Dataset Information

Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community.

ABSTRACT: Laboratory mice are widely studied as models of mammalian biology, including the microbiota. However, much of the taxonomic and functional diversity of the mouse gut microbiome is missed in current metagenomic studies, because genome databases have not achieved a balanced representation of the diverse members of this ecosystem. Towards solving this problem, we used flow cytometry and low-coverage sequencing to capture the genomes of 764 single cells from the stool of three laboratory mice. From these, we generated 298 high-coverage microbial genome assemblies, which we annotated for open reading frames and phylogenetic placement. These genomes increase the gene catalog and phylogenetic breadth of the mouse microbiota, adding 135 novel species with the greatest increase in diversity to the Muribaculaceae and Bacteroidaceae families. This new diversity also improves the read mapping rate, taxonomic classifier performance, and gene detection rate of mouse stool metagenomes. The novel microbial functions revealed through our single-cell genomes highlight previously invisible pathways that may be important for life in the murine gastrointestinal tract.

SUBMITTER: Lyalina S

PROVIDER: S-EPMC9007364 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community.

Lyalina Svetlana S Stepanauskas Ramunas R Wu Frank F Sanjabi Shomyseh S Pollard Katherine S KS

PloS one 20220413 4

Laboratory mice are widely studied as models of mammalian biology, including the microbiota. However, much of the taxonomic and functional diversity of the mouse gut microbiome is missed in current metagenomic studies, because genome databases have not achieved a balanced representation of the diverse members of this ecosystem. Towards solving this problem, we used flow cytometry and low-coverage sequencing to capture the genomes of 764 single cells from the stool of three laboratory mice. From ...[more]

PMID: 35417481

Similar Datasets

Project description:BackgroundSequencing variable regions of the 16S rRNA gene (≃300 bp) with Illumina technology is commonly used to study the composition of human microbiota. Unfortunately, short reads are unable to differentiate between highly similar species. Considering that species from the same genus can be associated with health or disease it is important to identify them at the lowest possible taxonomic rank. Third-generation sequencing platforms such as PacBio SMRT, increase read lengths allowing to sequence the whole gene with the maximum taxonomic resolution. Despite its potential, full length 16S rRNA gene sequencing is not widely used yet. The aim of the current study was to compare the sequencing output and taxonomic annotation performance of the two approaches (Illumina short read sequencing and PacBio long read sequencing of 16S rRNA gene) in different human microbiome samples. DNA from saliva, oral biofilms (subgingival plaque) and faeces of 9 volunteers was isolated. Regions V3-V4 and V1-V9 were amplified and sequenced by Illumina Miseq and by PacBio Sequel II sequencers, respectively.ResultsWith both platforms, a similar percentage of reads was assigned to the genus level (94.79% and 95.06% respectively) but with PacBio a higher proportion of reads were further assigned to the species level (55.23% vs 74.14%). Regarding overall bacterial composition, samples clustered by niche and not by sequencing platform. In addition, all genera with > 0.1% abundance were detected in both platforms for all types of samples. Although some genera such as Streptococcus tended to be observed at higher frequency in PacBio than in Illumina (20.14% vs 14.12% in saliva, 10.63% vs 6.59% in subgingival plaque biofilm samples) none of the differences were statistically significant when correcting for multiple testing.ConclusionsThe results presented in the current manuscript suggest that samples sequenced using Illumina and PacBio are mostly comparable. Considering that PacBio reads were assigned at the species level with higher accuracy than Illumina, our data support the use of PacBio technology for future microbiome studies, although a higher cost is currently required to obtain an equivalent number of reads per sample.

Project description:Seagrass form high productive ecosystems in coastal environments. However, the effects of these coastal plants on the structure and function of the belowground eukaryotic microbiome remain elusive. In this study, we characterized the community of microbial eukaryotes (microeukaryotes) in both vegetated and unvegetated sediments using 18S rRNA gene amplicon sequencing and quantitative PCR. Analysis of sequencing data showed that the eelgrass (Zostera marina) colonization decreased the alpha diversity indices of benthic microeukaryotes. Apicomplexa represented an average of 83% of reads across all samples, with a higher proportion at the vegetated sites. The taxonomic community structure was significantly different between these two types of sediments, for which the concentration of NH4+ in sediment porewater and salinity could account. Phylogenetic analyses of long 18S rRNA genes (around 1,030 bp) indicated these apicomplexan parasites are closely related to gregarine Lecudina polymorpha. Determination of 18S rRNA gene abundances provided evidence that the eelgrass markedly promoted the biomass of the gregarine and all microeukaryotes in the seagrass-colonized sediments and confirmed that the gregarine was hosted by a polychaete species. Significantly higher gene abundances of heterotrophs and mixotrophs were found at the vegetated sites, which could be explained by the finer sediments and short supply of dissolved inorganic nitrogen, respectively. The pigmented protists were more abundant in 18S rRNA gene copies at the lower and higher pH levels than at the intermediate. Nevertheless, the fractions of heterotrophs and phototrophs in the community were significantly related to porewater N:P ratio. These results indicate that seagrass colonization significantly induces an increase in overall biomass and a decrease in diversity of benthic microeukaryotes, making them more heterotrophic. This study also highlights that the hotspot of eukaryotic parasites could be linked with the high productivity of a natural ecosystem.

Project description:BackgroundStudies of shifts in microbial community composition has many applications. For studies at species or subspecies levels, the 16S amplicon sequencing lacks resolution and is often replaced by full shotgun sequencing. Due to higher costs, this restricts the number of samples sequenced. As an alternative to a full shotgun sequencing we have investigated the use of Reduced Metagenome Sequencing (RMS) to estimate the composition of a microbial community. This involves the use of double-digested restriction-associated DNA sequencing, which means only a smaller fraction of the genomes are sequenced. The read sets obtained by this approach have properties different from both amplicon and shotgun data, and analysis pipelines for both can either not be used at all or not explore the full potential of RMS data.ResultsWe suggest a procedure for analyzing such data, based on fragment clustering and the use of a constrained ordinary least square de-convolution for estimating the relative abundance of all community members. Mock community datasets show the potential to clearly separate strains even when the 16S is 100% identical, and genome-wide differences is < 0.02, indicating RMS has a very high resolution. From a simulation study, we compare RMS to shotgun sequencing and show that we get improved abundance estimates when the community has many very closely related genomes. From a real dataset of infant guts, we show that RMS is capable of detecting a strain diversity gradient for Escherichia coli across time.ConclusionWe find that RMS is a good alternative to either metabarcoding or shotgun sequencing when it comes to resolving microbial communities at the strain level. Like shotgun metagenomics, it requires a good database of reference genomes and is well suited for studies of the human gut or other communities where many reference genomes exist. A data analysis pipeline is offered, as an R package at https://github.com/larssnip/microRMS . Video abstract.

Dataset Information

Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community.

Publications

Single cell genome sequencing of laboratory mouse microbiota improves taxonomic and functional resolution of this model microbial community.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets