A genome catalogue of lake bacterial diversity and its drivers at continental scale

Garner, Rebecca E.; Kraemer, Susanne A.; Onana, Vera E.; Fradette, Maxime; Varin, Marie-Pierre; Huot, Yannick; Walsh, David A.

doi:10.1038/s41564-023-01435-6

Cited by 18 publications

(9 citation statements)

References 103 publications

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“…While multiassembly is less computationally demanding because it processes only a subset of the data at once, this also inherently limits the effective sequencing coverage of genomes, leading to insufficient sequencing coverage for low abundance species. Multiple metagenomic studies have observed multiassembly resulting in more fragmented and incomplete Metagenome-Assembled Genomes (MAGs) displaying less diversity compared to coassembling the same data [7, 10].…”

Section: Discussionmentioning

confidence: 99%

“…In our flagship case study analyzing the brackish wetlands of Twitchell Island, California, GenomeFace was able to produce 1,923 medium quality Metagenome Assembled Genomes spanning 69 phyla and candidate-phyla from a single assembly, each representing a unique species, with 90 percent of these species not previously cataloged. For perspective, we compare our analysis of only Twitchell Island’s wetlands to two of the largest consorted efforts to study aquatic communities ever performed, Tara Oceans and A genome catalogue of lake bacterial diversity and its drivers at continental scale [10], both of which encompass microbial communities sampled from a diverse range of geographic locations. These studies documented 1,888 and 1,008 prokaryotic species, representing 29 and 22 phyla, respectively.…”

Section: Introductionmentioning

confidence: 99%

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GenomeFace: a deep learning-based metagenome binner trained on 43,000 microbial genomes

Lettich,

Egan,

Riley

et al. 2024

Preprint

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Metagenomic binning, the process of grouping DNA sequences into taxonomic units, is critical for understanding the functions, interactions, and evolutionary dynamics of microbial communities. We propose a deep learning approach to binning using two neural networks, one based on composition and another on environmental abundance, dynamically weighting the contribution of each based on characteristics of the input data. Trained on over 43,000 prokaryotic genomes, our network for composition-based binning is inspired by metric learning techniques used for facial recognition. Using a task-specific, multi-GPU accelerated algorithm to cluster the embeddings produced by our network, our binner leverages marker genes observed to be universally present in nearly all taxa to grade and select optimal clusters of sequences from a hierarchy of candidates. We evaluate our approach on four simulated datasets with known ground truth. Our linear time integration of marker genes recovers more near complete genomes than state of the art but computationally infeasible solutions using them, while being over an order of magnitude faster. Finally, we demonstrate the scalability and acuity of our approach by testing it on three of the largest metagenome assemblies ever performed. Compared to other binners, we produced 47%-183% more near complete genomes. From these datasets, we find over the genomes of over 3000 new candidate species which have never been previously cataloged, representing a potential 4% expansion of the known bacterial tree of life.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GenomeFace: a deep learning-based metagenome binner trained on 43,000 microbial genomes

Lettich,

Egan,

Riley

et al. 2024

Preprint

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“…However, changes in community structure and functional potential are still correlated, indicating that functional redundancy, if important, is not complete 44,68 . Strong links between changes in the community structure and their functional potential have been reported in freshwater lake ecosystems 15,24,69 as well as in marine habitats 44 . This relationship was found even stronger when functionallyunannotated genes were included in such comparison 44 , indicating that much of the variation occurs in functions that are not yet properly characterized 70 .…”

Section: Peri-urban Lakes Display a Stable Gene-content Throughout Th...mentioning

confidence: 96%

“…31.596803 doi: bioRxiv preprint Microbial communities are key contributors to ecosystem functioning 11 , quickly reacting to disturbances, and are thus often investigated to assess lake 'health'. Eutrophication is a major driver of these communities [12][13][14][15] , promoting the growth of phytoplankton, including Cyanobacteria, as well as heterotrophic bacteria that degrade derived organic matter. However, the impact of eutrophication is often hard to disentangle from that of other variables (e.g., lake morphology, land cover and uses 12,14 ).…”

Section: Introductionmentioning

confidence: 99%

A summer in the Greater Paris: trophic status of peri-urban lakes shapes prokaryotic community structure and functional potential

Foucault,

Halary,

Duval

et al. 2024

Preprint

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With more than 12 million inhabitants, the Greater Paris offers a “natural laboratory” to explore the effects of eutrophication on freshwater lakes within a relatively restricted area. Here, a four-month time-series was carried out during summertime to monitor planktonic microbial communities of nine lakes located within a ∼70 km radius around Paris (Île-de-France, France) of comparable morphologies, yet distinct trophic statuses (mesotrophic to hypereutrophic). The contribution of the trophic status and intra-summer variations were investigated on prokaryotic community structures (16S rRNA gene sequencing) and functions (shotgun metagenomics). Sampled lakes harbored highly distinct and diverse prokaryotic communities. Although their gene pool was quite stable and shared among lakes, taxonomical and functional changes were correlated. The trophic status appears as the main driver of both community structure and functional potential. When focusing on function involved in biogeochemical cycles, responses to phosphorus limitation (mostly polyphosphate-related processes) were highlighted between trophic statuses. Hypereutrophic lakes communities displayed the highest contrast and heterogeneity over time, suggesting a regime shift compared to lakes of lower trophic status. This study explores the influence of eutrophication on lakes’ microbiome ecology, by comparing for the first time the planktonic microbial communities and their functional potential of lakes of distinct trophic status in close vicinity over a summer season.

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“…Profiling the composition and function of microbial consortia using metagenomic sequencing is aided by a high-quality reference database of relevant genomes. Such databases exist for the human gut 14 , soil 15 , ocean 16 and lake 17 . However, no such resource is available for fermented foods.…”

Section: Mainmentioning

confidence: 99%

MiFoDB, a workflow for microbial food metagenomic characterization, enables high-resolution analysis of fermented food microbial dynamics

Caffrey,

Olm,

Kothe

et al. 2024

Preprint

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Fermented foods, which contain a diverse array of microbial metabolites and microbes, are increasingly recognized as potential mediators of human immune and metabolic health. While there is growing interest in characterizing the microbial landscape of fermented foods, current characterization methods typically rely on 16S rRNA sequencing or marker gene-based methods, which have low taxonomic resolution and cannot functionally characterize microbes. Here we describe MiFoDB-workflow, a metagenomics workflow for the identification of microbes associated with food fermentation based on a primary database of 675 genomes of bacteria, yeast, fungi, and common fermented food substrates. We constructed the database using metagenome-assembled genomes (MAGs) derived from metagenomic sequencing of 90 fermented foods, combined with previously-published fermented food-derived MAGs, and relevant genomes deposited in RefSeq and GenBank genomes. We demonstrate the utility of MiFoDB for high confidence genome identification, including discovery of previously uncharacterized species, strain tracking across related foods, and functional analysis in fermented foods of different substrates. The workflow streamlines high-confidence characterization of the diversity of the fermented food landscape including novel ferments, and allows strain-level tracking of microbes across time, substrates, and producers.

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A genome catalogue of lake bacterial diversity and its drivers at continental scale

Cited by 18 publications

References 103 publications

GenomeFace: a deep learning-based metagenome binner trained on 43,000 microbial genomes

GenomeFace: a deep learning-based metagenome binner trained on 43,000 microbial genomes

A summer in the Greater Paris: trophic status of peri-urban lakes shapes prokaryotic community structure and functional potential

MiFoDB, a workflow for microbial food metagenomic characterization, enables high-resolution analysis of fermented food microbial dynamics

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