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Truong, D. T.

Publications and source records attributed to Truong, D. T..

2 recordsLinked to original sources

Accessible, curated metagenomic data through ExperimentHub

We present curatedMetagenomicData, a Bioconductor and command-line interface to thousands of metagenomic profiles from the Human Microbiome Project and other publicly available datasets, and ExperimentHub, a platform for convenient cloud-based distribution of data to the R desktop. The resource provides standardized per-participant metadata linked to bacterial, fungal, archaeal, and viral taxonomic abundances, as well as quantitative metabolic functional profiles. The datasets can be immediately analyzed in R or other software with a minimum of bioinformatic expertise and no preprocessing of data. We demonstrate identification of taxonomic/functional correlations, an investigation of gut \"enterotypes\", and a comparison of the accuracy of disease classification from different data types. These documented analyses can be reproduced efficiently on a laptop, without the barriers of working with large-scale, raw sequencing data. The building and expansion of curatedMetagenomicData is based entirely on open source software and pipelines, to facilitate the addition of new microbiome datasets and methods.

bioinformatics

Studying vertical microbiome transmission from mothers to infants by strain-level metagenomic profiling

The gut microbiome starts to be shaped in the first days of life and continues to increase its diversity during the first months. Several investigations are assessing the link between the configuration of the infant gut microbiome and infant health, but a comprehensive strain-level assessment of vertically transmitted microbes from mother to infant is still missing. We longitudinally collected fecal and breast milk samples from multiple mother-infant pairs during the first year of life, and applied shotgun metagenomic sequencing followed by strain-level profiling. We observed several specific strains including those from Bifidobacterium bifidum, Coprococcus comes, and Ruminococcus bromii, that were present in samples from the same mother-infant pair, while being clearly distinct from those carried by other pairs, which is indicative of vertical transmission. We further applied metatranscriptomics to study the in vivo expression of vertically transmitted microbes, for example Bacteroides vulgatus and Bifidobacterium spp., thus suggesting that transmitted strains are functionally active in the two rather different environments of the adult and infant guts. By combining longitudinal microbiome sampling and newly developed computational tools for strain-level microbiome analysis, we showed that it is possible to track vertical transmission of members of the microbiome from mother to infants and characterize their transcriptional activity. Our work poses the basis for surveying at larger scale the sources of microbial diversity in the infants and starts associating these transmissions with the subsequent longer-term development of a healthy or dysbiotic microbiome.\n\nImportanceEarly infant exposure is important in the acquisition and ultimate development of a healthy infant microbiome. There is increasing support that the maternal microbial reservoir is a key route of microbial transmission, yet much is inferred from the observation of shared species in mother and infant. Common species, per se, does not necessarily equate vertical transmission as species exhibit considerable strain heterogeneity and it is therefore imperative to identify shared strains. We demonstrate here the potential of shotgun metagenomics and strain-level resolution to identify vertical transmission events via the maternal source. Combined with a metatranscriptomic approach, we show the potential not only to identify and track the fate of microbes in the early infant microbiome but also identify the metabolically active members. These approaches will ultimately provide important insights into the acquisition, development and community dynamics of the infant microbiome.

microbiology