v.3.8.31; -maxiters 8) and automatically trimmed using trimAL 39 (v.1.2rev59; -automated1). All 16 alignments were concatenated and a phylogenetic tree was constructed using FastTree 40 (v.2.1.10; -gamma -lg).…”

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confidence: 99%

Metabolic Diversity within the Globally Abundant Marine Group IIEuryarchaeaOffers Insight into Ecological Patterns

Tully

2018

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Despite their discovery over 25 years ago, the Marine Group II Euryarchaea (MGII) have remained a difficult group of organisms to study, lacking cultured isolates and genome references. The MGII have 10 been identified in marine samples from around the world and evidence supports a photoheterotrophic lifestyle combining phototrophy via proteorhodopsins with the remineralization of high molecular weight organic matter. Divided between two Orders, the MGII have distinct ecological patterns that are not understood based on the limited number of available genomes. Here, we present the comparative genomic analysis of 322 MGII genomes, providing the most detailed view of these mesophilic archaea to-date. 15This analysis identified 17 distinct Family level clades including nine clades that previously lacked reference genomes. The metabolic potential and ecological distribution of the MGII genera revealed distinct roles in the environment, identifying algal-saccharide-degrading coastal genera, protein-degrading oligotrophic surface ocean genera, and mesopelagic genera lacking proteorhodopsins common in all other families. This study redefines the MGII and provides an avenue for understanding the role these 20 organisms play in the cycling of organic matter throughout the water column. Main textSince their discovery by DeLong 1 (1992), despite global distribution and representing a significant portion of the microbial plankton in the photic zone, the Marine Group II (MGII) Euryarchaea 25have remained an enigmatic group of organisms in the marine the environment. The MGII have been predominantly identified in the surface oceans 2 , account for ~15% of the archaeal cells in the oligotrophic open ocean 3 , and shown to increase in abundance in response to phytoplankton blooms 4 comprising up to ~30% of the total microbial community 5 . Research has shown that the MGII correspond with specific genera of phytoplankton 6 , during and after blooms 7 , and can be associated with particles when samples 30 are size fractionated 8 . Phylogenetic analyses have revealed the presence of two dominant clades of MGII, referred to as MGIIA and MGIIB (the MGIIB have recently been named Thalassoarchaea 9 ), that respond to different environmental conditions, including temperature and nutrients 10 . To date, the MGII have not been successfully cultured or enriched from the marine environment. Instead our current understanding of the role these organisms play in the environment is derived from 35interpretations of ecological data (i.e., phytoplankton-and particle-associated) and a limited number of genomic fragments and reconstructed environmental genomes. Collectively, these genomic studies have revealed a number of re-occurring traits common to the MGII, including: proteorhodopsins in MGII sampled from the photic zone 11 , genes targeting the degradation of high molecular weight (HMW) organic matter, such as proteins, carbohydrates, and lipids, and subsequent transport of constituent 40 components into the cell 9,12-14 , genes representative...

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Metabolic Diversity within the Globally Abundant Marine Group IIEuryarchaeaOffers Insight into Ecological Patterns

Tully

2018

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“…The phylogenetic tree based on 16 concatenated ribosomal proteins used in (Hug et al , 2016) (Figure 1) was constructed using the methodology outlined in (Graham et al , 2018). Briefly, open reading frames in MAGs and genomes were determined using Prodigal v2.6.3 (Hyatt et al , 2010), and ribosomal proteins were identified from these ORFs with HMMER v3.1b2 using the command hmmsearch with an e-value cutoff of 1E-5 (Eddy, 2011).…”

Section: Methodsmentioning

confidence: 99%

“…High-throughput sequencing of environmental DNA, metagenomic assembly of DNA sequence reads into contigs, and binning of contigs into metagenome-assembled genomes (MAGs) has provided unprecedented insights into the metabolic potential and evolutionary history of many uncultivated lineages (Hug et al , 2016; Brown et al , 2015; Woodcroft et al , 2018; Castelle et al , 2013; Crits-Christoph et al , 2018; Castelle et al , 2015; Anantharaman et al , 2016). In addition to revealing numerous new phyla, MAG analyses have identified new clades of microorganisms within longstanding phylogenetic groups, assigned biogeochemical roles to known but uncultivated lineages, and attributed new functions to diverse relatives of model prokaryotes (Graham et al , 2018; Mondav et al , 2014; Tully, 2019; Boyd et al , 2019; Solden et al , 2016; Singleton et al , 2018; Martinez et al , 2019). With these advances in sequencing technology, a more complex view of microbial evolution and metabolism has emerged.…”

