Charting the Complexity of the Marine Microbiome through Single-Cell Genomics

Pachiadaki, Maria; Brown, Julia; Brown, Joseph; Bezuidt, Oliver; Berube, Paul M.; Biller, Steven J.; Poulton, Nicole; Burkart, Michael D.; Clair, James J. La; Chisholm, Sallie W.; Stepanauskas, Ramunas

doi:10.1016/j.cell.2019.11.017

Cited by 185 publications

(306 citation statements)

References 107 publications

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“…Thousands of isolate, single-cell, and metagenome-assembled genomes are guiding us towards a better understanding of how microbes shape life on Earth [1][2][3][4][5][6][7] , thus bringing about a golden age of microbial genomics. An ever-increasing number of genomes and metagenomes are unlocking uncharted regions of microbial diversity 1,8,9 , providing new perspectives on the evolution of life 10,11 .…”

Section: Introductionmentioning

confidence: 99%

Unifying the known and unknown microbial coding sequence space

Vanni

Schechter

Acinas

et al. 2020

Preprint

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AbstractBridging the gap between the known and the unknown coding sequence space is one of the biggest challenges in molecular biology today. This challenge is especially extreme in microbiome analyses where between 40% to 60% of the coding sequences detected are of unknown function, and ignoring this fraction limits our understanding of microbial systems. Discarding the uncharacterized fraction is not an option anymore. Here, we present an in-depth exploration of the microbial unknown fraction through the lenses of a conceptual framework and a computational workflow we developed to unify the microbial known and unknown coding sequence space. Our approach partitions the coding sequence space in gene clusters and contextualizes them with genomic and environmental information. We analyzed 415,971,742 genes predicted from 1,749 metagenomes and 28,941 bacterial and archaeal genomes putting into perspective the extent of the unknown fraction, its diversity, and its relevance in a genomic and environmental context. With the identification of a target gene of unknown function for antibiotic resistance, we demonstrate how a contextualized unknown coding sequence space provides a robust framework for the generation of hypotheses that can be used to augment experimental data.

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Section: Introductionmentioning

confidence: 99%

Unifying the known and unknown microbial coding sequence space

Vanni

Schechter

Acinas

et al. 2020

Preprint

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“…Our results indicate that mutations leading to an addition of resources would be more common in nutrientrich environments. This is supported by previous observations regarding genomic and proteomic changes associated with environmental concentrations of nitrate (2,5,21,(51)(52)(53). We thus sought to quantify how resource conservation manifests in molecular changes to DNA and protein sequences.…”

Section: Supplementary Textmentioning

confidence: 57%

Resource conservation manifests in the genetic code

Shenhav

Zeevi

2019

Preprint

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9Ocean microbes are responsible for about 50% of primary production on Earth, and are strongly 10 affected by environmental resource availability. However, selective forces resulting from 11 environmental conditions are not well understood. We studied selection by examining single-12 nucleotide variants in the marine environment, and discovered strong purifying selective forces 13 exerted across marine microbial genes. We present evidence indicating that this selection is 14 driven by the environment, and especially by nitrogen availability. We further corroborate that 15 nutrient availability drives this 'resource-driven' selection by showing stronger selection on highly 16 expressed and extracellular genes, that are more resource-consuming. Finally, we show that the 17 standard genetic code, along with amino acid abundances, facilitates nutrient conservation by 18 providing robustness to mutations that increase nitrogen and carbon consumption. Notably, this 19 robustness generalizes to multiple taxa across all domains of life, including the Human genome, 20 and manifests in the code structure itself. Overall, we uncover overwhelmingly strong purifying 21 selective pressure across marine microbial life that may have contributed to the structure of our 22 genetic code. 23 could be attributed to the different rates of synonymous mutations between the high-and low-595 nitrate groups, which, combined with simplex properties, may affect observed nonsynonymous

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“…We mapped the short reads from 223 published marine viral metagenomic samples (29) to the 5539 single amplified genomes (30) Table S4. These virome samples come from 65 Tara stations.…”

Section: Virome Mappingmentioning

confidence: 99%

“…As there are no universal conserved marker genes for phages, we utilized recently published marine datasets (29,30) to demonstrate that PhageBoost can discover previously unseen prophage signals.…”

Section: Superimposition Of Tara Ocean Viral Samples On Single-cell Gmentioning

confidence: 99%

“…We mapped the sequence reads from 223 metagenomic marine viral fractions samples (viromes) from 65 different TARA oceans stations (29) to 5,537 single amplified marine microbial genomes (SAGs) (30). This gave a total of 22,659 unique regions with a signal out of 236,405 contigs (Tables S4-S5) that were consistently detected by at least 1x coverage.…”

Section: Superimposition Of Tara Ocean Viral Samples On Single-cell Gmentioning

confidence: 99%

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Rapid discovery of novel prophages using biological feature engineering and machine learning

Sirén

Millard

Petersen

et al. 2020

Preprint

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Prophages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.

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Charting the Complexity of the Marine Microbiome through Single-Cell Genomics

Cited by 185 publications

References 107 publications

Unifying the known and unknown microbial coding sequence space

Unifying the known and unknown microbial coding sequence space

Resource conservation manifests in the genetic code

Rapid discovery of novel prophages using biological feature engineering and machine learning

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