Section: Introductionmentioning

confidence: 99%

Evidence for non-methanogenic metabolisms in globally distributed archaeal clades basal to theMethanomassiliicoccales

Zinke

Evans

Schroeder

et al. 2020

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0Recent discoveries of mcr and mcr-like complexes in genomes from diverse archaeal 2 1 lineages suggest that methane (and more broadly alkane) metabolism is an ancient pathway with 2 2 complicated evolutionary histories. The conventional view is that methanogenesis is an ancestral 2 3 metabolism of the archaeal class Thermoplasmata. Through comparative genomic analysis of 12 2 4Thermoplasmata metagenome-assembled genomes (MAGs), we show that these microorganisms 2 5do not encode the genes required for methanogenesis, which suggests that this metabolism may 2 6 have been laterally acquired by an ancestor of the order Methanomassiliicoccales. These MAGs 2 7 include representatives from four orders basal to the Methanomassiliicoccales, including a high-2 8 quality MAG (95% complete) that likely represents a new order, Ca. Lunaplasma lacustris ord. 2 9 nov. sp. nov. These MAGs are predicted to use diverse energy conservation pathways, such as 3 0 heterotrophy, sulfur and hydrogen metabolism, denitrification, and fermentation. Two of these 3 1 lineages are globally widespread among anoxic, sedimentary environments, with the exception 3 2 of Ca. Lunaplasma lacustris, which has thus far only been detected in alpine caves and subarctic 3 3 lake sediments. These findings advance our understanding of the metabolic potential, ecology, 3 4 and global distribution of the Thermoplasmata and provide new insights into the evolutionary 3 5 history of methanogenesis within the Thermoplasmata. 3 6 3 7

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“…KEGG annotations can be used to estimate the completeness of various biogeochemically-relevant metabolic pathways in a genome using KEGG-Decoder v.1.0.1 (Graham et al, 2018; https://github.com/bjtully/BioData/tree/master/KEGGDecoder). Users can search genomes based on completeness of a pathway or function of interest.…”

Section: Description Of Methodsmentioning

confidence: 99%

MetaSanity: An integrated, customizable microbial genome evaluation and annotation pipeline

Neely

Graham

Tully

2019

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11Summary 12 As the importance of microbiome research continues to become more prevalent and essential to 13 understanding a wide variety of ecosystems (e.g., marine, built, host-associated, etc.), there is a 14 need for researchers to be able to perform highly reproducible and quality analysis of microbial 15 genomes. MetaSanity incorporates analyses from eleven existing and widely used genome 16 evaluation and annotation suites into a single, distributable workflow, thereby decreasing the 17 workload of microbiologists by allowing for a flexible, expansive data analysis pipeline. 18 MetaSanity has been designed to provide separate, reproducible workflows, that (1) can 19 determine the overall quality of a microbial genome, while providing a putative phylogenetic 20 assignment, and (2) can assign structural and functional gene annotations with varying degrees of 21 specificity to suit the needs of the researcher. The software suite combines the results from 22 several tools to provide broad insights into overall metabolic function and putative extracellular 23 localization of peptidases and carbohydrate-active enzymes. Importantly, this software provides 24 built-in optimization for "big data" analysis by storing all relevant outputs in an SQL database, 25 allowing users to query all the results for the elements that will most impact their research. 26 Availability and implementation 27 MetaSanity is provided under the GNU General Public License v.3.0 and is available for 28 download at https://github.com/cjneely10/MetaSanity. This application is distributed as a Docker 29 image. MetaSanity is implemented in Python3/Cython and C++.

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Potential for primary productivity in a globally-distributed bacterial phototroph

Cited by 208 publications

References 22 publications

Metabolic Diversity within the Globally Abundant Marine Group IIEuryarchaeaOffers Insight into Ecological Patterns

Metabolic Diversity within the Globally Abundant Marine Group IIEuryarchaeaOffers Insight into Ecological Patterns

Evidence for non-methanogenic metabolisms in globally distributed archaeal clades basal to theMethanomassiliicoccales

MetaSanity: An integrated, customizable microbial genome evaluation and annotation pipeline

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